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Kick-Start Your DataOps Journey in 3 Steps

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DataOps is the answer to enabling data agility, while business intelligence is the answer to data accessibility. Learn about the three steps to getting started with your DataOps journey.

Data is the new oil in the information age. It is the raw material consumed by transformative enterprises in the 21st century. But unlike oil, data is not a finite resource that can be stockpiled for later use. As a human race we are creating more data at an exponential rate, and our hunger to consume this data in whatever form is unprecedented. And the best data is fresh data. Organisations can no longer navigate their rapidly changing markets successfully if they are using static reports built on month old data. 

This means data must become a real-time resource, available at the fingertips of every user in a self-service format they can consume as insights. Driven by this need, we see an overwhelming demand to shift toward a way of working that enables data agility and accessibility. 

DataOps is the alignment of people, process, and technology to enable the rapid, automated, and secure management of data. The goal is to improve outcomes by bringing together those that need data with those who provide it, eliminating friction throughout the data lifecycle. 

DataOps is the answer to enabling data agility, while business intelligence is the answer to data accessibility.

So how should an organisation start to enable these crucial elements within their organisation, and how can we do it without getting into the technobabble all too common in these discussions? One of the key reasons for starting AtlasPlato and subsequently partnering with Delphix, was a desire to help organisations transform toward data-driven decision making. This is a cultural change for many organisations and should be led by executives, adopted at every level, and should permeate through every pillar from strategy development to customer service.

Here are three steps to starting your DataOps journey:

Step 1: Engage at the business level, not at the technical level.

Business level engagement should seem obvious, but it’s all too common to see a data project initiated because of the availability of a novel tool or platform, driven out of IT for their direct benefit. While this often addresses an immediate a technical need, resources may be invested in answering a problem that either wasn’t a high priority, or addresses a localised niche concern, resulting in a low uptake when rolled out to the broader organisation. This isn’t to say IT doesn’t have the best intentions as a business enabler, but in most organisations they simply won't have understanding of the perspective of someone who lives in the finance department, or drives the sales team, or owns people and culture. 

Business engagement begins with awareness workshops, where staff from across the organisation have their eyes widened to new possibilities. They are given the opportunity to envision a new way of working in a world where data and insights are truly at their fingertips. There is tremendous power in asking your people what questions they would love to have answered by the data in the organisation, and what the impact of that would be. Expect big and bold positivity; “huge” or “massive,” followed by “it would change what I do for a job” or “yes please.” Your people and their enthusiasm will draw you toward the highest priority use cases, and identify the highest impact challenges within the value chain of your organisation. Secondly, asking your staff to take time to reflect on what has been raised and systematically vote for the most important opportunities – this leads to a true weighting of their thoughts.

With this, we’re given a well-represented view of the questions to be answered, their priority and potential impact if solved correctly. With impact comes business justification for funding and true innovation and transformation follows.

Step 2: Start small and expand.

So now that we have the questions prioritised and the impacts documented, it is time to choose one to solve. Start small and look for short time-frame, maximum benefit styled answers.  Contrary to the belief of many a large hardware vendor, it is not about massive investment in infrastructure, nor attempting to ingest every dataset to solve every problem on day one.  Solving a small problem is cost-effective, especially if we can re-use elements of the solution for the second, third or fourth problem. 

Think about what the minimum viable product looks like; how can it be low in cost, how can I quickly obtain the set of data from my production data and move it to where it needs to go, how can I secure it, how can I repeat it and how can I test and validate my data before I visualise it, and how will I actually visualise my answers.

When it comes to quickly implementing a platform that can provide obfuscation and masking, while also providing data virtualisation capabilities with minimal effort, consider the Delphix Dynamic Data Platform. Attempting these tasks manually is a massive time sink to your data project, so why not utilise an out of the box solution that can provide all of the above so that time and effort are spent on answering the question put forward by your business?

Step 3: Socialise and repeat.

Now that we’ve come up with our amazing solution that is powered by a virtualised, obfuscated and validated version of our production data, presented by an amazing business intelligence dashboard through a repeatable process, what’s next? Well, socialise the answer. Put this into the hands of all the staff who asked the question in the first place. This is the ultimate way to test, and may either get the thumbs up or the thumbs down, but will solicit a lot of valuable constructive feedback. 

Failing fast with a feedback loop is a terrific way to tweak your solution over and over until it’s correct for the consumers, in this case the very same people for whom it was created. There is no point in keeping consumers in the dark until the very end of development. They are the ultimate test that no technology or pipeline can ever replace. We often find it’s useful to collate their ongoing inputs in the form of a feedback register, even keeping that register open post acceptance to help create a systematic way of providing constant improvements to the now working implementation. 

Summary

DataOps has the power to bring people and technology together to eliminate data friction as a barrier to innovation.

Get your questions and engagement from the business, start small, reduce your time to market by using market-leading tools and socialise for rapid feedback loops. Your people are the greatest asset available to any data project and should be used as much as possible. 

If you’re looking to get started on your DataOps journey or would like to start to think differently about how you approach your existing data projects, reach out to us at AtlasPlato or Delphix today.