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FYI: You’re a Data Company, and Chances are You’re Failing

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New report from top analyst firm 451 Research reveals moving fast in the age of digital disruption is hard and, unless you solve the data problem, things will only get worse.

In today’s highly competitive, app-driven business landscape, every company is a data company. Brands must deliver and monetize data-driven services to customers to out-innovate the competition. All the while, they must protect customer information and comply with a host of new data regulations. 

For enterprises in this new paradigm, data is both their most important asset and the greatest inhibitor to success. We’re seeing unprecedented levels of data growth, and demand for access by people and teams in all corners of the business. Nearly 30 percent of enterprise organizations say data is growing 100-500GB per day, according a new report by top analyst firm 451 Research. 

However, with more data comes more challenges. Delivering data at scale is slow, expensive, risky and painful for most companies. The same survey found the most common data management challenges to be data security (68%), data quality (60%), governance (51%), data provisioning (42%), among many others. 

It’s not all doom and gloom, but building and scaling your data company must start today. The quicker you implement these four strategies, the quicker you will create new revenue streams, improve quality, accelerate time to market, comply with regulations and lead the market. 

Agnostic to data source and data location 

Successful data companies achieve a high velocity of innovation by continually enabling new enterprise data sources to feed a web of digital transformation initiatives. But if you cannot get data to where it needs to be or to the teams that need it, you’ll lose to your competitors that are able to adapt to changing tides in the market more rapidly and efficiently. 

Let’s say one application team is gathering new data that needs to be fed to the data science team to unlock new business insights. If that data is unable to be transported from one group to another, quickly and securely, those data science initiatives will languish and ultimately fail. Failure to efficiently move and integrate data across the enterprise force team leaders to make suboptimal decisions that put their initiatives at risk. As a result, your competitors will be more adept in responding to changes in the market and deliver competitive differentiation because you can’t simply get your data where it needs to be. 

Self-service access to secure data 

Every employee in a data company is using data in some fashion, forcing you to think about how individuals on various teams get access to data and use it effectively. The only way to deal with this intense demand is to make data self-service to those that need it. Exactly what self-service means will vary from user to user. For instance, an analyst on FP&A who is looking at a financial report is going to be very different from a developer who is writing a new application. But at the end of the day, they need the ability to express what data they need, and get access to it in a form that they can use, without the overhead and wait times of complex IT processes.

Companies must replace the cacophony of bespoke tools with an enterprise platform that can effectively connect people to data so they can operate effectively in their day-to-day roles. Without self-service access, they’re forced to supplant that with people and process that slows things down. You don’t have to look far to find stories of users testing with five-year-old data, or refreshing environments only once a year because it’s simply too painful to get access to better quality data.

This is compounded by the growing complexity of the data landscape. If you’ve got 7,000 databases, 15,000 applications and 50,000 developers who all need access to data, managing that complexity is effectively impossible with self-service access. You end up with slower processes, less effective people and failed initiatives that impact that bottom line. 

Ability to manage, change and collaborate with data like code

DevOps was born out of a need for teams to better collaborate and manage the development and delivery of applications and digital services. One of the key technology enablers of the DevOps revolution was the ability to define alongside code the environments in which that code needed to be run. The revolution of infrastructure as code allowed teams to replace slow, error-prone, and inconsistent processes with rapid predictable deployments. This in turn enabled development and operations teams to collaborate more effectively, improving the feedback cycle and driving better outcomes for the business. 

Like code, data is always changing. Production data changes, test data changes and so does the structure of data as you’re evolving applications and integrating data sources for analytics. Having the ability to understand how you adapt to those changes is critical here. How do you refresh the latest version to test a new code change? How do you bookmark the state, so you can share it with a different team member? How do you test changes to your data model against the data used to train the previous version?

When users can’t access the right version of data for the task at hand, they turn to poor quality data and things break. When teams can’t easily share data to aid communication and collaboration, you end up with silos. And when changes are made within a silo, the rest of the world breaks. 

Minimize governance and compliance risks

We live in an era of heightened data privacy concerns and ever-increasing regulation. Data breaches and privacy lapses can irreparably damage a brand, and regulatory fines can cripple the ability for to invest in necessary innovation. Companies can no longer play fast and loose with customer data, but the natural reaction - locking down access - only serves to put critical transformation initiatives at risk.

Securing data so only the right people have access is necessary but insufficient. Systems and people are not infallible, and the only way to empower users with the data they need is to proactively identify and mitigate risk within that data. By employing techniques, like data masking, to eliminate sensitive data, companies can confidently provide data to the teams and third-party partners that need it. Sophisticated data companies can rest easy knowing that the inevitable security lapse cannot possibly jeopardize customer data, safely avoiding media and regulatory backlash.

Closing Thoughts

You can either be data-driven or perish. The old way is a patchwork of systems and manual processes that result in increased exposure to risk, disruptions amongst competing demands, failure to scale and slow time-to-market. Focusing on aligning the right people, process and technology around data, what we call DataOps, does the inverse. Successful DataOps drives revenue growth, higher customer retention and satisfaction, all while reducing cost and risk. When you’ve reached the the nirvana of a data-first enterprise, you’ll find yourself continually driving disruptive innovation that keeps you at the front the market and ahead of the competition.

Download “DataOps Lays the Foundations for Agility, Security and Transformation Change” analyst report to learn why your enterprise needs a DataOps strategy, not just more data people. 

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