Solving Unified Data Management & Analytics Challenges for Business and IT

Organizations in every industry are leveraging data and analytics to overcome roadblocks on the path to innovation and progress, plan for the future, and position their organization to competitive advantage. 

The entire enterprise must become more connected than ever before for expertise to come together to drive a shared vision for innovative change. This means transitioning away from legacy systems and adopting a modern approach to business processes and operations, and data and analytics. 

Ronald van Loon is an SAP partner, and as an industry analyst and insider for over twenty years, has an opportunity to further investigate the challenges emerging in the unified data and analytics domain.

To get the most value from data, businesses need to strengthen their data culture, and this continues to be an evasive objective for numerous organizations. With so many new systems, processes, and individual ways of thinking and working, creating a connected data ecosystem can be complex. 

But with a unified data management and analytics solution, the world of business and IT can unite to drive data initiatives forward, enhance productivity, and overcome the unique challenges that are inherent to business and IT.

Data and Analytics Complexities Grow

Data is unquestionably increasing in value, but it’s simultaneously growing in complexity. Organizations have to tackle this complexity in order to benefit from the potential value of data. But traditional approaches to database, data warehousing, and analytics can take years of development and deployment before they produce any benefits, and even then, companies can face limitations when pertaining to real-time analytics, complex data sets, and data streaming.

Businesses are also reporting that they’re grappling with advancing and managing data as a business asset, driving a data culture, accelerating innovation via data, and using data and analytics to compete. 29.2% of businesses report having accomplished transformational business results, 30% report having built a meaningful data strategy, and only 24% believe their company was data-driven this last year. 

A few of the primary challenges impeding analytics progress include:

• Lack of a strong analytics strategy, which may encompass under-utilized technologies, failing to set manageable goals that can provide quantifiable value, or lack of interaction and agility across data, IT, and business.
• Unbalanced analytics programs that don’t account for diverse user needs as well as enterprise-wide standards, which can result in inefficiency and prevent analytics from being scaled.
• Insufficient data governance and data hygiene that impacts data accessibility and often leads to data silos.
• Myriad data sources, overlap, and obscurity due to the adoption of new processes and systems throughout numerous layers of the organization.
• Legacy analytics initiatives that hinder organizations from developing, deploying, and scaling advanced analytics due to deficient features for collaboration, and limited artificial intelligence (AI), machine learning (ML), and big data capabilities. 

Companies can be further challenged in infusing data into the very DNA of their decision-making processes rather than just understanding or discussing the importance of including it. Ultimately, this puts a damper on creativity, curiosity, and an enterprise-wide data mindset that fosters dynamic, smart innovation across products and services. 

Business leaders need to approach data and analytics investments as an accelerant to ongoing business requirements, develop analytics capabilities according to relevant use cases and business problems, and build upon this foundation by strategically implementing new tools, technologies, and solutions.  

Business and IT Unified Data Management and Analytics Challenges

As data is one of the most powerful and critical assets an organization has, it must be available, accessible, and able to be leveraged by every user across the entire value chain. Business users have to be able to ask questions and get answers from their data and rely on it as a pillar of decision-making. This extends to both IT and business lines, though these two areas have distinctive roles, responsibilities, and purposes. 

If business and IT can work together and share their knowledge and experience when it comes to data, progress can be optimized across the enterprise. But business and IT have their own set of unified data management and analytics challenges that they face that they need to overcome.

Business challenges:

• Trusted Data: Lacking quality data, outdated data, or duplicate data. 

• Self-service: Overly complex systems and processes create barriers for business units who need simplified methods to get to the data that they need to make decisions. 

• Ease of use: Having the capabilities to work independently and access the data that they want without contacting and/or burdening IT teams. 

IT challenges:

• Hybrid Systems: Working on premise or in the cloud can be time consuming and overly complex. 

• Heterogeneous Data: Siloed data from multiple sources is too spread out.

• Security and Governance: The evolving landscape of security, privacy and regulatory environments are becoming complicated. 

When business and IT teams can’t effectively use data, they can’t work freely, make confident decisions, and leverage artificial intelligence (AI) and machine learning (ML) to help them transform data into solutions and meet the demands of evolving consumer behaviors, rapid transformation, and technological enabled workspaces. 

An Answer to Business and IT Challenges

Organizations need flexibility, agility, and connectivity in order to enhance collaboration and use data-driven insights to drive intelligent solutions forward. With a unified data management and analytics strategy, organizations can overcome the common challenges that business and IT are facing across industries. 

SAP Unified Data and Analytics connects business and IT professionals and improves end-to-end data management to simplify data environments and help organizations grasp and maximize the true value of their data to its fullest potential. 

In order to develop a unified data ecosystem, organizations are moving to cloud database as-a-service solutions, using an intelligent platform like the SAP Business Technology Platform that connects disjointed data from IoT, cloud and big data, creating a single source of truth. This helps businesses resolve data trust challenges and simplify IT disparate data and hybrid system complexities. At the same time, IT can better focus their attention on governance and model data in secure spaces. Data and workloads essentially become more integrated and connected, which helps business and IT better collaborate. 

With intelligent cloud solutions, like SAP HANA Cloud, SAP Data Warehouse Cloud, SAP Data Intelligence Cloud, and SAP Analytics Cloud, organizations can choose what data to keep on premise and which to keep in the cloud, scaling as needed, migrating legacy systems where necessary, and start the process of building a modern infrastructure. Organizations can give data purpose with SAP Unified Data and Analytics.

An intelligent platform is going to simplify data accessibility for business teams while simultaneously providing visualizations and dashboards. This brings real-time data insights to life, enabling business teams to establish a meaningful connection to their insights, which solves the previously discussed decision-making and accessibility challenges. 

Unified Data Management and Analytics Optimization

Eliminating unified data management and analytics challenges ensures that organizations are able to deploy their forecasts, contextualize data and analytics, and share insights across business and IT to continuously grow and innovate. 

To learn more and stay current with the latest data and analytics trends and information, you can visit the executive corner industry pages with a focus on retail, public sector, or consumer packaged goods on saphanajourney.com or sign up for the SAP Data Defined: Monthly Bytes newsletter.

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Will GPT-3 AI put authors out of work permanently?

In a world of GPT-3 AI-generated content, are writers even needed? In a recent business experiment, I set out to answer this question.

If you’re wondering, who am I to tell you anything about GPT-3 AI? Well, I’m Lillian Pierson, and I help data professionals become world-class data leaders and entrepreneurs – to date I’ve trained over 1 million data professionals on the topics of data science and AI. I’m a data scientist turned data entrepreneur, and I’ve been testing out GPT-3 AI for about 3 months now in my data business, Data-Mania. 

As a data entrepreneur, I spend a TON of my time, energy, and financial resources on creating content. From podcast episodes to YouTube scripts, to emails and social media posts, content creation eats up a huge chunk of my week.

So when I heard about GPT-3 AI copy services, I was curious to know: would this be a useful tool in my business?

Would I be able to 10x my content production rates? Replace freelance writers?

Rather than simply buying into the online hype, I wanted to conduct my own research – and today, I want to share it with you. Whether you’re a data entrepreneur, data professional, or simply a fellow data geek who LOVES reading about the smartest AI companies, read on to get the full scoop on GPT-3 AI and how I believe it will shape the content writing industry. 

In this article, we’ll cover:

• What is GPT-3 AI?
• The pros of GPT-3
• The cons of GPT-3
• 3 guidelines to use GPT-3 while maintaining brand integrity
• Will GPT-3 change the content writing industry? 

Let’s get started.

What is GPT-3 AI? 

First of all, what is GPT-3 AI? GPT-3 is a model for human-like language production written in Python. It uses large amounts of texts crawled from the web to create similar, but unique content. Since it was developed by OpenAI and released for public use in June of 2020,  there have been TONS of data entrepreneurs creating SAS products that run off of GPT-3. 

Some of the most common GPT-3 AI content services are Copy.ai and WriteSonic. I conducted my experiment using Writesonic. 

Pros of GPT-3 AI

Alright, let’s start with the good. 

1. Great for Product Descriptions

During my experiment, I have to say I was genuinely impressed by the product description snippets I was able to create using Write Sonic’s GPT-3 AI service.

All I needed to do was input the name of my product (in this case, it was my free Data Superhero Quiz) as well as add product info such as features and benefits. All I did was copy and paste some bullet points from my sales page and I was good to go. 

And wow! With the click on a button, I had ten high-quality product descriptions to pull from. The service was even suggesting some features and benefits I hadn’t even thought of. 

2. Unique and Anonymous

A big pro to using GPT-3 AI content is that everything it spits out is completely unique. There’s no need to worry about plagiarized content. Also, the service is totally anonymous – no one will know you’re using AI so there’s no need to worry about being judged. 

3. Good ROI on Your Time and Money

After reviewing the product descriptions created by Writesonic, I have to admit I liked them a lot better than the ones I’d written myself. Considering the fact they’d taken me a good 10-20 minutes to write, PLUS I’d purchased templates for $50 to speed up the process of writing them, the GPT-3 AI content is clearly better value. I had dozens of descriptions within just 30 seconds. 

Overall, if you are looking for a tool to help you quickly and easily create short content snippets (i.e. product descriptions!) you should definitely add a tool like Copy.ai or Writesonic to your toolbox.

Cons of GPT-3 AI

While I had some successes with GPT-3 AI, I also had some total failures. 

1. Lacks context

Unfortunately, GPT-3 is not great at generating content if it doesn’t have the context directly from you. 

I tried playing around with its article writing mode, which is still in beta.  

Essentially, you give it an outline and an introduction, and then it returns the entire article with all of the body copy.

While technically the information may be factually correct, it lacks context. It won’t have the context needed for YOUR particular audience, so it won’t be intelligible.

Information without context about WHY it matters to your customers is useless. They need to know why they should care and how what you’re sharing will actually have an impact on their life. Without that, you’re simply producing content for the sake of content, and adding to the noise. 

2. In some cases, it gets things wrong.

While in some cases the information might be garbled and lacking context, in other instances, the content GPT-3 AI provides could be flat out wrong. GPT-3 AI will lack the nuances about your industry that come naturally to you.

For example, when I was using Writesonic’s article mode, one of the headings was “What are the obligations of a Data Processor?”

However, the body copy that GPT-3 produced did NOT correlate with the appropriate heading. Rather than telling me the obligations of a Data Processor, it gave me content about the role of a Data Protection Officer. 

It brought up a totally different point. And while it may be related, if you had actually used this content on the web, it would’ve reduced your credibility and put your brand in a bad light.

In short, I would straight up AVOID GPT-3 AI for article-writing or long-form content. You could potentially use it as a research tool, to help you uncover relevant topics you may not have thought of, but always be sure to dig deeper into those topics and not rely on what GPT-3 gives you.

3 Guidelines To Make the Most of GPT-3

Here are three recommendations and safety guidelines for you to use in order to make sure that you’re protecting your brand integrity and the quality of the content you produce when working with GPT-3. 

1. Review GPT-3 AI Content Carefully 

GPT-3 is going to create a TON of content for you. It’s up to you to pick and choose what is valuable, and to make sure everything is factually correct and appropriate. 

 2. Add Personalization

Whatever content that GPT-3 gives you, you need to improve on it, add to it and personalize it for your brand. You know your customers better than anyone else.  I recommend seeing GPT-3 as more of a content research tool than as something to produce finished copy.

3. Add Context

No one on this planet needs more random information. What we need is meaning and context. So while the creators of GPT-3 are correct in saying it produces ‘human-like text, it’s not able to add the context readers need in order to create meaning in their lives.

Content without context doesn’t compel readers to take action based on what they’ve read – all it does is overwhelm them. Information for the sake of information simply adds to the noise – which is something all of us online content creators should be trying to avoid at all costs

4. Listen to All Content Aloud

And last, but not least, rule number four is to listen to your end text aloud.

You want to make sure that whatever content GPT-3 AI spits out, you’re listening to out loud so you can make sure it’s conversational and flows nicely. It’ll also be an opportunity to double-check everything is factually correct.

My favorite tool to do this is a TTS reader. 

By following these guidelines, you’ll be able to ensure that you can safely increase your content production WITHOUT harming your brand’s reputation.

Will GPT-3 change the game for writers?

After reviewing the results from my business experiment, I STILL believe that there is a need for highly skilled content writers. However, the rise of GPT-3 AI demonstrates how AI is certainly changing the content marketing landscape. 

While I do believe GPT-3 may replace low-level, unskilled writers (who, let’s be real, probably shouldn’t be pursuing writing in the first place) businesses will continue to require writers who can deliver nuance, context, and meaning to their customers. 

At best, GPT-3 will become a tool that helps smart writers speed up their writing process and make their lives easier. They may use GPT-3 content as a starting point from which they can create highly personalized and meaningful content. 

At worst, the web could become flooded with GPT-3 AI generated that only adds noise to the already crowded internet, significantly contributing to the overwhelm people are already experiencing when trying to find high-value information online.

In order to create long-form, meaningful content, GPT-3 AI content tools still have a long way to go, but they show promise as a tool to speed up businesses’ content workflows. 

About the Author

Lillian Pierson, P.E.

Mentor to World-Class Data Leaders and Entrepreneurs, CEO of Data-Mania

Lillian Pierson, P.E. helps data professionals transform into world-class data leaders and entrepreneurs. To date she’s educated over 1 Million data professionals on AI. She’s also been delivering strategic plans since 2008, for organizations as large as the US Navy, National Geographic, and Saudi Aramco.

Get the Data Entrepreneur’s Toolkit (free)

If you love learning about this GPT-3 tool, then you’re also going to love our FREE Data Entrepreneur’s Toolkit – it’s designed to help data professionals who want to start an online business and hit 6-figures in less than a year.

It’s our favorite 32 tools & processes (that we use), which includes:

• Marketing & Sales Automation Tools, so you can generate leads and sales – even in your sleeping hours
• Business Process Automation Tools, so you have more time to chill offline, and relax.
• Essential Data Startup Processes, so you feel confident knowing you’re doing the right things to build a data business that’s both profitable and scalable.

Download the Data Entrepreneur’s Toolkit for $0 here.

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Time to leverage data trust: Best Techs in Australia

Business intelligence has the potential to help small businesses, crisis management experts, or also business administrators evaluate and appreciate investment opportunities simply and straightforwardly. Furthermore, research is used in the placement of goods in the industry. In reality, knowledge management’s value cannot be comparable to that of any other business instrument. Analytics is a subset of marketing information, and it is the only tool that can help a company turn massive amounts of raw data into usable business information that can be used to make decisions. It is widely found that companies that specialize in data analytics outperform their competitors. Without a question, information has been an important weapon for higher management.

With time, the emphasis has turned to business intelligence, in-memory analytics, large data analytics, streaming analytics, and, most specifically, data science, however, both of these flavors are good at solving similar problems. With the passion of ‘Connecting with Strength, Core, and Insight,’ data analytics services Australia are leading data analytics providers, generating meaningful results.

Make use of Big Data architectures and IoT to help you achieve your goals

The concept of “data requirements” is crucial, but it is often ignored. There is no such thing as a universal Big Data approach that works for all. Rather, the data framework you choose can explicitly support your specific business objectives.

Working alongside a partner who is unable to adapt a personalized solution to the case is not a good idea. No two similar implementations can be the same, just like no two companies are alike. Look for the personalized choice to have a solution that fits right in with the company’s brand. The Internet of Things, or IoT, is the Big Data of the future, and it’s reinventing multiple businesses around the world. Businesses may obtain data straight from the original, without the need of middlemen or 3rd parties, by using the Internet of Things. This information is often very detailed. Internationally, there are already 2billion IoT-joined computers, with that number projected to increase to 63 billion by 2024. Much of such instruments can generate a large volume of data lines.

Data processing and cognizance are almost as essential as, if not more essential than, data storage when it comes to computers of Things. The Internet of Things has an abundance of evidence. And the amount of data is increasing every day. As a consequence, organizations can be faced with an enormous amount of data that they are ill-equipped to handle.

In this regard, choosing the best collaborator or supplier of data solutions is crucial. The Internet of Things (IoT) reflects an immense pool of quality that is only waiting to be exploited. However, you must be able to decide which data sources include this value while others do not.

This is an opportunity you cannot continue to pass up. You can install IoT hardware anywhere you could, or collaborate with a company that has the expertise and scale you require.

What are the reasons for the increasing demand for big data analytics supports?

First and foremost, if you see yourself with much more information than you know what to do about, you must take control of such an extremely useful resource. Creating the groundwork for gathering, managing, and analyzing this data would aid in the transformation of your business into a forward-thinking, additional perspective, and, most significantly, valuation enterprise. However, as with any good program, that once pillars and systems are in place, they often need upkeep and consideration to remain cutting-edge and relevant to the modern era.

It is highly beneficial to provide an urgent supportive role to manage specific patches, modifications, and enhancements to the company’s software to preserve and enhance its efficacy. All of this can be accomplished in the background, easily, and in line with best practices when you outsource the work to an analysis consultancy including Main Window, allowing you to concentrate on the projects and multiple stages that are most important to you. Many companies have agile teams, which means they only have one individual in charge of data. This guy, whether senior or professional in expertise, would frequently be tasked with managing the real deal framework, from monitoring to network management, insights development, troubleshooting, and a never-ending list of BAU items. When you move from an individual model to a squad of data engineers, experts, physicists, and architects, having tech expert testing on-demand help ensures that the analytics capabilities can skyrocket. A specialist would always function in the best way in the team, educating and bouncing thoughts off colleagues, looking out for answers quite quickly as compared with themselves.

Bottom line

Big Data is here to remain, which means the company would need to be prepared to store and successfully use ever-increasing amounts of data and this is the reason why flexible data storage is needed in any company as data analytics are a big game-changer for several industries.

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2021 Analysis of Leading Data Management and Analytics Market Solutions

There’s a lot of conversation in the industry about how data is key to unlocking powerful decision-making capabilities. Data can ignite a wildfire of change, creativity, innovation, speed, and agility across an organization.

But decision makers have to be completely confident in their data in order to leverage these kinds of influential capabilities. Data has to be trustworthy, unbiased, accessible, and timely for it to generate meaningful, analytics-driven insights. Companies need to derive purpose and value from both data and analytics, especially in this time of uncertainty, using a unified data management and analytics solution. 

Ronald van Loon is a SAP partner, and is applying his unique position as an industry analyst to take a deeper look into what different organizations are doing in the data and analytics space.

Cloud, artificial intelligence (AI), machine learning (ML), database and data management, application development, and analytics are pillars of transformation today. As organizations look to future-proofing their business, they have some critical decisions to make when it comes to unified data management and analytics solutions that meet their individual needs.

With this in mind, we’ll explore vendor differentiators to help executives better understand the market so they can develop and benefit from their data and modernize their data architecture to support changing and emerging requirements.

Emerging Data Management and Analytics Trends and Evolving Business Requirements

What are today’s organizations looking for in a data management and analytics solution?

• Greater agility, simplicity, cost-effectiveness, and ease of automation to accelerate insights.
• The capabilities to overcome challenges surrounding traditional on-premise architectures that inhibit organizations from meeting emerging business needs, including those pertaining to real-time analytics, complex data sets, self-service, and high-speed data streaming.
• The ability to surpass pervasive data challenges through the strategic application of both existing and new technologies to drive next-gen analytics. 
• The ability to move beyond cumbersome data warehouses that typically demand a multi-year commitment to build, deploy, and gain advantages.

This reflects a few critical trends that are supporting the movement towards a unified data and analytics strategy. Businesses are migrating or extending to the cloud, with 59% of enterprises anticipating cloud use to exceed initial plans because of the pandemic. Also, data lakes and warehouses will begin to assume similar qualities as the technology grows. Finally, according to SAP, companies will transition to “data supermarkets” to manage data consumption to clarify processes.

As a modern architecture, Data Management and Analytics (DMA) reduces complications related to chaotic, diverse data via a reliable model that includes integrated policies and adjusting to evolving business requirements. It utilizes a combination of in-memory, metadata, and distributed data repositories, either on premise or in the cloud, to provide integrated, scalable analytics.

Data Management and Analytics Solutions Per Vendor

DMA adoption is increasing as organizations make efforts to benefit from the next evolution of analytics, introduce more collaboration across teams and departments, and transition beyond data challenges. When evaluating a DMA solution, there’s a few key elements that organizations should keep an eye out for, including:

• Self-service capabilities that allow business users to ask questions to support decision making, drive data intelligence and aid in rapidly ingesting, processing, transforming and curating data through ML and adaptive intelligence. 
• Real-time analytics through the streaming of multiple sources, and performance at scale for diverse and large-scale project types. 
• Integrated analytics to help businesses better manage various data types and sources. This extends to storing and processing voluminous sets of both unstructured and semi structured data, and streaming data.

Organizations must also be able to leverage their DMA solution to support analytics-based processing and transactions across use cases like data science investigation, deep learning, stream processing, and operational intelligence.

There are several vendors in the domain who are offering data and analytics solutions to suit a wide range of use cases, though the following is not by any means a complete list:

Microsoft

Microsoft’s Azure platform suite offers a range of cloud computing services across on premise, hybrid cloud, and multicloud for flexible workload integration and management. They also provide enterprise-scale analytics for real-time insights, and visualizations and dashboards data collaboration.

SAP

SAP offers a complete end-to-end data management to analytics solution with SAP HANA Cloud, SAP Data Warehouse Cloud, SAP Data Intelligence, and SAP Analytics Cloud. These solutions are SAP Unified Data and Analytics and they coordinate data from multiple sources to fast track insights for business and IT and give data purpose. 

Amazon

Amazon Web Services (AWS) offers numerous database management services to support various types of use cases, including operational and analytics. They’re the largest global cloud database service provider, and offer cloud provider maturity, scalability, availability, and performance.

Google

The Google Cloud Platform (GCP) includes numerous managed database platform-as-a-service solutions, including migration and modernization for enterprise data. They offer built-in capabilities and functionalities for data warehouse and data lake modernization, and both multi and hybrid cloud architectures. 

Snowflake

Snowflake’s Cloud Data Platform is a solution that offers scalability for data warehousing, data science, data sharing, and support for simultaneous workloads. It includes a multi-cluster shared data architecture, and enables organizations to run data throughout multiple clouds and locations.

Empowering the Data Journey with Unified Data and Analytics

Unifying data and analytics can be problematic for organizations across industries due to increasing data sources and types, messy data lakes, unexploited unstructured data, and siloes that impede insights. 

Both business and IT teams need trustworthy, real-time insights and fast, seamless access to data to make sound, data-driven decisions. But business and IT worlds are often fragmented when they should be harmonized, and respective data and analytics needs often conflict, which can prevent a data culture from flourishing. 

The business side stresses data accessibility and self-service, while IT wants to strengthen data security and governance. These competing needs have to be balanced to support interdepartmental collaboration and maximize data effectiveness and productivity.

The SAP Data Value Formula conveys how each component of the SAP Unified Data and Analytics, the foundation of the SAP Business Technology Platform (SAP BTP), works cohesively to give data purpose:

This enables organizations to leverage capabilities to develop, integrate, and broaden applications and gain faster, agile, valuable data-driven insights. When different data sources are brought together in a heterogeneous environment, with a hybrid system for cloud and on-premise, business and IT departments can better collaborate to work towards shared organizational objectives. Basically, the end-to-end data journey is supported to help transform available data into actionable answers.

Unite All Lines of Business 

All aspects of a business can benefit from unified data and analytics, from finance and IT to sales and HR. Siloes are eliminated to facilitate an organization-wide approach to data and analytics, business and IT are united to accelerate data-based decisions, and data journeys are charged with agility and high quality data.

You can register for the SAP Data and Analytics Virtual Forum to learn more about powering purposeful data, or sign up for the SAP Data Defined: Monthly Bytes newsletter to stay on top of the latest data and analytics trends and developments.

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Why You Need Multi-Disciplinary, Integrated Risk Management

This article is excerpted from my upcoming book Agile Enterprise Risk Management: Risk-Based Thinking, Multi-Disciplinary Management and Digital Transformation.  The book provides a framework for evolving your Risk Management function to make it operate in a nearly-continuous fashion, which will allow it to keep pace with the rate of change required to remain competitive today.

We are advocating for your transformation to a more agile organization.  In all likelihood, you’ve already begun—created internal collaboration capabilities and customer-facing, web-enabled services.  But you probably have a long, long way to go before you have reached an optimal level of business agility.

Wherever you are in the evolutionary process, ERM must evolve and become more agile at the same time or you can impair your ability to recognize and manage risks as they are created or transformed by your evolving business.

Why Multi-Disciplinary?

The disciplines mentioned earlier—Enterprise and Business Architecture, Business Process Management, Transformation Portfolio, Program and Project Management—as well as Scenario Analysis, Strategic Planning and Transformation Roadmapping, are intrinsic to your managing your company.  There are, or should be, planning, operating, quality-controlling, monitoring and performance management processes and practices associated with each of them.  In addition to informing, guiding and governing how you do what your company does, you collect a great deal of valuable, raw information in the course of executing them.

Enterprise Risk Management is an information-intensive discipline; if you cannot see things that should be addressed, you will not address them.  Sitting in a conference room trying to build an inventory of these things is a sure way to miss some of them.  Extracting what is passively generated from your governance processes and your day-to-day activities and experiences is a good way to be more comprehensive.  You’re already doing it, more or less, but you need to develop a focus on root sources of risks, which may not be obvious to you.  So, looking at your company through the lens of each of the disciplines you use to run it will provide perspective that can enable you to put together a (more) complete and deeply-nuanced picture of where you should focus your risk management efforts.

Why Integrated?

Risks arise from decisions you make and actions you take.  Ideally, actions you take—execution of Business as Usual (BAU) operations, have had their risks addressed via policies, practices, processes and procedures.  It should be OK, once these are running smoothly to lower the scrutiny level.  Decisions, other than those integrated and embodied in BAU operational processes, may occur regularly or irregularly and your risk management team must be present for you to manage the risks associated with them. 

In the case of higher-level decisions, such as an acquisition, you would expect risk management to be an intrinsic component of the analytical process and it probably is, up to a point.  In such a case, due diligence is an important risk management tool, perhaps your only opportunity to identify and assess non-obvious risks that don’t appear in the acquisition target’s financial statements or that may involve differences in governance or operational processes or culture.  One important lens that you can apply to focus due diligence is a taxonomy or ontology that you apply to the risks that you identify, analyze and treat.  Taxonomies and Ontologies are classification schemes that you use to qualify types of risks so that you can better understand and work to manage them.  What you see as a risk, with a presumed cause or source, your acquisition target may view as something entirely different, something which doesn’t rate mentioning to you or a prescriptive treatment in the course of their operations.

In the case of BAU-related risks, decisions crop up when (a) a case arises for which there is no prescribed action or (b) when there is a need to revise the business process.  If your risk management team is not integrated to the degree necessary to recognize and respond to either of these events, then your risk management comprehensiveness will slip, just that little bit.  Obviously, if you amass enough of these cases, your control over your risks can be seriously compromised.

Depending on a periodic review process to identify new or morphed risks is a bit like driving while looking in the rear view mirror.  It’s OK for a few seconds while you are on a straight road but fails spectacularly when a curve comes along.  The process you will go through while you compile your starting risk inventory, which may be pretty well stocked with risks from your existing risk register, will hopefully be a one-time thing.  However, many existing risk inventories are structured around avoiding undesirable outcomes more than they are identifying root causes.  Revisiting the risks in the register to refocus on source-of-risk and risk/reward analysis is an important task and crucial to reorienting your risk management posture.  Once you have refocused and developed intuition about risk sources, you can apply continuous risk management best by integrating your risk team in tight collaboration with operating units whenever decisions are being made.

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6 Environmental Public Health Jobs Where Data Science Is Useful

These days talk of Public Health may send a shiver down one’s spine. After over a year of the Coronavirus pandemic, the term almost feels like a buzzword, having overwhelmed, and oversaturated the media for months with no end in sight. Plus, public health is heavily discussed and debated amongst politicians and government officials on any given day, let alone during an election year amidst a global pandemic. All of this results in much of the population believing that public health refers strictly to government health programs. However, public health actually refers to everything in the environment, communities, and population that poses a threat to the health of the population. 

With climate change on the rise and the wellness of our planet on the top of many people’s minds, careers in environmental public health are more important than ever. From handling long-term challenges, like protecting natural resources, to more immediate problems, such as disaster management, environmental public health “focuses on protecting groups of people from threats to their health and safety posed by their environments,” according to the Centers for Disease Control and Prevention (CDC). 

Data science skills are playing a crucial role in helping environmentalists model different stats and data together to forecast future environmental challenges. 

Let’s take a look at the top five environment public health jobs where having data science skills are useful: 

Epidemiologist

A very timely occupation, epidemiologists focus on investigating the origin and spread of disease in humans. They identify people and communities who are notably at risk and work to control or completely stop the spread. Furthermore, they develop ways to prevent these diseases from happening in the first place by working in labs and hospitals and educating the community at large as well as policymakers. Data management is essential for the epidemiologist especially experiences in R-software and other data visualization techniques.  

Environmental Specialist

These specialists regulate the containment and disposal of hazardous materials. They help to develop regulations at the federal, state and local levels and ensure that all waste streams are managed according to those regulations. These are the folks who inspect and interview any violators of the waste management system.

Regular data analysis is an integral part of the environment specialist job. Some of the critical data pieces that require further analysis are air and water pollution data, tree ring data, temperature records, etc.

Environmental Toxicologist

Toxicologists study the effects of toxic chemicals. This includes how toxic chemicals are metabolized by an organism, how they impact the ecosystem as they cycle through, and all lethal and non-lethal effects the chemicals have on an entire species. Some environmental toxicologists may also conduct testing on new chemicals before they’re released to the market, in order to ensure they won’t cause adverse effects in humans such as cancer or birth defects.

Looking at the past data is significant to track the effects of toxic chemicals. Hence, knowledge of data science is useful for this job.

Bioengineer

Another timely speciality, Bioengineers can follow a number of different, yet extremely valuable, career paths. 

Those with a degree in bioengineering can go on to become Pharmaceutical Engineers. Pharmaceutical Engineers create effective (and safe) pharmaceuticals that can impact lives for the better, and in the case of Covid-19, save hundreds of thousands of lives. These specialized engineers develop, create, and test medications for the treatment of a wide variety of viruses, diseases, and injuries. 

Bioengineers can also go on to study medical device engineering, which is the development of new medical devices like prosthetics, artificial organs, and other breakthrough technology. Yet another popular career choice for bioengineering grads is a Medical Scientist. Medical Scientists promote population health through a combination of bioengineering and medical science and can carry out important duties including conducting research, clinical trials, and more.

Every bioengineering career option requires knowledge of data science. Big data is crucial to decode the human brain to promote a better healthcare system. 

Air Pollution Analyst

These vital analysts collect and analyze data from polluted air. They trace their data to the source of the pollutants and work to develop future techniques for reducing or altogether eliminating air pollution. Air Pollution Analysts hold humans accountable and control pollution outputs in order to preserve our atmosphere and maintain the quality of the air we breathe.

It is the responsibility of the air pollution scientist to examine data from polluted air. Besides, they compile different stats to create a detailed analysis.  

Environmental / Health Inspector

The health inspectors that most people are familiar with scope out your favourite local restaurant for any health violations to keep you safe. Environmental health inspectors scope out all businesses, buildings, parks, waterways and other settings to ensure they meet health and sanitation standards. They search for any potential health threats and produce ways to correct and prevent problems. Some may be responsible for inspecting food manufacturing plants, landfills, or underground storage. These inspectors also make sure any mass-produced food supply is as safe as possible.

In order to pursue any of the above Environmental Public Health jobs as a career, a minimum of a bachelor’s degree is required with knowledge of data science. This degree does not necessarily have to be in environmental public health specifically but could center on occupational health and safety, or a related scientific field like biology, chemistry, or even engineering. Depending on the position, some career options may require a graduate degree such as a Master of Public Health (M.P.H.), or perhaps even a doctorate. This field has a considerable technical and scientific nature, so for most, getting a master’s degree in environmental public health is a good idea in order to advance in the field.

Final Thoughts

No matter whichever public health career you choose, data science is an integral part of every environmental job. Career opportunities in the environmental public health sector are predicted to grow due to the continuously rising challenges presented by climate change and other factors. Maintaining a healthy environment is integral to increasing general longevity and quality of life and the knowledge of data science is helping to study the past data to improve the future. 

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AI for Influencer Marketing: How It Is Transforming the Market

                               Image Credit: Instagram.com

Influencer marketing is human-centric. So, how can artificial intelligence (AI) for influencer marketing ever work? 

Surprisingly, the interplay of AI and humans is making influencer marketing more scientific and data driven.

To put things in perspective, consider these facts:

Shudu Gram who campaigned for Rihanna has 202K followers. Lil Miquela, Prada and Nike’s star influencer, has 2.4M followers. What sets Shudu and Lil apart is that they are virtual influencers powered by computer-generated imagery or CGI.

This means consumers don’t really care if their favorite influencers are humans or not. If AI-powered influencers excel at storytelling, there’s no reason they can’t win hearts as well as human influencers.

That’s how much the influencer marketing industry has transformed since the advent of AI.

In this post, I’m going to dig into four more disruptive applications of AI in influencer marketing. 

AI Helps With Influencer Discovery

When you want your brand’s content to stand out on oversaturated social media platforms, you need an influencer with a flair for storytelling and the right brand affinity. 

And that brings us to the crux of the problem faced by 61% of marketers: influencer discovery.

Image via Mediakix

In every major domain, there are hundreds if not thousands of influencers, many of whom are just bots or fake accounts. So, how do you evaluate an influencer’s authenticity?

Manually scouring their profiles to check their engagement and credibility is impossible for us humans. Hiring an influencer marketing agency for an audit may be heavy on your pocket.

Enter AI-based influencer marketing tools.

These tools can assess thousands of profiles in minutes. Equipped with big data functionality, they can collect and assimilate massive data from multiple touchpoints. They can even create detailed personas for each influencer on your radar, allowing you to compare them side by side.

Powered with predictive analysis, AI tools can pick winning content creators even before they create content for you.  They understand your audience even better than you do, thanks to their persona-building capabilities. Moreover, they keep “listening” to audience sentiment across channels and look out for red flags in your brand mentions.

Using these insights, they can predict with near accuracy how your audience will receive a piece of content. When you combine that super-power with their precise influencer evaluation, you’ve got yourself a machine that can spot a brand-influencer mismatch from miles away.

Some powerful tools like trendHERO also help you spot influencers who have got fake followers. As a result, you can avoid those influencers and partner with genuine ones who can help in taking your campaign to the next level.

Image via trendHERO

Also, the best part about working with tools is that you don’t have to worry about prejudice or subjectivity. Likewise, AI-based tools evaluate influencers objectively only on the basis of hard data and historical patterns. This enables you to make data-driven decisions with confidence.

AI Helps Measure Campaign Performance

Measuring campaign performance is another gray area for marketers. For years, they have been leveraging influencer marketing without having clarity about its ROI. As a result, they’ve had a hard time justifying their expenditure in front of their management. 

If you think that replicating brilliant influencer marketing campaigns can guarantee success and lock in ROI, think again. With unpredictable social media users, the move can backfire massively.

That’s where AI-driven influencer marketing tools come into play.

These tools can forecast campaign ROI with unbelievable accuracy. They can even drill down into each influencer’s performance, overall and per post. Using historical campaign data, these tools can predict your campaign and influencer performance even before you get started.

Not only that, but these tools can identify channels and strategies with maximum ROI-generating potential. By eliminating the guesswork from influencer marketing, these tools can help you generate good leads from your campaigns. You can fix budgets and targets and count on your influencers to deliver on them.

Once that happens, influencer marketing becomes a potent weapon in a marketer’s arsenal. 

AI Tools Assist Influencers with Content Creation

It’s well accepted that influencer-driven content strategies increase engagement rates exponentially.

But when AI is added to the mix, what you get is unbeatable content that is tailored to each platform and audience group.  As discussed at length before, AI tools can decipher an influencer’s tone by studying their past content. Artificial neural networks (ANN) enable these tools to analyze video and image attributes deeply.  Likewise, natural language processing (NLP) enables these tools to analyze user comments and figure out how they feel about different types of content.

Putting two and two together, these tools can determine if your influencer can cater to your audience’s tastes and needs satisfactorily. They can provide “intelligent” suggestions to fine-tune content copy to perfection. 

AI tools can go really granular in their analysis. They can predict minute elements such as typography and CTA copy that appeal to your target audience. When you target the right people with the right content through the right channels and tools, you can cut through the noise and get noticed on overcrowded social media platforms.

In a Gist

Brands and marketers who fuel their influencer marketing with AI find their ROI steadily climbing northwards. AI tools can not only arm them with information to make data-backed decisions but also reduce time and resources spent on repetitive tasks. I’m sure you’ll now agree that AI is reshaping the future of marketing in a big way. 

So, how do you apply AI to influencer marketing? Share your experiences and insights in the comments below. Perhaps, I can provide you with some optimization tips of my own.

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The Main Trends in Mobile App Development That Will Dominate in 2021

In the last few years, mobile apps have constantly changed our lives. And due to their great popularity and usability, they represent a significant opportunity for learners and businesses. According to Statista, mobile apps are expected to generate approximately USD 189 billion in revenue. Moreover, many experts have already stated that the mobile app development industry is one of the fastest-growing industries and shows no signs of slowing down in the future.

With recent technological advancements and new inventions coming almost every day, it is not wrong to believe that 2021 will be the year of mobile apps and that entrepreneurs and companies will have more opportunities to do business in the future. After our team of business analysts conducted extensive research, we have identified and listed below the most promising trends in mobile app development that will dominate in 2021.

The Augmented Reality and Virtual Reality Era is Just Beginning

AR and VR are cool! There is no doubt about it. But in 2021, their use will no longer be limited to gaming applications. Tech giants are already developing lots of new applications for both. For example, both Google and Apple released new AR demos on their latest devices, proving that AR/VR will change the game shortly. These technologies are also expected to be used on social platforms for branding and targeting potential customers through AR/VR apps beyond the screen.

Snapchat and Instagram, for example, have already launched their AR filters that can transform a human face into various fun digital characters.

Some examples of AR and VR trends

• Disrupting mobile AR
• AR in marketing and advertising
• AR in healthcare
• AR in manufacturing

Smart Things – The New Era of Mobile, Connected Bright Things

The words “smart objects” or “smart things” were initially coined by relatively new technology: the Internet of Things. Also known as IoT, it is essentially a network of physical objects with sensors, electronics, and software, all connected in the network itself. For example, Samsung, Xiaomi, Bosch, Honeywell, and many other major brands already have a significant market share. Recent trends in IoT app development include Kisi Smart Lock, Nest Smart Home, Google Home, etc. IoT is generally considered one of the game-changing technologies in the world of mobile app development. The global IoT market expects to generate a revenue of USD 1.335 trillion by 2021.

Future IoT trends

• Smart homes and intelligent zones
• Routers with more security
• Self-driving cars
• IoT in healthcare

AI and Machine Learning

Both AI and machine learning have penetrated quite deeply into the mobile application market. AI has mainly manifested itself in chatbots, while Siri, the combination of machine learning and artificial intelligence, has become an integral part of mobile app innovations. In 2021, AI and machine learning power will not be limited to chatbots and Siri. Many organizations have already begun to embrace the development of AI applications to increase profitability and reduce operational costs in various forms. In fact, according to IDC, over 75% of workers using ERP solutions will now harness the power of AI to develop their skills in the workplace. It means that not only are AI and machine learning embedded in today’s mobile applications, but they also offer a significant opportunity for future innovation.

Future trends in AI and MI you should watch for

• AI-enabled DevOps through AIOps
• Artificial intelligence chips
• Machine learning
• Neural network interoperability

Beacons – A Market Worth Millions

Beacons are not an innovation anymore. Several sectors, including museums, hotels, and healthcare, are now using beacon technology in their applications. We think it is fair to say that beacon technology has become more understandable to ordinary users. However, their applications will not be limited to 2021. Beacons have a much greater capacity than that. For example, beacons combined with IoT in retail can help users by providing them with valuable information about sales and other current offers they may find nearby.

Future trends in beacon technology 

• Beacons for mobile payments
• Artificial intelligence chips
• Machine learning
• Beacon scavenger hunt

Cloud – the must-have for future mobile applications

While many still consider the cloud a luxury option, this will no longer be the case by 2021. The world is already beginning to realize the benefits and opportunities of the cloud. For example, reduced web hosting costs, improved upload capacity, and streamlined business operations are just some of the benefits offered by the cloud. Today, many security-related issues are solved through the cloud, making mobile application development more secure, fast, and reliable. 

In addition, using cloud technology such as Dropbox, AWS, SlideRocket, and many others, it is now possible to develop robust applications that run directly in the cloud. This means that we should also expect to see other equally powerful applications that require minimal storage on the smartphone in 2021.

Cloud computing trends in 2021

• Quantum computing
• Hybrid cloud solutions
• Evolution of cloud services and solutions

Mobile Wallets – The Game Changer for Mobile Banking 

There is no doubt that demand for mobile payment app development is on the rise, and with security being the main concern for developers, the use of mobile wallets will only increase in 2021. Frictionless payment methods are what today’s customers like to see in the mobile applications they use.

So, by 2021, mobile wallets and the integration of payment gateways offering the highest level of secure encryption will become commonplace in all types of mobile applications.

 Mobile banking trends to look out for

• Over 2 billion mobile wallet users
• More secure mobile wallets
• Contactless payment

Blockchain – Things Beyond Bitcoin and Smart Contracts

Since its inception, blockchain development has opened up a world of new and more exciting possibilities in the IT industry. In 2018, we all saw the use of blockchain technology used to create cryptocurrencies and smart contracts. However, in reality, blockchain is more valuable than you can imagine. For example, decentralized mobile apps can develop using blockchain. Decentralized mobile apps, or DApps, are essentially apps that not only belong to no one but also can’t be shut down and have no downtime. Simply put, the blockchain expects to contribute more to the mobile app industry by making the mobile app itself decentralized, just as the bitcoin blockchain did for money.

Future trends in blockchain technology

• Asset tokens
• Blockchain as a Service (BAAS)
• Trading on cryptocurrency exchanges
• Cryptocurrencies and games

Wearables – The Essential Accessory of the Future

There is no denying that the wearable electronics market is increasing. According to Statista, the value of wearables expects to reach more than $44.2 billion by the end of 2021. It means that there is a precise volume of investment in the wearables market but that in the future, the word “wearable” will be as redundant as the word “smartphone” is currently. Currently, the main control panel of any wearable device is the smartphone and means that to create a wearable device, the devices need to be paired with it and need to be nearby. However, according to UNA co-founder Ryan Craycraft, our smartphone will no longer be the central hub shortly. Apps developed by wearable devices will have a more ubiquitous connection directly to the web and perhaps even to our bodies.

Future trends in wearable electronics for 2021

• Wearable technology takes the top spot in fitness trends for 2021.
• The rise of wearables is leading to a decline in sales of traditional watches.

On-Demand App Development – The Most Successful Business Model of Modern Times 

The on-demand business model was once considered an inevitable bubble in the mobile app world. Today, however, on-demand services are the future. Almost every industry has embraced the on-demand model and no sector will abandon this successful business model in 2021. To date, 42% of the adult population uses at least one on-demand service. And there is no sign that this on-demand trend is going away anytime soon. Overall, the on-demand trend is here to stay, and their competitors will surely crush companies that don’t adapt.

 On-demand trends in 2021

• Greater focus on the B2B sector
• More industries will embrace on-demand applications

2021 and Beyond…

We believe that keeping up with the latest trends and technologies is the key to keeping up with the ever-changing demands of customers and competitors. We hope we’ve shared some great insights on mobile app development trends for 2021 in this blog. While it’s hard to determine the exact benefits of all the mobile app development companies for your business, don’t hesitate to reach out to experts if you do. We guarantee that your app will stand out in the mobile app market.

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9 Top-Notch Programming Languages for Data Science & Machine Learning

Have you ever had a question about which programming language is best for data science and machine learning? To become a data scientist or a machine learning expert, you will have to learn various programming languages. So, in this article, we will be talking about the best programming languages you should learn to become a data science or machine learning expert. 

Python is the most popular and the most used programming language in the field of data science. Python is considered as one of the easiest languages to work with, its simplicity and a wide choice of the library just make it more convenient. 

Python is an easy-to-use open-source language and supports various paradigms, from structured to functional and procedural programming. Python is the number one choice when it comes to machine learning and data science.

You can’t talk about Data Science without mentioning R. R is again considered as one of the best languages for data science because it was developed by statisticians for statisticians to deal with such needs.

R is typically used for statistical computing and graphics. There are numerous applications of R in data science and have multiple useful libraries for data science. R is very handy in conducting ad hoc analysis and for exploring data sets and plays an important role in Logistic Regression.

JavaScript is an object-oriented programming language that is used in data science. There are hundreds of Java libraries available today that can cover every kind of problem that a programmer may come across. 

Java is a versatile language that can manage multiple tasks at once. It also helps in embedding things from electronics to web applications and desktops and can be easily scaled up for large applications. Popular processing frameworks like Hadoop also run on Java. 

This elegant programming language is comparatively new, created recently back in 2003. It was initially designed with the purpose to address issues with Java but nowadays Scala is applied in numerous places ranging from web programming to machine learning. 

As the name suggests, it is an effective and scalable language for handling big data. In modern-day organizations, Scala supports functional programming, object-oriented, and as well as synchronized and concurrent processing. 

Structured Query Language or SQL, is a domain-specific language that has become a very popular programming language for managing data. Although SQL is not exclusively used for data science procedures, knowing SQL queries and tables is really helpful for data scientists to deal with database management systems. SQL is remarkably convenient for storing, manipulating, and retrieving data in relational databases. 

Julia was developed for speedy numerical analysis and high-performance computational science which makes it an optimal language for data science. One thing that makes Julia undisputed is its speed. It is extremely fast and can work even faster than Python, R, JavaScript, or MATLAB.

It can quickly implement different mathematical concepts and excellently deals with matrices. Julia can be used for both front-end and back-end programming.

Julia comes with various data manipulation tools and mathematical libraries. Julia can also integrate with other programming languages like R, Matlab, Python, C, C++, Java, Fortran, etc. either directly or through packages.

Perl is widely used to handle data queries. Perl supports both object and procedural-oriented programming. Perl uses lightweight arrays that don’t need a high level of focus from the programmer and it is proved to be very efficient as compared to some other programming languages. 

The best part about Perl is that it smoothly works with different mark-up languages like XML, HTML and also supports Unicode.

C++ has a unique spot in the data scientist’s toolkit. There is a layer of a low-level programming language on top of all modern data science frameworks and that programming language is C++. You could say that C++ has a very big role in executing the high-level code fed to the framework. This language is very simple yet extremely powerful. And guess what? C++ is one of the fastest languages out on the battlefield. And as it is a low-level language, it allows the machine learning and data scientists practitioners to have a more extensive command of its applications.

Some of the biggest pros of C++ are that it enables System programming and helps to increase the processing speed of your application. Though knowing C++ isn’t essential for data science, but it helps you to find the solutions when all other languages fail.

MATLAB comes with native support for the image, sensor, video, binary, telemetry, and other real-time formats. It offers a full set of machine learning and statistics functionality, plus a few advanced methods like system identification, nonlinear optimization, and thousands of prebuilt algorithms for image and video processing, control system design, financial modeling. 

Well, if you look at it there are hundreds of programming languages in the world today, and the use-case of each language depends on what you want to do with it. Each of them has its own importance and features. So, it’s always up to you to choose the language based on your objectives and preferences for each project. 

To become an expert in data science or machine learning, learning a programming language is a crucial step. Data scientists should consider the pros and cons of the various programming languages before making a decision for their projects. Now that you know about the best programming languages for data science and machine learning, it’s time for you to go ahead and practice them! 

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Could Machine Learning Practitioners Prove Deep Math Conjectures?

Many of us have solid foundations in math or have an interest in learning more, and are passionate about solving difficult problems during our free time. Of course, most of us are not professional mathematicians, but we may bring some value to help solve some of the most challenging mathematical conjectures, especially the ones that can be stated in rather simple words. In my opinion, the less math-trained you are (up to some extent), the more likely you could come up with original, creative solutions. Not that we could end up proving the Riemann hypothesis or other problems of the same caliber and popularity: the short answer is no. But we might think of a different path, a potential new approach to tackle these problems, and discover new theories, models and techniques along the way, some applicable to data analysis and real business problems. And sharing our ideas with professional mathematicians could have benefits for them and for us. Working on these problems during our leisure time could also benefit our machine learning career, if anything. In this article, I elaborate on these various points.

The less math you learned, the more creative you could be

Of course, this is true only up to some extent. You need to know much more than just high school math. When I started my PhD studies and asked my mentor if I should attend some classes or learn material that I knew was missing in my education, his answer was no: he said that the more you learn, the more you can get stuck in one particular way of thinking, and it can hurt creativity. That said, you still need to know a minimum, and these days it is very easy to self-learn advanced math by reading articles, using tools such as OEIS or Wolfram Alpha  (Mathematica) and posting questions on websites such as MathOverflow (see my profile and posted questions here), which are frequented by professional, research-level mathematicians. The drawback by not reading the classics (you should read them) is that you are bound to re-invent the wheel time and over, though in my case, that’s the best way I learn new things.

Professionals with a background in physics, computer science, probability theory, pure math, or quantitative finance, may have a competitive advantage. Most importantly, you need to be passionate about your own private research, have a lot of modesty, perseverance, and patience as you fill face many disappointments, and not expect fame or financial rewards – in short, not any different than starting a PhD program. Some companies like Google may allow you to work on pet projects, and experimental research in number theory geared towards applications, may fit the bill. After all, some of the people who computed trillions of digits of the number Pi (and analyzed them) did it during their tenure at Google, and in the process contributed to the development of high performance computing. Some of them also contributed to deepen the field of number theory.

In my case, it was never my goal to prove any big conjecture. I stumbled time and over upon them while working on otherwise un-related math projects. It peeked my interest, and over time, I spent a lot of energy trying to understand the depth of these conjectures and why they may be true. And I got more and more interested in trying to pierce their mystery. This is true for the Riemann hypothesis (RH), a tantalizing conjecture with many implications if true, and relatively easy to understand. Even quantum physicists have worked on it, and obtained promising results. I know I will never prove RH, but if I can find a new direction to prove it, that is all I am asking for. Then I will work with mathematicians who know much more than I do, if my scenario for a proof is worth exploring, and enroll them to work on my foundations (likely to involved brand new math). The hope is that they can finish a work that I started myself, but that I can not complete due to my somewhat limited mathematical knowledge.

In the end, many top mathematicians made stellar discoveries in their thirties, out-performing their peers that were 30 years older despite the fact that their knowledge was limited because of their young age. This is another example that if you know too much, it might not necessarily help you.

Note that to get a job, “the less you know, the better” does not work, as employers expect you to know everything that is needed to work properly in their company.  You can and should continue to learn a lot on the job, but you must master the basics just to be offered a job, and to be able to keep it. 

What I learned from working on these math projects: the benefits

To begin with, not being affiliated with a professional research lab or the academia has some benefits: you don’t have to publish, you choose your research project yourself, you work at your own pace (it better be much faster than in the academia), you don’t have to face politics, and you don’t have to teach. Yet you have access to similar resources (computing power, literature, and so on). You can even teach if you want to; in my case I don’t really teach, but I write a lot of tutorials to get more people interested in the subject, and probably self-published books in the future, which could become a source of revenue. My math questions on MathOverflow get a lot of criticism and some great answers too, which serves as peer-review, and they even point me to some literature that I should read, as well as new, state-of-the-art yet unpublished research results. On occasions, I correspond with well known university professors, which further helps me not going in the wrong direction. 

The top benefits I’ve found working on these problems is the incredible opportunities it offers to hone your machine learning skills. The biggest data sets I ever worked on come from these math projects. It allows you to test and benchmark various statistical models, discover new probability distributions with applications to real-world problems (see this example), new visualizations (see here), develop new statistical tests of randomness and new probabilistic games (see here), and even discover interesting math theory, sometimes truly original: for instance complex random variables with applications (see here), lattice points distribution in the infinite-dimensional simplex (yet unpublished), or advanced matrix algebra asymptotics (infinite matrices, yet unpublished)  and a new type of Dirichlet functions. Still, 90% of my research never gets published. I only share peer-reviewed, usually new results. The rest goes to garbage, which is always the case when you do research. For those interested, much of what I wrote and that I consider worth sharing, can be found in the math section, here.

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