Data literacy skills can be grown internally, but it takes IT commitment from the top-down. See how to grow data literacy among your peers. Image: Friends Stock/Adobe Stock Must-read big Continue Reading
Data literacy skills can be grown internally, but it takes IT commitment from the top-down. See how to grow data literacy among your peers.
A business intelligence (BI) analyst does all of the things that a regular business analyst does, except the BI analyst has direct experience with BI tools and analytics.
A data scientist works on data collection, storage and integration like other IT data management professionals, except the data scientist has familiarity with machine language, process automation tools like RPA, statistics and algorithm development, big data analytics tools for visualization, and processing tools and platforms.
Organizations are short on both BI and data science skills, so is there a way resident data scientists and BI pros can coach their peers on data literacy?
What is data literacy?
Gartner defines data literacy as “the ability to read, write and communicate data in context, with an understanding of the data sources and constructs, analytical methods and techniques applied and the ability to describe the use case application and resulting business value or outcome.”
Business analysts are already adept at developing use cases, understanding the context of data, and seeking business value from data. What they haven’t mastered is the BI tool set that can make their efforts even more impactive.
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Data science literacy is a different story. There are IT professionals who are non-data scientists and who understand how to read, write and blend data into business contexts, but they don’t know how to work with unstructured, big data to accomplish these goals, nor do they understand big data platforms like Hadoop or programming languages like MapReduce.
They also lack knowledge of the iterative development methodology that characterizes big data analytics algorithm refinement and of the parallel processing that is required for vast volumes of unstructured data.
Building data literacy skills
The key to building data literacy skills in BI and data science is acquainting regular business analysts and data workers in IT with the tools, platforms and methodologies that are used in BI and data science.
To effect knowledge building, an internal training process must involve strong candidates in IT who are eager and able to learn new skills, patient mentors who are willing to teach them, and constant management supervision.
There are organizations that are succeeding in doing this, but they also abide by the following caveats.
Understand that teaching is not telling
Just telling a trainee about how something works and then leaving them with a stack of online manuals and resources is not enough. Knowledge transfer for data literacy occurs when trainees are able to apply what they have learned to real work.
For example, if a business analyst has been trained in how to incorporate BI tools into their analytics work, they should be using these new tools. In this way, both knowledge and self confidence are built.
Choose the right mentors
Not everyone is a good mentor. Some individuals don’t have the patience for mentoring a junior staff member, and others may not want to share their knowledge.
A mentor must be willing to teach an understudy, be patient when mistakes are made, assign real work for the understudy to do, supervise the work and do all of this while the mentor also carries on with their own workload.
Identify the right students
Not everyone who wants to learn BI or data science has the aptitude or the attitude for it. IT leaders should choose their trainees carefully. The ability to pick things up quickly is important, but attitude, the ability to work hard and a commitment to master the subject are absolutely essential.
Shepherd the process
If IT plans to develop BI and data literacy skills internally, it must be fully committed to the process. This means that IT leadership must be as actively engaged in skills and literacy training as it is in projects that it’s doing. It’s not good enough to assign teams of mentors and trainees and then to walk away.
Goals for knowledge and data literacy mastery should be set, and the individuals being trained should be infused into projects that allow them to exercise new skills. This can only be done if IT leaders stay on top of the training process, ensuring those being trained can actually do the work for which they have been trained.