A Smart Move—How Businesses Can Truly Become Data-Driven

Carl D'Halluin, Chief Technology Officer at Datadobi.

The "data estate"—the collection of the datasets owned, managed and used by the typical organization—has become one of the most valuable modern business assets. This information provides a comprehensive foundation for everything from decision-making, operational efficiency and cost optimization to regulatory compliance and the ubiquitous need for innovation.

While this is familiar territory to businesses around the world, many are still failing to turn the potential that exists within their data estate into positive outcomes. Whether it’s a lack of visibility, data silos, ineffective governance or any one of myriad other challenges, failure to leverage the value of data can have serious operational and strategic consequences.

In today’s "digital-everything" world, we operate in economies where data is afforded significant intrinsic value. As a result, the go-to approach is to collect data from just about every available source, even if the organization in question has no specific plans for it.

This has created an environment where, according to industry research, 64% of organizations already manage at least 1 PB of data—equivalent to 500 billion pages of text. On the face of it, that sounds like a lot, until comparing it to a significant number of companies managing hundreds of petabytes or more.

Being Data-Driven

If we revisit the core aspects of a data-driven organization, it means that decisions are made based on tangible elements of data rather than a collection of assumptions. Decisions are made to deliver specific outcomes, and attainment of these outcomes is measured.

But what are the challenges of being a data-driven organization? For one, data fragmentation, where data is stored in multiple locations and accessed over different protocols. Fragmented data provides a challenge in creating an organization-wide view of all stored data.

In addition to fragmentation, the inability to organize data into logically related groups makes analyzing and reporting on stored data difficult. Additionally, data quality is a challenge. Is the data relevant to the business or the specific outcome? Is the data old or outdated and, therefore, of little value? Is the data actively owned or is it orphaned by the departure of the original owner or decommissioning of the original owning application?

Effective adoption of smart data management solutions and practices begins with tackling the challenges inherent in being data-driven. Insight forms the foundation for all such smart data management initiatives. Insight provides critical visibility into how much data is present, how old it is, who owns it, what type of data it is and more.

Managing data without this information is like driving to a new area without a map, and just like a map will tell its user where to go, insights will provide the necessary information to make decisions about how data should be managed.

By The Numbers

The obvious question to ask at this point is: How? Given almost all businesses are data-driven, they need to be able to carry out the fundamentals of data management. This includes creating virtual views of their data and analyzing the details, addressing orphaned/non-owned files, creating workflows to handle the lifecycle of data as it ages and becomes ready for relocation to an archival platform, and identifying non-business related data and removing that content.

In this context, the choice of technology infrastructure determines how well the typical organization can translate its data ambitions into business outcomes. Today, this increasingly means the ability to manage both structured and unstructured data efficiently and is typically based on the use of advanced data orchestration tools, cloud-native architectures and highly scalable storage solutions.

With the appropriate software solution in place, organizations can then set out the policies and processes required to ensure data is accurate, organized and subject to proper governance. This relates directly to how confident leaders can be about data integrity and that the data they have collected is accurate, consistent and reliable. This work goes hand in hand with establishing a management framework that meets regulatory requirements while also reducing the various risks of non-compliance or data breaches.

Armed with these capabilities, businesses can successfully shift from merely positioning themselves as "data-driven" to a place where their technologies, processes and culture are fully informed by data-led insight—and that’s a very powerful place to be.

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