Defining intelligence can be difficult. But one way to look at it, when it comes to business, is to think of an intelligent business as one that is able to collect data and use it to make better decisions. In the past, business expertise was concentrated amongst a few people who were able to use their knowledge and experience to deliver insights that helped the business make better decisions. The downside? Decision-making was centralised and therefore limited.
In today’s fast-moving world, companies cannot afford to have bottlenecks that centralise intelligence and delay important decisions. Nowadays, everyone in the business needs to have access to intelligence so he or she can make more-informed decisions more quickly. The days of using data retrospectively instead of using live data to make real-time decisions are coming to an end. This is the world of the intelligent business.
The key quality of intelligent businesses — versus those that struggle — is that they are data-driven. Also, intelligent businesses have developed the capability to be proactive in their execution so that opportunities are identified and acted upon quickly.
To do that, companies need to identify and centralise their data assets and use an appropriate data management solution so they are accessible and usable. Data gaps need to be identified and filled so that a complete view of the enterprise is created. When that happens, businesses start to see things that were previously not visible.
For example, business improvements that involve making small changes to operations in order to improve the organisation’s performance become evident and allow enterprises to capture the low-hanging fruit that data visibility makes obvious. The business also likely will discover some of the more complex analytical capabilities they’ll need to deliver the next wave of benefits.
An important lesson is that analysis should not be conducted for its own sake. Instead, look at specific user stories and use-cases where data-driven insights and decisions can deliver tangible benefits. These user stories will then become part of an on-going “visibility extension” exercise which creates additional value at each iteration.
Once the user stories are developed, enterprises find opportunities to automate and enhance business processes. This can happen through robotic process automation or the application of artificial intelligence and machine learning. Automation cannot exist without both the data-visibility and the insight-gathering processes. As these algorithms are used, they will also feed back into further visibility and insight requirements, completing the cycle that allows for continuous business process improvement at scale.
The life cycle that shifts an organisation into becoming an intelligent business starts with better data visibility that gives the business insights to help drive better decision-making and automation.
It’s important to note that the shift to creating an intelligent business is not predicated on disposing of legacy systems. Legacy processes and systems are often fit for purpose, but not comprehensive. The key is getting the fuller view of the enterprise before taking steps to change either systems or processes. Further, these changes need to be developed as user stories and implemented using agile methods. This ensures that the business users are full partners in the evolving use of data in the enterprise.
While having complete data is important, do not ignore the importance of people. It is critical, when making the shift to becoming a data-driven intelligent business, that you recognise the value of your employees’ contributions. The move to becoming an intelligent business is an evolution; therefore, the capabilities of the current database administrators and business analysts are important in defining the data landscape. These people are vital to developing user stories, prioritising the implementation of these stories and performing the actions necessary to ensure the transition to a data-driven enterprise.
Finally, intelligent businesses are proactive. They use data to recognise what’s happening and use that to derive insights and use automation to recognise and quickly take advantage of changing situations. This doesn’t discount the importance of legacy systems or people, but builds on both to support businesses as they adapt to a faster-paced world.