The World Economic Forum has declared data a commodity as valuable as gold. Like gold, however, data must be mined. Data lying fallow in a database is no more valuable than gold resting in the ground. Deloitte analysts observe, “Many organizations are captivated by the discipline of data analytics — and for good reason. The availability of data and the technology needed to mine it have put analytics within reach of almost every organization. Data from disparate internal and external sources can be combined in innovative ways to produce previously inconceivable insights and generate value.”[1] The editorial staff at Business.com adds, “Big data is a big deal. The sheer quantity of data generated over just the last few years far exceeds the entirety of the previously accumulated human historical data record. … However, as Jonatahn Shaw pointed out in Harvard Magazine, it’s not the amount of data that makes it a really big deal, it’s the ability to actually do something with it.”[2] Most companies understand the value of data and, as a result, data scientists are now in great demand. The Business.com staff reports, “According to a 2015 MIT Sloan Management Review, 40 percent of the companies surveyed were struggling to find and retain the data analytics talent. And the picture is starting to look even bleaker. International Data Corporation (IDC) predicts a need by 2018 for 181,000 people with deep analytical skills, and a requirement five times that number for jobs with the need for data management and interpretation skills.”

Advanced analytics creators and users

Bill Franks (@billfranksga), Chief Analytics Officer for The International Institute for Analytics, argues some companies don’t understand where and when data scientists are needed. He explains, “The trends toward democratization of data and self-service analytical capabilities are powerful and both have driven a lot of value for organizations in recent years. At the same time, it is possible to go too far. I get concerned when I hear the suggestion that everyone in the organization needs to create, use, and understand analytics. Many people don’t (and shouldn’t!) understand analytics at all. There are absolutely people within an organization who must understand how analytics work. … However, in most cases, it is still a relatively small number as a percentage of all employees.”[3] His observation is followed by a big “but”. Even though most employees don’t need to be data scientists, they do need to understand how analytics can improve their job performance. “To say that many people in an organization don’t need to create or understand analytics,” he writes, “is not the same as saying that they should not make use of analytics. … The goal should be to permeate analytics throughout an organization and to allow it to impact virtually every employee and business process. But, it is perfectly fine to have many people unaware of the effort, complexity, and theory behind those analytics. As long as they change their behaviors and take action as the analytics suggest, success can be achieved. The secret is to enable such people to take advantage without having a deeper understanding of the underlying analytics.”

Once you realize there is a difference between understanding how analytics work and understanding what analytics can do, you are better situated to develop a corporate data strategy. Mathias Golombek (@EXAGolo), Chief Technology Officer at Exasol, asserts, “Organizations must move towards incorporating new data strategies to remain ahead of the competition. Only companies who work their data hard for insights are able to optimize their business, create new routes to market or create innovative new services and revenue streams.”[4] Franks believes a good place to start thinking about data analytics strategy is distinguishing being creators and users. He explains, “At a very high level, this issue can be broken down to the distinction between analytics creators and analytics consumers. There are degrees of sophistication within those high-level segments, but one of the biggest sub-segments will usually be highly unsophisticated analytics consumers. For those employees, the goal should be to provide simple, prescriptive actions based on analytics. The goal should not be for them to understand what is under the hood or to have them create their own analytics. Pushing people too far up the sophistication scale will not only waste a lot of resources but will lead to a range of issues from employee frustration to bad decisions.” Today, cognitive systems, like the Enterra Enterprise Cognitive System™ (Aila®), can help with both the data scientist shortage challenge and getting unsophisticated analytics consumers to leverage analytic insights.

Cognitive computing and advanced analytics

Addressing the data scientist shortage

Cognitive technologies can help address the data scientist shortage by embedding analytic expertise in the system. The traditional approach to analytics has been to assemble a team of three experts:

• A business domain expert — the customer of the analysis who can help explain the drivers behind data anomalies and outliers.
• A statistical expert — to help formulate the correct statistical studies, the business expert knows what they want to study, and what terms to use to help formulate the data in a way that will detect the desired phenomena.
• A data expert — the data expert understands where and how to pull the data from across multiple databases or data feeds.

Having three experts involved dramatically lengthens the time required to analyze, tune, re-analyze, and interpret the results. Cognitive technologies empower the business expert by automating the statistical expert’s and data expert’s knowledge and functions, so the ideation cycle can be dramatically shortened and more insights can be auto-generated. Golombek explains, “There are data projects that specialize in analyzing complex graph structures, text sentiment analysis tools for analyzing your customers’ support emails appropriately, and in-memory databases that give you fast access to your data analysis, and each of these was built according to specific requirements, in line with a specific data strategy. Today, a heterogeneous, agile data ecosystem allows companies to do unprecedented things with data, opening up a whole new space to become better in business by utilizing automated, predictive and prescriptive processes rather than just creating reports about the history.”

Helping unsophisticated analytics consumers

Franks insists organizations must ensure “analytics are disseminated to employees who are unsophisticated consumers in a way that they can make use of the results and take action.” Today’s cognitive platforms use natural language processing (NLP) to ensure users can both ask questions and receive answers in language they understand. Using systems like Enterra’s Aila platform, you don’t need to simplify a question or master the complexities of modelling data, constructing queries, or performing mathematical and statistical calculations. Just ask the platform in plain language receive easily understood answers.

Summary

Deloitte analysts conclude it is important to foster an “organizational environment where analytics is perceived as an opportunity to support people, and not as a threat to the status quo.” Leveraging state-of-the-art cognitive technologies can help achieve that end by increasing analytical capabilities and making them available to the technically unsophisticated end user.

Footnotes
[1] Deloitte, “The People Dilemma of Analytics,” The Wall Street Journal, 3 November 2015.
[2] Editorial Staff, “Big Data, Big Problem: Coping With Shortage of Talent in Data Analysis,” Business.com, 22 February 2017.
[3] Bill Franks, “No, Not Everyone Needs To Understand Analytics,” International Institute for Analytics, 12 October 2017.
[4] Mathias Golombek, “Big data analytics is dead – long live data analytics, says CTO of Exasol,” Computing, 11 January 2018.