DataOps A Competitive Advantage

Companies that want to speed up their end-to-end processes and gain business insights can no longer use the data in the way they have for decades. Data is the fuel for innovation and sustaining a competitive advantage. It is the key ingredient for driving analytics and understanding business trends and opportunities.

The amount of data that companies generate and collect continues to grow, pouring into organizations from sources such as IoT sensors, social media feeds, and enterprise systems. Half of today’s business leaders are unsure what their industry will look like in just three years, and no one wants to be tomorrow’s Blockbuster.

There is a great deal of focus on intelligently capturing, orchestrating, and analyzing data to better business outcome – Right Data, Right Place, Right Time.

Companies that oppose the introduction of DataOps Innovations, will spend even more time responding to data errors and faulty manual processes, and fall even further behind when it comes to providing timely and accurate information to business leaders.

All modern enterprises need to deliver the right data to their growing customer-base that wants to consume data as quickly as possible. But many of these organizations are failing to deliver a seamless customer experience, despite investing a large chunk of investment in data science applications.

Adopters of DataOps will be well positioned to create an optimized and automated data pipeline that enables business processes to be streamlined and  shift focus on high-quality tasks, and support decision-makers with the best possible intelligence. More personalized customer experiences, greater process efficiencies, enhanced productivity, new digital revenue streams and the transformational possibilities of automation and artificial intelligence (AI) can all be unleashed by a fresh approach to data management.

Defining DataOps? DataOps is an organization-wide data management practice that controls the flow of data from source to value, with the goal of speeding up the process of deriving value from data. The outcome is scalable, repeatable, and predictable data flows for data engineers, data scientists, and business users. DataOps is as much about people as it is about tools and processes.

It builds on the foundations created by agile development and DevOps; has as a goal to integrate similar principles into data analytics and data science to improve data quality and shorten the time needed to achieve actionable business intelligence.

DataOps can enhance quality by surfacing key processes that impact the organization. The data preparation may involve time-consuming tasks such as gathering requirements, modeling data, creating a report, and arranging for distribution of reports. But understanding the value of these tasks encourages the removal of siloed processes.

While DataOps began as a set of best practices, it has now matured to become a new and independent approach to data analytics. DataOps applies to the entire data lifecycle, from data preparation to reporting, and recognizes the interconnected nature of the data analytics team and information technology operations.

Due to the enormous size of the datasets, data analytics today requires automation that performs validity tests, analyzes large amounts of raw data, such as data from multiple sources, and detects anomalies and outliers that could indicate quality problems in the data pipeline. The basic concept behind DataOps is “analytics is code”, which means that even analysis routines that control metadata and business intelligence need to be automated.

Why DataOps now? The tasks of data prep, integration, monitoring, and governance tasks have been done for decades. What is new now? Consider the following forces:

Consumerization of enterprise and the rise of data fluency. Then: Can IT get this data for me? Now: Why can’t I do this myself?

Rise of the digital natives: To disrupt a sector, software needs to connect with data in that ecosystem. Then: Sell technology to non-tech sector Now: I will leverage data in this sector to change how things work.

Buy, not Build: Build your core competency, buy everything else means more B2B partnerships, more B2B data. Then: Can I get a report? Now: I need raw data, along with report.

Big Data Maturity: Many companies have built and operationalized their analytics stacks. Then: What analytics system should I use? Now: I need to feed more data into my AI

More Data, More Problems: Data doubling every 18 months. Then: I have a few data sources Now: I have a long roadmap of data I want to use. How can I get to that faster?

DataOps misconceptions. First, get out of your head any notion that DataOps is just DevOps applied to data. However, the two concepts do connect, so it is important to understand the baseline concepts. If your company has dipped its toe into the waters of both agile and DevOps, you have a head start. Those experiences can give everyone a better perspective about bringing the development and deployment practices to data processing and integration pipelines. Key differentiators:

Unleashing DataOps – Data friction is the issue. Businesses everywhere are looking for ways to improve their operational efficiency and effectiveness to enable the best decision-making, especially due to many silos within an organization. For organizations seeking a transformation within their data operations, automation technology can deliver a competitive advantage. Data becomes valuable when trusted business ready data helps drive differentiated insights and operational excellence for organizations.

DataOps has already enabled companies to transform their data management and analysis processes. It enables teams to easily develop isolated, secure, and disposable test environments that allow them to experiment and innovate. The principles of DataOps also enable companies to use their vast production data sets in ways that were unimaginable just a few years ago.

Businesses that require timely and actionable business intelligence will see benefits in these key areas:

1. Efficiency Driven: DataOps is basically a process-oriented automation method that dramatically increases work efficiency. Creating automated, repeatable processes and controlling rollouts reduces the likelihood of human error spreading that cripples a network or leads to erroneous results. By integrating smart testing and observation mechanisms into the analytics pipeline, teams can focus on strategic tasks rather than scanning tables for anomalies. This reduces the time and cost of data management and processing as well as the number of tasks.
2. Data Quality Improvements: Reducing effort and improving data quality leads directly to faster access to actionable business intelligence. DataOps can provide a more efficient and efficient approach to data management and analysis. Automated capture, processing, summary, and analysis, combined with error elimination, provide faster, more accurate, and more reliable data collection and processing.
3. Actionable Intelligence:  DataOps can reveal important information about the health of an organization, such as the number of employees, the size of the organization, and the impact of changes within the organization.

This view would not be possible without teams constantly reacting to anomalies and errors through manual processes. By replacing repetitive and monotonous processes in fast-moving, innovation-oriented organizations, companies can benefit from increased employee satisfaction and retention.

The core problem is not data itself, but the data friction caused when constraints on data prevent people from meeting the ever-growing demands of the business.  As a result, companies are locked in a battle with data friction across people, process, and technology. Companies made headway with Agile, cloud, and DevOps — for a while. Predictable delivery of compute environments has gone from weeks to minutes, with automated, elastic, and on-demand infrastructure. But data is unlike compute. It is expensive to maintain, full of sensitive information, difficult to copy, hard to track over time, and slow to deliver to the teams that need it.

As DevOps and cloud tore down the barriers between people and infrastructure, more environments, more automation, and more speed meant increased demand for data, faster. IT is still struggling to manage, secure, and deliver the data environments that the business demands. And users are still struggling to access, manipulate, and share the data they need.

When data friction becomes the blocker to innovation, customers leave, competitors win, and businesses spend more time reacting instead of leading.

A successful DataOps Practice. We need DataOps, a new means to connect people to data, empowering them to overcome data friction and achieve the velocity of innovation demanded by the digital economy.

Do you really know where all your data is, how to access it and who can see it? Do you trust your data? If you are not sure, you are not alone. According to IDC, the reality is that only 2.5% of all data is analyzed. That means most of your data, arguably your organization’s most valuable asset, is totally underutilized.

To deliver data that meets the needs of businesses, it is vital for organizations to focus on three important pillars of DataOps: People, Process, and Technology.

Make people a priority: Define rules for an abstracted semantic layer. Ensure everyone is “speaking the same language” and agrees upon what the data (and metadata) is and is not.

Manage the processes: Design process for growth and extensibility. The data flow model must be designed to accommodate volume and variety of data. Ensure enabling technologies are priced affordably to scale with that enterprise data growth.

Technology: Automate as many stages of the data flow as possible including BI, data science, and analytics. DataOps is not one tool that can be bought and then completed. Essentially, any DataOps solution ought to enhance your business’ capacity to collaborate, build data pipelines, automate testing and monitoring, and deploy new features.

Achieving that requires changes to IT and corporate culture. A redesign and re-implementation of both how data flows and how it is governed by re-architecting how supporting operations’ infrastructure works. These sound like big changes, and they are, but there is a proven way to becoming DataOps powered.

DataOps also mandates a comprehensive technology approach that eliminates key points of friction across:

• Governance: Security, quality, and integrity of data, including auditing and access controls.
• Operation: Scalability, availability, monitoring, recovery, and reliability of data systems.
• Delivery: Distribution and provisioning of data environments.
• Transformation: Modification of data, including masking and platform migration.
• Versioning: Capture of data as it changes over time, with ability to access, publish, and share state across users and environments.

A modern approach: Store, Activate, Enrich, and Monetize. For DataOps to succeed, you need to avoid creating complex data management infrastructures that only a handful of experts can understand or improve. Policy-based configuration and automation are the power of DataOps. Understanding how and why these technologies are key to DataOps success is important, but technology is only part of the story. Your enterprise will also need to reconsider some of its cultural structures and habits. Achieving DataOps excellence demands expanding metadata, automation and policy-driven control and configuration.

Conclusion. The data belongs not only to IT, data scientists and analysts, but to everyone involved in the business, not just the data scientist or analyst. The first step to succeed in today's digital age, therefore, is to become a data-driven organization with its own data strategy. The changes that DataOps will deliver to an enterprise culture may be the most profound impact it has in the long term. Areas to focus: Democratize Data Ownership. Use Existing Skills in new ways. Adapt command data ecosystems roles. Manage data as a core business asset. Make decision-making collaborative.

• Apply lessons from agile, DevOps, and lean manufacturing principles to DataOps.
• Spearhead a campaign to align data analysts, engineers, and scientists to conduct the development process in parallel, according to the functions to which they are best suited.
• Standardize the processes involved in building data pipelines, and potentially use technology solutions to ensure they stay on track.

The digital economy has created an unquenchable thirst for data across all aspects of business. Every company is now a software company, and the market shift from complex, feature-heavy products for the masses to highly personalized, on-demand experiences, mandates a sophisticated data strategy. Data, and access for those that need it, is a competitive advantage. Those that can leverage data to drive innovation will win; those that cannot, will lose. DataOps is your Competitive Advantage.

References: Hitachi "Unlock your DataOps Advantage". CIO "The Power of DataOps". IBM "Deliver Business Ready data fast with DataOps". HPE "How to get started with DataOps".