Home TECHNOLOGY Data Literacy: What Is It For

Data Literacy: What Is It For

Changing an organization into an information driven organization requires an information driven culture. Information proficiency is an essential piece of this change, where information education is a main concern for corporate workers. Information education is a fundamental piece of the information driven culture.

However, to change an organization into an information driven organization, it is important to zero in on the viewpoints that create esteem. Information itself has no worth, yet it is made by the way that the information is gathered, ready, dissected, or used to help compelling business use cases with extensive business-grade information methodologies.

What Is Data Literacy?

Data literacy is the ability to read, understand, create, and communicate information, therefore giving meaning to data, interpreting them correctly, telling a phenomenon through data, properly selecting the most relevant information. Data literacy is data literacy and focuses on skills related to working with data. Just as literacy refers to the skills needed to read and interpret a text, data literacy requires creating a fertile environment for extracting values ​​from data.

Why Data Literacy Is Essential For Businesses

Information proficiency is fundamental in an organization because, in the period of information, the board in the entirety of its structures, can assist with basing the security of its creation chain on AI, guide promotion through robotized crusades, give clients help using artificial consciousness, etc. all that an organization needs to develop efficiency and intensity further.

As information investigation and ample information become integral to an information-driven organization, information education is an unquestionable requirement in the endeavor.

It is related to information science and empowers representatives, all things considered, to pose the proper inquiries about machines and information to make added esteem, settle on the best choices and convey importance to other people. Information proficiency changes the corporate business and works on the unwavering ness of the labor force, which, from strengthening, attracts energy to upgrade proficient turn of events and help the information-driven organization be serious in an inexorably forceful market.

What Are The Benefits Of Data Literacy?

Information education offers an organization the benefit of reacting speedily in aggressive settings that buyers are progressively requesting because of information-driven emotional cycles. An information-driven organization is more aggressive in the worldwide economy because of the abilities of its representatives. Information proficiency should turn into a cross-over ability, open to different organization figures at numerous levels, and at this point not simply be saved for subject matter experts.

Because of information education, information from the executives is presently not simply an IT issue but a chance for top and center administration to be refreshed, continuously and straightforwardly from a portable application, on the creation of deals. Information education – while never supplanting information science – further develop the dynamic course of top and center administration, which can get to pivotal data all the more rapidly and quickly, on account of creative, passionate, and intuitive designs, without sitting tight for intermittent and static revealing.

What Are The Main Characteristics Of Data Literacy?

Business Intelligence

Business intelligence (BI) enables companies to make more effective decisions by showing current and historical data within the business context. BI is a set of processes and tools that allow companies to group data from different sources, analyze it and extract strategic decisions.

To support data literacy, business intelligence – whose task is to present analytical results in the form of dashboards, graphs, reports, graphs, etc. – has been enriched with new features in the direction of a better user experience for non-analyst users of the data, but with the need to access information in a more timely and straightforward way.

Data Visualization

The most advanced data visualization software offers the possibility to access data and insights more quickly and autonomously. Data visualization boasts the opportunity to create standard graphs (for example, pie charts, histograms, linear graphs, etc.) in a user-friendly way. It allows you to build predictive models, modify data, and integrate new data sources. , create complex queries and analyze unstructured data.

All without having to possess programming skills, but in a code-free logic, where it is sufficient to access pre-set menus and thanks to the support of visualization. The available dashboards are increasingly dynamic and interactive: access the mobile apps to get real-time updates on the production or sales situation.

Self-Service Data Analytics

A data-driven company rethinks how it interacts with data, embracing greater autonomy for business users. This trend has been called self-service data analytics. This is the diffusion of tools that allow business users to independently manage the data query process, ranging from exploration to analysis to the visualization of insights. Top and middle management are increasingly asking to access information more quickly, as well as static, standardized, and periodic reporting.

Data Quality

The quality of the data serves to clean up and manage the data, making them available in the company simultaneously. The high-quality data allows systems to integrate all related data to provide a comprehensive view, including interrelationships. The reliability of the decision-making process depends on the quality of the data. Integrating data quality management into company functions also entails a return on investment in the data sector, not only from a monetary point of view but also from the point of view of the time of company resources.

The Ten Best Practices For Data Literacy

Adopt the ten best practices to become a data-driven company.

To drive data-driven enterprise transformation, a company must adopt best practices. A holistic data strategy is the most effective strategy. Here are the ten best practices:

  • Give priority to the data vision, involving all top management and dedicating priority investments;
  • Transparent approach: focusing on business use cases, making the positive impacts of implemented initiatives, but also negative ones to be avoided;
  • Making data scientists happy: these talents are scarce, but they must be motivated and satisfied with their commitment. Data scientists are not so much interested in company benefits or career advancement as in doing challenging work. It is crucial to create a work environment capable of attracting talents, perhaps by creating a center of excellence, where skills inspire each other and learn from each other;
  • Data governance is a jungle, and it is forbidden to get lost: the best way is to align the governance of all activities by prioritizing business use cases (point 2) to ensure that the necessary data is available in the right quality;
  • The quality of the data is not everything: it must be at the right level without making it a totem. On the other hand, it is necessary to immediately understand in which fields the quality of the data is crucial, accepting gaps in other sectors. In this regard, it is essential to define the KPI of data quality (Key Quality Indicators, KQI): the transparency of data quality, the measurement of the KQI, and the report of this indicator becomes a driver for improving the quality of data in the long term;
  • Dare a little with privacy, but be careful with security: Gartner does not recommend circumventing the GDPR (it costs very high fines and violates the law), but convincing customers to opt-in data processing, even through win formulas -win (opt-in in exchange for loyalty programs);
  • The transformation architecture requires intelligent methods: a data-driven company is based on a modern IT architecture (modular, flexible, scalable, fast, with added value). How to do it? Separating the data into a dedicated data layer enables the transformation; Hybrid data layer settings typically configure real-time data warehouses and new data lakes, making it easier to manage complexity and leverage the synergies of pre-built standard solutions. Critical data services are exposed, and key data elements gain a greater audience;
  • You have to climb before reaching a point of no return: speed and tangible results are essential to developing a better data culture. A scalable and sustainable architecture is more accessible to implement a little at a time than a complete development, without generating rejection phenomena;
  • Conquer the minds, hearts, and hands of employees: human skills are essential to succeed. Reasons, to promote the business value and other rational benefits; seats, to generate passion and enthusiasm; provide the training necessary to be successful (training, to empower all employees);
  • Learn from others: many companies have implemented data strategies in recent years. There is nothing better to do than learn the lessons taught by the pioneers and the best, without haste and humility.
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