I’m confident 2020 will be remembered as the year DataOps came of age, as companies are discovering the need to maximize the inherent business value of their data. DataOps adds the observations that are needed in all aspects of data management that keeps tracks of data moving at speed across the organization.
That data will rewire the way businesses operate is now an inevitability. But to truly make the most of it, a DataOps function has to be included — it’s the only way to make use of data’s huge opportunity.
As we head into the new year, here are my three predictions on how DataOps is likely to develop over the course of 2020.
1. Data hygiene will become standard
Dirty, incomplete, and inaccurate data is currently the greatest threat to digital transformation projects with8 out 10 AI and machine learning projects stalled due to poor data quality. Poor data can leave businesses with crippling losses, from heavy administrative burdens, to regulatory breaches, and a painful lack of visibility into critical deal information.
Businesses today are currently saddled with disparate, outdated, and complex legacy systems and are sinkingtrillions into technology and services that enable digital transformation. While the right vision may have been set, it is too often undermined by the wrong data architecture; largely because of the way it addresses ‘the plumbing’ of data across integrating systems.
The success of front-to-back processing of multiple internal and external data systems is hinged on its ability to achieve standardization across every business stream. The introduction of DataOps across businesses will see a change from completely overhauling legacy systems to fine tuning them with added functionality to operate with a common layer of consensus and governance.
DataOps will soon sit at the heart of modern business as the requirement to manage a universal view of all data assets and data pipelines becomes standard. Without it, large volumes of known and unknown data quality issues will cause data to fail to live up to its potential.
2. DataOps drives a cross-team culture
Despite many of the world’s best data scientists and data engineers now working in machine learning and automation, there continues to be a lack of cross-team engagement due to siloed ways of working; often resulting in crossed wires and delays in the software release process.
The art of efficient deployment has remained a complex barrier to innovation for many firms. A key hurdle to overcome is the lack of a central team in place that ensures collaboration and communication regarding data across teams isn’t stifled.
Looking ahead we will see a major transition towards DataOps forming the connective tissue within an organization that brings data engineers and scientists together with business operations to ensure everyone is working on a common understanding of data, its currency, quality, and operational resiliency.
Embedding DataOps principles across an organization is a signal towards a more intelligent and strategic use of data. In an era where data can provide real-time metrics faster than ever before, companies will look to encourage cross-functional teams to improve their chances of working at scale and quickly adapting to data flows on a continuous basis.
Once more businesses implement a DataOps culture, we’ll see a more closely aligned end-to-end collaboration between data engineers and operations; making it more feasible to shorten development cycles and improve the integration and release of data projects to increase feature release frequency.
3. DataOps will prioritize cost and operational resilience
In today’s business environment, every team is under pressure to deliver against aggressive timelines and fixed budgets; which has often led to poor ‘lift and ship’ data platforms that are at risk of becoming obsolete even before completion. Businesses have had to learn the hard way about implementing data platforms without refined tuning over time.
The most competitive companies now recognize that data platforms can no longer be overlooked as the cost of flawed data are simply too high. These organizations have and will continue to identify that the agile, more accurate and systematic approach of DataOps principles provides a chance to explore data productively.
This is in part due to the way in which it observes all the data pipeline interactions to better analyze data and its management over the course of its lifecycle; enhancing the ability to catch critical platform failures, data transformation errors or data quality issues ahead of time.
To enhance operational resilience, the fusing of data scientists, developers, and business leaders will become commonplace as enterprise models see the value in ongoing monitoring and optimization. Those that see this model as part of the changing landscape of data and best manage internal resistance will come out on top.
Putting DataOps at the heart of your data strategy
With the rate of data innovation continuing to accelerate, 2020 will be the year DataOps becomes mainstream, and for good reason. The key takeaway to remember is that for DataOps to become an inherent part of business culture, everyone from the CEO to Technology and Operations needs to be brought in.
By putting a DataOps culture in place, companies stand to gain deeper visibility across the entire data lifecycle and so that is a crucial competitive edge. To become a real contender in the data-driven road ahead, DataOps has to be at the heart of your data strategy.