BI Analytics: An engine for your business strategy

Strategy drives the value an organization creates for its customers. Be it through their products or services. And data has an immense role in defining this strategy and executing this journey. Business Intelligence helps companies learn what has worked and what hasn’t, and advanced analytics can further guide the future decisions of business proactively. BI & Analytics hold the key for empowering consumers/users make informed decisions.

While there are short-cuts to enable BI and Analytics, the best path is to start by defining the Data strategy. Having a data strategy in place enables clarity among all the stakeholders and delivers greater return on investment. Establish the business objectives like improve customer satisfaction, expand to new markets, increase revenue, etc. and work backwards to enable solutions that deliver these objectives.

There are many BI and analytics maturity models that one may come across. Most of them will assess the extent to which an organization is Descriptive, Diagnostic, Predictive, and Prescriptive aspects of data.

• Descriptive: What happened?
• Diagnostic: Why did it happen?
• Predictive: What will likely happen?
• Prescriptive: How can we make it happen?

Data is an asset, which when managed and leveraged well, can drive organization growth. BI & Analytics platforms enable an organization to harness data and its power to drive business. It is important to have a good data foundation for faster and better BI & Analytics outcomes.

This is possible when all three dimensions of people, culture and processes come together. Everyone in the organization must align on the metrics, dimensions and definitions and then work towards common goals. And a single source of truth, the foundational layer, is key to making this happen.

There is no one-size fits all solution in the market. Choosing the right option will start with asking the right questions to the right people. While the wish-list can be endless, it is important to prioritize the expected functionality from the platform and work towards identifying options that best meet those priorities. And the technology options could be commercial or open-source or even custom-built.

For yielding better ROI in BI and Analytics, start at the end goal. Have business needs captured and prioritized. Ensure there is alignment on these priorities across the organization. Have an impact vs. efforts matrix defined for all prioritized needs, and assess the time and resources that will be required for each line item. Remember to have a feedback loop in place to iterate and deliver outcomes that can be better adopted.

Few common pitfalls to quote are –

• Lack of a well-defined problem statement
• No support from stakeholders
• Lack of governance
• No agility and feedback loop in place
• Gaps in tools/technologies

While new age tools offer out-of-box insights, they still have a long way to go before they can be blindly trusted. Real world problems, especially when data is involved, are complex to complex. And the insights offered through tools have to be in a consumable format too. For these insights to be trusted, consistency in the definition of KPIs and a single-source-of-truth would be critical.

Yes, new-age tools are offering self-service capabilities. A point to note with enabling this feature is to have good governance in place. The usual perception of governance is that there will be a lot of control, approval processes, etc. and it is therefore important to balance business empowerment and IT controls.

It all begins with defining the business purpose. And then having the right team and solution approach. Couple of points to call out are the experimental nature and the iterative efforts required to deliver better outcomes in predictive analytics and AI. Competency also plays a key role in ensuring that advanced analytics efforts don’t end at a Proof-of-Concept stage but are indeed deployed to production, providing sustainable business value.