There is no silver bullet for how organizations can drive data literacy throughout their organizations, but a few general elements include technology, processes, training and programs, and change management. Once an organization starts using data on a daily basis to extract actionable insights – and both the business leaders and line managers start valuing data as a strategic asset – that is when building and maintaining data literacy programs can begin to sustain the data culture. Data literacy assessment, in turn, is essential for measuring the success of these efforts.
A strong data culture makes every employee aware of the power of data, so that they are inspired to adopt data-driven decision-making in their work. A Forrester Analytics survey reports that currently companies only make “48% of decisions based on quantitative information and analytics.”
To measure the impact of data culture in an organization, conducting and evaluating periodic data literacy training programs or workshops can prove effective. The reported success or failure of these programs uncovers any data literacy skills gap.
The training teams can use the results of these workshops to motivate everyone to adopt data culture across the organization. Thus, tracking and measuring individual progress during these training programs is necessary to improve and refine the programs.
The measurement aspect is designed with a single goal in mind: to check whether the organization is becoming more data-literate or not. This is where the need for data literacy assessment comes in.
The Importance of Data Literacy Assessment in an Organization
Gartner defines data literacy “as the ability to read, write, and communicate data in context.” This broad definition assumes an understanding of data sources, data analytics, and data use cases.
In layman’s terms, “data literacy” describes an individual’s ability to identify and understand data sources, analyze data to derive insights, and use these insights to make value-added decisions.
When an ordinary business user learns to read and interpret data in context, they will eventually become an expert at deriving value from data-driven insights without the help of data professionals.
For example, customer-support representatives need to be comfortable reading data and understanding how to use data to make smart, customer-friendly decisions. Companies need more people who are trained and equipped to interpret data, derive insights from data, and ask the right questions about data at the outset.
The goal of any data literacy program is to ensure that some specific training objectives are met after the conclusion of the program.
Data literacy assessment may be a combination of quantitative and qualitative measures, which are based on the results of user surveys conducted by the Data Management team. Data literacy metrics ae not only used to gauge the technical qualities of a training program or workshop; they can also be used to determine the non-technical aspects of a training program or a workshop, such as employee interest in and attitude toward data, and their overall willingness to become data advocates within and outside their peer groups.
To measure the success or failure of any such program or workshop, the following metrics may be collected from the trained workforce:
- How many workers are asking the right questions?
- How many workers are making informed decisions based on data?
- How many workers are able to communicate data-driven findings to others?
The Role of Tools in Data Literacy Assessment
Globally, businesses are accessing and using more data than ever; still, many businesses are not able to maximize the ROI on their data assets, because they fail to grasp the full potential of data for driving value. The ability to interpret data is a skill anyone can build, and now there are a variety of ways to empower individuals and companies, elevate capabilities, and promote change.
Modern, self-guided, data literacy training platforms and tools can help enterprise teams access, review, and interpret the success or failure of data literacy programs. Typically, these data literacy assessment platforms measure a worker’s number skills, ability to recognize sources of data and their purposes, knowledge of data tools and techniques for visualizing data, and capacity for generating insights and making data-driven decisions. However, these online platforms seldom measure data ethics, which is a critical skill for gaining data literacy. The platforms typically include:
- Data Literacy Program Surveys with Reports: These surveys allow individual data literacy program participants to answer questions and access reports on their data literacy skills, with some surveys gathering information on how much data is used in decision-making across an organization.
- Self-Driven Data Literacy Leaning Modules with Tests: As employees continue their efforts toward data literacy, periodic self-assessments may act as a checkpoints for assessing and building custom learning programs with a focus on particular learning areas.
- Custom Data Literacy Training Conducted by HR: Once HR and managers get a broad sense of the strengths and weaknesses related to an individual employee’s data literacy, HR can start building effective training programs tailored to individual needs.
- Data Literacy Pilot Tests: These tests may be conducted to discover data literacy skills gaps throughout the organization. The test results should be communicated to others to refine existing data literacy programs.
- Specialized Data Literacy Assessment Workshops: Data literacy assessment is conducted in less than 90 minutes to determine skill levels, as part of the analytics maturity assessment. In under 90 minutes, all of the basics are taught relating to data collection and analysis, determining metrics to monitor, learning how data can be used to inform strategy, and building the dashboard.
- Augmented Data Analytics Platforms: The modern, artificial intelligence- and machine learning-equipped data analytics platforms are playing a crucial role in helping business users become more data-literate. In this scenario, capable technologies aid curious business users to develop data literacy on their own.
How Organizations Are Measuring Data Literacy Programs
To emphasize the importance of data literacy within an organization, corporate leaders can begin by using data to explain daily business decisions. According to 91% of business leaders, improving data insights into decisions is a challenge facing their organizations. Clearly, providing organizations with a wealth of information and end users with the latest analytics tools do not result in widespread, data-driven decision-making.
To leverage the true power of data, employees need to get familiar with data and data analytics methods by participating in solid data literacy programs or workshops on a periodic basis. The way the business leaders and managers can foster the data culture is by demonstrating in their everyday work life how data is used as “evidence” for decisions, competitive intelligence, and forecasts.
To encourage employees to take leadership roles in boosting the data culture, managers should think about rewarding employees who are going the extra mile in using data to enhance business performance.
The top executives generally contribute toward capacity-building in data literacy training programs, while individual employees across the organization remain engaged in promoting successful programs. Any program relating to data literacy skills development should be continuously tracked and monitored to keep the leaders informed about the progress of data culture.
Conclusion
There is an expectation that everyone, in every role, must possess the core skills necessary for data interpretation, integration, and analysis. Global organizations have realized that, to stay competitive, it is now critical for every single person in their organization to possess the
basic skills needed to interpret, read, and utilize data.
If an organization has data-literate employees, it means that they know how to gather and correlate the correct data, ask the right questions, apply techniques to extract meaning from data, and communicate results. These employees know the culture and understand an organization’s goals in more depth, and they can be incredibly effective at teaching others how to analyze and make decisions from data.
There are a number of methods that organizations can employ to foster the benefits of data-driven decision-making, but the key to increasing adoption among employees is helping them to be self-sufficient. Like any other skill, becoming self-sufficient with data analytics comes through a combination of training, mentorship, education, and experience.
Fuente: www.dataversity.net
