Data literacy skills can be developed internally, but top-down IT commitment is required. See how to increase data literacy among your colleagues.
A business intelligence (BI) analyst does all the things a regular business analyst does, except BI Analyst with direct experience with BI and analytics tools.
A data scientist works on data collection, storage, and integration just like other IT data management professionals, except data scientists with familiarity with machine language, process automation tools such as RPA, statistics and algorithm development, big data analysis tools for visualization, and processing tools and platforms.
Organizations lack skills in BI and data science, so is there a way that resident data scientists and BI experts can coach their colleagues on data literacy ?
What is data literacy?
Gartner definition knowledge of data is “the ability to read, write, and communicate data in context, with an understanding of data sources and structures, applied analytical methods and techniques, and the ability to describe use cases.” application and the resulting business value or results.”
Business analysts are adept at developing use cases, understanding the context of data, and finding business value from data. What they haven’t mastered is the BI toolkit that can make their efforts more aggressive.
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Data science savvy data science is another story. There are IT professionals who are not data scientists and understand how to read, write, and incorporate data into a business context, but they don’t know how to work with big, unstructured data to achieve their goals. this goal, as well as not understanding big data platforms like Hadoop or programming languages like MapReduce.
They also lack the knowledge of the iterative development methodology that characterizes the fine-tuning of big data analysis algorithms and the parallel processing required for large volumes of unstructured data.
Building data literacy skills
The key to building data literacy skills in BI and data science is to familiarize business analysts and regular data workers in IT with the tools, platforms, and methodologies. used in BI and data science.
To effectively build knowledge, the internal training process must involve strong candidates in the IT field, who are eager to learn and have the ability to learn new skills, who try to Counselors are patiently available to teach them and provide ongoing management supervision.
There are organizations that are successfully doing this, but they also adhere to the following considerations.
Understand that teaching is not talking
Just telling a student how something works and then leaving them with a bunch of documents and manuals online is not enough. Knowledge transfer for data insights happens when students are able to apply what they’ve learned to real work.
For example, if a business analyst has been trained on how to incorporate BI tools into their analytical work, they should use these new tools. In this way, both knowledge and confidence are built.
Choose the right mentor
Not everyone is a good mentor. Some individuals do not have the patience to mentor subordinates and others may not want to share their knowledge.
A mentor must be willing to teach subordinates, be patient when making mistakes, give the actual work to the students under them to do, supervise the work and do all this while the mentor also shoulder their own workload.
Identify the right student
Not everyone who wants to learn BI or data science has the aptitude or attitude to it. IT leaders should choose their interns carefully. The ability to pick things up quickly is important, but attitude, the ability to work hard, and a commitment to mastering the subject are absolutely essential.
Shepherd the process
If IT plans to develop BI and data skills internally, IT must be fully committed to the process. This means that IT leaders must actively participate in skills and knowledge training as in the projects they are working on. It’s not enough just to appoint teams of mentors and interns and then walk away.
Data literacy and proficiency goals should be set, and individuals in training should be channeled into projects that allow them to implement new skills. This can only be done if IT leaders stay at the forefront of the training process, ensuring those trained can actually do the work for which they were trained.
