Driving More Data Value as the Outcome with DataOps

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Click to learn more about author Itamar Ankorion.

We know data increases business success if utilized well and in the right situations. But, how do we know we have the right data? How do we dig through thousands of data sets to leverage the one insight that will drive a specific, improved business outcome?

This challenge is why DataOps is a rising trend in the data industry. This new methodology helps fill in the missing link from pipelines to collaboration. However, for DataOps to become a real asset and enable real-time analytics, we need to understand the importance of data as an outcome.

DataOps: Business Execution and collaboration at the Speed of Data

Companies of all sizes are still looking for the secret sauce to make their data work better for them. Bringing the whole universe of possible data in line to be analyzed for actionable outcomes means organizations first need to bring together modern technology, new processes, and teams in a way that connects the dots between the business strategy, data, and analytics.

This is where DataOps shape-changes a company. Through real-time data integration and modern architectures, DataOps positively disrupts how data is shared and consumed. It advances digital transformation by leveraging new technologies that support a data paradigm shift for speed and accuracy, accelerating cycle times and reducing the time to insight.

In moving forward with DataOps, one of the most important parts that is usually left for last is collaboration. While the technology is fundamental, breaking down data silos and bottlenecks is essential as well. DataOps brings together data specialists, data users, and the business decision makers in a common shared purpose, leading them towards a more agile integration.

To successfully leverage DataOps, those involved need to have an understanding on where their data comes from; when and how it might have been changed; and the data sets available for analysis. A data catalog is a critical part of that process as it allows users to trust the data they will use to create business outcomes.

Data catalogs present all the data sets available and offer data quality assessments including details about if it is being used by other teams; if it complies with specific policies; and if it is clean or has been modified. Individuals then can take full advantage of what is available without having to reinvent the wheel – or the data set!

Data Outcomes Build Data Literacy

With a DataOps strategy driven by data integration and data catalogs, individuals will have the information they need to execute analysis towards outcomes. At this stage, increasingly we find another challenge: user understanding of and decision making with data.

Employees at all levels need to be data literate. A lot of data’s value for a company is not uncovered because there is a lack of understanding. A company can invest in new technologies; roll out a full DataOps strategy; and engage in a ‘new collaborative culture’ internally, but if the data scientists, business users, and decision makers can’t derive proper meaning from the data, business outcomes will get lost in translation.

According to Qlik’s Data Literacy Index, companies with higher rates of data literacy have 3 to 5 percent higher values, which translates into $320 to $534 million higher valuations per company. This confirms what we all intrinsically know about the value of data. By increasing the data literacy of every employee, organizations will see data become a cornerstone in every aspect and decision of the workforce’s daily lives, which will lead to bottom-line results. Qlik works with enterprises looking to benefit from DataOps and Data Literacy, supporting the creation of a complete and integrated data supply chain.

With all that in mind, we need to pay attention to our Data Strategy from a technology, processes, and talent standpoint, making sure we are putting in place the tools and resources needed, as well as the training and the mindset shift to build from the ground up an agile, data-driven enterprise.

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