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The data-driven world

The world is growing at an incredibly fast pace and things are becoming increasingly inter-connected. In his latest book, Thank you for being late, Thomas Friedman captures this aptly: “It was only a few years ago when Facebook did not exist, twitter was still a sound, the cloud was still in the sky, 4G was a parking place, linked in was a prison, application is what you sent to colleges, big data was a rap star, and skype was a typographical error.” 

In this inter-connected world, every activity, every device, every click is producing rich data. Understanding and analysing this data is becoming more and more important as this is where new customer experiences, new efficiencies, new business models, and new inventions can be derived. The International Data Corporation estimates that companies that are leaders in using data assets to their advantage will capture $1.6 trillion more in business value than those that lag behind.

Business Intelligence and Advanced Analytics in daily life

Have you noticed how advanced analytics influences how you and I make decisions in daily life? It influences how we work, drive, eat, exercise, and shop etc. We search for the best possible route to get to a place, look for recommendations on places we want to visit, the things we want to eat and buy, the music we want to listen to. For example, a simple search on my gym yields the following results:

It tells me the most busy and slow times during the day and the average time people spend there. This gives me an indication on when I should plan my visit.

Take online shopping as another example. Ever noticed how the advertisements on your browser are similar to something you have been looking to buy or recently bought? This is because your clickstream data is recorded, complex recommendation algorithms process this, and companies use this to target you with ads meant specifically for you.

Streaming services like Spotify and Pandora use big data to deliver music that is tailored to your taste. Every time you skip or like a song, that data is added to the pool of people who have skipped or liked that song and helps the streaming services customise the right music for you with the help of machine learning algorithms.

The fitness industry uses big data to its advantage. The fitness bands that are so popular these days analyse your everyday activities to tell you how healthy or unhealthy you are. Many of these have services that let users compare their habits and lifestyles with those of similar weight, age and activity. This data is also used by the medical industry to trace health patterns ad find cure to diseases. On the other hand, insurance companies use this data to decide the premium they want to charge their customers based on their lifestyle. 

Business Intelligence and Advanced Analytics in organisations

Today, many organisations realise that data is a strategic asset and analytics can provide an important competitive advantage. They want to foster a data-driven culture where analysis plays an essential role in all decisions. However, like everything else in life, organisations go through a Business Intelligence maturity curve represented below:


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 represents a stage where organisations take an insights driven approach and work in a diagnostic mode. As an outcome of this phase, users are able to 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 foresights 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. For example: 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.

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. 

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 | 29 August 2017

Tags: Power BI, Data Analytics, Digital Transformation, Data Intelligence, #datareimagine, Less Busyness More Business

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