Terms come in and out of vogue on a regular basis. In recent years, the use of the term Machine Learning has surged. What I struggle with is that many traditional data mining and statistical functions are being folded underneath the machine learning umbrella. There is no harm in this except that I don’t think that the general community understands that, in many cases, traditional algorithms are just getting a new label with a lot of hype and buzz appeal.
It wasn’t too long ago that many people espoused the decline, if not death, of the SQL language and relational database technology in general. As a level set, remember that relational technology stores data into rows and columns and that the way to access relational data is through Structured Query Language (SQL).
Just like the value of the Internet itself wasn’t really understood until it was in place, I suspect that we’ll all be surprised at how fast the Internet of Things becomes a part of our lives and how much we value it. However, there is an underbelly to the IOT that has the potential to severely disrupt how much of its potential is realized.
As we enter 2014, we are in the middle of a fundamental transformation in the way businesses view analytics. Analytics are now seen as core to a business. Analytics matters. We are just starting to see analytics used as the basis for new products and revenue streams. The breadth of decisions analytics support is increasing every day. The next few years are going to provide a lot to blog about and I am looking forward to it.
You think big data is big today? Just wait until next year…or the year after that…or the year after that. It is growing exponentially. Whatever seems big now will likely seem relatively small just a short time from now. My inner analytics geek is thrilled when several times per week I see an article or have a discussion that calls my attention to yet another company or industry with yet another source of data growing explosively. It is just amazing how quickly these sources of data are growing. With that comes an explosion of analytic potential as well.
Over the past year, I have seen a very positive and encouraging shift in my discussions with organizations about the analytic talent that they employ. More and more discussions are about how to best structure an analytics organization as many companies have now found themselves with enough analytic professionals to make it necessary to figure out how to make the most of them. The speed of the shift from “should we hire anyone?” to “how do I organize all these people?” has surprised me.
Throughout my career, one of the primary ways to classify a company has been its industry. Knowing a company’s industry gave you a solid start on understanding the company. From an analytics perspective, you could make highly accurate assumptions about what data an organization would have, what problems it was trying to solve, and what types of analytic processes would be beneficial for the organization’s business.