The world over, two pragmatic ideas are taking root. Businesses want employees to spend less time tinkering with their devices instead of being productive. They also want their IT teams to invest less time fixing PCs and more time providing value.
Kulvinder Dosanjh (Kully), Group Director IT Service Delivery and Business Information Security Officer at BMI Group, explains this from his experience in the new normal. His organization, a leader in the roofing and waterproofing segment, faces a routine Monday morning productivity loss familiar to many businesses: Service Desk tickets balloon, employees wait idle or are handicapped while their systems are fixed; the Service Desk spends time taking remedial action and hundreds of productive hours are lost across the organization.
The Work from Home trend gaining traction has compounded the problem. Employees are struggling with new processes, applications, and security protocols. They expect plenty of hand-holding from IT. Kully‘s organization is solving this by deploying automation and AI in end-user computing environments.
Kully sees the automated world delivering a different perspective to both business users and IT support teams. He strongly believes automation is and will continue to change the way IT support works and business users get support. With the intelligence built around end-user devices, huge amounts of system data get generated —which no human can practically go through— this data gets automatically captured, digested, and parsed. An AI layer is then used to create insights at a device level. These trigger self-heal processes, self-service recommendations, chatbot assistance, and a task list for the IT team.
“What this does is simple,” says Kully, “It proactively prevents incidents that affect productivity. It provides the IT team with tasks to follow up instead of having to take calls or emails from users.” Now, employees don’t wake up on Monday—or any other day—with the question, “What is happening and when can it be fixed?”
While a customer-focused process is better than an SLA-focused process, security has become a major consideration in the new world of accessing data from the outside. The old world of connecting to data and systems from inside to outside has shifted 180 degrees to access data from outside in– especially after the pandemic has affected global business. And with employees doing their own thing, enterprise systems and networks are more vulnerable. However, security systems, processes, and protocols can’t be made so complex that they inconvenience employees and hinder productivity.
Sujoy Chatterjee, Vice President, Infrastructure Services at ITC Infotech believes that a balance must be struck between the levels of security and the flexibility available to employees to use technology. To decide the right level of security, the business must be involved. At BMI, a central team uses a risk-based approach and ensures that decisions don’t impact business. As a matter of abundant caution, processes are in place to mitigate the impact of any breach in security.
“The goal,” says Kully, “Is to bring data back to business—without which, we may have no business!” The takeaway from the BMI experience can be boiled down to two essentials: One, leverage AI to make data more effective and improve productivity/ efficiency by empowering users and making their lives easy and two, do not overdo security to the point where it has the potential to paralyze business.
One question haunts every CXO: “How can we make our company and products better for the customer?” The answer, today, is straightforward. Data is what makes and breaks organizations. Data allows organizations to look back with surgical precision on their past actions and allows them to look forward with confidence to remodel the business in near-runtime. For the insurance industry, data has extraordinary significance. “It allows us to tailor services for customers instead of having a set of products you try and sell,” says Paul Johnson, CIO, and COO of PIB Group (an ITC Infotech customer), which is a dynamic and diversified specialist insurance intermediary that provides bespoke solutions for personal and business customers. Johnson is mindful of the fact that in a service-oriented environment, data provides the means to know the customer accurately and improve customer experience, boosts organizational efficiency, meets compliance requirements, and understands how markets may be shifting. Despite the upside, why is selling a modern data vision to the Board so difficult?
The reason Boards are wary of CIOs pushing ambitious data agendas is that they are never shown reasonable ROI. In the experience of leaders who have driven successful data modernization programs, the board needs to see two outcomes of data modernization:
ROI—which is not necessarily in terms of monetary impact—is possible in three years. For most insurance companies, legacy databases are, by nature, reporting systems. Boards need to be sensitized to the fact that modernizing data is preferable to overspending on a reporting database as the investment helps shape business strategy through insights. Data modernization must be seen strategically.
Quick wins that allow the board to buy into the data modernization journey. This then allows the organization to do all the other exciting things it can to make business better, including revenue uplift, improve operational efficiency, and ways to serve customers in different and more personalized ways for more effective upsell and cross-sell.
Five key shifts make investments in data modernization an industry imperative:
Direct, digital, and embedded sales will become dominant channels for growth, which directly enables cross-sell and upsell insurance
The subscription revolution will see insurance deeply woven into consumers’ everyday life
Ecosystems will expand as the cloud and new connections enable radical innovation
Real-time risk visibility and responsiveness will become a reality. Huge savings due to faster processing and elimination of fraudulent transactions
AI adoption will accelerate change
A simple scenario helps contextualizes the shifts. Imagine a customer asking for motor insurance. The right way to tailor the insurance is to seek data on car usage and build an AI-driven risk profile of the customer. Based on this, insurance is offered in a subscription model to fit customer needs.
While there are several challenges to modernizing data related to scalable architecture and questions around trust, security, and governance, all can be solved by leveraging an intelligent cloud. But it pays to pay close attention to Johnson’s words of wisdom that come from experience: Don’t bite off more than you can chew—don’t promise the world, promise what you can deliver; get IT and business to collaborate so that a business understanding drives data modernization; and don’t rush it—if you do, you’ll make mistakes.
Insurance organizations have always understood the value of data. It allows them to build risk models which are the bedrock of business. Now, they need to harness internal and external data and use it in real-time to improve customer experience, raise organizational efficiency, and meet complex regulatory requirements.
I’m a HUGE believer in the liberating mindset of Design Thinking. For example, Design Thinking not only makes Data Science more effectively, but it helps avoid the devastating unintended consequences of an AI capability that can continuously-learn and adapt at speeds exponentially faster than humans. I’m honored a speaker at the Catalyst Empowerment Summit on August 2. I will present “Blending Design + Data to Scale Innovation” in this lightning-paced event.
In preparation for the event, let me explain how Design + Data scales innovation by exploring how Design Thinking bookends Data Science.
Defining Value. The role of Design Thinking before engaging Data Science is to clearly and thoroughly define what one is trying to achieve. This includes clarifying the intended benefits and “values” across a wide range of perspectives (Figure 1).
There needs to be a wide range of dimensions of value against which the AI model’s effectiveness and accountability will be measured, and it is NOT just financial metrics. Get this wrong, and the AI models will optimize exactly what you told them to optimize, which may not have been what you intended (see Terminators, ARIIA, and VIKI for examples of AI models optimizing EXACTLY what the value statements asked them to optimize).
Data Science Customer Journey Map. We modified the Customer Journey Map to help the data science team identify the key decisions, analytics, and data necessary to help stakeholders successfully complete their journeys (Figure 2).
The Data Science Customer Journey Map identifies the data and analytic requirements necessary for customers to successfully complete their journeys.
Data Science Collaborative Engagement Process. The role of Design Thinking during the Data Science engagement process is to create a mindset and operating culture of exploration, trying, and learning. Throughout the Data Science Collaborative Engagement Process, design thinking drives ideation, exploration, and experimentation in tight collaboration with the business stakeholders – where all ideas are worthy of consideration with a “diverging before converging” mindset (Figure 3).
The Data Science development process is a non-linear “rapid exploration, discovery, training, testing, failing and learning” process, perfect for integrating design thinking capabilities.
Hypothesis Development Canvas. The Hypothesis Development Canvas ensures the data science effort directly supports the organization’s critical business and operational initiatives. A well-structured Hypothesis Development Canvas, like a well-structured storyboard, provides a concise yet thorough way to make a use case come to life. I spend an entire chapter in my eBook “The Art of Thinking Like a Data Scientist” on how to create the Hypothesis Development Canvas, if you are interested in learning more.
Design Thinking + Data Science. Design Thinking and Data Science both embrace a curiosity-driven, rapid exploration, rapid testing, continuous experimentation, failure-enabling, continuously-learning and adapting mindset (Figure 4).
The primary difference between Design Thinking and Data Science engagement methodologies? One is focused on machine learning, and the other is focused on human learning. And when you blend that learning together, it will be the ultimate empowerment of an AI-to-human continuously-learning and adapting environment.
From Technology Outputs to Business Outcomes. The role of Design Thinking after the Data Science engagement is to support a culture of continuous measurement, experimentation, learning, and adapting focused on business and operational outcomes (Figure 5).
Design Thinking techniques can drive the collaborate with the business stakeholders to continuously learn and refine the desired business and operational outcomes that leads to new sources of customer, product, and operational value creation.
Empowerment. Finally, design thinking guides the empowerment of individuals and teams necessary to nurture and scale innovation (Figure 8).
Key #1: Create a common vision and shared purposed around the organization’s “True North.” That is, what does your organization seek to accomplish and who do they seek to serve.
Key #2: Establish a Common Language (using Design Thinking – the language of your customers) so there is no confusion about what is being said, and a standard engagement “framework” that guides the value identification, capture, and operationalization processes.
Key #3: Embrace organizational Improvisation (improv) with the ability and agility to move team members in and out of teams while maintaining operational integrity.
Key #4: Create a culture built on transparency and trust that values everyone’s contributions and their unique and inherent assets and capabilities. Remember, you can’t mandate trust, you must earn it!
Key #5: Sharing, Reusing, and Refining Operating Environment. This is the heart of a learning organization, an organization that a share its learnings (both good and bad) and can reuse and refine what others have learned. This is the ultimate of standing on each other’s shoulders.
Design Thinking + Data Science scale innovation by identifying, codifying, and operationalizing the sources of customer, product, and operational value creation. But to create a sustainable innovation environment requires empowering everybody; that everyone feels empowered to contribute and lead. It is within this culture where the AI-to-human collaboration will drive game-changing innovation and new sources of customer, product, and operational value creation (Figure 7).
The phrase “All models are wrong, some are useful” is quite loosely used. Some take it in a very literal sense to imply that “Modeling is a futile exercise”.
This is a terrible misunderstanding and we shall see why shortly.
“All models are wrong, some are useful” is an aphorism (meaning it is a concise expression of general truth). But the aphorism in this case leads to misinterpretation.
Firstly, it is important to understand what modeling is.
The purpose of modeling is to provide an abstraction of real process. Basically, a good approximation of reality.
Anybody who mistakes the abstraction for the real, commits the Fallacy of Reification (yup, one more fallacy to add to the list of all fallacies which we data scientists/statisticians commit).
Anybody who mistakes the abstraction for the real, commits the Fallacy of Reification
Coming to why the phrase should not be taken literally, a good analogy would be that of an example of a Map.
In an exact sense, a map is also wrong because it does not provide 1:1 mapping of the real world.
So, George Box’s phrase should be construed the same way as a map is considered wrong because it does not represent the real world.
Does a map provide 1:1 mapping to the real world?
No.
But is it mighty useful?
Hell yeah.
Also, talking about utility of the models, John Tukey’s words conveys the essence clearly.
“Far better an approximate answer to the right question, which is often vague, than an exact answer to the wrong question”.
So, to summarize
“All models are wrong, some are useful” ≠ “Modeling is a futile exercise”.
Reference: On Exactitude in science — Jorge Luis Borges
E-Discovery is becoming the number one priority tool for seamless transition day by day. It has significant implications for retaining, storing, and managing the electronic content of organizations.
Businesses of all sizes have to go through litigation at some stage. Most civil and criminal cases are accompanied by electronic discovery requests. And if it is your first legal case, this is the best time to get familiar with eDiscovery.
About eDiscovery
E-discovery is simply an extension of the traditional discovery process, which involves any Electronically Stored Information (ESI) that an organization possesses like email messages, presentations, voicemails, word processing files, tweets, spreadsheets, and all other relevant communication or information that can come in handy in a litigation case. eDiscovery can be extended to any platform where ESI has stored: computers, laptops, servers, tablets, smartphones, and other electronic devices.
The fact is that all the documents these days are stored electronically. Single-plaintiff employment matters, commercial disputes, divorce, personal injury – all type of evidence is now created, collected and converted into electronic form. Emails, word processing, financial data, social media postings are all stored electronically, and even if something is on paper, it is probably printed via a computer file.
In addition, for the minor cases as well, lawyers must know oh to handle ESI in order to keep discovery transparent, responsive, and proportional to fulfill the factual needs of the case.
How does eDiscovery works?
The standard eDiscovery procedure starts when a lawsuit is anticipated. Attorneys who represent litigants from both parties will establish the scope of the eDiscovery request, research the relevant ESI, and put it on legal hold.
Once the request regarding eDiscovery is issued, the litigants must submit all related ESI for collection and analysis. At this point, it will be converted into a PDF or Tiff file for court use.
E-discovery in cloud
E-discovery may present an Information Technology challenge for firms as they have to govern their ESI to be in compliance with legal and other regulatory requirements. However, cloud-managed services in eDiscovery, whether IaaS or as part of a hosted service, can address most of these Information Technology difficulties.
Cloud-based workflow management can automate the complete eDiscovery process and implement more secure, less error-prone eDiscovery compliances and regulations. In addition, the companies also get the extra benefit of reduced data storage costs and data archiving costs.
The accessibility of the data also becomes more efficient as cloud-stored information is readily available when required outside the organization for the purpose of eDiscovery.
Three key issues that decision-makers should consider:
eDiscovery is gaining attention rapidly. However, most corporate decision-makers are unsure that whether they are prepared to make changes or not.
Many organizations have encountered some eDiscovery issues focused on email. In addition, an increasing number of data types and venues are further increasing the difficulties of eDiscovery and managing content in general.
Rules and regulations regarding ediscovery are continuously evolving, which has burdened decision-makers with additional demands to fulfill with respect to efficient management of eDiscovery.
Key practices firms can follow
There are a variety of practices that an organization can consider for developing an eDiscovery strategy:
Focus on the involvement of employees
The policies, procedures, and technologies might be an essential component of a solid eDiscovery strategy. But it is equally necessary to educate the employees, consultants, and other members of the organization regarding the importance of retaining content that can be valuable, making use of corporate communication and collaboration mediums according to the corporate policies, and being cautious about deleting any crucial document. Educating employees can play a significant role in implementing or improving eDiscovery.
Ensure that IT and legal work together
To establish a robust eDiscovery capability, it is important that the legal and IT departments collaborate to set up a fundamental approach. E-discovery in the cloud can streamline the review and analysis workflow of legal firms, and the eDiscovery data stored on the cloud can be easily accessed irrespective of location, time, or and device.
Develop robust eDiscovery policies
It is necessary to create a procedure of data retention and deletion for types of content. However, many firms do not complete this with adequate urgency even if they undertake this issue. It is crucial for any firm or organization to retain all electronic data that can be utilized for the current and anticipated eDiscovery. Also, other retention requirements include data types like social media.
Implement deletion policies
Many firms either over-specify the amount of data that must be retained or do not establish effective data deletion policies that lead to retention of more data than is required, creating unnecessary liability. Along with that, it also leads to increased eDiscovery expenses as more data is retrieved for the purpose of reviewing and also results in more than necessary storage costs.
Therefore, it’s important for an organization that legal and the IT team work together to conduct a review and ensure that everything complies with the regulatory and statutory requirements. Data classification is an essential element here as decision-makers must set up specific parameters regarding what is needed to be retained, what can be deleted, and the positioning method that will be used.
Acknowledge the importance of litigation services
Suppose decision-makers believe that litigation is reasonably anticipated. In that case, it is important that the organization immediately starts to examine and preserve all the data that might be considered relevant during the complete duration of the litigation.
For example, a claim for a breach of contract with some contractor will require the retention of emails and other electronic documents that have been exchanged between the employees and contractor. Also, the papers in which the employees talk about the contract or the performance of the contractor must be retained.
A structured and configured eDiscovery and data storing capability will help organizations to immediately place their hold on data when a request is made by a court or regulator or on the advice of legal counsel and retain it for as long as they want to. And if there is a need for an extra hand to execute things efficiently, firms can take the assistance oflitigation support services.
Implement the correct technologies
At last, it is essential that firms adopt systems with appropriate capabilities like archiving, storage, etc., that will allow the firm to be efficient in the process of eDiscovery. Optimum capabilities ensure that all the relevant information is easily accessible and reviewable at an early stage of a legal case.
In addition, the correct technology platform will allow the classification of data as it is created and then locate content wherever it exists, irrespective of its location.
Key takeaway
The most brilliant way to ensure that your firm is well prepared for electronic discovery requests is to store each and every email sent received via employees. As technology evolves, many more unique issues will present themselves, leading to the implication of more advanced solutions. Today we may feel disrupted by any new technology that, along with promising a better future, brings unexpected issues relating to it.
However, the latest methods of communication will lead to the creation of more potential avenues for discovery. Therefore, the problems which we are observing now won’t be a trouble in the upcoming years.
Data governance is the process of managing the data’s usability, security, availability, and integrity within an organization using internally set and enforced rules and policies. Effective data governance ensures that data is consistent, accurate, and secure and that it is not misused.
Why does it matter?
Data governance is a must for any organization that seeks to use its data for analysis. It creates an environment where data can thrive as a source of actionable insights that enable the organization to thrive. Without it, data may fail to meet the quality standards required for usable insight extraction or may be exposed to security threats that compromise its integrity, putting the enterprises at risk of legal action.
Data governance improves reliability across all the organization’s businesses thereby making efficient data integration possible. For instance, a distributor’s name may be mentioned differently in the procurement office and the factory’s database. Analysts who have never interacted with the supplier may pose a challenge during data integration.
Governance ensures that there is uniformity and that the analyst does not need to consult the departments generating the data in order to gain an understanding of the data. It goes beyond insight extraction and security by governing who within the organization has access to the data and how they do so. It is therefore important to understand what data each individual member of staff needs before setting the rules or policies of accessing the data internally.
Goals:
1. A key goal of data governance is to break down data silos in an organization. 2. Ensuring proper use of data. 3. Improve data quality 4. Ensuring compliance
Benefits:
Data Protection regulations such as GDPR, PCI DSS, and US HIPAA are very strict on how data should be managed. Failure to comply with these laws can lead organizations to incur hefty fines and damaging their reputation. Data governance takes into consideration applying laws early on thereby protecting the organizations’ data.
Strong governance ensures that all points of data creation function with data quality as a priority. This leads to an overall improvement of data quality within the organization.
Data governance works like an address book for all the data in the organization. This ensures there is no data that is isolated by errors of commission or omission from the overall organization’s rules and policies.
The code of conduct and rules established by governance ensure that data management is made easier. It makes it possible for the management of the data’s security and legal compliance.
Strong governance ensures that the data is secure in storage when it’s being accessed and high quality when being created. The governance matters to an organization that intends to use their data for analysis and remain compliant because it ensures consistency, integrity, and security of the data. By improving data quality and proper data use, increases efficiency within the organization and saves a lot of time for the data users.
To successfully implement it in an organization, the data users should be trained on the policies of governance to ensure they understand what will be required of them.DQLabs’ agile data governance toolmakes it easier for an organization to set up strong governance by providing a framework that is comprehensive and is guaranteed to increase the organization’s data value as well as ensure compliance.
The advent of the digital age is considered one of the most revolutionary eras in human history. The digital revolution colloquially called the ‘third industrial revolution, started with the shift from analog and mechanical technologies to digital technologies. This shift introduced the field of computer science and its related technologies.
With massive improvements in processing power, storage, cloud technologies, and hardware devices, the digital space has become a vast ecosystem that is ripe for commercialization, with the digital economy generating trillions of dollars each year around the world. Data is shared on the internet is now worth as much as gold or oil. But just like oil, data in its raw form is useless. It’s only worth something after it is processed into a more useful form.
This is where a new army of data scientists and data analysts comes into the picture. They are responsible for creating programs to process this raw data into a more useful form that can be exploited for commercial purposes.
These commercial purposes can be anything, from finding out the most popular flavor of ice cream in an ice-cream shop based on the number of customers who buy them, to plotting out the best design for a road to ensure the minimum possibility of accidents derived with data from previous accidents. We can predict the future based on the past experiences of such events.
WHO IS A DATA SCIENTIST AND DATA ANALYST?
Data Analysts are professionals who take raw data, process them using mathematics and statistical analytics, and present them in such a way that everyone can understand. They provide valuable insights that can be used to improve business practices and increase profits.
A data scientist on the other hand uses the power of arithmetics, statistics, and sometimes calculus to draw conclusions from raw data. They derive meaning from data and use that to theorize or predict what can happen in the future.
A data analyst simply interprets data in layman’s terms. A data scientist analyzes the data, extracts meaning from it, and draws conclusions that the company/organization can work or improve upon to increase their revenue and their customer experience.
DATA SCIENCE AS A CAREER:
The average salary of a data scientist who is just getting into the field is Rs.500,000 per year, and those with 1 to 4 years of experience can expect that amount to go up to ₹610,811 per year (Source). Career opportunities in the field have exploded in the past few years, with almost all major software companies hiring data analysts and other professionals related to the field to work on various projects. With data science making rapid progress over the past twenty years and expected to significantly impact the lives of everyone on the planet over the next few years, companies are getting aboard to get their share of the pie. Almost all industries, from hospitality to space exploration to even government organs are utilizing the power of data to better conduct their business.
India is the second-largest country, after the United States, in terms of job opportunities for data scientists in the world. There are almost 50,000 jobs generated per year in the field of data science in the country, with career prospects looking plentiful. As you gain experience in the field, your value will only increase exponentially. You can also expect MNCs to post you abroad in Europe or the US with salaries increasing to almost $250,000 per year.
There is a huge scope for data analysts and scientists in almost all professions today, but it is getting competitive due to the lure of better career prospects in the field.
TOP 10 DATA SCIENCE APPLICATIONS:
1) HealthCare:
Ever since COVID-19 was declared a pandemic by the World Health Organization (WHO) in March 2020, the data on the cases and infections being released by governments worldwide, along with data released by the WHO and UN, have successfully been used to track and trace the spread of the disease and refining approaches to the way the disease is battled safely.
It has significantly improved contact tracing in countries like South Korea and has helped governments to establish networks to analyse infections and alert citizens about possible virus cases. They have helped in slowing down the spread of the virus.
Another important application has come out in Japan where data science is being used to identify cancerous cells in patients to better fight them without harming the healthy cells.
2) Planning Airline Routes:
Airline industries are one of the most cash-intensive industries in the world. It is also one of the riskiest industries currently in existence, where each error results in life or death instances and profits margins are always thin.
With fuel prices increasing every day, along with the massive competition that exists, every rupee counts. To ensure the cost of operations of airplanes is kept reasonable, airlines use data science to predict optimal flight paths, weather conditions, flight delays, change seat prices, and the time taken to arrive at the destination hours before the aircraft leaves the runway.
They also use data science to find which aircraft to use based on fuel consumption and passenger occupancy on each flight, and which aircraft to buy in the future.
Furthermore, tare is also used to ensure the best experience of customers and lower the cost of operations and maintenance of crew and aircraft.
3) Weather forecasting and analysis:
The meteorological departments around the world use data analytics to predict storms, floods, and rains that will take place hours or weeks before they happen. These predictions have a huge impact on the economies of coastal areas like fisheries, shipping, and aircraft movement.
In India, the India Meteorological Department uses data science to predict cyclones and storms in the Bay of Bengal every year during the monsoon season to chart out evacuation procedures and give out warnings in advance. Every year, almost 12 lakh people are evacuated from their homes in Odisha, Andhra Pradesh, and West Bengal during cyclone seasons, resulting in lakhs of lives being saved.
4) Targeted Advertising:
Software and social media giants like Facebook, Google, and Twitter use data science to post targeted ads to users to increase their ad revenues. They use traits of specific users that the advertiser is looking for and record user interactions to various posts to target them with ads based on that data.
Last year, the money spent by companies for targeted advertisements increased to almost $70 billion worldwide, as they are revealed to increase consumers to click on them 2.68 times more frequently than regular advertising.
With the internet playing a larger role in the lives of everybody, consumption of digital media is only going to increase, and it’s a good bet that targeted ads will too.
5) Banking:
The use of data science in banking is mostly related to security and fraud detection. Transactions that deviate from a standard set of rules can be flagged suspiciously and sent to the supervisors without any human intervention.
Data science is also being used to prevent money laundering and financing terrorism by blacklisting suspicious transactions and tracing them to report to the authorities.
6) E-Commerce:
Giant e-commerce companies such as Flipkart and Amazon use data science and data analytics to improve upon the products being displayed to their customers. They try to display the needs of the customer without them having to search for them individually. One big example is the ‘commonly bought together’ feature, which shows the items other customers have usually bought along with the present item the customer has put in their cart.
Amazon tries to build a profile of the customer based on their purchases and recommends items to them based on their history. All this is done using data analytics.
E-commerce is also one of the highest paying fields for data engineers, where starting salaries of good jobs usually start on the higher side from ₹8,00,000.
7) Transport:
The most significant achievement using data science in the field of transportation is the evolution of self-driving or driverless cars/vehicles. With the pairing of data science with the Internet of Things (IoT) technologies, computers are pegged to replace human drivers in cars, trucks, and many other transport mediums. Algorithms have been developed that allow these vehicles to recognize traffic signals, signs, zebra crossings, objects such as traffic cones, and so on. These algorithms also enable the vehicle to learn from them. The job of the data scientist here is to design the algorithms in such a way that the necessary data that is collected is categorized properly to better optimize the performance of the vehicle.
Data science currently is used to predict fuel consumption of vehicles on different terrains and temperatures, along with predicting the behavior of drivers and traffic. Data science is extensively being used to identify obstacles on the road to prevent accidents.
Online cab aggregators like Ola and Uber are using data science to analyze passenger and driver profiles, and better match them to ensure a smooth ride. They also use it to save fuel and time, further increasing profits in a cash-intensive industry.
8) Education:
Data science is being used in education to evaluate student performances and guide them to improve on their learning weaknesses. They’re also being used to pair them with instructors who can specifically help them in academics and non-academics alike, and tailor course material based on the comfort of the student.
Universities also use them to innovate on their curriculum and see which courses are more popular among students to invest in them accordingly.
9) Manufacturing:
Data science is being used extensively in manufacturing to increase throughput and effectively managing raw materials. This can be done by predicting wastage and ensuring an adequate and timely supply of raw materials. Monotonous jobs are being automated at a rapid pace, and data science is being used to improve machine work, particularly those that are involved in precision manufacturing and autonomous robotics.
With improvements in the field of automation and data science, processes such as 3D printing, batch processing, and repetitive manufacturing have seen giant improvements, resulting in an increase in throughput all over the industry.
10) Gaming:
Data science is extensively used to improve game design and gameplay. Complex scenarios and improved interactive gameplay have been the direct result of creating levels using data science.
In online gaming, the company uses data science to create profiles of users and uses them to pair with each other in tournaments. Achievements such as levels crossed and missions completed are used as data to profile them.
CONCLUSION:
In conclusion, data science is a rapidly growing technology that is found in many critical and commercial fields, with no sign of slowing down. Data is said to be the future, and data analysts and scientists will be paid very well as the applications of the technology grow.
Companies and governments are realizing the need to incorporate data science in their work, and are not shying away from paying big bucks to invest in the technology and manpower that drives it!
The digital era calls for every business process, decision, and action to be fed with analytics. Data-spewing technological innovations have led to abundant availability of data and data analytics has become a staple process across business sizes and verticals. As businesses take every possible pain to collect this data to gain a competitive edge in the industry, data collection companies become an enabler in their quest – pooling, categorizing, and processing data to derive business-critical insights. They assist the organizations in leading successful data-driven initiatives by overcoming three challenges – accumulation, analysis, and action.
Consider the statistic sourced from PwC’s Global Data and Analytics Survey that states that data-driven organizations are three times more likely to report substantial improvement in decision-making. Unless the data is credible and strategically processed, any insight derived out of it will be flawed, costing resources and time. Data collection services enable businesses to obtain relevant data required for sound strategies and business decisions. Cost-efficient collection of accurate and domain-specific data has been a cornerstone of the knowledge economy, the bedrock of firms ranging from aggregator startups to global corporates. Data collection services offer the right approach towards the first step of business intelligence, assisting companies to ace their peers in the industry.
Data-Enabled Use Cases
The data collection companies have the potential to capitalize on what data has to offer. In other words, they have superior technical capabilities to collect, analyze, and visualize the data through automated processes, supported by a highly competent pool of data mining professionals. Cross-functional and agile data management structures allow them to assist client organizations in gaining the right insights, thereby pivoting them in the cut-throat competitive landscape. Three major ways through which data collection providers help client organizations to accentuate the business impact are:
1. Implementing New Business Model
Data-powered implementation of new business models aligns business objectives with the current and forecasted state of demand and supply. Professional data collection services help organizations to expand the company’s portfolio to a wider range, add more value, respect, and credibility supporting the value proposition. The provider can offer relevant data or actionable insights or any other valuable secondary information gauged from data.
2. Customer Experience Strategies
Space-and-shelf optimization, cross and upselling, stock and replenishment optimization, dynamic pricing strategies, and assortment optimization are some of the activities that require substantial customer data. Leveraging insights-driven results can help stakeholders effectively manage such customer-centric activities. Data collection companies provide the required data based on business objectives and processes. Essentially, they facilitate a company to effectively map out their customer experience and offer services accordingly.
3. Streamlining Internal Business Operations
Data-driven insights help in streamlining a company’s internal processes. Supply chain optimization, workforce planning, predictive maintenance, demand planning, and fraud prevention are some of the processes that can be enhanced with the benefit of data. Companies that themselves deal with the collection of massive amounts of data can outsource their primary collection to data collection services, essentially offloading the work for cost-optimization.
From Insights to Action: Converting Data into Business Value
The digital wave led by shifting consumer preferences has compelled companies to collect and analyze data to thrive. However, investing in data collection when consumers have real-time expectations from brands and companies is challenging.
Millions of pieces of data floating around in the form of applications, consumer feedback, advertising, attribution, etc make this task even tougher. So, the businesses that engage data collection services emerge as the front runners in the ever-evolving landscape. Besides, they gain advantages in terms of technology and infrastructure allowing access to insights derived from advanced analytics that deliver business impact.
Collaborating with experienced and accomplished outsourcing data collection companies can help businesses to tap the true potential of data. All the major online data collection services providing firms can easily integrate various data sources, leverage the most advanced technologies to deliver quicker and in-depth analyses, as well as extract insights that lead to better business performance. The insights derived from harnessing data assist the leaders in future-focused strategies that contribute to the growth of the organization.
Variance is one of the best measures of dispersion which measure the difference of all observation from the center value of the observations.
Population variance and standard deviation
The average of the square of the deviationstaken from mean is called variance. The populationvariance is generally denoted by σ2 andits estimate(sample variance) bys2.ForNpopulation values X1,X2,…,XNhaving the population mean μ, the population variance is defined as,
Where, μ is the mean of all the observations in the population and N is the total number of observations in the population. Because the operation of squaring, the variance is expressed in square units and not of the original units.
So, we can define the population standard deviation as
Thus, the standard deviation is the positive square root of the mean square deviations of the observations from their arithmetic mean. More simply, standard deviation is the positive square root of σ2.
Sample variance
In maximum statistical applications, we deal with a sample rather than a population. Thus, while a set of population observations yields a σ2 and a set of sample observations will yield a s2. If x1,x2,…,xn is a set of sample observations of size n, then the s2 is define as,
Properties
Effect of changes in origin: Variance and standard deviation have certain appealing properties. Let each of the numbers x1,x2,…,xn increases or decreases by a constant c. Let y be the transformed variable defined as,
where, c is a constant.
Finally we get that any linear change in the variable x does not have any effect on its σ2. So, σ2 is independent of change of origin.
Effect of changes in the scale: When each observation of the variable is multiplied or divided by a certain constant c then there occur changes in the σ2.
So, we can say that changes in scale affects and it depends on scale.
Uses of variance and standard deviation
A thorough understanding of the uses of standard deviation is difficult for us as this stage, unless we acquire some knowledge on some theoretical distributions in statistics. The variance and standard deviation of a population is a measure of the dispersion in the population while the variance and standard deviation of sample observations is a measure of the dispersion in the distribution constructed from the sample. It can be the best understood with reference to a normal distribution because normal distribution is completely defined by mean and standard deviation.
Conversational User Interface (CUI) is an artificial interface with which you can communicate to either ask questions, place orders, or get information.
Top-notch CUI’s offer a more human-like conversation. This helps in bridging the gap between physical and online conversations.
Many companies have started understanding the importance ofconversational AIby incorporating them into their marketing strategies. Statistics show that automated conversational marketing companies witnessed a10% increase in revenue within 6-9 months.
Even from a customer’s point of view,86% of online buyers preferred quick and immediate customer support, which chatbots for small businesses provide.
There are two main types of CUI’s. First is the chatbots where the interaction and communication takes place in the form of text. The second one is voice assistants like Google Assistant, with which you can talk to provide input.
Game-Changing Conversational User Interface Examples
Here are 5 of the top CUI’s and chatbots for business that cover all bases and provide a smooth and happy experience to all users.
1. Skyscanner – Travel Search Website
Skyscanneris an online travel agency that launched in 2003. It allows its users to compare and find cheap flights and hotels and also hire cars.
Skyscanner is the world’s biggest independent flight search engine. In 2016, itraised $192 millionto grow its engine and services. In the same year, whenconversational AI and chatbotsstarted receiving more recognition, Skyscanner joined the league by introducing their Facebook Messenger bot.
The purpose of this chatbot is to help customers search for flights to any destination through a simple conversation.
A Brief Walkthrough
Skyscanner’sFacebook Messenger botbegins well by providing the necessary information on its home page. By displaying information like “The world’s travel search engine” and “Typically replies instantly,” it tells you what it is capable of doing.
When you continue, the bot welcomes you by your name, thus providing a personalized experience. You can then find flight deals, explore new destinations, or get tips on the best time and route for travelling.
After selecting the origin city, destination city, and travel dates, the chatbot shows a list of flight options from various airlines along with their rates. It is also capable of sending alerts if there is any change in the pricing.
Once you compare and choose a flight, the chatbot redirects you to the website to complete the payment.
Few Brilliant Features
Anywhere
The “Anywhere” feature is one of Skyscanner’s best features. If you are unsure of your destination, simply typing “anywhere” in the text box will display a list of travel suggestions from the origin city.
Throughout the process of searching and selecting a flight, Skyscanner’s chatbot constantly confirms the cities and dates that you have chosen. It also allows you to change the details with ease.
Adapting to New Trends
Skyscanner is one great example of a company that follows and adapts to new trends. With many people using the Telegram messaging service, Skyscanner introduced a Telegram bot to target a wider audience to search for flights and hotels easily.
The bot can even understand colloquial terms like “next weekend” or “next Monday” and display the correct options.
Skyscanner has also added a live chatbot on the Skype platform.
In 2016, Skyscanner also partnered with Amazon’s Alexa allowing users to search for flights through a voice conversation. By asking simple questions “Where are you flying from?” and “Where are you flying to?” Alexa can get the travel details from you and talk you through the relevant flight details.
Results
The easy-to-use conversational user interface of Skyscanner is effective in providing relevant details to all customers. In just a few years since the chatbot’s introduction, Skyscanner managed topass one million traveller interactionswith chatbots across all platforms by 2019.
All the minute details show the thought put into designing the chatbot, making it a huge success.
2. Duolingo – Language Learning Platform
Duolingois a language learning platform that provides its services for free to all users on its website and mobile app. Officially released in 2012, Duolingo now offers courses in 38 languages, including fictional languages like Klingon.
Over the past few years, Duolingo has started to leverage the power of artificial intelligence to alter the courses and make them more convenient for the user.
With the help of a conversational user interface, Duolingo has revolutionized the language learning sector.
The Problem
Duolingo is an example of a great company that analyzes and understands their problems and brings out solutions to overcome them.
Duolingo understood that the most significant problem they would face would be helping users effectively learn a language. Conversing is what helps learners practice and retain the language. Simply reading words and phrases on a screen would not help in the same way.
The Solution
Chatbots
To overcome this obstacle, Duolingo implemented the use ofAI-based chatbots. They created and assigned a few characters to the bots, allowing you to have a real conversation in your learning language.
If you get stuck and don’t know how to reply during the conversation, you can also use the “help me reply” option to get assistance from the bots.
Duolingo recently took conversational learning to the next level by introducing conversational lessons. This new feature offers practice with words and phrases used in real-life scenarios and will enable you to put those words together to form meaningful sentences.
Duolingo allows you to listen and repeat commonly used sentences. It also corrects you when you speak or type the wrong word and explains its correct usage. This way, you can learn a language with Duolingo through textual and voice conversations.
As you learn more words, the difficulty levels increase, giving you thorough learning of the entire language.
Outcome
Duolingo’s chatbots and conversational lessons give the user the experience of having a conversation in reality. Duolingo is known for its conversational AI and conversational marketing strategies.
Since its inception, they have added over 500 millionregistered users, out of which 42 million are active every month.
The coronavirus lockdown between March 11 – April 30 increased Duolingo’s users by30 million people. These statistics show the magnitude of Duolingo and its CUI’s success.
3. Domino’s – Pizza Restaurant Chain
Domino’sis one of the most successful pizza restaurant joints across the globe. Today, Domino’s operates17,800 storesin more than 90 countries selling an average of 3 million pizzas every day.
Over the years, Domino’s has introduced different ways through which customers can order food. One such way is online ordering.
From 2017 to 2020 alone, Domino’s made27 million Facebook impressions. This figure alone shows the success of online ordering. But Domino’s did not stop there. They introduced CUI into their business, allowing customers to order food through a bot on Facebook Messenger.
Here are some highlights of Domino’s chatbot for business.
Meet Dom
Domino’s named their chatbot “Dom,” giving it a character. This makes the user feel that they are conversing with a person on the other end rather than a computer. Dom makes digital ordering more conversational and simple.
Pre-set Options
Dom has pre-set default options programmed into its interface. So, when you want to place an order with Dom, options like “Pizza,” “Pasta,” “Sandwiches,” etc., show up on the screen. All you have to do is select an option and continue to the next step. This eliminates the need to type in your order, thus saving time.
Dom is also aware of current deals and allows you to apply a deal or coupon to your order.
Constant Summarizing
The entire process of ordering a pizza occurs in multiple steps. It includes choosing the size of the pizza, crust, and toppings. Dom makes sure that it constantly summarizes your order while simultaneously adding new information to it at every step.
Dom also simplifies the process of making changes to the order. Even if you are in the last step (say you are choosing toppings) and feel like changing the pizza crust, Dom will make that change for you while retaining other information (like pizza size) in your order.
Accepting its Shortcomings
When Dom is unable to understand the customer’s input, it apologizes and lets the customer know about it. This gesture is appreciated rather than displaying information that is not related to the customer’s request.
Domino’s Voice Ordering
Dom’s skills also include its ability to place orders through voice commands from users, making pizza ordering easier.
Domino’s also offering its services on voice-based CUI’s like Amazon Alexa, launched in 2017, and Google Assistant, launched in 2019. Through these mediums, you can place your most recent order or track your ongoing order by asking the voice bot to do so.
Domino’s Anyware
Apart from ordering through chatbots and voice-based CUI’s, the Domino’s Anyware initiative allows all users to literally order from anywhere. This includes ordering from your car, smart TV, smartwatch, and through tweets, SMS, and zero-click app.
Interface
To put it in a nutshell, Domino’s conversational AI chatbot makes online pizza ordering simple for all customers. The linear flow in Dom’s CUI makes it easy to order food when compared to other alternatives.
4. Lark – Digital Healthcare Platform
Larkis a digital healthcare company that offers services in various sectors. It keeps track of your daily activities like food habits and sleeping patterns and aims at improving your fitness and health. It helps people in reducing weight and also focuses on reducing stress and anxiety among people.
Lark’s chatbot is an app that dedicates itself to all these activities. Users can interact with their bot through text, voice, and button options.
Varied Responses
One aspect that sets a fundamental difference between ordinary bots and top chatbots like Lark is its varied responses to the same topic. Even if you type in the same sentence repeatedly, Lark will respond with a different answer. This small attribute enormously improves its human-like conversational style.
Lark’s responses are also friendly and caring. This is crucial, especially for conversations about mental health and stress. These responses help in motivating the users.
A Knowledgeable Bot
While conversing with a healthcare bot, knowledge about everything must be its top priority. Lark is one such bot that knows stuff related to its field as it was created with the help of experts and professionals in the healthcare sector.
For instance, when you tell Lark what you ate for lunch, it can recognize it and place it under a particular category (like veggies or meat). It can then make recommendations (like switching to other categories) so that you consume all kinds of nutrients to maintain a balanced diet.
In a way, Lark acts as your fitness coach and nutritionist.
Proven Research
Acomprehensive studywas performed on the Lark Weight Loss Health Coach AI (HCAI) to evaluate its effectiveness in weight loss. The results of the study showed the following:
It increased the consumption of healthy meals by 31%.
An in-app survey showed a 100% response rate.
High-risk diabetes patients using conversational AI lost a magnitude of weight compared to the loss achieved with lifestyle change programs.
The health coach also encouraged positive behavioral changes.
Achievements
Over the years, Lark and its conversational user interface have received a few achievements.
5. Erica – Bank of America’s CUI
In 2018,Bank of Americalaunched their own chatbot “Erica” to help their customers in their transactions on the mobile app.
Created using AI, predictive analytics, and cognitive messaging, Erica can help customers in numerous ways like,
Making payments
Checking account balance
Tracking daily expenditure
Locating past transactions
Checking FICO score
Receiving notifications on pending bills
Highlights of Erica’s Conversational User Interface
The home page of the app displays a greeting message that welcomes the user. Through the prompt at the bottom of the page, you can type or voice out your task or query. Erica also displays a message, “See what Erica can do,” which shows all its functions when clicked upon.
Versatility
Erica can efficiently understand voice, text, as well as tap inputs from the users. Erica indeed shows its versatility when it comes to understanding the customers’ varied questions. Currently, Erica canunderstand almost 500,000 different variationsof the questions that customers ask.
Erica provides meaningful insights that help customers in making better decisions. These insights can also help in saving money. Erica can do this by suggesting stuff like putting the cash rewards from your credit cards to better usage.
Instead of asking detailed questions or sending out long forms, Erica asks for feedback subtly. Once the tasks are completed, a smiley and a sad emoji appear. You can easily give feedback by tapping on any one of them.
Analysis of Erica’s Success
Around500,000 new usersmake use of Erica’s services every month. At the end of 2019, Bank of America stated that Erica alone had witnessed over10 millionusers and was about to complete 100 million client requests and transactions.
These statistics show that Bank of America hit the bulls-eye with its conversational AI.
Why is Conversational User Interface Important?
Using Artificial Intelligence (AI) and Natural Language Processing (NLP), CUI’s can understand what the user wants and provide solutions to their requests.
Some of the best CUI’s provide the following benefits to the customer and the owner.
Provide a personalized and unique experience to all the users
Offer 24×7 support to all customers and clients, eliminating the need for man-power for the same job
Can efficiently speak to thousands of customers at the same time
Convenient to use as they are user-friendly
Available across various platforms and channels
Easy to set up for business owners as it requires little to no knowledge in coding
Real-time analytics help business owners and marketers improve their marketing strategies.
Conclusion
Going through these game-changing conversational user interface and chatbots for business, it is clear that using them in conversational marketing strategies increases a company’s sales and leads to more happy customers.
Most of these chatbots also prove that thinking about all the small and minute details and incorporating them in the CUI’s can take the company a long way forward.
