How Data Analysis is Taking Over the World

By Chris Douglas

It’s Cyber Monday, but before you dive into online shopping on company time ahem, let’s explore some information about data.


No…not THAT Data… You know, data – the information that’s gathered and reported whether you realize it or not. The amount of data being collected is out of this world! And it keeps growing. A study was done by Dun & Bradstreet and Forbes Insights regarding the current state of data analytics adoption from over 300 executives in North America, U.K. and Ireland. Several different industries were a part of it, and it seems the landscape has shifted pretty heavily over the past several years.

Analyze this:

  • There is a skill gap in the data analytics industry, according to Forbes Magazine. This gap persists across the entire IT enterprise. Of the professions surveyed, 27% of them stated that this skills gap slowed down and prevented growth in their data ventures
  • Data Analytics is not just growing within the IT realm. It now plays a major role in the majority of business especially in marketing
  • Practice makes perfect: The need for better data analytics practices is vital. Forbes surveyed professions and 19% of those individuals stating that they use only basic data models and regressions
  • Data Overload:  Companies that plan to achieve data analytics superiority need to embrace a hybrid expertise model using multiple data analysts with a variety of skillsets.  A survey was taken of major companies and the results found that 60% of them are using third parties to support organizational bandwidth while 55% are outsourcing some or all of their data analysis requirements because they don’t have the resources to cover all of their needs.

Armed with this information, let’s take a look at the top skills companies are seeking out in order to meet these shifting analytical needs:

  • Data Analysis: Odd I know, but you really have to be able to discern what you are analyzing. Besides knowing all the tips and tricks, bells and whistles, it is important to understand which data is meaningful. That way, your next move is one that makes good business sense.
  • SQL: Data is all about information. In order to retrieve said information from a database, SQL takes the cake as a programming language made to do so. Structured Query Language (SQL) is  an important tool in which to be fluent.
  • Learning a Statistical or scripting language: Learning Python or Matlab can be beneficial on the scripting language side. R and SAS are examples of statistical languages to learn to be data- dangerous.
  • Data Management: Data management relates to how you structure databases which can have complex rules around who can access different pieces of information. And there are different approaches to storing data as efficiently as possible. Looking at these options and tools can really be effective in driving your data analytics.
  • Adobe/Google Analytics: Having a good grasp on the most popular analytical tools would be a great way to advance in the data realm. Google analytics is one of the most popular.  Adobe is another well-known tool to help enable powerful analytics.

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