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Where are you on the business intelligence maturity curve?

If you want an indication of the power of data analytics, look no further than the fitness bands hundreds of thousands of Kiwis now wear to track their steps and physical exercise.

The sensors in those trackers and even the smartphones we carry around for much of the day, deliver an array of data points that are converted into metrics that we can track over time to monitor our sleep, weight, heart rate and determine how active we are.

Many of the services from the likes of Fitbit go a step further, comparing your habits and lifestyle with those of similar weight, age and activity to give you insights into how the state of your health compares to your cohort. Much of this anonymised data is also being used by the medical industry to trace health patterns and to find cures to diseases.

Analytics in daily life

Now fitness trackers are going further, with the likes of the Apple Watch letting you touch the digital crown to take an electrocardiogram to check your heart rhythm for irregularities. Apple’s Health Records app is also now allowing a patient’s records, including diagnoses, prescriptions and past test results from multiple providers, to be accessible on the iPhone.

The next step is the giant leap forward – combining that health tracking data with a patient’s records and using artificial intelligence (AI) to predict future risk of disease and suggest treatments to keep the patient healthy. That is the game-changer that is coming for the health sector, the era of personalised medicine and it will have implications for primary care through to health insurance.

Every industry, from financial services and retail, to entertainment to government are on this journey as they move from simple business intelligence giving them the state of play currently, to smart analytical applications that help predict the future.

The incorporation of AI capabilities into business intelligence and data analytics is transforming businesses and the early adopters are the ones gaining a valuable edge over their competitors. If you aren’t thinking about how your own business is evolving in its use of data, you are likely to struggle in an increasingly data-driven world.

The Business Intelligence maturity curve

Figuring out where you currently are on what is known as the Business Intelligence (BI) maturity curve is a very useful exercise. We can characterise that journey as having four distinct phases and at Intergen, we are working with organisations at each point of the curve.  

The Business Intelligence maturity curve

Phase 1 represents a pre-analytics environment where most people within the organisation are not utilising analytics, except for spreadsheets with some basic reporting. Users have access to some information and are looking at things in hindsight to describe what happened. There may be pockets of people throughout the enterprise who may be interested in analytics, but there is no real support for the effort. The culture is still not data driven and decisions are made based on gut instinct rather than on facts.

Phase 2 is the stage where organisations take an insights-driven approach and work in a diagnostic mode. As an outcome of this phase, users can assess the past and present – slice and dice data, ask questions to their data like what is happening in their business, why did it happen, how many, how much, how are we performing etc.

Phase 3 moves to a foresight-driven approach where enterprise users start using machine learning and predictive models to predict what will happen. As an example, with the help of predictive models, organisations can predict which of their customers are likely to churn and use corrective measures to prevent it, or predict fraud, or suggest the next best action for a customer; the use cases are infinite.

Phase 4 is the prescriptive phase, often referred to as the ‘Holy Grail of analytics’. Most organisations are striving towards this state. This is a highly mature phase and is based on recommendations, automations and simulation algorithms that provide advice on possible outcomes and recommends to the users what they should be doing e.g. predicting which products will experience the most growth for a valuable customer profile, justifying investment in marketing and product development etc.

The maturity model above works best when the three different cogs of the wheel – People, Process, and Technology work in synch. Often, technology is the easy part; it is the people that need to be taken on a journey. With analytics becoming more self-service and democratised, alongside the advent of cloud, agile methods are becoming more common helping users realise business value from projects sooner.

It is when we get to phase 4 that we see organisations really begin to unlock their potential. The melding of artificial intelligence with business intelligence is the big trend that will drive the productivity and competitiveness of companies and industries in the coming years.

But every journey starts with a first step. So, go ahead, find a business question to answer, get a group of people together, and leverage some easy to use BI tools to start small.

Don’t be afraid to fail – just fail forward and think big!

This blog is part of the #datareimagine series. For more experts' insights, clients' experiences and to download our datasheets, click the banner.

For more experts' insights, clients' experience and to download our datasheets, click the banner #datareimagine

Posted by: Priyanka Roy, Head of Data & AI, Intergen | 02 September 2019

Tags: Power BI, Data Analytics, Data Intelligence, #datareimagine

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