Business decision-makers are programmed to maximize ROI in a number of organizational areas – especially technology. In an article called 8 tips to get more bang for your big data convergence bucks, Thor Olavsrud of CIO.com compiled tips for harnessing converging technologies to maximize return without breaking the bank.

Olavsrud does a good job of calling out some key issues for companies wrestling with Big Data, but it should be no surprise given the forum that most of his tips are targeted at the enterprise level. For example, suggestions about “the basic tenets of Hadoop and distributed computing,” “Using Spark, Apache Drill or other in-memory processing technologies,” and “Hybrid Transactional/Analytical Processing (HTAP)” are significant focus areas if you have a team of data scientists and are managing your own data analytics infrastructure, but what about everyone else?

If you are not one of the big guys, there is little chance that you have the resources required to engage with Big Data at this level. As the article points out, these are difficult issues even if you are the CIO of a large company. The good news is that for an SMB or any other business with limited “big data convergence bucks” there are options.

Data Science-as-a-Service companies like Actus Data take care of crucial details such as linkages between “established enterprise standards like SQL, NFS, LDAP and POSIX” so you can concentrate on what most matters to your business. Businesses of all sizes can outsource all of the technology heavy lifting required to deliver the analytics insights necessary to drive growth, improve profits, and gain a competitive advantage.

There is no shortage of confusion in the market about how to use data to make better business decisions – that is one reason we like to highlight articles like Olavsrud’s. At the same time, it is also very important to know that even if you are not a top tier CIO there are affordable solutions available and that practically every business can have the capabilities necessary to earn big returns on Big Data.