Data Literacy: the Key Role of Data Visualization
Data Literacy means the ability to identify, locate, organize, use and communicate information. In other words, this term means data literacy, the ability to interpret them correctly and to tell a phenomenon through them, correctly selecting the most relevant information.
This competence should not be exclusive to specialists, but should involve the various company figures at various levels. The spread of Data Literacy certainly passes through the inclusion of professionals related to data, such as Data Scientists or Data Analysts, but it goes much further. First of all, one can think of organizing courses and training activities on the subject also for figures belonging to the business lines.
Another aspect, probably the most important, deeply involves the mechanisms for transmitting information in the company. It is a question of rethinking the ways of interacting with data, in the direction of greater autonomy of business users: this trend is known to international analysts with the term Self-Service Data Analytics.
Self-service Data Analytics: autonomy through visualization
Self-service Data Analytics means the dissemination of tools that allow the business user to independently manage the data query process, from exploration to analysis, up to the visualization of insights.
The starting point is the request by decision-making roles such as Top and Middle Management to access information in a more timely manner and to go beyond static, standardized and periodic reporting. This is due to competitive contexts and increasingly demanding consumers and, in general, to the need to make decision-making processes data-driven. In response to this demand, the offer of Business Intelligence and Data Visualization solutions has been enriched with new features, in the direction of a better user experience for non-data analysts, but with the need for speed and simplicity purely business. Today, therefore, this technological evolution can support and at the same time be a consequence of the growing diffusion of Data Literacy in the company.
From Data Literacy to Data Visualization
The most advanced Data Visualization software on the market allow not only to build standard graphs in an extremely user friendly manner (e.g. pie charts, histograms, line graphs, etc.), but also allow you to build predictive models, make changes to data, integrate new data sources, build complex queries and analyze unstructured data in totally code-free logic, only thanks to the support of pre-configured “Menus” and visualization.
These aspects are then linked to the increasing dynamism and interactivity of the dashboards that Top and Middle management have available. This means being able to offer, for example, a real-time update of the production situation or sales of a store directly on mobile applications.
Data Literacy and Visualization: Where are the companies located?
While in small and medium-sized enterprises, data management continues to be seen, with traditional logic, as “an IT problem”, large companies register from the various business functions the need to access data and insights in a faster and more autonomous way . This leads to investing in Data Visualization & Reporting software which, as mentioned in the previous paragraph, are expanding their potential with additional features, greater interactivity and a set of innovative graph types.
However, it should be emphasized, in conclusion, that greater autonomy of business users in carrying out analyzes is not to be considered as a replacement for some specific roles dedicated to Data Science. Companies that declare, in 2018, to guarantee autonomy in accessing data to business figures also show a greater organizational maturity of the functions related to Analytics: 57% have already equipped themselves with a Data Scientist (against 46% of the average of large companies) and 30% see the presence of a Data Science Manager (against 22% of the average).
The theme is therefore how to combine a process of adoption of Data Science skills with an increasingly Self-Service approach for business users, succeeding in this double objective means being able to make the most of specific skills, employing specialized figures in activities with greater added value, and instead make more traditional reporting activities more efficient.