With data being today’s most valuable commodity, companies are embracing DataOps as an integral part of their business

A relatively new concept that came into being less than 5 years ago, DataOps has already managed to catch the eye and ear of progressive technology leaders who were open to the idea of making real-time, relevant data fuel their day-to-day decisions and drive operational efficiency to a new level. The following are the benefits they expect from investing in advanced analytics:

Source: Business Wire

Now that data operations are increasingly playing a leading role within the framework of digital transformation and building of data-driven business models, it is important to recognize the importance of DataOps as an overarching approach to handling data inside organizations and executing long-term data science and big data strategies.

So how exactly can DataOps alleviate the pains of data engineering and analysis specialists?

Let’s find out in the following chapters:

• ​DataOps 101
• DataOps drives innovation in the data field
• Planning for DataOps implementation

DataOps 101

First things first, so let’s start with definitions. As IBM puts it, “DataOps (data operations) refers to practices that bring speed and agility to end-to-end data pipelines process, from collection to delivery.” There are many other definitions, but the general consensus is that DataOps is, well, DevOps for data, and the difference between DevOps and DataOps is very slight. In fact, they have a lot of shared goals and characteristics:

• Both rely on Big Data and the corresponding cloud infrastructure.
• Both use advanced process automation for testing, data orchestration, deployment, and continuous monitoring.
• Both lean on continuous integration and continuous deployment/delivery.
• Both aim to curtail the duration of release cycles for software products and valuable datasets by following key Agile principles.

DevOps once revolutionized the way companies were managing their software development workflows through continuous integration and continuous delivery. Internal DevOps services also helped businesses make a smooth transition to cloud computing and achieve new levels of operational efficiency. DataOps promises to do the same for data operations.

Data science and big data projects rely on an uninterrupted flow of data and its round-the-clock availability for end users and AI/ML algorithms. This is where DataOps ties it all together by cultivating a culture based on target-orientedness, flexibility, self-organization, and continuous improvement.

The primary purpose of DevOps is the quick and seamless delivery of fully tested software to business users in 24/7 mode. In a similar fashion, DataOps aims to deliver up-to-date, relevant, and ready-to-use data to each business shareholder within an organization. Implemented properly, DataOps practices help close the gap between all data users: data analysts and scientists, managers, and other big data beneficiaries.

Source: Medium

Making valuable business data accessible to pretty much everyone within an organization has a profoundly positive impact. The most notable advantage is the substantially reduced time of reaction to changes, which is of paramount importance for modern business decision-making. Being able to apply modern data analysis technologies and track frequent changes in vast amounts of big data in near–real-time mode makes the entire company more agile and effective.

At the same time, this approach highlights yet another difference between DevOps and DataOps — the latter often requires the adoption of a new managerial mindset, or even the transformation of the entire corporate culture, to ensure that no insights and opportunities are wasted or overlooked. After all, what difference would it make if a game-changing ML-based prediction is not promptly submitted to a corresponding executive and is only attended to when it’s way too late?

Now that we’ve learned about the importance of DataOps, it’s time to ask: what is DataOps in terms of its transformative power?

DataOps drives innovation in the data field

Although DataOps is often mentioned in the context of machine learning and other promising big data trends, it’s not necessarily limited to these areas. It’s a great fit for nearly all data-related activities thanks to the wide gamut of its potential applications and the multiplied benefits it provides to companies working with their data in the cloud and using microservices for their data pipelines.

The most important thing, as mentioned, is that the adoption of DataOps is not only and not as much about making infrastructural changes and using new tools. In many cases, it’s a lot more about creating and implementing new processes and rethinking the way things are currently being done.

Ask yourself the question, “What is ‘data operations’ for my company?” Chances are that your organization may be working with data very selectively, keeping both data and data specialists siloed and isolated from the rest of the company. Occasionally, your data analysts will produce reports and projections intended for particular teams or individuals, but for the most part, no one will really know what’s going on “behind the scenes”.

DataOps is going to change this for good. Isolated teams lack the flexibility and swiftness that are must-haves for companies undergoing digital transformation, so they become natural bottlenecks and a drag on their company’s performance. To achieve greater performance, the DataOps framework suggests the following:

• Bridging the gap between production units and business users
• Making data updates as frequent as possible
• Making data easily accessible to all relevant parties
• Creating cross-functional workgroups augmented with data engineers and scientists who ensure that data governance and orchestration tools are embedded at every step (design, development, and testing)

The last point in the list above may well be the most important one. Tight collaboration between data professionals and software engineers guarantees that both sides get heard and that both functional and non-functional business requirements are observed with maximum efficiency.

Planning for DataOps implementation

Now that we’ve answered the question “What is DataOps?”, it’s time we covered some DataOps implementation tips. The good news is that you may not need to start with a clean slate. If a company is considering adding DevOps to its arsenal, it may already have one of the key items — a DevOps team. The next step would be to add a data engineer for building data pipelines and top this off with a data scientist who will put the collected data to good use by building ML models and extracting value from them.

That would take care of people, the first ingredient in the three-line DataOps recipe. The remaining two are processes and technologies. We already mentioned that the adoption of DevOps requires a substantial transformation of the existing paradigm of data governance and cross-departmental cooperation, all for the sake of getting aligned towards a common goal.

On the process side, the following actions may be required to ensure a steady flow of data from multiple sources through teams and internal services to designated destinations:

Source: MIT Technology Review Insights

Finally, from the technology perspective, deep automation appears to be the greatest contributor to the success of DataOps implementation. It ensures that the most data-intensive AI/ML/DL services technologies get enough data or the right quality at the right time, maxing out the accuracy of data analysis and rewarding the company with an up-to-date set of business insights.

Other elements of a fully functional DataOps infrastructure may include the following:

Okay, what is “data operations”?

So what is “data operations” in reality? Definitely not a key to every door, or a magic pill, or a silver bullet that will deal with every challenge out there. As someone who lives and works in a world where conventional businesses are awakening (en masse!) to the necessity of becoming software houses in order to carry on, you know just how massive such initiatives may initially appear.

Many of those businesses have already made solid headway towards complete digitization and adaptation to the new all-digital reality while some are yet to make that leap of faith. However, it won’t be long before this switch becomes a matter of survival. Early adopters will have the extra benefit of having been experimenting with and streamlining data management and analysis techniques for longer, thus securing long-term stability and leading positions on the market.

Even if you don’t think that DataOps is your absolute priority at the moment, it definitely deserves to be on your agenda. Its adoption requires time, so you can start working on it in small increments at different levels (people, processes, and technology). This way, you’ll be ready when your very own tsunami of data shows up roaring on the horizon.

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