A lot has happened in the last 5-6 years in the field of data science. A large number of data analysts are finding newer grounds to launch their career aspirations in business intelligence domains where organizations are hiring suitable talent to meet their future goals. In a competitive market, organizations can no longer be left to the mercy of half baked data analytics and half hearted strategies. Today, these organizations seek superlative projections from a group of specialized professionals called data analysts who come up with newer ways to treat data like an asset — an irreplaceable resource that can help organizations differentiate their overall positioning from the rest of the competition. In the last 2 to 3 years, companies have raised the bar so high in data analytics that it is no longer an easy industry to break into, particularly for professionals who lack the intelligence and skill to differentiate between data intelligence and data literacy.
Wait? Did you think data literacy and data analytics are one and the same? If you thought they are the same, you are not alone. 99% of the professionals think alike before they acquire data analytics certifications and are able to fully understand the differences and the converging similarities between data literacy and data analytics.
In this article on the role of data analytics certifications, we have highlighted the importance of data literacy and what business outcomes should analysts expect to garner once they become fully compliant with modern norms of BI applications.
What is data literacy?
Newer dimensions in data science applications have shown that a lack of data literacy can cause serious ambiguity among analysts, particularly when it comes to setting the stage for up and coming technologies such as Cloud computing, Blockchain, Cognitive intelligence, NFTs, and Fintech automation. Bow, if you haven’t heard of these tools, you need a course in data analytics for data literacy subject specifically. Now, do you get where we are heading to with our discussion on data analytics certifications for data literacy programs?
Data literacy is the generalized term used to define the family of knowledge management techniques, approaches, and tools used to understand the properties of different types of data from a business perspective. Data analytics is a subdomain within the data literacy program where analysts are trained to make an appropriate action plan to use data in a meaningful manner. In the larger context of how data literacy programs are designed, you are required to understand the various nuances of data management such as extraction, storage, analysis, visualization, and storytelling.
How to set the correct narrative on data literacy and data analytics in your company?
Data literacy initiatives are supervised and controlled entirely by the team of business decision makers who have the strategic vision and professional mileage to take the necessary actions in this regard. Seldom would you find an executive from lower than Director level in the hierarchy calling the shots on data literacy programs?
Here’s a quick overview of what the data literacy strategies roadmap looks like:
Step 1: Identify the data management goals and governance frameworks for the current infrastructure
Step 2: Hire or train executives involved in the business processes that are directly using data and analytics for their operations, for instance, business intelligence groups and analysts
Step 3: Establish a chain of command consisting of managers and supervisors who can build a concrete foundation for data science applications
Step 4: Bring together different business leaders on the same platform and under the same roof. These could be the Chief Information Officer (CIO), Chief Operating Officer (COO), CMO, and the Chief Data and Privacy Officer.
Step 5: Look out for options to augment data analytics and data literacy initiatives through the smart implementation of machine learning and AI solutions integrated for better digital transformation results.
Step 6: Finally, monitor and analyze the performance of each model, participant, and platform for superior outcomes in the short and long term future.
What is the future of data literacy programs in the company?
Data literacy is expected to move from a task focused activity to a culture driven phenomenon where certifications and recommendations would become a norm. People with a high data literacy ranking would be looked upon in the organization to lead activities related to business intelligence goals and ensure everything stays within the ambit of existing IT governance, trustworthiness, and operational security.
Should data literacy be picked over data analytics?
Most analysts confuse data literacy with analytics and vice versa. On the surface, these may sound like one and the same, but a deeper study would reveal that data analytics is a universe where data analysis or a family of data analysis tools play a part as subdomains. Without gaining an upper hand on data terms and concepts, it is impossible to make a foundation in the analytics field. In fact, a majority of the data scientists insist on including data literacy skills as part of hiring and selection processes at the entry level to any data science or analyst job. For companies that have managed to establish this benchmark in their hiring rounds, the success rate of projects has gone up by 60% and the team attrition has bottomed at less than 2% per quarter. This saved companies at least 1-2 million dollars every year. Therefore, it is in the best interest of the organization as well as the data analyst to top data literacy concepts.
What companies are doing to enhance data literacy rates?
Within companies, managers and strategists are emphasizing on the need to train the existing workforce on data and how their interaction with data can help to improve the overall productivity levels and efficacies for the entire organization. So, organizations are partnering with top data analytics course providers who train employees on data literacy and enable these professionals to derive actionable insights from whatever data they have in their systems. For example, the Marketing analysts would be utilizing the bulk of website and landing page information to craft personalized brand campaigns. Advertisers could be utilizing the data from web ads and display ads to identify the scope of their branding practices beyond the usual engagements. Similarly, finance teams would utilize the financial data and revenue stream to identify the bottlenecks causing the maximum loss of revenue and plan newer ways to plug the gaps so that future goals could be met. In the medical and healthcare industry, patient data is used to identify how certain population is impacted by a disease and what remedial practices and drugs could be used to limit the infection rates and control mortality in serious conditions. The application of data literacy is endless, but it should begin at the organizational level.
Another way to enhance data literacy rates is to provide Scholarships for students and rewards to aspiring students who want to take up data analytics certifications. These could be propped up as a part of rewards and recognitions, a standard practice in a majority of tier 1 companies around the country.
Thirdly, providing internship opportunities to students currently pursuing UG courses in BE/ B Tech and Computer Applications has also delivered good outcomes in taking data literacy to newer heights.