It wasn't too long ago when business users struggled to answer simple questions like, “Who are my top 10 customers? Who are my top five suppliers? Are people opening and reading our email campaigns?”
As recently as the early 2000s those questions were nearly impossible to answer. The advent of out-of-the-box business intelligence made those questions much easier to answer by managing and delivering static and descriptive analytics.
But while businesses were able to query (including ad hoc), report and analyze the process of creating these reports, delivering agility throughout the analysis process was difficult, time consuming and didn't address business needs. And, with answers to these initial questions in hand, stakeholders increased the breadth and depth of their inquiries.
IT picked up the brunt of the demand as data and analysis requests became backlogged. Ultimately, business users began thinking of IT as a bottleneck to accessing data.
Fast forward to today’s surge of data growth and complexity, and the frustration around the analytical process has only heightened.
Modern-day businesses face a typical, but entirely different scenario. Perhaps you are a data-driven business that started out with a data lake built on top of Hadoop that stores your unstructured and structured data. In order to combine all of your data assets you need to have resources, expertise and tools. In general, the DNA of your business dictates that you can, and will, “build” it yourself.
However, the requirements to build seem limited and costly. For example, Hadoop is not inherently easy to leverage for analytics and modern-day businesses need resources such as a data scientist as well as access to training, tools and resources. In this manner, these businesses build analytic applications on top of Hadoop structures to create insight and opportunity using the raw data found inside and outside the firewall.
Custom solutions like this “build-focused” infrastructure on top of the data lake quickly turn from an agile and quick approach to deducing insight from your data, to a headache for IT management. Soon, the business starts to question the governance and accuracy of the data and analysis, which becomes a slippery slope.
This slippery slope leaves modern day businesses in the same situation as enterprises — needing a complement to existing infrastructure that can help manage solutions and deploy self-service analytics.
Both of these scenarios exist in the wild: we see big businesses that invested in early business intelligence solutions and newer medium-sized businesses looking to invest in big data analytics. How can we meet all of their needs at once? The answer is a hybrid solution — build some of that in-house and buy some of this technology so that you can get the best of both worlds.
Throughout my experience with a variety of clients, I have seen the implementation of best practices that incorporate this solution. These best practices will help you make better decisions up front and help the business make more prudent investments.
Here are few steps to take when you start down the path of building a modern BI platform.
Take an inventory of what is happening in your business today to define your business needs.
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