This post will explain why data literacy is crucial and why some businesses aren’t making the most of their data.
The capacity to read, write, analyze, and interact with data is referred to as data literacy. It is not essential to be Shakespeare to be literate, nor is it necessary to be a data scientist to be data literate. Data literacy does not imply knowledge of all computer languages or advanced data science techniques. Instead, it is about comprehending and making evidence-based decisions.
Data literacy solution is a critical step on a company’s path to data fluency. It teaches employees how to make data-driven decisions, engages critically with data, implement effective data governance, and make ethical data judgments.
Making Data-Driven Decisions Requires Data Literacy
Employees are more likely to accept a change if they understand it completely. Employees that understand data science and its commercial applications may be able to lead the creation of use cases from start to finish, inspiring others to make data-driven decisions.
Employees must be data literate to effectively and critically interact with data insights.
Data-literate employees may create and assess data visualizations that are employed in the company’s decision-making process. Furthermore, data literacy is required to check the veracity of the data used to build visualizations.
Employees who investigate the reliability, quality, and consistency of data sources may uncover expensive errors, increasing the company’s confidence and efficacy in making data-driven choices.
Effective Data Governance Is Built On Data Literacy
Data governance refers to the set of rules, policies, and organizational structures that outline how data is managed in an organization. It guarantees that data is easily accessible, useful to the company, of high quality, and in compliance with current requirements. Before developing data policies, executives must first understand the company’s data context and needs.
A three-part data organizational architecture underpins excellent data governance.
1. The data management office (DMO) is in charge of creating policies and standards.
2. Domain leaders are responsible for formulating and implementing domain-specific plans.
3. The data council, which brings together domain leaders and DMOs.
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Leaders in each of these components should presumably be familiar enough with data processes to provide clear and equitable data governance roadmaps. For example, data-literate domain executives who see the value of data may work successfully with the DMO to create and manage a domain-specific corporate data lake.
With Data Literacy, Businesses Can Make Ethical AI Judgments
AI system dangers for organizations that use AI systems for real-world applications include AI accidents, data privacy breaches, and AI prejudice. Engaging technical and non-technical stakeholders to critically analyze the data science system’s techniques and conclusions is one strategy to mitigate such a risk. Data-literate business stakeholders are critical in detecting the hazards of AI systems and ensuring that existing AI systems are fair and ethical.
Increasing Data Literacy In Organizations
There are several advantages to instilling excellent data literacy in all staff. Existing skills projects, e-learning courses, and specialized classroom training are all part of data literacy programs. Creating in-house data literacy programs is a time-consuming and labor-intensive process that requires extensive planning.