We’re well past the point of realization that big data and advanced analytics solutions are valuable — just about everyone knows this by now. In fact, there’s no escaping the increasing reliance on such technologies. Big data alone has become a modern staple of nearly every industry from retail to manufacturing, and for good reason.
IDC predicts that if our digital universe or total data content were represented by tablets, then by 2020 they would stretch all the way to the moon over six times. That’s equal to 44 zettabytes of data, or 44 trillion gigabytes.
There are many reasons why data is being generated so quickly — doubling in size every two years. The birth of IoT and connected devices is just one source, while the need for more reliable real-time data is another. What’s more interesting, however, are the trends formed as a result of the newer digitally-reliant solutions. They specifically help shape the industry, altering how business analysts work with data.
What will our digital future look like? How will we manage all this information? What skills should business analysts be focused on developing?
For quite some time, the data analyst and scientist roles have been universal in nature. It’s not that specializations didn’t exist, they always have but companies are now starting to look for professionals with industry-specific experience. They want someone well versed explicitly in the kind of data they’re dealing with.
Everything from financial services to manufacturing and logistics is being upgraded to rely on more digital services and as a result an influx of real-time data. There are plenty of opportunities, so choosing a specialization won’t hurt career choices, but doing the opposite can. It’s important to build a solid CV by working with businesses and teams that fit a specialization, so choose one.
By 2020, over 40 percent of all data science tasks will be automated. Machine learning technology and its growing capability is a huge driver of that automation. It’s for good reason too because automation and powerful machine learning tools can help extract insights that would otherwise be difficult to find even by skilled analysts.
The entire process is also achieved much faster, boosting not just general efficiency but an organization’s reaction time to certain events, as well.
Quantitative analysis, experimental analysis, data scaling, automation tools and, of course, general machine learning are all skills that modern data analysts should seek to hone. The more direct experience and talent an analyst has with automation technology, the more desirable they will be.
GDPR helped to spur the demand for prioritized data governance, and frankly, it happened so fast it left many companies scrambling to comply — even still some are fumbling with the idea. But it’s not the only regulation or guideline that’s making waves. More recently, the California Consumer Privacy Act reared its head, which will go into effect in 2020. It won’t be the last either, not by a longshot.
These regulations have a monumental impact on data processing and handling, consumer profiling and data security. Businesses are under intense pressure not only to comply with the requirements established but also to understand the impact on current and future operations.
Data scientists and analysts who understand the ramifications can help organizations navigate the guidelines, and are skilled in both data privacy and security are in high demand. As more regulations come to be, that demand will continue to grow making it a viable specialization for current and future professionals.
Admittedly, it’s no small feat to stay up-to-date with anything that relates to modern technology.
