Essay Example on Data Literacy: Propelling Workforce to Success

Introduction

Data literacy is the ability of individuals to comprehend, make use and effectively disseminate data. It gets demonstrated by the employees' ability to read, write and communicate data in context. The employees should possess the requisite skills to analyze and draw meaning when presented with data. It makes renders the ability of employees to decode data critical for organizations.

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Data literacy is a propelling force towards success and momentum. It is of importance that all employees in an organization understand the need to improve their data literacy. They should be in a position to utilize data in their routine activities as well as applying it to their decision making. When used right, data literacy will ultimately assist every employee in achieving their objectives (Doug, 2018). Besides, employees will perform their duties better. Thus contributing to an improvement in the overall performance of a company. Notably, when data is made accessible to all the employees, the operations of an organization become efficient and streamlined

Data literacy divide is the gap created by the employees' inability to understand and use data efficiently. As a result, data literacy divide inhibits organizations from realizing higher rewards accruing from investing in data.

Data Knowledge

Every industry, discipline and company designs a unique set of data sets and data terms. The ability of an organization's employees to understand from a business perspective the company's data places them in a favourable position to make use of the data. For instance, an online marketer must be familiar with standard marketing metrics such as sessions, page views, bounce rate and unique visitors (HITEQ, 2020). Besides, for an online marketer knowing data, they must learn how numbers operate.

Data Assimilation

When presented with data never encountered before for interpretation, one needs to be skilled at handling raw data before using it. Data assimilation entails internalizing what the new data present (Doug, 2018). There is no data analysis or concluding the date at this stage. One should be familiar with inspection of charts or tables for individual elements and looking for clarification if some items are missing or are ambiguous

The title and labels of graphs should be correct, descriptive and clear. The time frame should get inspected for the range it represents. The employee must also understand the source of the data before working on it. The units of measurement should get clearly understood in terms of the metrics represented by the tables (Doug, 2018). The employee should inspect if the scales in the graph axes are useful and clear. The need for understanding how rates, ratios and formulas get calculated cannot get overlooked. Besides, inspecting if the dimensions that segment the data are meaningful and transparent is an integral aspect of data assimilation. The filters should get checked for clarity and if they have been applied correctly to a data set. Understanding the criteria used in sorting the values and the ranking of the benefits is necessary. Finally, the targets should get checked if they have been added to the tables and is they are clear on what they represent.

Data Interpretation

Data interpretation is the next step after the assimilation. It entails the employees interpreting and analyzing the data. The approach to be adopted depends on the presentation format and the type of data. An employee should be knowledgeable on nine observations to make on the charts. The first one is the trends. It is the direction that a metric adopts (down, up or flat). Next are patterns, which refer to the repetitions and cycles present in the data. Thirdly are the gaps or the omissions current in the data set (Badke, 2015). Next is skewness; it represents the distribution of values on the chart. The outliers are another element to look out. These are the data points that detached from the other data points. The seventh element to look out for is the focus. These are the emphasized aspects of the tables or charts. It calls for critical analyses to understand the reason behind the highlighted parts of a data set (Badke, 2015). Noise is an element is the unnecessary data that is not related to the message of the table. Lastly, the data should be logical. The data should assist in answering a specific business question. It should be in support of a proposed argument.

Data Skepticism and Curiosity

Besides data analysis and interpretation, an employee must critically think about it. Data gets accepted on the face value on numerous occasions (Brent, 2017). It is essential to slow down and consider the less obvious factors that affect the outcomes and their interpretation. For instance, the method used when collecting data, or the manner the data got obtained may influence the results. The source of the data should be credible. The bias of the producer of the data or the consumer of the data should get evaluated. Also, the data should be analyzed for manipulation and implied assumptions that could potentially influence the way numbers are interpreted (Brent, 2007).

Furthermore, the context should get explained to include missing background information. If supplementary data got covered during the comparison process, the comparison offered should be relevant and fair. Besides, causation should not be confused with correlation. The data should be practically and statistically significant, and the quality should be distinguishable.

References

Badke, William. (2015). "Why Information Literacy Is Invisible." Information and Data Literacy, pp. 137-153. DOI: 10.1201/b19843-15.

Bordonaro, Doug, and Doug Bordonaro. (2018). Data Literacy: What It Means and Why It's Essential for Your Business. Retrieved from www.infoworld.com/article/3259744/data-literacy-what-it-means-and-why-its-essential-for-your-business.html

Dykes, Brent. (2017). Why Companies Must Close the Data Literacy Divide. Retrieved from www.forbes.com/sites/brentdykes/2017/03/09/why-companies-must-close-the-data-literacy-divide/.

HITEQ Center. (2020). "Building Data Teams and Skills: Maximizing Data Literacy and Data Governance for a Data-Driven Culture." HITEQ Center, HITEQ Center. Retrieved from hiteqcenter.org/Resources/HITEQ-Resources/building-data-teams-and-skills-maximizing-data-literacy-and-data-governance-for-a-data-driven-culture.