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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 Already being a few months old probably makes it a little out-of-date, in this fast changing world we live in, but I think it still makes fascinating reading.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 sent uniformed officers to make “ custom notification visits to individuals whom they had identified, using a computer generated list, as likely to commit a crime in the future. Just one step towards the “Minority Report”?
  2. Hiring algorithms. Companies are using computerized learning systems to filter and hire job applicants. For example, some of these algorithms have found that, statistically, people with shorter commutes are more likely to stay in a job longer, so the application asks, “How long is your commute?” Statistically, these considerations may be accurate, but are they fair?
  3. Marketers target vulnerable individuals. Data brokers have begun selling reports that specifically highlight and target financially vulnerable individuals. For example, a data broker might provide a report on retirees with little or no savings to a company providing reverse mortgages, high-cost loans, or other financially risky products. Would we want our own families targeted in this way?
  4. Driving analysis devices may put you in the wrong insurance category. Since 2011, car insurance companies like Progressive and Axa, have offered a small device you can install in your car to analyze your driving habits and hopefully get you a better rate. But some of the criteria for these lower rates are inherently discriminatory. For example, insurance companies like drivers who stay off the roads late at night and don’t spend much time in their cars, but poorer people are more likely to work the late shift and to have longer commutes to work — both of which would be strikes against them when it comes to calculating their auto insurance rates.
  5. Walmart and Target determine your life insurance rates. OK, not directly, but Deloitte has developed an algorithm, based on “non-traditional third-party sources” that can predict your life expectancy from your buying habits. They claim that they can accurately predict if people have any one of 17 diseases, including diabetes, tobacco-related cancer, cardiovascular disease, and depression, by analyzing their buying habits.

Marr starts this article by very briefly discussing privacy and inherent biases in data. I think these issues are far more urgent than deciding whether it is market research, business intelligence or data scientists that are in charge of the actual data analysis. Perhaps we all need to work together so that the “Human” side of data is not forgotten? After all, most data comes from people, is understood – if no longer strictly analysed – by people, for the benefit of people, to help change people’s behaviour. What do you think? Join the conversation and let your voice be heard. (I’ll be presenting this very topic at the Swiss BI-Day this coming Tuesday, so I do hope that you will pop by and listen)

Winning Customer Centricity BookThis post includes concepts and images from Denyse’s book  Winning Customer Centricity. You can buy it in Hardback, Paperback or EBook format in the members area, where you will also find downloadable templates and usually a discount code too.

The book is also available on Amazon, Barnes and Noble, iBook and in all good bookstores. If you prefer an Audiobook version, or even integrated with Kindle using Amazon’s new Whispersync service, it’s coming soon!

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.

statistic id forecast big data marketThe 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.

How business intelligence fits into the data world of businessThe 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 cleaning, integration and transformation, and only 20% on its analysis!

Google glass provides access to business intelligence

In February GigaOM echoed these thoughts, claiming that we are not in BI 2.0 but rather 4.0. They said the volume of data and the number of people now exposed to it, makes data availability to everyone essential. No longer does BI involve only the CEO and IT specialists, it concerns everybody.

Google glass provides access to business intelligence
Google glass, as tested by Virgin
, is a good example of this. It delivers real-time, on time and relevant information to Virgin’s hosts and hostesses, to meet, greet and advise its passengers. Their customer support team can accompany their VIP guests and warn them of delays and gate changes as they happen. Google Glass enables them to get out from behind their desks and interact more with the guests they are trying to please.

BI must Deliver More Synthesised Knowledge

According to a recent Business Intelligence report on management’s opinion of their data, they are currently frustrated. They say that it comes from many disparate sources and is rarely if ever available in real-time. They can’t easily access it without the help of IT and it takes too long to customise it to what they need. What is particularly interesting in the findings, is that management were not saying that they don’t need information; in fact it actually looks as if they want to have access to more data. BUT more of it in a way that makes it easy to find what they want, when they want it.

Another finding from the survey shows executives’ thoughts about data delivery. Currently they are getting their information primarily through emails and spreadsheets. I find this shocking that today we still expect management to take the time to wade through all the data in order to draw their own conclusions. Less than one in eight of the C-suite is getting dashboards, which is their preferred medium (>>Tweet this<<). They also want mobile delivery so that they can access information on the go.

This study provides us with a simple plan to satisfy their needs and to help us meet our own challenges of data abundance. This is what we should prioritize, since we can no longer continue to do what we’ve always done in the same way we’ve always done it. The BI priorities are as simple as ABC; accessibility, business impact and consistency (>>Tweet this<<).

BI needs to Provide Simplified Access

Information should be provided where and when it is needed and in such a way as to have most impact on the business. This means making it easy to review, and quick and simple to draw conclusions. This is why the number one dem and from business is dashboards.

Dashboards have the advantage of imposing consistency (>>Tweet this<<) so no time is lost in underst anding what the information is showing. With the availability of more information, comes the challenge to make it available to more people. And more people will also mean more and different needs.

Business Intelligence data warehouses are like a tree of knowledgeTo underst and the accessibility challenge I find the tree is a great metaphor for what we struggle to achieve. The roots can be compared to all the different sources of information we have at our disposal. The trunk is like all the integrated information that is reported in dashboards and the branches, twigs and leaves are the different data warehouses we create.

Whilst a one-page overview is sufficient for management, others will need greater granularity. Therefore we need to make information available at different levels of detail. My experience suggests three types of information sharing.

  • The leaves are like data warehouses where the raw or nearly raw data sits
  • The twigs are the information repositories where analysed data and information resides
  • And the branches are the knowledge libraries where the integrated actionable insights sit

What I have learned from setting up numerous data warehouses, information repositories and knowledge libraries, is that it is not easy. Not because of any technical complexity, but because of winning the needed  internal support for the project and getting the essential acceptance for global access to the information. It takes more than technology, it takes a culture change in many cases too, and this is the real challenge. Stopping the “information is power” mentality means finding ways to counter the opposition who claim  confidentiality of their own data whilst also requesting access to everyone else’s. In addition, even if people need information, they will generally not make the effort to go looking for it, if there is an easier way, such as by asking someone else! All these issues need to be resolved for an integrated database project to succeed.

Business Impact

One way to encourage the culture change mentioned earlier, is to demonstrate the business impact of what you are providing. The desired impact won’t come by delivering spreadsheets, it will come from dashboards (>>Tweet this<<).

So how do you summarise a company in a one-page dashboard, especially those which are present in multiple categories, globally? Well, often the simplest way is not to try to cover the total business, but rather the top categories and markets that would cover 70% – 80% of total sales. In most cases this would be sufficient to underst and the main priorities for management.

Of course at category level each business unit should be able to get access to more detailed information, as should the regional presidents, if you are working in such a complex business environment.

The real power of dashboard information will come from data integration, where both internal and external information are synthesised, for a holistic view of the business. I have worked on several projects that combined internal information with consumer data for a complete business report. The consumer information came from promotions, call centres and CRM activities, and was combined with market research on product and communications performance, to provide a solid base of consumer underst anding. This can then be presented alongside the more usual financial information that executives are already receiving. Having a complete overview of the business has far more impact than individual, silo’d summaries and enables management to make decisions more quickly and easily.

Increase Consistency

Another challenge when setting up and integrating databases, is in the harmonisation of their master data. When you are working with consumer data, this challenge can be multiplied by ten if not one hundred. For example, consumers will talk about a pizza, without specifying the br and, sub-br and, variant, flavour, packaging and size that would be used by the business to define it. So you have to find a way to translate what the consumer is saying, into the products as recorded internally.

The consistency of the master data will even increase in importance and complexity, with the expansion in available data sources. In addition, the fact that more people will get involved, will confound things even more, since their needs will differ.

Asking Better Questions of the Data

Accessibility, business impact and consistency are vital to the success of the new BI’s data management and usage, but I feel the urge to add one more thing. That of asking the right questions of the data. Although BI is used to asking questions, I think Market Research (MR) are the real experts in questioning. Therefore they should be involved in ensuring integrated databases are combined in such a way as to permit easy extraction of whatever level of information is required, or whatever perspective might be taken.

For example, BI is used to running forecasts. Those usually start from a review of past data and current reality to develop forecasts based on complex algorithms. They will do this within their teams with perhaps input from finance. MR on the other h and, is more likely to work from societal trends and develop plausible future scenarios, brainstorming across the organisation to gather a wide array of perspectives. Both perspectives are complementary and combined, they make a powerfully readied organisation.

Making more data more accessible to more people will certainly help this question development, as I think getting the right answers depends upon asking the right question, don’t you?

These were just a few of the ideas I shared at the Swiss BI Day in Geneva. How do you see business intelligence adapting and changing as a result of the increased information availability happening today?

C³Centricity used images and graphs from Statista, Microsoft and Virgin in this post.

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