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Why Data Literacy and Data Quality Are Essential to Your Digital Business

As businesses keep gathering more data, you need an effective data governance strategy to manage it.

CDW Expert CDW Expert

Data quality means that a company’s data is trusted, leveraged and understood. One of the biggest issues IT leaders face is a lack of trust around their data.


Today’s IT organizations face many different challenges in the effort to establish an effective data governance plan for their business. Several factors come into play, such as lack of data management, confusion around data ownership and budget, and inadequate understanding of the business value that data governance can deliver.

Perhaps two of the biggest issues IT leaders should tackle on the road to data governance are data literacy and data quality.

Data Literacy is Data in Context

Data literacy is fast becoming a core capability of today’s ever-evolving digital society. Gartner defines data literacy as, “The ability to read, write and communicate data in context, with an understanding of data sources and constructs, analytical methods and techniques applied, and the ability to describe the use-case application and resulting business value or outcome".1

So, how do you know if you have the data literacy you need to establish an effective data governance program?

Generally, companies that base their policies on data, instead of on their intuition, are off to a good start. Often, it helps to appoint someone — usually an executive — to act as the data literacy champion. This helps establish leadership that is supportive of a company culture shift and demonstrates to the organization the commitment to enhancing data governance and elevating the value of their data.

Companies with solid data literacy think of data first, not last. Reporting and analytics are built into the core of the organization, they are not treated as an afterthought. Why is this important?

“By 2023, data literacy will become an explicit and necessary driver of business value, demonstrated by its formal inclusion in over 80 percent of data and analytics strategies and change management programs.”1

Not only is the importance of data literacy fast-increasing, but the need for good data quality is more important than ever.

Get More Leverage with Data Quality

Data quality means that a company’s data is trusted, leveraged and understood. One of the biggest issues IT leaders face is a lack of trust around their data. Company stakeholders can have trouble believing IT is providing accurate or timely information because they are churning out disparate data.

If you are struggling with this challenge, you know better than anyone that your legacy technology is likely holding you back. Data is different now. It’s generated differently, captured differently, analyzed differently and leveraged differently. Outdated data management systems were not designed for today’s data.

For one thing, the sheer volume of data has changed. Old hybrid or legacy technologies can’t handle the massive amounts and different types of data companies generate today. The amount of data being generated on your company’s social media platforms alone is enough to make your head spin.

How do you capture this data so it’s meaningful, and how do you govern it in a way that makes sense?

The variety of data has also changed. Today’s data falls into three classifications: structured, unstructured or semi-structured. It’s the unstructured or semi-structured data that legacy systems just aren’t designed to handle.

Structured Data

This is data that is the easiest to search and organize, because it is usually contained in rows and columns and its elements can be mapped into fixed predefined fields. Examples include Excel spreadsheets, tabular databases and more.

Unstructured Data

This is data that cannot be contained in a row-column database and doesn’t have an associated data model. A much larger percentage of data in the world is unstructured. Videos and images are examples of unstructured data.

Semi-structured Data

This is data that doesn’t conform to a rigid tabular structure that can fit within a relational database. There are some organizational properties such as tags or metadata to make it easier to organize, but there’s still fluidity. XML or JSON data associated with web services are examples of semi-structured data.

Today’s Data is Born in the Cloud

For an idea of the volume of data anticipated worldwide, Statista predicted that 149 zetabytes of data will be generated by 2024. That’s a lot of data for enterprises to manage.

Since cloud technologies are able to handle unstructured and semi-structured data classifications, as well as structured data, companies are struggling to modernize their data estate without leveraging the cloud.

Most modernization efforts look like a combination of a cloud or hybrid approach. Having data that’s generated and created in the cloud and then bringing it back into an old legacy on-premises system doesn’t make sense anymore. You can now have secure and governed access to data in the cloud. There are a lot of advancements in cloud data management and security that can really help in your modernization journey.

4 Steps for Data Governance Success

More and more, IT professionals are realizing that their data is just coming at them too fast. To really get a handle on it, there are four steps to consider for data governance success.

  1. Start by analyzing your current environment to get down to the root cause of where your issues reside. Think of the three C’s when evaluating your existing ecosystem. If it’s costly, complex and has too many constraints — which is often the case with legacy technologies — it may be time to consider an update to cloud.
  2. Break down your silos to put together a centralized data governance program. You involve your C-level executives, as well as compliance and security teams. Look at the stakeholders impacted by data and analytics across your organization and work with this group to establish a data governance and data literacy plan that will take you into the future.
  3. Make plans for modernization. If you find you are having data access, data quality and data performance issues, these are red flags that you need a modernization strategy. In fact, modernization efforts are often the foundation of governance. Plus, IT modernization can bring additional benefits, such as: lower maintenance costs and TCO, increased processing and easy cloud integration.
  4. Consider a data governance workshop. When you engage with IT experts who have decades of experience in evaluating data and analytics ecosystems across a multitude of industries, you will gain insights and recommendations that will help your organization quickly stand up a data governance program and a roadmap to manage your data as an organizational asset.

Get More from Your Data with Data Governance

A strong data governance strategy helps ensure that your data is usable, accessible and protected, guaranteeing trust in the quality and consistency of the data — and helping you leverage your data to deliver better business outcomes.


SOURCE:Gartner: “Data-Driven Foundations: Getting started with data literacy and data-driven business transformation,” presentation, 2021.