Self-service analytics are here. Do business intelligence teams face extinction?

The world of analytics is changing. Self-service analytical tools like Tableau, Qlik and Power BI enable business users to perform reporting and analytics with little to no support from the IT organization. This trend has evolved due to several factors:

• Organizations are flooded with data, and IT organizations are not able to keep up.
• Easy-to-use business intelligence (BI) tools make it more efficient for business users to create their own reports.
• IT organizations' analytical projects can take several months, even when a business needs the information in weeks.

This trend has moved the work efforts of IT business intelligence (IT BI) teams to spend most of their energy gathering, cleaning and structuring data for the business to perform their own analytics through a self-service mode. In special cases, they provide industrial-strength reporting that can scale to thousands of users where trusted verified data is needed. However, they’re still being shown the door when it comes to some of the more interesting work.

How can IT BI teams stay relevant and valuable to the business? We have suggestions that embrace self-service analytics as the new reality and transition core competencies of the BI team from descriptive reporting to diagnostic and predictive analytics.

Let’s explain that concept before prescribing several solutions. The current state of most analytics is descriptive in nature and helps answer what occurred. Diagnostic and predictive analytics help answer why it occurred and sets the stage to answer what we can do about it. Diagnostic analytics help organizations not only understand what but also why and what decisions and actions can be taken to solve the issue. This type of analytics is more valuable to the business and requires competencies that IT BI teams are uniquely qualified to bring from years of building analytical solutions.

IT BI teams can move from providing commodity reports to analytics that drive the business by adding these components:

Build out a data science capability

Data scientists are starting to appear in analytical groups within companies. However, they’re still not pervasive, and the function is understaffed. Also, most groups within a company can’t justify the cost of a full-time data scientist. By adding data scientists to the IT BI organization, you create a centralized team that can provide analytics to underserved parts of the organization. This team is already focused on creating analytical data structures and reports, and adding the ability to derive insights from the data is a natural extension.

Make it about the decision

In your requirements process, add the decision architecture practice as a capability. Most methodologies for capturing analytical requirements focus on the questions asked of the data to surmise the dimensions and facts. While this provides great descriptive analytics, it doesn’t move the needle on the analytical maturity curve. A deeper focus on the decisions the business makes and centering analytical capabilities to enable actionable insights will move the needle.

Add decision theory to your analytics

If data science helps you turn information into actionable insights, decision theory helps you structure the decision process to guide a person to the correct choice. Decision theory, along with behavioral economics, is focused on understanding the components of the decision process to explain why we make the choices we do. It provides a systematic way to consider tradeoffs among attributes that help us make better decisions. Tools such as thresholds, alerts, decision matrices and choice architecture should be considered important capabilities to add to your team’s tool chest.

Create a report certification process

Report proliferation can occur with self-service analytics. If the analytics are consumed and trusted by a broad audience, you should create a standard for the health and validity of your reports. For example, you may have one group create a report with key metrics sourced from uncertified data sources. When you put reports through a certification process, consumers will know the level of scrutiny by data governance and IT teams and trust the data and metrics.

As IT BI teams look to move up the value chain in the capabilities they offer to their internal customers, adding skill sets, methods and tools is a necessity. The capabilities outlined are a great fit for a centralized team to own, and this transformation in IT BI teams can speak to the value enhanced capabilities provide to the business.

— By Andrew Wells and Kathy Chang