You have a product that has taken off. Your daily active users metric has been growing exponentially. The number of events per day you’re logging is now in the 100’s of millions.
As a result you now find yourself with terabytes of data or if you have become really successful hundreds of terabytes.
You begin to wonder if you could use all of this data to improve your business. Maybe you can use the data to create a more personalized experience for the users of your product. Or maybe you can use the data to discover demand for new products.
You request that your data team come up with way to leverage this data to do just these types of things.
The data team that you have hired recommends that you develop a data pipeline. An end-point of that pipeline being the data warehouse.
You may get something like this:
Data Pipeline and Data Warehouse
But after months of work, and many dollars spent building the data warehouse, the data scientists that you hired can’t come up with the insights.
How could all of that data, all of those IT consulting hours, and those cloud computing resources be marshalled to not produce the insights?
The problem likely lies in one of the important components of your pipeline: the data warehouse
Here are some of the painful things you can experience in the data warehouse:
Poor Quality Data
Data that is Hard to Understand
Inaccurate / Untested Data
A Slow Data Warehouse
A Poorly Designed Data Warehouse
A Data Warehouse that Costs Too Much
A Data Warehouse that Does Not Factor in Privacy Requirements
Poor Quality Data
You data may be streaming in from multiple sources. When an analyst runs a JOIN on this data, it could result in a table that is inconsistent. Inconsistent data can manifest itself as missing columns that are required to properly identify each data item. Or the data may contain duplicates that take extra space and prevent from performing the aggregations necessary to achieve insights without extra work (meaning extra analyst time cleaning the data via interpolation, and extra compute hours deduplicating the data).
Data that is Hard to Understand
You have PhD’s on your analyst team. Why are they scratching their heads and shrugging their shoulders after looking at your data? It could be that the tables in the data warehouse are an enigma.
A lot of times, the data warehouse is built by a different team than the analysts. Both groups are trying to manage data but are not necessarily playing for the same data team.
Oftentimes the tables are created in a way that makes it easy to create the table and but not easy to be processed downstream. The table is created without taking the downstream requirements into consideration! Noone thought to begin the data warehouse design with the end goal in mind of quickly enabling insight generation.
Inaccurate / Untested Data
Data items can be wrong. Data items may reflect something that is not possible. The data may reflect something going on in society that you do not want to serve as a basis for downstream analysis. The data must be accurate otherwise, it will lead your analysis to wrong or detrimental insights. Untested data is worse than not having any data.
A Slow Data Warehouse
A data warehouse can be of no use because it takes too long to query, or goes down often. If users are not trained on how to write efficient queries or if the warehouse is not developed to automatically scale with the growth of the data, and if there are no protections in place to prevent abuse of the compute resources of the warehouse your insights will never materialize.
Poorly Designed Data Warehouse
Business leaders who launch a data warehouse without first considering the business needs and translating these into actionable tasks will likely get a data warehouse that does not meet their business needs.
Not understanding these business needs upfront leads to miscommunication amongst the analysts, which leads to confused insights.
A Data Warehouse that Costs Too Much
One possible cause of a costly warehouse is not matching the right warehouse implementation option to your needs. Not every organization needs to create a from-scratch, on-premise, data warehouse. Doing this takes a lot of time, a lot of the right human resources, and equipment. This can yield a project that is late, over budget, and expensive to maintain or upgrade. As a result over time your warehouse becomes less useful as other priorities consume the organization’s resources.
A Data Warehouse that Does Not Factor in Privacy Requirements
Even if your product is a game, or something purely consumer oriented, and even if you spell out clearly in the terms of service that whatever data the user shares is yours, you still can’t ignore how the data warehouse will protect your user’s identifiable information.
Not taking this into consideration can result in people in the company being able to look up specific users for non-business purposes. It can result in people in the company misusing personally identifiable information, which can hurt your users, and negatively impact daily active user growth. It can result in personally identifiable information inadvertently leaking somewhere downstream.
How to Deal?
There is no magic bullet to addressing these many issues. While some of these issues are technical in nature (and just require the right no-how), others are organizational–meaning you can’t just download a free-ware tool to solve them.
But briefly, some of these issues can be addressed by:
Have a well organized product development process. Using agile
Realize that there is no one-size fits all data warehouse. You will have to some warehouses that are configured to be high-speed data stores to capture data streaming in from your product. These are data warehouses that are configured to prioritize transactional activity. Other data warehouses will be configured to be always-on, highly-available, scalable, and reliable data stores whose purpose is to hold your 100s of terabytes of data in a queryable form to enable the data analysts.
Nowadays, businesses have to rely on data in unprecedented ways. In fact, businesses hailing from various disciplines use massive amounts of data on a daily basis. They gather data from several sources, offline and online. However, it is also important to compile and process that data and analyze it using apt software solutions. That is why Data visualization applications are used by so many companies. From technology giants to leading MNCs, plenty of companies are relying on BI and data visualization solutions like Microsoft Power BI. For effective Power BI implementation, hiring a veteran development agency is recommended.
Understanding the Importance of Data visualization
Before you invest in a specialized data visualization tool like Power BI or buy it, it is necessary that you know the significance of data visualization. Your business may obtain data from myriads of sources. Analysis of that data helps in understanding customer preferences, areas of improvement and market trends, etc. This, in turn, helps the businesses take key decisions and make strategic moves. To understand the analyzed data properly, presenting it in a comprehensible visual manner is necessary. That is where data visualization steps in.
Microsoft Power BI as a data visualization tool
Microsoft Power BI is a BI solution that has robust and embedded data visualization capabilities. The data compiled and analyzed by the tool is visually represented using several elements. These include graphs, videos, charts, images, etc. These visual elements help the users and viewers understand the data well. It is possible to use many filters or parameters to represent data in specific ways. Dashboards and reports are also key features present in the application. Of course, you will gain from the services of a veteran Power BI developer for utilizing these elements.
The elements in Power BI used for data visualization
As it is, Power BI comes with a wide range of data visualization elements. These include:
Charts of varying types
Area Charts– It is also referred to as a layered area chart. It is used to indicate a change in one or several quantities over time. Area charts should be used when the user wants to display and see any variable’s trend over time. For example, it can be deployed to get a glimpse at the workforce productivity in various quarters. You may also use it to analyze the sales and expenditure of the company quarter-wise.
Line chart– A line chart is one of the widely used visual elements in Power BI. It is useful when you want to visually represent trends over time. The data points are joined by a straight line horizontally. For example, it can be used to represent the sales figure of a company in a financial year.
Bar Charts– These charts are used to represent categorical data through horizontal bars. This is used a lot on Power BI as they are easy to comprehend. This chart can be used to represent the growth rate of various departments in a company, per quarter, for example.
Combo chart– A Combo chart blends a column chart and line chart. They can be useful when you want to compare several measures with varying value ranges. They can be used to illustrate the association between two measures in a single visualization.
Doughnut charts– A doughnut chart is much like a pie chart, and it is used when it is necessary to display the relationship of a section to a whole. However, users need to remember that doughnut chart values should add up to 100%. Using too many categories in a doughnut chart makes it hard to read.
Funnel charts– Funnel charts are used when it is necessary to illustrate sequential connected stages in any process. It is used widely to show sales processes. Each funnel stage denotes a certain percentage of the total amount. A funnel chart resembles a funnel, with the first stage being the biggest in size.
Pie charts– Pie chart is somewhat like a donut chart, and the combination of all segments must add up to 100%. The data is segregated into slices, and it is useful for representing the similar category of data.
Gauge charts– A gauge chart may remind you of the speedometer used in regular car dashboards. In it, a needle is used for data reading.
There are some other types of charts available in Power BI, like the waterfall chart. However, these are typically used by the Power Bi Experts.
Maps
In Power BI, you can make use of maps to represent sales data. This is accessible through the globe icon in the tool’s visualization pane. You have to pick the required categories.
There are three types of maps, namely Flow maps, point maps, and regional maps.
R and Python for data visualization
Microsoft has made it possible to use R and Python to enhance the data visualization prowess of Power BI. This can be immensely helpful for the end-users who want their reports to be as information-rich and visually enticing as possible.
R is a language that is used extensively for graphics and statistical computing. For that, it is necessary to have R studio and necessary packages and libraries in place. R provides a robust platform for data visualization and analysis. In fact, with it, you can visualize data prior to the analysis.
Python is another programming language that can be used with Power BI. It is necessary to set up Python with the necessary libraries and packages in the system. Python, in fact, has been used for years for data visualization needs. However, it lacks robust chart generating options, which can be achieved by integrating it with Power BI.
It is hard to locate another BI and data visualization tool that is enriched with so many visual elements like Power BI. After you equip the dashboard with various visual elements and feel happy with the visual representation of data, you can publish it. Based on the version of Power BI you have, it is possible to share reports that can be seen only by other Power BI users and those who do not use the platform.
Data visualization tips for Power BI users
As it is, Power BI is laden with so many visual elements that using them in the right way can be tedious for some users. This may be tougher for those who are new to the platform. Listed here are some effective tips for extracting the most out of data visualization features in Power BI.
Before using any visual element such as a type of chart or map, think of the purpose and type of data to be represented.
Do not clutter the dashboard using too many visual elements at a time. Also, customize the charts with an apt color and label for making these easy to comprehend.
You can also add visual elements in Power BI that you may have used in the MS Office suite. These include shapes, text boxes, and images. After adding, you can resize these elements as well.
Summing up
As you can see, Power BI is a powerful data visualization tool, and you can use many of its embedded visual elements to showcase your data effectively. However, it is also necessary that you pick the visual elements cautiously and evade overdoing things. You may also seek services of the Power BI development services for creating killer Power BI reports.
Legal Tech refers to the technology used in the legal sector. It has significantly transformed how attorneys and other legal professionals perform their duties. Moreover, it has brought a lot of opportunities for law offices (solo legal practices, law firms, and corporate/government legal departments) by digitally transforming legal operations, helping them meet client demands efficiently and timely.
More interestingly, the scope and adoption of technology are not limited to top legal organizations. Small-size law firms and even legal startups have also invested in technology, taking advantage of the opportunities it offers.
4 Ways Technology is Transforming the Legal Sector:
Advanced technologies simplify lawyers’ work and improve legal services’ quality, all while reducing operational costs. Here’s how.
Bridging Communication Gaps between Lawyers & Clients
Lawyers can now collaborate more effectively with their teams and establish a flexible, more secure medium to communicate with clients by utilizing unified communication tools. This can result in enhanced productivity and client satisfaction.
The Era of Automated eDiscovery
Searching for documents and highlighting or tagging relevant evidence pieces are parts of case preparation that consume a lot of time. Nowadays, most of the paperwork is digital; here, eDiscovery automation software (powered with advanced analytics) can automatically find and tag keywords and key phrases and eliminate irrelevant documents, helping attorneys speed up the entire process.
Case Management Becoming Easier
Many software on the market enable attorneys to manage different case management functions using one platform. For instance, schedule preparation, contact-list organization, document management, billing data entry, etc., are now easier to manage. Besides, any cloud-based case management software allows attorneys to store all the case-relevant data (helpful information) in a centralized location and access it anytime from anywhere. This is even more beneficial for those working remotely.
The Rise Attorneys’ Online Communities
By coming together in large numbers, attorneys create community groups, most often to help people who don’t have access to professional legal advice and counseling. Another motive is to include law students and solo attorneys in community groups and discuss several legal profession-related topics, such as issues, trends, news, etc.
Social media sites and apps, especially Twitter and LinkedIn, are becoming more popular as a forum platform for attorneys to connect with other legal professionals and establish a strong network in the industry.
Challenges Brought by Technology for the Legal World
Undoubtedly, technology is making an attorney’s job and life more manageable. However, it also brings along various challenges; let’s discuss a few.
The Sudden Knock on The Door
A few years back, the sudden occurrence of the technology revolution has left many attorneys (mostly veterans) baffled in their profession. Typical legal processes such as research, documentation, case preparation, etc., used to be managed manually. However, with digitization, many legal tasks are handled by automation-powered software and tools, arising several challenges for attorneys.
Lack of Technical Knowledge & Expertise
Many legal professionals are still unable to understand which technologies are best for their practice and how to make the best use of them to obtain favorable results. Due to this, many law firms often choose legal process management servicesprovided by external firms having skilled legal professionals with all required technical knowledge and capabilities.
Issues on Organizational Level
Due to the rapid and intense emergence of technology in legal, lawyers and law firms need a massive operational overhaul, transforming several processes. From lead generation to revenue recognition, everything needs to be changed now. Consequently, law firms now have to deal with challenging situations such as determining the usage scope of legal tech, developing new business models, establishing policies, etc.
Financial Restraints
The cost of technology adoption and maintenance put a question on many law firms’ budgets, forcing them to think twice before making any tech investment. Many legal organizations overlook the advantages of technology for their practice because of financial problems.
Nowadays, individuals or businesses in legal need would prefer choosing a tech-driven legal services firm. Besides attracting more potential clients, here are some other benefits of the continuously growing legal tech.
Benefits of Legal Tech
Reduction in Manual Effort
Legal tech, for example, data processing, document management, eDiscovery software, etc., automatically manages these processes, allowing lawyers to free up a significant amount of time. As a result, they can utilize this time to communicate with clients and prepare case files for the effective representation of clients in the courtroom.
Research Work becomes Easy
Legal research tools assist lawyers in becoming updated about different rules and regulations. Such software can also identify relevant documents on the internet, shortlist them, and even highlight key phrases that can be helpful for an attorney to make their argument stronger.
Better Management of Resources
Various applications for title management and calendaring allow attorneys to efficiently manage work related to titles and get valuable insights regarding all tasks scheduled for a particular workday. This enables senior attorneys to utilize resources (paralegals or other clerical staff members) more effectively, bringing better results.
Minimized Risk of Mistakes/Errors
Tech-driven data entry and management solutions restrict access to sensitive and confidential information a law firm holds, for instance, clients’ case details. Besides, integrating such tools with analytics can help make better use of the available data.
Enhanced Transparency
With the help of reliable law practice management software, law firms can better control their processes and eliminate workflow issues. These software record real-time information (for example, how much time a paralegal has to spend on a particular client’s case) in a centralized, secure location. This data can then be used for billing and analyze staff performance, productivity, and much more.
Excellent Customer Experience
Using AI-powered legal software, attorneys can send personalized emails to clients. Also, by collecting client data, such software allow law firms understand their clients’ need better, helping them meet their demands well in time. Thus, legal tech can help law firms enhance clients’ experience by providing highly customized legal services.
Conclusion
Solo attorneys, law firms (of all sizes), and corporate/government legal departments must become aware of the technology they can use to improve legal operations. Since many legal processes are shifting toward digital platforms, they need to catch up with legal tech trends and adopt all easily applicable tools. It is time not to be afraid of being replaced by that might be developed in the near future; instead, it is time to gain knowledge, improve technical skills, and utilize technology for the betterment of legal functions, processes, and the overall growth of legal business at large.
Data Science is a broader concept and multidisciplinary.
Data science is a general process and method that analyze and manipulate data.
Data science enables to find the insight and appropriate information from given data.
Data Science creating an opportunity to use data for making key decisions in different business domains and technology.
Data science provides a vast and robust way of visualization techniques to under the data insights.
Machine Learning
Machine learning fits within data science.
Machine learning uses various techniques and algorithms.
Machine learning is a highly iterative process.
Machine Learning algorithms are trained over instances.
Machine Models are learned from past experiences and also analyze the historical data.
Machine Model able to identify patterns in order to make predictions about the future of the given data.
“The main difference between the two is that data science as a broader term not only focuses on algorithms and statistics but also takes care of the entire data processing methodology”
Let’s see quickly the Machine Learning Process – Overview and jump into Train and Test.
Understand the scenario
Certainly, you can assume how the students are getting trained before their board exams by the great teachers in School/College.
At School/College level we use to undergo many more Unit-test/Term exams/Revision exams/Surprise tests and etc., Here we have been trained on various combinations of questions, mix and match patterns.
Hope you all come across these situations many times in your studies. No exceptional data set that we’re going to use in Data Science. All because we need to build a very strong model before we go into deploy the model in a production environment.
Similarly, in the Data Science domain, the Model has been trained by the sample data and makes them predicts the values with the available data set after data wrangling, cleansing, and EDA process, before deploying into the production environment, before the model meets the real-time/streaming data.
This process is always helping us to understand the insight of the data and what/which model we could use for our data set to address the business problems.
Here we must take care of the data set and it should match with real-time/streaming data feed (To align with all combinations), while the model performing in a production environment. So, the choice of data set (data preparation) is really key before the T&T process. Otherwise, the model situation becomes pathetic… as below in the picture. There might be huge effort loss, impact on the project cost and end up with unhappy customer service.
Here you should ask me the below questions.
Why do you split data into Training and Test Sets?
What is a good train test split?
How do you split data into training and testing?
What are training and testing accuracy?
How do you split data into train and test in Python?
What are X_train and Y_train X_test and Y_test?
Is the train test split random?
What is the difference between the training set and the test set?
Let me answer one-by-one here for your benefit to understand better way!
How do you split data into training and testing?
80/20 is a good starting point, giving a balance between comprehensiveness and utility, though this can be adjusted upwards or downwards based upon your model performance and volume of the data.
Training data is the data set on which, you train the model.
Train data from which the model has learned the experiences.
Training sets are used to fit and tune your models.
Test data is the data that is used to check if the model has learned well enough from the experiences it got in the train data set.
Test sets are “unseen” data to evaluate your models.
Architecture view of Test & Train process
CODE to split give dataset
# split our data into training and testing data X_train,X_test,y_train,y_test = train_test_split(X_scaled,y,test_size=.25,random_state=0)
What are training and testing accuracy?
Training accuracy is usually the accuracy we get if we apply the model to the training data
Testing accuracy is the accuracy of the testing data.
It is useful to compare these to identify how Training and Test set doing during the Machine Learning process.
Code
model = LinearRegression() # initialize the LinearRegression model model.fit(X_train,y_train) # we fit the model with the training data
linear_pred = model.predict(X_test) # make prediction with the fitted model
# score the model on the train set print(‘Train score: {}\n’.format(model.score(X_train,y_train))) # score the model on the test set print(‘Test score: {}\n’.format(model.score(X_test,y_test))) # calculate the overall accuracy of the model print(‘Overall model accuracy: {}\n’.format(r2_score(y_test,linear_pred))) # compute the mean squared error of the model
X_train — This includes your all independent variables, (Will share detailed notes on independent and dependent variables) these will be used to train the model.
X_test — This is the remaining portion of the independent variables from the data which will not be used in the training set. Mainly used to make predictions to test the accuracy of the model.
y_train — This is your dependent variable that needs to be predicted by the model, this includes category labels against your independent variables X.
y_test — This is the remaining portion of the dependent variable. these labels will be used to test the accuracy between actual and predicted categories.
NOTE: We need to specify our dependent and Independent variables, before training/fitting the model. Identifying those variables is a big challenge and it should come out from the business problem statement, what we are going to address.
Is the train test split random?
The importance of the random split has been explained in the below picture clearly in a simple way! You could understand from pictorial representation!
In simple text, the model could understand what all data combination are is exists in the give data set.
The random_state parameter is used for initializing the internal random number generator, which will decide the splitting of data into train and test.
Let say! random_state=40, then you will always get the same output the first time you make the split. This would be very useful if you want reproducible results to finalize the model. from the below picture you could understand better why we prefer “RAMDOM Sampling”
Thanks for your time in reading this article! Hope! You all got an idea of Train and Test Split in the ML process.
Will get back to you with a nice topic shortly! Until then bye! See you all soon – Shanthababu
Email Marketing can be challenging. I learnt this lesson from my experience in the digital marketing sphere and being a support representative at an email software company.Why?There are a number of reasons. They come in different forms and from various places and refer tosegmenting an audience, finding contacts, designing a perfect subject line, to name a few.
Such activities require from marketers tons of creativity, consistency and research. Yeap, email marketing is stillone of the most efficient marketing channels due to ROI. This fact only fuels the competition in the industry, leading to seeking new solutions.
Notably,email campaign software has become the go-to optionfor many brands and businesses. Automation interferes in many spheres and enterprises, while digital marketing is not an exception. Email campaign software makes a difference there.
However, how many email platforms are there? A lot. I have been working in digital marketing for some time and understand why one can find very confusing the amount of software available before marketing teams.
That’s whyI have designed a list of the top email marketing software that can add to your small business, startup, or long-term campaign. This post will be helpful for those who have doubts about which email marketing to use or have just started a journey into the marketing world.
Top Email Marketing Services
Before all, the automation tools I am listing in this post are different and answer to similar needs of a marketer. Some of them are all-in-one solutions; others aim to facilitate a specific issue. Interestingly, you can combine one tool with another.
How to choose the best marketing software? Pick the one that will help your business needs or goal.The right email marketing tools are about answering the challenges.What are some that marketers consider crucial? Scheduling, organization,personalization, segmentingand data collection. Each of them is equally important for the open and click rates within lead generation.
At the same time, many of you have struggled with email templates; there are tools for it as well. Among other things, the platforms help to track results and report on valuable data. All in all, it is what a reliable marketing tool is to be expected of.
Let’s look at the options that can help you with the email marketing objectives.
1. Constant Contact
Constant Contractis at the beginning of the list as it has a specific focus on email marketing and has been long enough in business. Despite the idea that I had used it only for a while, many colleagues of mine refer to it as an excellent solution for small business. Why is it good?
First of all,it puts simplicity and accessibility in email campaign designation. For instance, the particular platform offers the management of emails, sending schedule, and content. It refers to template and newsletter creation, together with the insertion of CTA buttons. Importantly, it has integrations with Shopify underlining its usability for small businesses.
Also, it offers email list management and segmenting for better targeting. In the end, it is used by many small companies to generate leads. However, what I heard is that their users wish they paid less for the simplicity the particular platform offers.
2. GetProspect
Have you ever struggled with your email list enrichment? I bet you are.GetProspectemail findermay be a solution with its simple interface, easy-to-use functions, and extracting possibilities. I have worked at this company for some time and must say it does a pretty good job in what they offer. What exactly is it, and what value does it provide to your business?
Well, small businesses usually struggle with getting contacts of their target audience. If you are a b2b service, they may be business owners, CMOs or CEOs of firms. If you are a marketer or SEO specialist, they may be influencers or bloggers. Lastly, if you already have an extensive database, you may need to verify it.GetProspecthas these functions. With it,you can extract the emails from Linkedin or any corporate website.
It’s not the only email finder on the market. Still, it can be integrated into other CRMs byZappierand has a very minimalistic design. Thus, you can extract your groups of contacts, transfer them to the greater platform and produce the campaign you want.
Many of its users say that that simplicity and straightforward solution to email enrichment captivate them.
3. Mailchimp
You probably have heard of this marketing tool. It is one of the leaders for a reason. If I haven’t mentioned this in my post, it would be a mistake. Why is it good? There is a free package, providing valuable functions, while paid options are to bring even more.
I usedMailchimpfor its easy-to-use tracking and email building. Particularly, it has thedrag-and-drop feature, which can help a lot if you are new to email design.
Simultaneously,Mailchimpcan be handy in segmenting audiences. I had to use it on my first marketing assignments and was very glad it had a drag-and-drop function. Making discount coupons and give away campaigns required much less time, thanks to a large collection of templates.
However, looking back,I can say it has basic analytics and segmentation, while for the advanced ones, the user should pay. Notably, a friend of mine had some issues with the support department and their responses. Bad luck, possibly.
Lastly, integration capabilities with other platforms can significantly add to the user’s experience, though. It will be a great choice if you are supposed to level your email creation before entering a larger market and nurturing more leads.
4. Hubspot
HubSpotis another popular solution that many businesses use.The pros of this email marketing software lie in its universal nature. The particular software offers an all-in-one automation solution for many marketing platforms. However, it as well as a separate email marketing tool that is free.
Similar toMailchimp, it providesassistance in preparing visual materials and producing the body of emails. Some of my colleagues did like the interface and the follow-up sequences upon purchasing via websites. However, as it is a free tool, though, by a recognized company, it has some limitations, while the full version can be costly for small firms.
I would be using it if I have plans of enlarging my business, where email won’t be the crucial part of my marketing activity but add to the social media strategy. At the same time, it would be great if you are trying and experimenting with email marketing or considering unifying all of your channels under one CRM system. Then,Hubspotwill be the perfect solution.
5. Sendinblue
Sendinbluehas made it to this list due to its surprising features, considering the time we live in. Who sends SMS messages today when we have messengers? However,the particular tool does!It as well facilitates email campaigns management, having automation and personalization possibilities.In short, it is excellent for transactional messages sending. I had my team use it for one event project, and it did great.
Simultaneously, the template options are not as advanced as the top marketing email services above provide. Thus, choosing this option would be suitable for those who have their template game on an adequate level. That is one downturn among some other ones.
They refer to a limited free package and multiple logins only under advanced packages.
Still, it is affordable and should be a good choice if it suits your goal and strategy.
6. Sender
In regard to this email marketing software,you may want to use it if you pursue your deliverability improvement.The algorithms behindSenderfocus on tracking delivery rates. At the same time, there is a facilitator for template creation. One can add different visuals that will for sure optimize the engagement rates of the campaign. The service pays attention to details making your email marketing campaign bright.
Still, I heard that they had some lags within their segmentation feature, which the company is likely to have taken care of. Why? Their customer support is friendly and lends a helping hand irrespective of the issue’s complexity, despite thatthe pricing is relatively low.
7. Drip
You may think that this tool can be helpful in drip campaigns. This mailing campaign softwarehas a powerful segmenting focus and synchronizes with many website constructors.
Such a combination makesDripuseful for many entrepreneurs or small business owners that conduct their business online. In addition, they have a bunch of personalization features. That’s why many consider it ideal for firms with small operations in specific niches.
One of the cons is thatit can be a bit pricey. Yet, it offers some educational materials for users. Again, thedata analytics, targeting features, and personalizationwithin this email automation service can become a game-changer for an owner of a small firm.
8. Convertkit
Convertkitis another email marketing tool that is handy in email campaign designation. AsDriporMailchimp,it is excellent for segmenting the audience. However, compared to them, this service offers it throughtagging. Some colleagues of mine have said that it is easier to have different groups and target them by tags at your display, especially if there is only one product of yours.
On the other hand,the particular instrument can be challenging to use at first.You may need some time to comprehend all the functions. This happened to me, and I decided to go for another solution. Still, if you want to enhance your lead generation funnel, this can work.
9. Aweber
Aweberis atraditional and straightforward mailing campaign softwarethat was designed solely for email marketing. It has bothadvanced and drag-and-drop featuresfor template creation. Besides, as it is a long time on the market, it has an extensive knowledge base and support.
Moreover, it has all the standard features referring to personalization, follow-up automation, listing and segmentation. Notably, what is the most important thing is its simplicity.
I believe I have started my email marketing journey with this tool, andfor me, as a newbie in marketing, it was pretty easy to use.That’s why it can be a universal tool for tiny companies who just start selling their product and have not developed large lists yet.
10. Omnisend
Omnisendcan be a great choice if you are developing your business on several channels. Although it has a basic set of features, it has SMS automation features and can work with numerous platforms.
You can have different campaigns, while theOmnisendreporting system will show from where you got the revenue. It is essential for prioritizing the campaigns and offers for the customer groups.
Except for simplicity in management, automation and the beautiful design of templates, it can offer affordable packages.Suppose a person needs something for a small business related to visually pleasing products, like jewellery or craft. In that case, they are likely to benefit from the templates of this email campaign software.
Lastly, if you want something that would better align with other strategies or website designing, another option can be a better solution for you.
Bottom Line
There are many email marketing software, andpicking the right one depends on your goal and your business.You may need an email marketing tool solely for email campaigns or contact research. The best is the one that is the most efficient. I have made this list due to what I experienced and heard from my colleagues.
When choosing the best tool,look at what challenges you have or how a tool can give you an advantage. If the issue refers to contacts extracting, then,Getprospectis a solution. If you have multiple products and many platforms or channels,MailChimporHubspotcan be a pick.
If you need some help with templates, picking an email automation service focusing on their designation would increase your engagement rates. Lastly, if you lack segmenting,DripandConvertkithave efficient mechanisms and reporting to work with contacts’ data.
Education, especially college education, is facing an existential crisis. Partially due to demographic factors, and in part due to decisions made by policy-makers at national, local, and academic levels, colleges and universities are struggling to stay afloat. What’s more, there are signs that conditions are likely to get far worse for the academic world for at least the next couple of decades. The question this raises ultimately comes down to “what is it that we as a society want out of our education institutions, and what is likely going to need to change for them to survive moving forward?”
We are less than half way through a broad decline in the birth rate globally since the early 1990s, with the worst yet to come.
The Looming Baby Bust
While there are many factors that influence the future, there are a few indicators that futurists watch as closely as the birth rate. This measure – the number of births per 1000 women per year – has a profound influence upon everything from the economy to the rate of innovation to social trends. If you know how many people are born today, you have a surprisingly good idea about what the world will look like in 30-40 years, more even than technological trends or sociological shifts are likely to influence.
There have been a few major inflection points in the birth rate over the last century. The rate had increased and decreased with regularity in the United States in particular (where this article will remain focused) until the Great Depression in 1929, where it was at an historical low. However, by 1936 the population had started to increase dramatically, ultimately resulting in the single largest increase percentage wise in population of any generation in the last four hundred years. By 1955, when the population known as the Baby Boomers peaked, there were 3.9 births per 1000 women, which translated roughly to a family size of almost four children per set of parents.
For reference purposes, a population needs to have 2.1 births per 1000 women for the population to otherwise remain stable (for births to exceed deaths). It’s worth noting that the United States has not been above this reproduction rate since 1972, which means that the entire increase in population in the United States since then has been due to immigration.
There was a second echo boom that peaked about 1993 (when the birth rate was just a hair under the stability rate at 2.07), plateaued before peaking again in 2008, then started declining dramatically thereafter before hitting bottom (?) in 2019. The birth rate for 2020 was only slightly above where it was in 1972, at 1.78 births per woman. We could be looking at the start of a new cycle at this point, but the stability point is also determined by the death rate, which has taken a significant hit due to Covid-19.
There are a number of implications that this brings, especially with regards to the educational system. Someone born in 1991 is now 30. By 2010, the number of entering college students peaked and plateaued for about four years. The students entering college today were born after 9/11, and colleges and universities are already seeing a drop-off in the number of students. However, in four more years, we will start seeing students reaching 18 (nominally college age) who were born on or after 2007.
What’s significant about that year? That was the year the Great Recession hit, when there was a sharp drop off in the birth rate that would end up lasting more than a decade. Federal funding of post-secondary education had been declining, leaving states to pick up the slack at precisely the time they were already hammered by lower tax revenues due to declining enrollment from the recession. This pushed more students (and their families) into taking out student loans, saddling those same students with higher school debt even as jobs remained scarce, and putting a damper on college for those coming in since then.
In 2025, those born in 2007 will start going to college, but there will be fewer of them, not just in relative terms but even in absolute terms, and this trend will continue until at least 2039, when those born today start college. What that means in practice is that colleges and universities are going to be facing the biggest student drought in history, with enrollment down by as much as 25% from current levels by the end of it, assuming the current composition of students.
The Covid-19 pandemic is another system-level shock, albeit one that will likely fade over time in terms of impact. One thing that it has done, however, is to force the adoption of online telepresence to happen five to ten years sooner than it would have otherwise. Put another way, without the influence of the pandemic, social inertia would have likely meant that it would take another decade to get to a point where we’d be interacting at the same level we are now. Once the threat of Covid-19 finally fades (hopefully by the end of 2021), there will be some return to older patterns, but far less than many managers today currently believe.
For businesses, the move towards working from home has been mixed, especially outside the digital space. Digitally oriented businesses have generally thrived during the pandemic, but physically based businesses, from restaurants to manufacturing to entertainment to mostly brick and mortar retail, have been hit hard. In theory, colleges and universities should have been able to adapt, but while most universities would have seen themselves as being digital in nature, the physical constraints and tacit assumptions that colleges work under proved far more limiting than expected.
Covid uncovered an uncomfortable realization. It was perfectly possible for students to attend remotely, assuming that most universities have only a small percentage of students attending remotely. However, all too few schools actually had the infrastructure to go wholly virtual, and the requirements and complexities inherent in maintaining a large scale telepresence operation went far beyond what all but the most far-sighted of university chancellors had foreseen. Without the inadvertent cocooning effect that many schools had because of these assumptions, the attractiveness of universities as institutions was called into question. With tuition dropping, other sources of income – from football game revenues to the chance to appeal to alumnae – also suffered, making it evident just how dependent these institutions were on the notion of geophysicality, and the funding that came as a consequence of that.
Many colleges are also facing lawsuits from students or their families as those students were already charged for classes that were cut short, and tuition revenue has continued to drop as students became unsure whether or not they would, in fact, be returning to physical campuses in the fall of 2020 (or the winter of 2021, or the spring of 2021, for that matter). This has starved university budgets that were already being overtaken by administrative and facility costs, with the very real likelihood that by the end of the 2022 school year, many colleges and universities will be hopelessly in the red with little in the way of support revenue.
Covid-19 hit at the worst possible time for Universities, ironically because of the effects of the Internet (itself largely an academic innovation) on the availability and dissemination of information. One of the primary values of universities has long been its role in the acquisition of specialized knowledge by students, with a secondary value being that such schools also provided the means to certify that a person had a sufficient grasp on that knowledge to be able to employ it. Yet while university costs have climbed, overall the availability of that knowledge outside the formal educational system has also increased, raising the appearance that universities are less about teaching than they are about certification and gate-keeping. Given the fact that wages have, outside of a few in-demand verticals, remained largely stagnant, this has caused a growing number of students to question the value of that education in the face of rising tuition.
To make matters worse, corporate training programs and certifications are now competing with university degrees as sources of accreditation, and are increasingly becoming preferred over four-year or higher degree programs by employers, especially in areas where technology is changing so quickly. These programs also typically attract teachers that may not necessarily have advanced degrees, but often have developed industry knowledge through experience in the field.
Finally, there has been a hollowing out of the pipeline of graduate students and assistant professors as wages have generally not kept pace with even the tepid rise in wages in the private sector for skilled talent, and as the ultimate academic prize – tenure – has been phased out in university after university. While associate professorships and above have generally paid moderately well, typically the principal benefits that have accrued there come not to those who publish, but to those who patent, usually with the university claiming a significant chunk of license royalties.
These were existential problems even before Covid-19 manifest itself, but with the severe market downturn in 2020, potential students and their parents began to raise the question most university provosts dreaded hearing – was the value to be gained by the students worth the cost, especially when that cost might take decades to repay in full?
A university is a business, and any business that fails to adapt to changing market conditions will likely fail. The triple threat of demographically-induced declining student enrollment, the rise of the Internet-enabled competitors challenging the fundamental nature of education, and an overreliance upon external factors such as sports revenues and aggressive claims on patents (exacerbated by the Pandemic undercutting both), have all contributed to a situation where the question comes down to not if education is likely to collapse, but when.
In any discussion about academia – colleges, universities and post-graduate schools – one of the usually-unasked questions involves the role that these institutions need to play in a healthy society. Do we need a post-secondary education system as a society? I would argue that, if anything, we need it now more than ever because the need to learn has only grown in the last half-century.
The skills that you need for almost any job today have certainly changed wildly even from what they were a decade ago. Even in fields that would seem on the surface to be fairly timeless – such as archeology – changes in other disciplines (such as genetics, computer visualization, the rise of drones and satellites, and material science) have forced a radical re-evaluation of the models that we’d assumed fixed even a few decades before. The rise of ever more powerful digital tools is giving experts within any given field lenses that would have seemed fantastical at the start of their career.
Indeed, one of the hallmarks of the early twenty-first century is the emergence of the subject matter expert or research specialist as a key part of any organization. Similarly, while information technologies are eroding the traditional role of librarians in society, what they do today – helping to create information systems, establish classification models and taxonomies, and provide the infrastructure to better perceive insight from that information system – is critical for every organization. The technologies to manage these things are young, many appearing in the years since these data curators went to school and without an educational infrastructure in place, there is little cohesiveness for gaining the skills that are necessary to work with these tools.
Moreover, what that education should provide is not necessarily how to use the tools, but rather the context to make the most use of those tools. It has been well demonstrated that most innovations, in science, technology, the arts and elsewhere, occur when disciplinary domains collide. The most exciting discoveries in the world of archeology, for instance, are not coming about because of an uptick of new digs, but because archeologists are now able to synthesize their own domain knowledge with what’s coming out of population genetics, are able to visualize what ancient cultures look like with the use of artificial intelligence and architectural visualization, and able to take advantages of advances in climatology to get a better sense of the overall gestalt of those long-gone cultures.
These discoveries and innovations are occurring not because John, a geneticist, met Jane, an archeologist, at the rare university interdisciplinary luncheon, but because the Internet has made it possible to be aware of what’s going on outside of one’s discipline and having done so, encouraged communication between potential colleagues. To use a knowledge management metaphor, expertise has become siloed in universities.
This is not just an academic issue, admittedly. The same expertise has also become siloed within corporate organizations, as organizations, including publishers, want to be able to monetize their experts by controlling access to them. This has always been a thin edge for organizations to walk, as expertise is also typically expensive, but at the same time, the value of that expertise is not so much in what the expert can do but in the reputation that they bring to an organization.
As the reputation economy becomes more dominant, this too provides a conundrum for universities and corporations alike. Reputation comes about due to exposure, and one role that universities in particular play is to provide that forum for exposure to expertise. However, pre-Internet, that exposure came about primarily by being able to reach out to 200-300 people in a small amphitheater on a weekly or biweekly basis. Today, a high school student can have a following of a million people in a live stream, and the wages that the university would pay to that professor are dwarfed by what can be made from advertising revenues on YouTube.
Not surprisingly, those at the lowest rungs of the the educational hierarchy have taken a keen interest in what’s happening here. Ironically, tenure may be to blame here. If you think of a university as a network, advancement takes place through vacancies. In most corporations vacancies take place all the time: people leave for higher paying positions or are promoted up through the ranks, and the need to fill those positions ensures a certain degree of mobility within the organization.
In universities, on the other hand, tenure means that available positions open up far more slowly, not just within any given university but in all of them where tenure is present. This in general creates a Hobson’s choice for senior administrators – expel professors for real or imagined wrongs (creating a public relations nightmare), push more tenure track professors into administrative roles that they may be ill-suited for (and in general adding to the administrative overhead while reducing the reputation value of that professor), or live with a certain degree of churn at the bottom as grad students and non-tenured professors become disillusioned. As the goal of hiring very intelligent people is to increase the reputation of the college, none of these is or should be palatable choices, but they are all too often made without much strategic thought.
A similar factor involves the production of textbooks. The more forward-thinking textbook publishers came to the realization fifteen to twenty years ago that with the advent of the Internet (and eBooks), their business needed to adapt, or it would die. Textbooks are expensive to publish, require a huge amount of work to pull together, and typically have a comparatively small audience, all of which result in textbooks often costing from $50 to $250 or more. On the other hand, professors and universities both wanted to have their names on a textbook, especially if it became a de-facto standard, because it increased their respective authority in the field. The high costs of producing such textbooks also provided yet another gating factor towards keeping out competitors, as you had to be fairly large to field both the financial and production wherewithal to create them.
The eBook, digital production, and the Internet as a distribution platform, changed that equation completely. Instead of focusing on the book, the publisher began focusing on the chapter, with the idea being that good editing could make chapters become largely stand-alone entities. Digital production meant that you could aggregate different sets of chapters together, possibly with some intelligence in those chapters to allow for differences in graphical branding, as well as the use of specific metadata that would control how content would be displayed in different contexts. Semantic linking to tie related content together and the introduction of search capabilities have also been deployed, neither of which existed in any meaningful way for printed books.
Digital, distributed publishing further meant that such chapters could be combined with others upon request (or even made available as solo content), and eBook distribution platform meant that books could be made available upon your phone, laptop, or tablet as necessary, which reduced both distribution costs and cut down dramatically on student chiropractic bills. That such an approach gave instructors much more say in presenting the material that they felt was relevant was a nice side benefit, while also giving professors the chance to publish parts of more work while still focusing on teaching and/or research, rather than having to take sabbaticals (and the likely financial hit that would come because of it) to solely author a single textbook. These approaches increased their citation count, while at the same time facilitating revenue earlier and faster.
Not surprisingly, as the Internet has made the dissemination of ever more complex forms of media trivial, this has also meant that many instructions now regularly supplement their income with the creation not just of written text but of full curriculum materials, adding to their reputation in the process. This has changed the nature of the relationship between instructor and university, shifting from being an employment arrangement to an affiliation relationship. Not surprisingly, while this may reduce the direct costs to the University, it comes at the expense of a weakening in the relationship between the two agents, part of a broader trend occurring between organizations and the people who work with (rather than ostensibly for) them.
Of course, it should be noted that the publishers that didn’t adapt are no longer around, having been acquired for their catalogs by those that did.
Therein lies a strong cautionary tale for universities. The Case of Too Many MBAs provides another such tale. The very first Master of Business Administration, or MBA, was presented by Harvard University in 1908. For a number of years, the exclusivity of the degree meant that it was a highly sought-after certification. By the 1970s, most major universities with graduate schools had MBA programs, and there was a growing backlash as people who were hired because of the MBA were proving unable to be effective managers in those businesses that required specialized technical knowledge – which usually meant most companies. By 2020, MBAs were given about the same weight as a Master’s degree in any other field, and in many cases, less.
The MBA was devalued by ubiquity. Unfortunately, the Internet is all about ubiquity.
Given all of these factors, it is a safe bet to expect that Academia will need to change dramatically to survive. There are several trends that will shape what the education of tomorrow (and it may be a very soon tomorrow) will look like. This includes many or all of the following:
Distributed and De-Localized Education.The trend of untying education from a physical place and making it virtual was already underway even before Covid-19, but it has accelerated dramatically in the aftermath of the Pandemic. In effect, universities are becoming media publishing companies, with the media in question being “educational” in nature. Hybrid models may very emerge as a consequence, but such a hybrid would treat universities as being more like retreats rather than physical institutions.
Decommissioning Campuses.Just as many banks are becoming nervous about commercial downsizing of both office space and production facilities, so too should university boards of directors be sweating the closure and decommissioning of campuses in favor of virtual instruction. A significant number of college campuses were either constructed or expanded in the 1960s and early 1970s as the Boomer generation started to go to college. These facilities are now 50-60 years old and showing their age as the costs to keep these campuses functional continue to climb. As classes empty, the incentive to just sell off the campus property outright will become unavoidable.
Become Global.The flip-side of this is that the location (or even nationality) of the student should not be a gating criterion. The European Union already provides an example of this, where anyone within the EU can attend an EU university without penalty for being “out of state” or an “international student”.
A Move Towards Certification Rather Than Credential Oriented.In many respects, academia needs to become more agile, and one way it can do that is to reduce the overall time it takes to receive some form of certification, possibly down to the half-quarter or quarter level (i.e., six weeks to three months).
Make All Education Continuing.One major problem with the credential approach is that it tends to force education into the period from age 18 to 25 with continuing education often given short shrift by universities, which sees it as not profitable. However, by moving towards a certification model, you give people who are in the midst of their career the opportunity to learn from the best, without forcing them to take a hiatus from their careers. It also increases the “fatness” of the tail, so that the drop in immediate post-secondary students can be offset by more, older students.
Sell Certification, Not Education.Colleges need to acknowledge what has largely been unsaid for decades – you go to school to get the certificate, not to get an education. Once you do that, it opens up an alternative way towards paying for education: you can take the class repeatedly for free, but, especially with advanced classes, you only get credit (and only pay for credit) when you pass.
Move Towards Electronic Mediation.Teaching a class with ten thousand students is far different from teaching with thirty, especially when it comes to grading homework and tests. Using a combination of AI for essays, electronic mediation of tests, crowd-sourcing, and rules-based analysis, an instructor can more readily identify where students are having trouble, which is the real value of tests. Currently, most of this work is done by graduate students, at the cost of their own studies.
Individualized Curricula of Study.Most traditional universities work upon the assumption that there are specific courses that you have to have, in a specific sequence, in order to achieve a degree. This may force a student to take courses that they have already mastered, that are outside of their desired area of expertise, and in general serve to reduce the possibility of cross-specialization, at a time when cross-specialized is a highly desired trait. If a student lacks the skills to pass an advanced class, then they will drop back to a simpler class at no penalty.
Student Community MOOCs.Give more senior students the tools to create educational materials for junior students. Build an active community of users, combining the features of MOOCs and virtual worlds, with some mediation from instructors, and give credit for this in completing advanced classes.
Drop Educational Degree Requirements for Instructors.Teaching is a skill, like any other, and you do not need a Master’s (a six-year degree) or Ph.D. (an eight-year degree) to teach. There are a great number of people with real-life skills and experiences that are prohibited from teaching because they cannot take the time from already busy careers to spend two years learning what can be taught in six months. They are going elsewhere to teach, to academia’s loss.
Spin Off Research Institutes.This focuses on the fact that all too frequently professors treat their graduate students as free labor, often taking advantage of their ideas and input while delivering little of value in return. This is a toxic relationship that has become institutionalized. Universities would be better served spinning off their research work as separate businesses and then hiring graduate students as associate researchers. There is nothing that says that a professor cannot be employed in both capacities, if he or she so chose, but by making these two different roles, you don’t end up with brilliant researchers but awful teachers being forced to teach (or vice versa). This also holds true for the arts and humanities (consider the Clarion Writer’s conference as being a graduate school for writers).
A League of Their Own.Football has been a defining trait of universities in the US for decades, but that’s an aberration. Baseball, hockey, and similar team sports have a tiered farm-team system where young players are able to concentrate on learning the basics, why not football? As with research institutes and retreats, it’s possible for a university to spin off its football team into an (already extant) league, letting them negotiate both physical and pay-per-view rights independently of students seeking an education while still maintaining branding ties with them and supporting revenues.
Integrate Vendors and Companies.Education is an expensive proposition for most corporations, and many are loathe to create extensive training processes without some kind of financial backstop to make training revenue neutral at a minimum. One idea that may come about from reimagining the post-academic world is to build out a conceptual platform that both governmental and private organizations can plug into. This way, organizations can specialize in providing education that may be of value to both customers and users while at the same time being able to be findable within a broader network of curricula.
Begin With the Community Colleges.Most community colleges are already experimenting with several facets described here, and there’s a push at the federal and increasingly the state level to make at least the first two years of community college free. This is a good thing, but it also needs to be done in such a way as to be both sustainable long term and largely protected from shifts in the direction of political winds.
Speculative author William Gibson has been attributed as saying “The future has arrived — it’s just not evenly distributed yet.” This holds very much true for post-secondary education. No, we’re not going to see a wasteland of boarded up university campuses any time soon, but it is very likely that even while ivy-covered halls will continue to stand for decades, its important to realize that the underlying nature of how we educate people is undergoing radical change right now, and that what education will look like a decade from now may look very, very different than it is today.
Kurt Cagle is the editor of The Cagle Report, and is the Community Editor forDataScienceCentral.com.He lives in Issaquah, Washington with his family and his cats.
Enterprise systems help integrate various business functions across an organization. As good as it sounds, developing an enterprise system has hidden pitfalls that can affect your spending. Read our in-depth guide to get the best out of enterprise software development.
What is enterprise software development?
Enterprise software development focuses on a company’s needs rather than individual user’s needs. Since enterprise apps are used within a company, they are developed based on the internal environment and business processes. Organizations of different sizes and industries have different demands. The goal of enterprise applications is to fulfill a company’s specific needs and meet specific business goals.
The majority of modern enterprise systems implement theSaaS modelwhen software is developed using web technologies and is hosted on a cloud. Sofware as a service approach ensures rapid performance and flexibility of a system. However, some organizations still prefer traditional desktop apps as they provide greater security and control.
The three main types of enterprise systems
The first and foremost task of any enterprise software is to store, process, and transfer data. Most systems provide various workflow automation and document management tools designed for a specific industry or company’s activity field. The larger the organization, the more complex system they need. For example, large corporations combine several applications or choose integrated enterprise systems for their supply chain, inventory, accounting, and sales management.
Here are the most common types of enterprise software used in retail, pharmacy, real estate, manufacturing, and other industries.
Customer Relationship Management systems
Customer retention can be challenging. Companies make tremendous efforts to find new clients, but they put even more resources into establishing solid relationships with existing customers. CRM systems centralize and streamline all customer-related operations by gathering, categorizing, and analyzing customer data from various channels. That’s why the CRM marketkeeps growingyear after year and will reach $114,4 billion by 2027.
The benefits of using CRM systems include:
Defining consumer trends and insights based on customer behavior
Enhancing the quality of brand communication and client service
Automating sales funnels
Increasing customer retention rates
Protecting customer data privacy
Engaging customers through loyalty campaigns and other marketing tools
Here are the top requested features for CRM software:
Contact management
Interaction tracking
Scheduling
Email marketing
Pipeline monitoring
Reporting and analytics
Integration with other platforms
Mobile accessibility
Artificial intelligence
Workflow automation Summing up, CRM systems help companies sell faster and provide better customer service. To keep all business processes in one place, many organizations integrate CRM within ERP systems.
Enterprise Resource Planning systems
ERP solutions are used to manage day-to-day business activities, including planning, budgeting, procurement, human resources, risk management, etc. ERP systems tie together different business processes and ensure safe data flow within an organization. This type of software is crucial for managing medium and large-sized businesses. The global ERP market worthwill hit$48,22 billion by 2022.
There are numerous benefits of using ERP software:
Lower operational costs thanks to optimized business processes
Data and infrastructure consistency across departments
Risk reduction through data integrity and control
Actionable insights based on real-time data analysis
Enhanced cross-team and in-team collaboration
Workflow efficiency
Lower maintenance costs
The most requested features for ERP systems include:
Accounting
Finance
Human resources
Planning
Marketing
CRM
Inventory management
Order management
Invoicing
Project management
Scheduling
Reporting
Although ERP solutions offer great opportunities for business growth, not every company can fully leverage them. To get the most out of ERP software, make sure it’s accessible for employees and aligns with current company processes. The safest option would be to choose software development services that combine ERP development with excellent client support, software maintenance, and integration.
Supply Chain Management systems
According to The BCI report, 69% of companies don’t have total visibility over their supply chain operations. Supply chain management systems increase visibility and control throughout supply chain activities. SCM applications are involved throughout the whole supply chain lifecycle, from production to logistics and warehouse management. They take care of end-to-end supply chain transactions, data and document flow, supplier relationships, and other related processes.
The advantages of using SCM systems include:
Lower costs and better financial control
Eliminating logistical errors and other risks
Maximizing customer value
Effective forecasting and decision-making
Faster time-to-market
Better customer service and communication
Here is the essential functionality of supply chain management software:
Inventory planning
Warehouse management
Logistics
Returns management
Sourcing and supplier management
Order management
Inventory management
Order management
Data analytics
Procurement
Production lines maintenance
Supply chain management software development is vital for companies looking to keep up with the market competition. The best option is to partner with a company that provides custom software development services. This way, you get a system that seamlessly integrates into your workflow and serves your specific company needs.
Data Cleansing and analysis are the first steps in managing the quality of data. Cleansing is the process of correcting and detecting inaccurate data from your datasets. Being a very critical step it directly affects the accuracy of the data.
Data Analyst spends lots of time in data analysis to ensure there are no inconsistencies, fixing errors, correcting invalid nulls among others. All good analysis relies heavily on clean data. Achieving this result of clean data without any negative impact on the data’s integrity is very difficult to achieve using traditional cleansing methods.
Traditional cleaning methods are tedious and repetitive that will make data analysts exhausted before they even get to the data analysis part. This cleansing has now been made bearable by modern analytics such as DQLabs.ai
Challenges of Traditional data cleansing:
It is very hard for data analysts to track all the actions in data sets and tables.
Data analysts spend lots of time cleaning the data.
Difficult in tracking all the changes.
Modern data Cleansing:
1. Data Structures Visualization – Critical decisions such as removal of data are made from an overall understanding of the impact on the quality of data. Visualization of all the data can help analysts to wrangle some of the data while still maintaining focus on the bigger picture.
2. Cost-Effective – Cleansing can now be undertaken by one analyst this saves time and money.
3. Can Track the changes – When the contractor undertakes the tasks of cleansing, clients are able to able to track all the changes they made and the impact they had on data quality.
4. Deduplication – This modern data cleansing is possible to remove the duplicates in order to attain a single golden record. This is a very critical step in data analysis as it increases the overall data quality and the accuracy of the analysis findings.
Traditional cleansing methods are not only time-consuming but also increases the chances of negative data quality. Modern data analytics tools have simplified the process of cleansing and their effective analysis of data sets consistency increases the quality of data. With all these benefits in mind, it is no wonder many organizations are saying goodbye to traditional data cleansing techniques and adopting modern platforms with AI and ML capabilities.
The figures speak for themselves: there are currently more than 1.5 billion websites on the Internet. According to internetlivestats.com, with new websites created every second. However, there are only about 200 million active websites. These digital benchmarks continue to change, as do trends in web development and technology.
With changing emphases and new technology players, companies must always stay on top of the latest web development trends, technologies, and approaches to take advantage of new opportunities.This means that it is essential for web developers to be well informed about current trends to deliver relevant and quality software products.
There are several key trends emerging in 2021 for web development:
1| Artificial Intelligence (including chatbots
AI and bots are among the major trends in web development 2021. It is not surprising – the demand for automated communication has increased significantly this year. The popularity of AI chatbots and virtual assistant applications is therefore on the rise. Case studies include vacuum bots and Netflix or TechCrunch bots designed to provide movie suggestions or news updates based on deep analysis of human behavior and all previous interactions, to name but a few. As AI solutions gradually replace human intelligence, AI bots can eliminate many customer service professionals, which will undoubtedly reduce costs for companies.
2| Blockchain
Why is Blockchain on the list of the fastest-growing web programming trends? First of all, it is a high level of security. As there is no intermediary between transactions, every transaction is verified. In addition to the safety of the transactions, there are many benefits, including improved cash flow, website interactivity, reduced financial costs for businesses, fewer transaction contracts, etc. So, if your company needs total data security, it is an excellent decision to involve Blockchain in the web project.
3| AR/VR
Augmented and virtual realities have rapidly gained popularity in recent years. E-commerce is one of the sectors where virtual reality and augmented reality enhance the user experience, turning user stories into business success stories. Augmented and virtual realities, on the other hand, are among the most popular new technologies in web design, as they are intelligent tools that help potential consumers choose the most suitable product. The creation of virtual showrooms can hardly do without VR, and virtual fashion shoots look visually appealing while saving buyers time.
4| Progressive Web Applications, or PWAs
According to Wikipedia, a progressive web application (or PWA) is a type of web application software built using standard web technologies such as HTML, CSS, and JavaScript.
The notion of a progressive web application is not original, but PWAs remains one of the most popular trends in web programming. Apple, Google, and Microsoft are betting on its effectiveness, while Forbes and Twitter are reaping the real benefits of PWAs, such as increased impressions and reduced bounce rates.
The main advantage of PWAs is their efficiency. According to Google’s report, 53% of mobile site visits are abandoned if they take longer than three seconds to load. PWAs are always accessible and allow their users to enjoy the whole app experience without sacrificing convenience and quality.
5| Single Page Applications or SPAs
Another trending technology for web development, particularly attractive to developers because of its high performance, is the single page application (SPA). Based on JavaScript, SPAs do not require page reloads during use and provide users with a comfortable web space with clearly organized content. Easy to develop and fun to use, single-page applications are fast and responsive. Their users appreciate their simple, linear UX and comfortable scrolling.
6| Accelerated Mobile Pages, or AMP
AMP is an open-source library created by Google to improve websites, web applications, or web pages and reduce their loading time. The fast loading speed of AMP allows websites built with this framework to rank high in search engine results pages (SERPs). AMP remains the modern technology for website development for several benefits, including higher rankings in mobile searches, impressive loading speed, higher levels of audience engagement, and maximum user satisfaction.
7| Motion UI
Let’s take a closer look at user interfaces and the latest technologies used in web design. Motion UI is a Sass library for quickly creating CSS transitions and animations. Simply put, Motion UI provides a variety of pre-defined animations that can use in web design.
It’s an easy way to combine fun with real value, using primary and background animations, colorful hovers, animated graphics, eye-catching CTAs, and other elements as tools to grab the user’s attention and contribute to a more efficient web browsing experience. Motion UI websites keep users engaged and promote better interaction between businesses and their customers when done well.
8| Responsive design
When we talked about single-page applications, we already mentioned responsiveness as one of their strengths. Now it’s time to explore this concept in detail.
The more straightforward, the better – today’s businesses feel a direct correlation between the ease of use of their website and the number of loyal customers. Responsive form plays a vital role in the usability of websites. It includes responsive navigation on all devices and screen resolutions. If website users face minimal resizing or scrolling, it promotes their positive experience and increases the website’s ranking positions.
9| Push notifications
Push notifications initially design to inform users about current events and news, but they have quickly become an essential tool for driving conversions. This type of push notification intends to keep users’ attention and increase user retention. Cost-effective and affordable push notifications are actively used by companies seeking to provide a more interactive experience for their website users, whether small, medium, or large.
10| Cybersecurity
Recent trends in web development cannot go without security. Remote working will be a hot topic in 2021, and more and more organizations are looking to secure their data as much as possible to become digital businesses. As mentioned above, technology tops the list of innovations in developing customized web applications, as its distributed nature offers greater security and makes it more difficult for hackers to break into systems.
The research and development (R&D) phase of building an AI model to address a business problem is characterized by rapid exploration and iteration. Everything is on the table and experimentation is encouraged, from understanding how to frame the problem, to determining how to most effectively use the data on hand, to discovering the model architecture with the best performance.
In stark contrast to this, the operationalization phase of AI model development requires that the model be completely characterized, produce reproducible results, and be stable for integration in automation processes. Model versioning best practices and version control tools are essential to successfully navigating and overcoming this gap between R&D and production engineering.
Version Control Fundamentals
The practice of version control is nothing new. What developer isn’t interested in tracking and managing modifications to their software applications? Due to the large number of benefits associated with adopting these practices, ecosystems of software tools and applications exist in order to facilitate frictionless version control. The industry standard for this is Git, with a number of hosted software products based on Git being available including GitHub, GitLab, and Bitbucket.
While these solutions are necessary for managing complex software projects, they aren’t sufficient by themselves. In the data science ecosystem, there are emerging challenges that go beyond versioning software code and require innovation to address. For example:
Challenge: AI model development often produces very large model artifacts that exceed the capacity of traditional version control software.
Solution: Technologies such as Git Large File Storage (Git LFS) allow Git to be extended in order to accommodate very large non-source code files.
Challenge: The fuel for an AI model is data, however, datasets are massive, and storing multiple processed versions of the same dataset can be prohibitive.
Solution: Technologies such Data Version Control provide Git-like primitives for efficiently storing and versioning raw and processed datasets.
Key Takeaway: Developing a team that is well versed in version control principles will position you for success in adopting emerging data science version control tools.
Tracking Experimentation with Code and Data Version Control
During the R&D phase, fast-paced development has many advantages to allow for rapid exploration of ideas without much overhead. If this process becomes unprincipled, however, it can lead to loss of knowledge, duplicated effort, and expensive technical debt.
Fortunately, rigorous model code and data versioning (even during the experimentation phase) can serve as a fossil record for the iterative process needed to produce high-impact, AI models. In this way, developers can:
Preserve and disseminate institutional knowledge about an AI model, especially when there are multiple developers working on a model simultaneously
Revisit and reproduce the performance of different versions of a model after being unattended after some period of time
Ensure the model is in a state that can easily be transitioned into a production environment
Key Takeaway: Taking a proactive approach to experiment tracking and model versioning creates a streamlined process for achieving sustained value from AI models. In the domain of experiment tracking, tools such as MLFlow and SageMaker are good tools to evaluate.
AI Deployment and Model Versioning
To build off of version control best practices, the Modzy approach embraces versioning practices at all levels. Not only do we use source code and data versioning during the process of model development, but we also provide a universal template for AI models that incorporates unique identifiers (UIDs) and versions for each model on the Modzy platform.
This provides a single source code repository to develop and release multiple versions of an AI model. These model versions each have their own full description and code state recorded. This is a huge win for development velocity. It also makes for a straightforward process to crank out updates and variations on models in a reproducible manner.
Conducting model versioning in this way is not just useful for accelerating internal model development, but it is of critical concern when it comes to AI governance and auditability. Since all models deployed via the Modzy platform are packaged and set up for versioning in this way, every model prediction can be associated with a model and its history of development, data source, training procedure, and source code.
Key Takeaway: Modern version control practices are a foundational component for building the right ModelOps pipeline for your organization.
Version control practices consistently applied all the way from R&D to production deployment of an AI model are core to building a strong ModelOps pipeline. Dedicated technologies that are widely available make adopting these best-practices easier than ever. With a dedication to these principles, bringing model versioning to the domain of data science ensures quality, stability, and auditability of models.
Be sure to check out the other blogs in this series on Modzy.com:
