A few years ago, it was hard to talk about enterprise technology without quickly coming across the phrase “digital transformation”. Today, a new term is gaining ground: data-driven. Organizations have become increasingly data-focused ever since “Big Data” was a buzzword in the middle of the last decade, and this focus is reaching a fever pitch.

Why is data so important to businesses today? Simply put, poor data management or insufficient data analysis impacts the bottom line. Organizations collected data, but there was generally no overall operational approach or focus on the skills needed.

This led to abundance without intelligence. Most companies have data silos in each department, which limits the ability to create a holistic view of corporate data. Data comes from new sources, such as social media and smart devices, but there is no structure to translate this data into business decisions. Although there is a wealth of historical records, there is a clear lack of expertise in using these records to develop future plans.

As they try to acquire the right expertise, companies discover that the problem is deeper than they originally imagined. The ultimate goal for most businesses is data analytics, finding new insights from their data to help understand the past and predict the future. Unfortunately, most companies lack strong processes around data management, and there are also mismatches between technical efforts and business goals.

A good way to understand the scope of the problem is to take a holistic view of how data flows through an organization. Dividing the overall data activity into three fundamental steps allows companies to ask relevant questions:

  • Where does our data come from?
  • How fast do we want to process different data streams?
  • What information should be provided during data analysis?

Digging deeper into these questions helps companies understand the technical challenges of modern data management and analytics.

The big picture will inevitably lead to skill gaps that need to be filled. For a large organization, several skills gaps can be filled by several specialists. There might be database administrators responsible for implementing the data architecture and performing the initial processing. There might be data analysts who mine the data, find patterns, and report the analysis back to decision makers. And there could be data scientists tasked with building complex models based on underlying patterns and strategic business direction.

For small businesses, however, building an army of specialists is probably not practical. The most likely approach is to focus on the role that meets the ultimate goal: the data analyst. This analyst may also need to work on the infrastructure elements, but then they will have the skills to turn data into actionable results.

CompTIA’s Data+ certification validates these skills that are rapidly becoming critical in the digital economy. While the focus is on the second stage, manipulating data and providing analytics, there is also an infrastructure element. This verifies that a candidate understands common data structures and formats, and that the knowledge can be used to build a basic architecture, especially in a cloud environment where many tools are provided as a service.

Additionally, the CompTIA Data+ certification is useful for candidates with a technical or business background. Depending on the organization, a data analyst may have more affinity with the IT function or a business unit, so being aware of both sides is a must. The Data+ certification proves job candidates have this balance while ensuring they can implement data governance for a robust process.

In the field of data, time is running out. Companies have huge amounts of data. Now the question is what to do with it? The ability to analyze data quickly and efficiently can improve time to market, improve customer satisfaction and drive growth for the future. Taking a holistic view of enterprise data and creating data scientists will accelerate this process. As organizations prioritize accelerating data analytics, they should also prioritize accelerating discovery and skill development.