Business Intelligence Software

Why don't people use business intelligence software?

This is a contributed post by Satyen Sangani, CEO and co-founder of data accessibility startup Alation

The benefits of data are seemingly obvious: data removes bias; data can enable us to see and learn about trends faster; data can allow us to service our customers better. Moreover, databases are fast and business intelligence (BI) tools are ubiquitous.

Yet, for all this data goodness, people don’t use data. Gartner estimated that, on average, approximately 28% of a company’s workforce uses BI and more recent estimates peg that number at 24%. Of those that have BI tools, business consultant Wayne Eckerson estimated that only 20% actually know how to use them. Why are these numbers so low?

Consumption requires context

If we think of data as food for our mind, the nutrition movement might offer some clues. Today the state of labelling data for appropriate use is akin to the opaque labelling of food products over 40 years ago. Until relatively recently we had no idea whether the food we ate contained inorganic products, genetically modified ingredients, lead or even arsenic. Today we have raised nutritional awareness by listing critical ingredients and encouraging nutritional literacy that can assist in making healthy eating a conscious behavior.

Consuming data appropriately requires the same type of conscious evaluation of  ingredients. Here is a relatively common and simple example:

At one large multinational company, it turned out that the Date of Birth field is generally not populated. Rather, it’s defaulted to Jan. 1, 1980. As a consequence, if you did not know this fact and tried to find the average age of your customers, you would come to the conclusion that your customers are younger than they really are. This mistake happens so often that it has created a myth within the institution that they service young customers when their actual customers are typically middle-aged.

Drawing incorrect conclusions from data often does more damage than not using data at all. Consider the spurious relationship between vaccinations and autism or that six of the 53 landmark cancer studies are not reproducible. An Economist survey revealed that 52% of surveyed executives discounted data they didn’t understand, and rightfully so. The Economist reminds us that a key premise of science is “Trust, but Verify”. The corollary also holds true — if we can’t verify, we won’t trust.

Packaging data

No one wants to consume something that they’re not expecting. If someone expects a red velvet cupcake and you feed them pizza, they might live with it, but the initial experience is going to be jarring. It takes time to adjust. So, what does this have to do with data?

Data doesn’t really speak your language. It speaks the language of the software program that produced the information. You say sales, and the dataset says rev_avg_eur. You say France, and the data set says CTY_CD: 4. Can these labels be learned? Sure, but even in a relatively small organization, there might be 20 software programs in use every day, each of which has hundreds of different codes, attributes and tables. Good luck if you are in a multinational organization with tens of thousands of such programs.

This translation has a larger unseen cost. A recent industry study highlighted that 39% of organizations preparing data for analysis spend time “waiting for analysts to assemble information for use”. And another 33% spend time “interpreting the information for use by others”. If every time we need an answer it takes us hours or days to assemble and interpret the information, we’ll just ask fewer questions – there are only so many hours in a day. Making data easy to consume means ensuring that others can easily discover and comprehend it.

Data catalogs come to the foreground

Beautiful reports and dashboards often hide complexity. Metrics and statistics are wonderful, but we need to surround data with more context and lower the costs of using data. Context informs the modern world. Context helps us wade through complexity, and the phenomena represented by data are nothing if not complex. Historically, providing context – otherwise known as data about data – has been the province of metadata management tools. These tools have been severely constrained in the levels of description they provide, are massively difficult to manage, and almost impossible to use for all but the most grizzled information managers. Exclusively useful to data managers, they prove irrelevant to data consumers like analysts, data scientists or business people.

On the internet, LinkedIn is a catalog which provides context about our professional networks, Amazon is a catalog which provides context about goods we could want to purchase, and Yelp is a catalog that provides context about local businesses. Similarly, enterprise data needs a catalog enabling people to use data. Our traditional solutions of hiding complexity haven’t worked – it’s time to put more trust and better context into the hands of the people consuming the information.


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