Today’s business titans face a Thanos-sized problem: exponential growth, with increasingly scarce resources to keep it in check. In the case of the modern enterprise, however, the issue isn’t one of population control but of information overload. How do you avoid being overwhelmed by data when even its growth prediction are increasing every year?
One obvious option follows the Mad Titan’s preferred modus operandi: cut out whatever data you can’t handle. But doing so, while lacking the ethical issues of turning half of all known life to dust, potentially deprives businesses of mission-critical data and insights that could dramatically improve performance. If you simply reduce the amount of data you analyse at scale, you don’t know if you’re removing white noise or valuable information.
There is, however, a more elegant-sounding strategy doing the rounds of boardrooms and IT forums. It’s called DataOps, and it could greatly reduce, if not eliminate, many of the issues that make huge data volumes so troublesome to business.
What Is DataOps? Not What It Sounds Like
Despite the extremely similar-sounding names, DataOps bears only partial similarity to DevOps, which is arguably a part of most digitally inclined enterprises today. The DataOps approach involves the same agile methodologies as DevOps, applying principles like continuous testing and iterative improvements to how the organisation receives, processes, and analyses its data. However, DataOps also involves a range of other elements—most notably statistical process control, a methodology for monitoring data to ensure analytical outputs remain within statistically valid ranges. In other words, DataOps combines the best of several different fields, of which DevOps’ software development is only one.
So, what does DataOps bring to the table? When applied strategically, it can bring about huge boosts to the accuracy, transparency, and relevance of data analytics within the organisation. This is because DataOps simultaneously refines the data flowing to different teams, as well as the analytics processes those teams use to make sense and extract value from the data. Those constant, incremental improvements to both data quality and analytical validity can result in exponential increases in efficiency—enough to keep up with the pace of data growth, or at least better identify what data is worth keeping.
At this point, DataOps may seem like the Infinity Gauntlet to many a CIO or CTO, suitably powerful to overcome all sorts of issues associated with data growth and the ensuing analytical complexity. It can help pare down the array of analytics, Business Intelligence, and big data tools employed within the organisation, with all the cost and speed benefits brought about by simplification. But like the Gauntlet, DataOps can only be wielded well with care—and even then, not by everyone.
Only for the Strong
A successful DataOps program assumes your organisation already has a strategy for data analytics in place—and ideally, a dedicated team of data scientists or engineers responsible for its execution. Without an existing data pipeline, there’s simply nothing DataOps can improve. At the same time, IT leaders will struggle to build a new data pipeline and implement DataOps. This is because many of the initial insights which will guide the focus of DataOps, like what tests to adopt or analytics procedures to tweak, can only come about after evaluation of the data pipeline’s performance over time. Otherwise, you’re just shooting (or snapping) in the dark.
Effective DataOps initiatives require operators who are both familiar with the principles of DevOps and intimately acquainted with data analytics on a theoretical and practical level. They need both competencies to achieve DataOps’ twin goals of continuously improving data quality and devising better ways to analyse data. If those skills reside in different individuals—the most likely situation—then DevOps and data science teams will also need to collaborate closely for continuous, iterative improvement to occur.
IT’s Mightiest Heroes
And at that level, DataOps is precisely the same as DevOps—its success hinges less on technology and theory and more on how human beings work together. For DataOps to succeed, individuals from a range of disciplines must not only understand one another, but also put aside their differences for the sake of common objectives: managing exponential data growth, streamlining access to that data, and improving on the analytics models designed to improve the rest of the business.
This means there’s no one-size-fits-all or out-of-the-box DataOps solution, and any successful implementation will likely look much less elegant than the name might suggest. The C-suite might be more inclined towards a “big bang” Thanos-style solution, but effective DataOps bears more likeness to the Avengers: combining different, often discordant disciplines from data science to software development to create a fast-moving, agile whole. The best thing most businesses can do to get on board with DataOps, and put information overload in check as soon as possible, is to first build up those individual DevOps, analytics, and project management heroes before bringing them together.