John Pocknell
John Pocknell is a senior solutions product marketing manager at Quest Software. Based at the European headquarters in the U.K., John is responsible for developing and evangelizing solutions-based stories for Quest’s extensive portfolio of database products worldwide. He has been with Quest Software since 2000, working in database design, development and deployment. John has spent over 18 years (including 12 years in Product Management) successfully evangelizing Toad to customers at conferences and user groups around the world. He blogs and has produced many videos for Toad World, the Toad user community, and has authored technical papers about Toad on the Quest Software website.

DevOps has seen growing adoption over the last several years as companies look to improve the quality and speed of software delivery in order to increase business agility. However, developing software rapidly is just one piece of the larger puzzle.

Developing the right software applications to address the right issues requires insight into business’ needs, which depends on access to and understanding of the data — where DataOps comes in. Done right, DataOps can accelerate business intelligence and the rate at which companies can extract maximum value from their data.

The practice of DataOps has emerged with the notion of democratizing data, which has become increasingly popular over the last 10 years. This collaborative approach is supposed to bring people, data, and applications together, to ensure a more business-driven focus. However, this way of handling data also has its faults, such as centralized data making it difficult for understaffed business intelligence teams to meet business performance, as well as difficulties in implementation due to teams being siloed and lack of skills.

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To ensure the right data is getting in front of key decision-makers to drive business results, it’s important to understand the challenges around DataOps and data democratization.

Initial Challenges to Address

DataOps provides greater collaboration and delivery of data and insights at real-time speeds to decision-makers or decision-making applications. However, well-entrenched siloed systems and risk-averse corporate culture prevent organizations from successfully implementing and realizing the benefits of DataOps, in turn impeding data democratization.

Most enterprises are unprepared for DataOps, often because of behavioral norms — like territorial data hoarding — and because they lag in their technical skills, they are often stuck with cumbersome extract, transform, and load (ETL) and master data management (MDM) systems. As new technologies and techniques related to data continue to emerge, furthering the IT skills gaps, this also means having to hire from a smaller talent pool.

Additionally, it’s important to acknowledge the need for organizational leadership to be involved and for there to be buy-in from employees. DataOps requires a change in culture, so it’s important that executives are on board and back the initiative and that teams across the organization support it.

Data is siloed, disconnected, and generally inaccessible across many enterprises. There needs to be a way to map IT capabilities to the business functions they support and determine how people, processes, data, technologies and applications interact to ensure alignment in achieving enterprise objectives. This requires understanding the enterprise architecture and improving data intelligence to better understand the data ecosystem. With DataOps, data becomes democratized, enabling organizations to act “data first” and be more competitive, own more market share, and drive growth.

Ensure Its Secure

When it comes to approaching DataOps, what needs to also be a first step is ensuring the proper security is in place. Teams must check that the necessary solutions and processes are in place to fulfill regulatory compliance needs such as GDPR, CCPA, and HIPAA. This can be done by automating data discovery of personal and sensitive data across databases so the right data protection can be applied as appropriate to the environment, like masking, encryption, and redaction and auditing.

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Also beneficial is centralizing data governance. With an array of data sources specific to multiple applications and siloed provisioning procedures for test data, many organizations lack the full view on their data. Central data governance provides more visibility and standardization of who has access to what data, when, and for how long whether on-premise or across clouds.

Data-Driven Business Results

DataOps is well-suited for environments that require intelligent, responsive analytics in which data sources are dynamic and evolving. It provides a well-functioning platform for decision-making, both high-level and at day-to-day levels. It also provides the real-time data needed for automated applications powering edge devices and enterprise systems.

Today’s data-driven companies require rapid and continuous decision-making, based on the latest and most accurate actionable data available. DataOps applies the combined power of human collaboration and intelligent automation to deliver data when and where it is needed.

When done right — obstacles addressed upfront and proper security in place — organizations with DataOps initiatives are likely to see earlier success in their data analytics capabilities and achieve data democratization across their organization.