It may seem counterintuitive to think of data engineers needing to improve Data Literacy, the ability to read, work with, analyze, and argue with data. After all, data engineers apply technical Data Literacy skills to build and optimize operating systems and pipeline channels. At the same time, data engineers show gaps in using organizational Data Literacy and communicating internally across their companies, and this is where they require Data Literacy training.
Managers see that data engineers apply their Data Literacy skills well to solve problems like prompt data delivery among several services. However, the same data engineers work inefficiently by moving data across more services than necessary, suggesting poor organizational Data Literacy.
Consequently, data engineers produce mixed results, saving the business money in some areas while costing companies 11.4% in resources – and wasted time starts to add up quickly when you consider that the average data engineer gets a salary of $117,000.
Businesses tend to frame this problem as needing better project management. While better project management helps to leverage data engineers, organizations also need to understand this unevenness in Data Literacy exhibited by data engineers and its impacts.
Organizations must empower data engineers to share their Data Literacy strengths with non-technical businesspeople while advancing the data engineers’ Data Literacy through this collaboration. By addressing this organizational Data Literacy imbalance, data engineers will have an expanded toolset applying organizational Data Literacy.
Leverage Data Engineers’ Outstanding Technical Skills
Get a team of sound data engineers, and you will find they understand many Data Literacy basics – statistical concepts about data throughput, how to read a dashboard to identify and solve a data blockage, why a system has outputted some data, and so on. Those Data Literacy skills used to manipulate, comprehend, and argue with data are invaluable beyond data engineering.
When other businesspeople learn these kinds of Data Literacy skills, they can make better decisions based on repeatable and systematic findings. Ninety percent of business leaders agree that Data Literacy dramatically impacts a company’s success.
Data engineers help increase overall Data Literacy in the organization by showing businesspeople how to best interpret, explore, and process data through a user interface. In these activities, businesspeople can gain and apply basic Data Literacy skills.
Data engineers step in where situations call for more Data Literacy than required in a typical businessperson’s role. They can leverage automation and build and maintain a good Data Architecture behind the scenes. For example, data engineers from team A make software services, recording when changes to big datasets occur. Other data engineers from team B use these algorithms in their constructions to act on those significant dataset changes.
The technical Data Literacy skills applied by both teams work underneath the hood of the user interface. But the businesspeople can skip knowing these inner workings to accomplish their tasks.
A Gap in Organizational Data Literacy
While advanced in technical Data Literacy, data engineers require support to improve their organizational Data Literacy. They get “overwhelmed with developing and maintaining enterprise data systems” and end up overworked.
Many data engineers have left or will consider leaving their jobs because of their organizational Data Literacy gaps. This reality bodes poorly for companies.
Finding sound data engineers with needed programming and statistical skill sets alone poses significant challenges, even with a recessionary economy. Add to these demands to use and communicate key organizational data capabilities across different divisions and businesses and ask the almost impossible.
Some companies try to fill in organizational Data Literacy gaps through their Data Governance programming, sending representatives back to educate data engineers about dataset accessibility.
In addition, some organizations employ a project or team leader on a data engineering team, who gives feedback, reinforces priorities, or redirects the data engineers’ work. Think of Data Governance representatives and project leaders as having to train data engineers and communicate about organizational Data Literacy.
In the meantime, data engineers need more organizational support and professional development to communicate what they learn and need in a shared business language. Also, data engineers need opportunities to demonstrate to other teams that they can read, use, visualize, and interpret data to make data-driven decisions.
Why the Organizational Data Literacy Gap Exists
Companies equate Data Literacy with technical skills like automation and data storage. However, Data Literacy covers more than just graphs and numbers. A survey of about 190 U.S.-based executives revealed that firms want to take advantage of data assets, but cultural and operational challenges beyond technology stand in the way.
This organizational Data Literacy chasm has serious implications. Data engineers may mistakenly create a solution that another data engineering team has solved. Also, workers may fix data pipeline issues for one business unit and break functionality for another division that relies on older code.
For example, when team A designs a solution to track changes to big datasets among systems, they create a MySQL storage solution to track data activities. Another set of data engineers, team B, already has an excellent data storage system using Java, designed for large data volumes and quick access. Now a firm has two different data storage technologies that overlap in functionality.
Filling the organizational Data Literacy gap means data engineers have awareness and access to existing solutions and consider them before building a technical data solution or applying new technologies. Additionally, data engineers would consider potential fixes and impacts on other divisions before fixing any issues.
Embrace an Organizational Data Literacy Perspective
Addressing the organizational Data Literacy gap among data engineers requires a cultural shift where companies embrace an organizational Data Literacy perspective. To do so means necessary changes in how each corporate manager and data engineer approaches Data Literacy.
Executives need to create dedicated time and data resources for data engineers to grow their Data Literacy skills. To do so, managers must create good Data Literacy training services, providing data engineers with communication resources.
Resources available to data engineers need to expand beyond Data Governance initiatives and management. Instead, organizational Data Literacy should function separately from Data Governance.
Involving the corporate communications department with managers and data engineers promises to promote organizational Data Literacy among data engineers. Furthermore, communications staff can tailor content to meet the organizational Data Literacy training level that data engineers need.
Data Engineers Can Learn Organizational Data Literacy from the Business
While the typical non-technical businessperson tends to shy away from data-literate applications that mirror data engineering work, the data scientist turns away from organizational Data Literacy. Instead, data engineers rely on Data Governance representatives or project managers for collaborative participation, contribution, and engagement with data-enabled problem-solving and communications.
This people skill set resonates with the businessperson’s strengths and work activities. Data engineers manipulate data technically but forget the people aspect. So, interactions between data engineers and businesspeople to learn these skills would help. A toastmasters club provides a way to accomplish these connections.
Data engineers best approach their system operations and development with a product mindset, something gained through organizational Data Literacy. Then, data engineers will build and optimize Data Architecture to the most significant business need matching the organization’s strategy.
With this organizational Data Literacy mindset, the data engineers can investigate creating new algorithms or applying the latest technology. Think of organizational Data Literacy as a business Data Literacy skill data engineers use in addition to applying their technical strengths and solutions.
Conclusion
Companies must provide a balanced perspective on what skills data engineers bring and how they need to grow their organizational Data Literacy skills through training. Data engineers shine in the technical nuts and bolts of data.
These workers can share their robust technical data understanding by training others outside their team on the UIs. Likewise, data engineers can apply their Data Literacy skills to support business roles.
At the same time, data engineers need to develop organizational Data Literacy. In combination with corporate communications, managers can provide materials for data engineers to grow their business communication skills.
Moreover, in embracing an organizational Data Literacy perspective, organizations can create dedicated times for data engineers to participate in Data Literacy training. In return, managers should hold data engineers accountable for applying their Data Literacy through a product lens.
Furthermore, expand data engineers’ capabilities in collaborative-data problems solving and tapping enterprise-wide data resources. Reward these workers for demonstrating organizational Data Literacy and solid technical Data Literacy.
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