For data engineers and developers, each day is a new challenge in delivering solutions faster, better, and cheaper to digitally transform. Businesses in every industry are data-driven, and data professionals are feeling increasing pressure to work more efficiently and accelerate time to market for their products. The last thing a data professional wants is to become a bottleneck in the process. Could DataOps be the answer?
Firstly, let’s cover one of the top challenges. Today, too many strategic project teams are dependent on a small, centralized engineering or IT team for basic data operations such as exploring data or even building most data processing applications. As a result, we’re seeing too many engineering teams being the bottleneck for strategic digital transformation initiatives needlessly.
For organizations seeking to accelerate their digital transformation initiatives, onboarding new users potentially directly onto a data platform is an important requirement. Many of these users are outside IT and engineering and may lack the deep technical knowledge they need to work with the latest data tools. A DataOps approach empowers these business-focused users to work with data on their own in a self-service fashion. We’ve seen significant success in organizations that have been able to apply a DataOps approach to better align their business and technical teams with improved access to data. DataOps helps organizations accelerate the delivery of their strategic projects, reduce costs, and minimize the need for hard-to-source engineering skills.
But care needs to be taken when exposing technology to less technical users. These users need to be onboarded fast and must ensure they have the tooling and resources required to get to work on their projects. It’s not a trivial matter for any company, let alone a multinational enterprise where exposing users to a data platform introduces significant challenges on compliance and governance.
Security, governance, and compliance remain top of mind for data projects as well. People are rapidly learning that their personal data has tremendous value, and initiatives like the General Data Protection Regulation (GDPR), and California Consumer Privacy Act (CCPA) have brought respect for data privacy to the forefront. When it comes to safeguarding data, the stakes are increasingly high, and regulators are unforgiving. Data Breaches have cost some organizations several billion dollars in fines, lawsuits and loss of market cap.
After you provide access to a data set, you need to apply stringent management, control and real-time monitoring from a governance perspective. You never know when a compliance auditor will stop by for a surprise visit. You’ll need to answer questions like which applications are reading credit card data, how you are keeping customer data safe, who has access to data — and what are they doing with it. Even as you lock down for compliance, you also need to help your business stay agile and avoid slowing down data initiatives.
Should You Build or Buy to Streamline Operations?
Although these constant concerns are daunting, taking a few steps to optimize your data strategy can help take the worry out of managing your data projects — and put you on a path to faster execution and accelerated time to market.
For better onboarding, you’ll need to choose between a build vs. buy approach. Developing your own system to onboard users and clients takes time and resources. In fact, it could take years to build your own technology in-house. That’s a big issue if your engineers are spending all their time working with open source technology to build tooling for onboarding, instead of focusing on better ways to use data and get solutions to market fast.
Once you’ve onboarded your users, the cycle starts all over again, as users request access to more data sets. That means more time at the command line console, and less time focusing on the big picture.
A DataOps Approach Provides Real Advantages
Purchasing a commercial solution to support onboarding requires an initial investment, but a robust solution based on DataOps principles will enable you to support the managed onboarding of clients onto your data platform at scale while enabling you to maintain security and compliance. A DataOps approach is designed to foster more open communication between data and business professionals, and share data more effectively to build a data mesh architecture.
By making it easier for more people across the organization to work with your organization’s data, you can help your people align data initiatives more closely with business goals.
For example, digital bank Avanza needed to ensure the continuous improvement of the customer experience of their digital services to remain the leader in the Swedish market. Doing so required giving developers direct and constant access to real-time business data in Apache Kafka.
The organization applied a DataOps approach and intelligent data masking rules to meet GDPR and protect customer confidentiality, featuring a large single data platform to address how they delivered data access and applied data governance to support more than 20 strategic projects. Using the platform, tenants who build streaming applications gained visibility into the health of their Kafka clusters, and improved insight into data flows. This self-service approach means fewer support calls to data engineers, minimizing bottlenecks and freeing them up to work on more strategic initiatives.
Ultimately, data is the fuel that propels insights by tying back to analytics, decision making, and business systems. The better you can share that data, the more you can apply it to supporting better business decisions and enabling better customer experiences.
A good DataOps offering will also enable you to build compliance and governance into your processes. It provides the visibility needed to understand who is accessing data, and how, so you can maintain governance and uphold data ethics.
The challenges you face each day in a data-driven organization aren’t going away — in fact, they will likely continue to grow. But with a DataOps-driven approach, data engineers stay out in front of these challenges and build a more competitive business as they do so.
Feature image via Pixabay.
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