Data is the new oil, they say.
But like oil, big data is messy. It has departments fighting over ownership, not knowing how much to mine, and muddled in regulations.
And yet, companies have no choice but to collect it, analyze it, extract insights from information, and use them to raise the bottom line.
Image credit: New Vantage Big Data Executive Survey
Until now, organizations have relied on advances in analytics, software, and automation to produce business outcomes and grow. Nevertheless, collecting and managing data in a timely manner and using it to make decisions continues to pose a huge challenge. With the proliferation of various analytics models and AI-enabled tools, the C-suite is simply bombarded with more data, instead of being presented with refined choices that enable faster decision-making.
That’s where DataOps comes in.
What is DataOps?
Back in 2014, Lenny Liebmann, Contributing Editor to InformationWeek, introduced the concept of DataOps in the context of big data as a set of best practices that improved the coordination between data science and operations as a moniker for the term “data operations.”
Since then DataOps has expanded to mean a process-oriented, Agile methodology that focuses on continuous delivery to improve the quality of data analytics and align it better with business goals. Gartner defines it as “a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and data consumers across an organization.”
DataOps adds data analysts and scientists to the DevOps “people mix” of software developers and IT admins. “More and more people are injecting some sort of data science capability into development, into systems, so you need someone on the DevOps team who has a data frame of mind,” said Ted Dunning, co-author of Machine Learning Logistics.
The result is better flow and use of data across the organization.
But what happens next? What processes or structures can be improved in data-driven organizations by implementing DataOps?
The Goals and Benefits of DataOps
Can a company develop and implement a data management strategy to collect and process data effectively, analyze it at scale, and use it to respond appropriately to real-world events as they happen?
DataOps, at its best, increases the velocity, reliability, and quality of data analytics. But it can offer only benefits. Goals, on the other hand, are what the company sets out to achieve by making better use of data.
A Harvard Business Review survey reports that they are already seeing transformational changes, including better knowledge of customer expectations, effective decision-making, and cost savings.
Image credit: Harvard Business Review Analytic Services
The question arises, how do you mobilize all these benefits and port them into mission-critical processes and applications for your business?
1. Treat data as a cross-functional utility
Most present-day companies would agree that data is a critical organizational asset. However, many are unsuccessful in making it readily available to all departments regardless of source or origin. While there are many relevant roles, there is no one “Chief Data Officer” or single owner of data whose responsibility it is to ensure that data consumers’ needs are being met.
In order to make data flow a part of the organizational structure, you need to build a framework consisting of processes, roles, teams, and resources that are in line with your overall strategy.
Image credit: Happiest Minds
2. Clarify the value proposition of analytics as a business enabler
Data and analytics facilitate multiple business use cases such as creation of new products or services, entering new markets, fraud detection, supply chain optimizations, generating new revenue streams, as well as governance. You need to make each stakeholder and every team in your organization aware of the importance and role of more reliable data and the contribution of DataOps in increasing its quality and velocity.
3. Apply Agile techniques to data and analytics processes
The speed and reliability of turning data-capabilities into processes depends on how successful the organization is in leveraging DataOps across various facets of production. These include
- Predefined rules and semantics: Make sure all teams are on the same page about all the terms related to data, metadata, and data flow.
- Data virtualization: Organizations today collect and generate data from a variety of disparate sources. Much of this data is unreliable, unverifiable, and possibly useless. Data virtualization helps you overcome this challenge by creating a “single version of truth” and delivering consistent, customized and actionable intelligence to end users.
- Data and logic tests: Tests are the equivalent of statistical process control and quality assurance in manufacturing operations. They can be applied to data, logic, and models in order to ensure that the inputs are free from issues, the business logic is correct, and the outputs are consistent. There needs to be at least one test that returns errors and warnings for every step in the data analytics pipeline.
- Data governance: Ensure ownership of data, transparency, and two-way control on its movement. All of these will prove critical to reliable tracking and usage in your business.
4. Put humans in the driver’s seat
Today’s complex data pipelines and analytics use cases need multi-skilled people from inside and outside IT to ensure successful project completion. Job roles could include data architects, data analysts, data scientists, data engineers, business analysts, ML model developers, application developers, database administrators, and data security officers. Many organizations are already filling these strategic positions.
Good DataOps means getting all these people with disparate roles and responsibilities to share goals and metrics, work together, communicate clearly, understand what they can expect from each other, and collaborate on unifying data functions and pipelines. Data owners need to leverage automation, software, and technology wisely to bridge any functional or technical skill gaps among different teams and keep them on the data-to-value path.