Embedded BI Solves the Data Scientist Shortage

By May 31, 2016Industry Trends

Embedded BI Solves the Data Scientist ShortageBy 2018, the number of data science jobs needed in the United States will exceed 490,000, with fewer than 200,000 data scientists to fill the positions, according to a McKinsey study.  Also, lacking will be 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions. One question that isn’t asked enough is whether a top-down approach – driven by highly skilled data scientists and technical, data-literate managers – is the only viable approach.

Data scientist and technical managers can produce value. But to be efficient and effective they need to have context and a deep understanding of the business in general and particularly about the people, process, and technology involved in what they are analyzing.  For many organizations, those insights can’t be derived from data or implemented effectively without the involvement of front-line employees who have context.

Embedded BI Increases Efficiency, Decreases Costs

So that begs the question – why not provide those frontline workers and ultimately all workers with the BI and analytics capabilities in the applications they use every day? This expanded or democratized the use of data to make more effective decisions is one part of the value of embedded BI.

Embedded BI is less expensive. Big Data on top of the data scientist and technical training for management demands considerable infrastructure requirements and technical investment. You have to add the infrastructure (people, process and technology) to put those insights into action in the front line.

Embedded BI is more efficient because analytics are fused with the workflow that employees utilize every day. Workers who have the context are empowered to be entrepreneurial and can take action immediately within the application they know. This right sized analytics delivered at the right time to people with the right context presents a stark contrast to the deluge of data that requires a data scientist to interpret.

Data Visualization Helps Reveal Trends

Data visualization tools, often touted as part of a Big Data solution, are common within embedded BI solutions These tools serve to further empower frontline workers by helping them more quickly and easily spot issues and take action. Possessing context they can also use ad hoc reports, dashboards and visualizations to anticipate potential issues.

Izenda clients working in high-paced environments where multiple groups depend on the actions of others have remarked that users who can now create and customize reports and dashboards in real-time are able to anticipate potential issues much faster because “they know where to look.”

Decision intelligence expert Lorien Pratt provides a good explanation in why 10% of data holds 90% of the value and 4 reasons why.  She notes decisions are not made of just data, they are also based on human expertise and things you may not have thought yet to measure.  Employees with context for business can apply expertise and develop new measurements faster.

To be clear, data science has value and employees who resist following the conclusions that data presents do so at their own peril. Interestingly valuing intuition over data was acknowledged by executives in a study produced by The Economist, which found that when making a decision if data contradicted the gut feeling 87% would reanalyze or collect more and 3% would ignore it.

As with any form of business intelligence and analytics the goal is to make better data-driven decisions faster. Embedding BI and analytics into the applications users know to enable self-service into actionable insights is a means to achieve this.

What are your next steps?

Is the right approach to build or buy an embedded BI and analytics solution? Download Izenda’s white paper for questions to ask your team and of BI vendors.