Looking Towards the Future with Business Analytics

This blog post is part of the Big Data Week Speaker interviews series. We sat down with data strategist and tech leader,  Jen Stirrup, discussing the key differences between BI and BA, the impact of AI on these respective fields, as well as her keynote at Big Data Week London Conference.

Reserve your seat for Jen’s talk at the upcoming Big Data Week London Conference, on October 5. Get your Early Bird ticket today!

1. In recent years, organizations have increasingly turned to advanced software solutions to manage workloads, maintain profitability and ensure competitiveness within their respective industries. Business intelligence (BI) software and business analytics (BA) programs are arguably the most widely implemented data management solutions. So, what the key differences are between business intelligence vs. business analytics?

In the world of data, we have crossed the Rubicon which now includes business analytics as well as business intelligence; both streaming, real-time analytics as well as longer-term business analytics.

What are the stages of Business Intelligence? There is first generation Business Intelligence – this was the world of corporate Business Intelligence. You’ll know this by the phrase ‘the single source of truth’. This was a very technical discipline, focused on the data warehouse. Unfortunately, the business got lost in all this somewhere, and they reverted to the default position of using Excel as a tool to work with Excel CSV exports, and subverting the IT departments by storing data in email. This meant that organizations had robust data warehouses, but also a shadow data system where data was subversively hidden in Excel.

Then, we had the era of second generation Business Intelligence – the industry pivoted to bring the Business back into Business Intelligence, and we see the phrase ‘self-service business intelligence’. Here, the business user was serviced with clean data sources that they could mash and merge together, and they were empowered to connect to these sources.

Next, we have the third generation of Business Intelligence, which splits off into business analytics. In this era, data visualisation software has taken a priority. There is the distinction between business puzzles and business mysteries: puzzles are a well-defined question to a well-defined answer. Business mysteries are questions that are not well-defined, and the answer depends on the way in which the question was framed. In business analytics, the answer can depend on future data as well as existing and past data, so the time of business analytics embraces a larger time slice. Business Intelligence will always look in the rearview mirror, but business analytics will refer to the future, too; it’s the data equivalent of looking out of the front windscreen, not the rear view.

2. How do you predict ML and AI will transform business intelligence and analytics?

I believe that the industry will start to place artificial intelligence in business intelligence and business analytics technology. For example, instead of designing dashboards as part of a business intelligence project, I believe that AI will be used to push the dashboard report elements and parts proactively to the viewer so that the data that needs your attention is placed into your visual field.

3. Tell us a bit more about your topic at the BDW2018 London Conference. Why did you choose this particular subject?

Speaking to customers, they don’t always believe that AI is accessible to them. However, they risk the possibility of becoming late adopters of technology that is already very accessible. We have AI technology in our smartphones, in our Alexa home devices, to help us handle our emails, to help us identify the best route to work, and even to help make our shopping easier. I chose this topic since I wanted to share strategic and practical insights to success with Artificial Intelligence.

4. Who do you think should attend your talk at Big Data Week? Why?

This technology can be available to small businesses too, and it’s a matter of giving them a roadmap in order to help them to get started on their journey to becoming AI-efficient businesses. Businesses need to also consider important aspects such as automating Artificial Intelligence so that it can become part of their production systems.

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