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What Is DataOps And Should It Matter To Enterprise CIOs?

The last decade has been rife with technological advancements driving us forward at a blazing speed. We are now in a world full of data, constantly being produced at an accelerating rate.

Think of any daily routine. Got a reminder from your insurer to renew your liability policy? Sent a long-forgotten invoice based on a prompt from the financial system? Scheduled maintenance for a complex piece of equipment before any sign of failure? Data enables all those decisions. Global data storage is even estimated to exceed 200 zettabytes by 2025, including cloud storage.

Data is also essential in enterprises to:

  • Design a common operational picture (COP)
  • Facilitate analyses relevant to the business
  • Derive executable operational strategies
  • Develop solutions to enterprise problems
  • Create informed strategies and prioritize tasks
  • Keep a track of all benchmarks and progress
  • Make data-guided real-time decisions

To actualize data-driven plans, it is important to treat data as an asset, and yield value that keeps increasing with time. In this data-driven world, real-time decision-making is essential to beat the competition.

The real problem today is that the information collected from insights is not immediately translated into actionable plans. And real-time decision-making depends on analyzing a reliable set of data within a limited time, to create actionable plans. This is where data operations (DataOps) gain significance.

What is DataOps?

DataOps is the DevOps-inspired methodology of data management and quality data delivery for analysis. Stated simply, DataOps is the ability to continuously leverage data, develop solutions, and design data products to unlock the real potential of data in all sections of a business at speed and scale.

Why should DataOps matter to enterprise CIOs?

The DataOps manifesto says that DataOps ardently believes in the power of data analytics and measures its performance by insights.

Today, DataOps fits with microservices architecture, data-oriented enterprise operations, and has transcended machine learning. CIOs are also getting interested in the potential of data to transform their organizations for many reasons like:

  • Collaborative ventures

The discussion around the collaboration of business units and IT departments is also getting significant. With DataOps, all the departments can collaborate and act as the building blocks of their organization's success. DataOps makes actionable and governed data assets available to everyone so they can analyze and collaborate in real-time to generate positive outcomes.

  • Data literacy

CIOs are now focusing on strategic data literacy to make all the branches of an organization thoroughly skilled. By making quality data easy to access, use and analyze through DataOps, companies can enable data democratization at the pace the business requires.

  • Agility

Agility is the key to successful businesses in the VUCA world. By integrating real-time insights as enabled by DataOps, companies can immediately organize and ingest data. DataOps can further reduce the cycle time of data analytics and automate the data life cycle. Effectively, improving the data flow between departments speeds up the process.

  • Data quality

DataOps is designed to flawlessly manage a large (and growing) volume of data without compromising the quality. It also offers the details of data sources, accessibility, and recent changes for further transparency.

Challenges in implementing DataOps

While DataOps can solve many problems for CIOs, there are some roadblocks to its implementation. By addressing and understanding these complications, companies can take remedial measures to leverage DataOps.

  • Poor training

It is the enterprise's responsibility to ensure that its employees and teams are well-trained to implement DataOps practices. These include best practices and processes around data collection, validation, storage, processing, and maintenance. It also includes understanding the changes that would come about as process change to act on the insights being delivered in real-time. Continuous practice equips the teams with all the tools required to deal with such challenges.

  • Not having well-defined objectives

Organizations should have clear objectives behind the implementation of DataOps. From the cycle time of delivery to metrics for measuring success, companies need to think over their strategies and measurement criteria. If any metric is not achieving expected results, they should also define how to make it better and when such action is required.

  • Low automation

It’s 2022. Everything is about digitalization and automation. The lack of automation puts trust in manual testing and deployment, which can be an error-ridden path. By taking time and resources to automate, companies can get ample tools and high-quality data to implement DataOps.

  • Low rate of deployment

DataOps delivers the greatest value when it is adopted as an enterprise-wide strategy. Deploying it in pockets will cause inconsistency and create silos. Hence, constrained deployment will make it very risky and challenging for companies to make any meaningful changes.

  • No demarcation between code and data

In the IT industry, code and data today have very thin boundaries, but it is significant to remember that all code is data, but all data is not code. Code is anything structural defined in a programming language, but data is all kinds of information. If there is a lack of clarity on this topic, then companies cannot make the best out of DataOps.

Summing up

By strategically implementing DataOps, organizations can become autonomous digital enterprises and generate insights to drive future decision-making too in real-time. It can also be a partner of digital transformation for companies and improve their profitability and performance. Sure, there are challenges on the way, but the results can be transformative for any organization.

ITPN excels in master data management, data strategy, analytics, and reporting, making it a great data analytics collaborator too. It also maintains special consideration for data quality, governance, and privacy, so that security is never compromised. With DataOps, business teams get the liberty to experiment with data, while the IT team can ensure its credibility and accuracy. To make informed business choices and stay a notch ahead, connect today!




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