Market Research, Business Intelligence & Big Data: Have we Forgotten about Human Data?

The annual pilgrimage to the ESOMAR Conference took place last week in Dublin. I heard that there was much discussion, both on and off the stage, about Big Data and the future of market research. Hopefully, the whole profession will get behind one initiative, instead of each individually trying to “solve world peace” on their own!

This week sees the second Swiss BI-Day taking place in Geneva and there will no doubt be similar discussions about Big Data and the future of Business Intelligence.

It appears that Big Data is not just a buzzword or a commodity that has been likened to oil; it has become the centre of a power struggle between different industries. Many professionals seem to be vying for the right to call themselves “THE Big Data experts”.

This got me thinking about the future of data analysis in general and the business usage of Big Data more specifically. There seems to be no stopping the inflow of information into organisations these days, whether gathered through market research, which is proportionally becoming smaller by the day, or from the smartphones, wearables and RFID chips, that get added to every conceivable article, more generally referred to as the IoT (Internet of Things). Who will, and how are we to better manage it all? That is the question that needs answering – soon! (>>Tweet this<<)

Data Science Central published an interesting article earlier this year called “The Awesome Ways Big Data Is Used Today To Change Our World”. Already being a few months old probably makes it a little out-of-date, in this fast changing world we live in, but I think it still makes fascinating reading. It summarises ten ways that data is being used:

  1. Underst anding and Targeting Customers
  2. Underst anding and Optimizing Business Processes
  3. Personal Quantification and Performance Optimization
  4. Improving Healthcare and Public Health
  5. Improving Sports Performance
  6. Improving Science and Research
  7. Optimizing Machine and Device Performance
  8. Improving Security and Law Enforcement
  9. Improving and Optimizing Cities and Countries
  10. Financial Trading

Many of these are not new in terms of data usage nor business analysis. What is new, is that the data analysis is mostly becoming automated and in real-time. In addition, the first and second items, which were largely the domains of market research and business intelligence, are now moving more into the h ands of IT and the data scientists. Is this a good or bad thing?

Another article posted on Data Informed a few months after the above one, talks about The 5 Scariest Ways Big Data is Used Today   and succinctly summarises some of the dynamic uses of data today. The author of both pieces, Bernard Marr, wrote that “This isn’t all the stuff of science fiction or futurism. Because the technology for big data is advancing so rapidly, rules, regulations, and best practices can’t keep up.” He gives five examples of where data analysis raises certain ethical questions:

  1. Predictive policing. In February 2014, the Chicago Police Department
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Try a New Perspective on Business Intelligence: How to get More Impact & Answers

Last week I presented at the first Swiss Business Intelligence Day. It was an inspiring conference to attend, with world-class keynote speakers opening the day. They included Professor Stephane Garelli from IMD, Philippe Nieuwbourg from Decideo  and Hans Hultgren from Genesee Academy.

After such an illustrious start, you can imagine that I was more than a little nervous to present my very non-IT perspective of business intelligence. However, the presentation did seem to go down well, so I want to share with you some of the ideas I talked about. Not surprisingly, with my passion for customer centricity and always with the end-user in mind, I took quite a different perspective from that of the majority of IT experts who were present.

BI should Collaborate More

With the explosion of data sources and the continuous flow of information into a company, managing data will become a priority for everyone.

The Big Data market, which more than doubled last two years, is forecast to triple in the next four, according to Statista. BI will have to exp and its perspective, work with more varied sources of information and exp and its client base.

In the past BI was inward looking. It ran data-mining exercises, reviewed corporate performance, developed reports and occasionally dashboards. It was, and still is in many organisations, mostly concerned with operational efficiencies, cost-cutting and benchmarking.

The above plot is my own, simplified view of how BI fits into data management within most organisations today. The other three quadrants are:

  • Competitive intelligence (CI) uses external competitor knowledge to support internal decision-making. Although BI is sometimes considered to be synonymous with CI because they both support decision-making, there are differences. BI uses technologies, processes, and applications to analyze mostly internal, structured data. CI gathers, analyzes and disseminates information with a topical focus on company competitors.
  • Investor Relations (IR) uses internal data to get external people, such as shareholders, the media or the government, to support and protect the company and its views.
  • Market Research (MR) on the other h and is mostly outward looking. It studies customers’ behaviours & attitudes, measures images & satisfaction, and tries to underst and feelings & opinions. That information is then used, primarily by marketing, to develop actions and communications for these same customers.

The four quadrants, even today, usually work in isolation, but that will have to change with this new data-rich environment in which we are working.

BI is Ripe for Change

 

According to a recent (Jan 2014) Forbes article, BI is at a tipping point. It will need to work in new ways because:

  • it will be using both structured and unstructured data
  • there will be a consolidation of suppliers
  • the internet of things will send more and more information between both products and companies.
  • thanks to technology, data scientists will spend more time on information management & less time on data preparation. At present it is estimated that they spend 80% of their time on data
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