Digital transformation is a broad term that has various meanings by application, but in general, it means that more and more of what organizations, people, governments do is happening in computers, mobile devices and networks. As a result, the way things are done is changing, especially in the way things are connected. So in this new world of data flying everywhere, being generated and consumed, where does one stop for a second to take a look at what’s going on?
Do you remember those word problems on tests like the SAT or the ACT?
They would go something like this: Train A leaves the Station at 1:15PM, gradually accelerating for 7 minutes until reaching a speed of 52mph for a distance of 76 miles. Train B leaves 12 minutes later, accelerating to a speed of 47mph in 11 minutes. After one hour, how far behind Train A was Train B?
This sort of problem reminds me of the Business Intelligence (BI) business, alternately known as Decision Support, Analytics, Reporting, Data Discovery, etc. It seems that no matter how fast Train B can go (organizations implementing BI), they can only, at best, keep up with Train A (the relentless march of technology).
In biology, this is called the ‘Red Queen Effect’. named after poor Alice in 'Through the Looking Glass', where the faster she runs with the Red Queen, the faster the landscape moves with them so they have to go as fast as they can to merely keep up.
Data. It isn't a process. It's an asset.
Welcome to the 1st in a series of 8 blogs, where I will dive-in to separate and clarify both concepts and relationships across Business Intelligence and its active component OLAP, predecessor technologies, and data. OLAP in particular has suffered from issues of scale and speed, but the need for this type of analysis is greater than ever. And while, the industry of analytics has been overrun with big data and data science, there is a general lack of understanding that previous drawbacks of BI and OLAP have been solved with the new architecture of big data, Hadoop/Spark and Cloud.
Join me as I uncover both the vital need for multi-dimensional analysis and the vastly improved capabilities that exist for big, nae massive, data of today. I believe you will find some rather interesting surprises.