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Much has been made of the importance of data in the digital economy. While digital data has been present for decades, it has taken on a greater priority in an age of artificial intelligence, predictive analytics, and customer personalization. Data has been called the “new currency” and the “new oil” of business – an asset so critical that it undergirds the entire operational structure. Yet for all the value being ascribed to data, companies are still in the early stages of building their data discipline and skills.

Why is business data so important?

Simply put, poor data management and insufficient data analysis are impacting the bottom line. Organizations have been collecting data for a long time, but there has typically not been a comprehensive operational approach or a focus on the necessary skills to address this data effectively. Companies are starting to feel the effects of poor data management or insufficient data analysis, with wasted time being a top concern – hunting for data consumes time that could be used to focus on core functions and new innovations. Increased efficiency, another common business goal, comes as a result of well-designed systems and workflow – both of which can be optimised with effective data analysis. Data management and analytics should therefore be treated as a comprehensive program, rather than a collection of point tools for specific purposes.

Focus on data skills

As more companies use data to improve their internal operations and to better understand their customers, the development of new and improved data skills will bring them big success. These skills address a wide range of common business problems, from building a resilient data architecture, to improving the speed of data analysis, to mining data for new insights. In essence, the new currency of business requires new data specialists to extract value for all stakeholders.

Handle data properly

A solid understanding of the functions of data provides the context for building an effective corporate strategy, while an appreciation for primary data job roles makes it easier to develop important data-related skills. In this regard, the recent acceleration in the amount of data and the types of data that a company can manage has brought existing specialized skills to focus as part of a broader data management strategy. For example, many firms begin their data function strategy as an offshoot of software development. The skills and critical thinking needed in development translate well to data, where there is an abstract component of dealing with bits and bytes. Many data specialists also use the same tools as software developers, including programming languages such as Python or Javascript. However, while the recent rise in data types and volumes has brought focus to these and other specialized skills, there is still a foundation that must be built before moving to more advanced applications. Just as the data toolset is growing, so is demand for data skills throughout the organization, and the data function may therefore have outposts within business units.

Forming data teams – Job roles and required skills

Whether all data skills are centrally located within an IT department or spread across multiple business units, companies are taking steps toward establishing data teams. The concept of data teams is relatively new, and given that the CISO position was created in the mid-1990s and the CDO position was created in the early 2000s, it makes sense that data teams would be lagging behind security teams. In fact, only 44% of companies say that they have internal employees who are dedicated to data management or data analysis, and even among these companies, there is still high demand to develop more data skills that drives new business value.

For the data function, these four distinct roles can help fill out an organization’s data team:

  1. Database administrator
  2. Data analyst
  3. Data scientist
  4. Data architect

When considering the areas of the data function where businesses are seeking improvement, there is a clear priority out of the above roles: Companies want to improve their analytics capabilities. Data visualization is an especially interesting task, as it combines technical knowledge with business savvy and communication skills. However, the other roles play a key part in a comprehensive data strategy that ultimately provides high-value data analysis – meaning none of these roles should be neglected.

Is there a data skills shortage?

Given the relative novelty of the data function, one would expect that companies have a particularly strong demand for entry-level positions, but this is not the case. In fact, companies with hiring plans in 2021 are looking for more mid-level data specialists than in any other field. Part of the reason for this is that teams in the emerging fields of data and cybersecurity are often created from existing software and infrastructure teams. However, the high demand for specialized skills indicates a pipeline problem that has no easy solution.

Without well-defined entry-level roles, companies have less ability to rely on historical means of skill development, where they are able to obtain a qualified candidate from a traditional pipeline like a four-year degree program and then give that candidate the job experience and training opportunities that build more advanced skills. The explosion in demand for the advanced skills exacerbates the problem.

In the data field, time is of the essence.

Companies have massive amounts of data—now the question is what to do with it?

The ability to rapidly and effectively analyse data can improve time to market, enhance customer satisfaction and drive growth for the future. As organizations place a priority on speeding up data analysis, they should also place a priority on speeding up skill discovery and development.

CompTIA is responsible for all content and analysis. Any questions regarding the report should be directed to CompTIA Research and Market Intelligence staff at research@comptia.org.