Three Big BI Market Drivers to Watch for The Rest of 2017 and Beyond

May 24, 2017

Author: Tony Tauro, Performance Architects

In 2017, we’ve seen an expansion of business intelligence’s (BI’s) scope, changes in consumption, and shifts in the roles of BI consumers and creators. Traditional and fundamental BI practices and processes, however, remain more important than ever.

As a result, the three major market drivers of BI trends so far in 2017 include:

  1. Get more from your data
  2. Do it faster and cheaper
  3. Make your data better

These are not mutually exclusive, but instead tend to reinforce each other and the general direction of BI trends.

1. Get more from your data

Data is just bytes (or even bits) till someone can process it into information. Ideally, all the data sitting in our data warehouses has already been processed into information…of course, there is always better information if only we could read the data correctly. Data discovery and visualization are currently the hot tools to help us achieve more complete and better analysis of our data.

These tools are especially relevant because of the advent of another hot trend: big data. An easy way to understand big data is to think of the progression from to-do list to contact list to spreadsheet to relational database, and try to fill in what comes next: a solution that can handle data sets that are too big for traditional databases. And we are seeing more and more of such data sets now.

Once upon a time, manual data entry was the primary way to build data sets. Now data is introduced to data storage solutions automatically. Transactions are mostly electronic, and we have sensors producing data as well. It’s no wonder that our datasets are doubling in size every 2-3 years!  Big data tools are getting more and more prominent as companies realize the need to harness the power of this data.

Data discovery at its core is about interacting with your data the way you would with a search engine: ask a question and get an answer. Unlike a search engine, your data discovery solution gives you an appropriate (contextual) answer, considering items such as your role and permissions inside your company.

Visualization is about…visualizing your data, but it’s also about moving beyond the traditional graphs and charts that have always been used for BI. If data discovery is like using a search engine, visualization is a little like Wolfram Alpha, where you can query on a general topic, get in-depth information and find answers to questions you did not even know to ask.

Essentially data discovery and visualization techniques and solutions allow the consumer to create and discover the information needed, which brings us to the next topic.

2. Do it faster and cheaper

Since the days when humans fought velociraptors to win the evolutionary wars, “business people” have fought “IT people” for control of the reporting and analysis (BI) environment. Actually, one of those two things is pure hyperbole, but that is not the point.

“Self-service BI,” while not a new concept, is getting more traction now. While a diverse group, “business people,” are getting more savvy with BI solutions. At the same time, BI environments are getting more complex, making it even more important to get architecture and processes right.

Self-service BI is the concept that BI can be centrally managed, while also allowing “business people” to create their own set of reports, charts, graphs: basically, have their own BI and let IT manage it, too.

The savvy reader will note that data discovery and visualization are also forms of self-service BI, though that is not what is usually implied in general usage of the term “self-service.”

3. Make your data better

Data discovery pushes the boundaries for how we source data, going beyond the limits of the traditional data warehouses and bringing in data from more and newer sources (hence the search engine analogy earlier). this introduces questions about how to control data quality and how to improve data context.

Sometime after the Dark Ages, we came to the realization that the shiniest of dashboards get their credibility from boring old data quality and master data management processes. Transactions (e.g., sales orders, invoices, material movements, accounting documents) are great. They represent action and contain numbers that can be put into reports (like financial statements) and (gasp) glorious visualizations! However, without tying back to master data, the transactions are just business data (not information), and certainly do not provide context.

Ultimately data quality and management is about ensuring that the consumers of the data have a solid set of assumptions to use while translating that data into information. Keeping those assumptions true in the light of growing data sets and sources is a challenge (or opportunity… which one is it?), but is essential for the data discovery, visualization and self-service capabilities to stay relevant.

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