chicken  or the egg? hand holding a number of eggs with many chickens in the background
PHOTO: Daniel Tuttle

As more and more businesses become fully digitized, the instantiation of their business processes and business capabilities becomes software based. Any software implementation involves decisions being made which can result in a business getting stuck in time or creating a basis for business differentiation. A lot of these decisions have to do with simple things like what fields are made operational and what functions get implemented or not.

Focus on Business Goals First

Several years ago, I was involved in helping an insurance company with its analytics software implementation. Everyone on the management team wanted the analytics software completed so they could improve their business, but one of the project leads wanted the analytics task completed after an upgrade to their key transactional processing software.

However, the firm’s CIO understood that decisions made during the transaction processing software implementation could impact whether critical metrics and KPIs could be measured or not. And that these decisions would determine whether improvement goals were met.

So instead of doing analytics as an afterthought, the CIO had the analytics done up front. He slowed the transactional software implementation. At the same time, he got his team to start by thinking about the enterprises's business goals with the software implementation. With this change, his team determined the metrics and KPIs to measure to achieve improvement goals. They then required the transaction software project team to ensure that the upgrade implemented the necessary fields to support this measurement. In some cases, it was as simple as turning on a field or training users to enter data in a field after the transaction software went live.

Related Article: How Business Architecture Turns Digital Transformation Goals Into Reality

Make Analytics Part of Everyday Business Decisions

In his book, "Analytics at Work: Smarter Decisions, Better Results," Tom Davenport wrote, “if you really want to put analytics to work in an enterprise, you need to make them an integral part of everyday business decisions and business processes — the methods by which work gets done.”

For many, this means turning application development on its head, just as the insurance CIO did. In particular, it means development teams should no longer rush to implement applications. They need to be more deliberate, by first identifying the business problems the software instantiation of a business process would solve. Consideration also needs to be given to how they can improve processes with the software, rather than thinking about analytics and data as an afterthought of a software implementation. Why does this matter so much? As Davenport writes, “embedding analytics into processes improves the ability of the organization to implement new insights. It eliminates gaps between insights, decisions and actions.”

Related Article: Why Business Intelligence Has a Role in the World of AI 

Look Beyond Immediate Decisions to the Business Capability

Davenport said enterprises need to look beyond their immediate task or decision and appreciate the whole business process. The argument is that analytics should focus on the enterprise capability system. Clearly, maximizing performance of the enterprise capability system requires an enterprise perspective upon analytics. In addition, it should be noted that a systems perspective allows business leadership to appreciate how different parts of the business work together as a whole. Analytics, therefore, allows the business to determine how to drive better business outcomes for the enterprise.

At the same time, focusing on the enterprise capabilities system can over time lead to a reengineering of overarching business processes and a revamping of supporting information systems. This allows the business to capitalize on the potential of business capability and analytics improvement. From my experience, most organizations need some time to see what a change in analytics performance means. Therefore, it makes sense to start by measuring baseline process performance before determining the enhanced business process. Once defined, however, refinement for the enhanced process can be determined by continuously measuring processes performance. 

Analytics Drives the Ship

Artificial intelligence and  process have become inexorably linked, argue Marco Iansiti and Karim Lakhani in "Competing in the Age of AI." Analytics, for them, is about managing process. They give an example of Ant Group (formerly known as Ant Financial). With a staff of a few thousand people, Ant has grown from 10 million to 700 million users. Its secret is using an integrated platform that uses AI to power application processing, fraud detection, credit scoring and loan qualification. Everything is automated using analytics. To put this in perspective, JP Morgan Chase — a much larger business — has 82 million online customers with 250,000 employees worldwide.

Born digital, fintech companies like Ant and SoCal in the US are using analytics to create digital operating models that leverage digital to transform the financial services industry. We are sure to see data and process increasingly come together to drive more effective and efficient business outcomes.

Put Data and Analytics in Your Plans Now

Data and analytics can no longer be an afterthought. They need to be at the forefront of business decisions, especially as they become linked together in AI-enabled processes. Today, the game is about automation and continual improvement. This is a huge change, but as analytics-first disruptors start winning against legacy market leaders, it will become a step that everyone needs to consider taking today rather than tomorrow.