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5 Steps to Implementing a Data Literacy-Driven DataOps Framework 5 Steps to Implementing a Data Literacy-Driven DataOps Framework
DataOps is a new framework that has been gathering greater attention in the past year since it first appeared on the Gartner Hype Cycle.... 5 Steps to Implementing a Data Literacy-Driven DataOps Framework

DataOps is a new framework that has been gathering greater attention in the past year since it first appeared on the Gartner Hype Cycle. DataOps is defined as a new way of thinking related to data which encompasses people, processes, and technology, resulting in improved collaboration and streamlined decision-making at the speed of business. It creates better alignment for enterprises to work with and use their vast data stores and talent to drive better business outcomes. 

[Related Article: Why Use Continuous Intelligence in DevOps/DataOps]

The first thing to note is that companies can’t just add DataOps as a budget line item—it is not a solution to be purchased. Rather it is a change in the mindset of how data is used across the organization by all levels. DataOps works best when it’s being driven by a corporate initiative to improve data literacywhere knowledge of where to find and how to use data that allows them to make smart data-driven decisions—is valued. 

Organizations where data scientists and business analysts yearn for reliable, governed data access are ideal candidates for implementing a DataOps framework. Issues around getting users the data they need can be solved with more self-service options; however, this only masks the real problem of open data access and the data literacy gap that exists in most organizations. DataOps can help drive greater data literacy across departments while encouraging greater agility and operational efficiency. By following these steps, companies can be best prepared for a successful implementation of DataOps with data literacy in mind.

Step 1: Embrace a culture of data literacy

We want our organizations to be driven by data for competitive advantage, but we first need to understand what data exists and how it’s being used. An audit will help set a company benchmark of current data use and enables a better ongoing measure of the growth in data use through DataOps. This sets the stage for creating a culture of data literacy that will be enabled through providing greater access to data and data-related training. Executives need to put this mandate in place and lead with data use as a guiding principle.

Step 2: Build enthusiasm for data literacy from the top

DataOps consists of three core components: people, processes, and technology. As the methodology itself requires a culture change, you will need the key people in your organization to charter data literacy and enterprise data integration. Key stakeholders such as analysts, data scientists, CDOs, CSOs from a governance perspective and other data users—including the business leaders—can champion the push for greater use and lead by example on how data can be at the core of all activities.

Step 3: Create collaborative data processes

For DataOps to be successful, you need processes in place to encourage collaboration through data use, making it as simple as possible for data to become a part of the everyday tasks and decisions. What processes do you have that accelerate or measure data use? Forming these allows for the creation of KPIs for data-driven decisions. It also will uncover where any bottlenecks are when providing data access or the need for missing data sets.

Step 4: Implement technology in a pragmatic fashion

Technology is a bedrock of DataOps frameworks. IT departments should look at solutions such as enterprise data integration and data catalogs, which bring all the organization’s available information together in a governed format for easy access for analytical purposes. This enables data democratization, where every user can use trusted and relevant information to uncover better business insights. Enterprise data integration technology based on change data capture (CDC) operations will provide the real-time, automated data streams so any analysis is current. However, it is not a ‘set it and forget it’ process. The creation and validation of new data sets should be an ongoing process, driven by the speed from which raw data can be turned into insights. By augmenting intelligence and analysis, companies think outside the box to uncover the more interesting correlations.

Step 5: Measuring data literacy success

DataOps is an evolving framework and through continuous measurement and refinement, it only gets better over time. Review data usage from the catalog on an ongoing basis. Which data sets are being used most frequently? Is there a way to refine that data to make it easier for use? Is there a continual request for a specific dataset? Through understanding how the data is used and any gaps in data availability, you have a better sense of if your company is becoming more data literate. You can then measure the growing number of people interacting with data against the baseline set in Step 1 to see if you are achieving the goal of being a data-driven company.

[Related Article: 5 Mistakes You’re Making With DataOps]

A change in how companies use, interact and rely on data must come from the top down. It starts with the C-level suite embracing the value of data and encouraging every individual to use it. And while we have focused on implementing the people, processes, and technologies associated with a DataOps framework, the real end goal is greater ROI by building everyone’s data knowledge and use. DataOps enables both data literacy and better business outcomes.


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Dan Potter

A 20-year marketing veteran, Dan Potter is VP of Product Marketing at Attunity, a division of Qlik. In this role, Dan is responsible for product marketing and go-to-market strategies related to modern data architectures, data integration, and DataOps. Prior, he held executive positions at Datawatch, IBM, Oracle and Progress Software where he was responsible for identifying and launching solutions across a variety of emerging markets including cloud computing, real-time data streaming, federated data, and e-commerce. 

To read more about DataOps, data integration and data literacy, check out Dan’s blog at: https://blog.qlik.com/dan-potter/.

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