Data: a concise word for a tremendous amount of information. Once, it was manageable. Today, with data scattered everywhere – even systems you don’t control – data often veers toward the realm of chaos. As a data professional, your mission is to conduct that chaos.

All data is big data

In the realm of modern business, data is the lifeblood that courses through every department, process, and decision. It’s no longer just a byproduct of operations; it’s a strategic asset. The term “big data” has become synonymous with this era of information abundance. But what exactly is big data?

Big data is more than just its name suggests. It’s not just about size; it’s about volume, velocity, and variety. In other words, it’s data that is massive in size, generated at an incredible pace, and comes in various formats from numerous sources. For most large organizations, it’s reasonable to suggest that all data is now big data.

According to statista.com, “the total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 64.2 zettabytes in 2020. Over the next five years up to 2025, global data creation is projected to grow to more than 180 zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just two percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of 19.2 percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached 6.7 zettabytes.”

Making sense of it all

In this era of data abundance, traditional approaches to data management no longer suffice. Historically, data management was often seen as an IT or compliance problem. While the end goal was usually to support advanced analytics, research shows that investments in data integrity have far-reaching positive impacts across various business metrics.

As data volumes and complexity continue to surge, it’s imperative to formalize data practices that identify critical data, ensure its quality, and make it accessible to authorized users. This isn’t merely about compliance; it’s about maximizing the value that data can bring to your organization.

Shifting the perspective on data governance

The term “data governance” has, in the past, been met with resistance in many businesses. The compliance-driven data governance initiatives of the mid to late 2000s often hindered business agility and innovation rather than facilitating them.

Today, data governance needs to shift its focus towards enabling businesses. It should simplify the process of finding and accessing the data that knowledge workers need to perform their jobs effectively. Data governance should be about formalizing decision-making regarding data, starting with a consensus on what data is essential and where data management investments are needed most.

Data governance structures should be driven by the organization’s data strategy, which should be intricately tied to business objectives. As data becomes less structured and volumes increase, understanding how data is created, stored, and consumed, as well as who is responsible for it, who should have access to it, and its trustworthiness, becomes paramount in prioritizing data for maximum value.

The importance of context

In 2014, concerns arose that data lakes would become unmanageable without context. By 2018, it became clear that chaotic data lakes were impeding the goal of data democratization – making data accessible to those who need it.

To address this challenge, technologies like enterprise data catalogues and metadata tools have emerged. These tools bridge the gap between business and technical data contexts, providing data intelligence. Additionally, data quality tools have gained prominence in improving the content of data.

When selecting these technologies, it’s crucial to keep the business knowledge worker in mind. They should cater to a variety of business cases and stakeholders, from decision-makers seeking trusted insights to analytics teams and data scientists aiming to create innovative data products.

Operational teams and IT must be equipped to run agile DataOps programs to deliver trusted, reliable data at scale. Compliance and privacy teams must have the capability to demonstrate compliance to both external and internal auditors.

A business-first approach

To navigate this data-rich landscape successfully, organizations should adopt a business-first, top-down approach. This approach integrates data governance processes and structures that foster collaboration and prioritize data management efforts across the organization.

In conclusion, data is no longer a manageable entity but a vast and ever-expanding resource. To unlock its full potential, businesses must shift their perspective on data management and governance. It’s not merely an IT or compliance concern; it’s a strategic asset that, when managed effectively, can drive positive impacts across various business metrics. Embracing a business-first approach and leveraging the right technologies are essential steps towards making sense of the data chaos and harnessing its true value.