You’ve probably heard that insights from data analytics allow companies to make more strategic decisions, but what exactly is data analytics and how does analyzing data help create opportunities for growth?
Data analytics can inform organizations about current and projected workforce trends, and help them make more strategic decisions such as improving inclusivity, diversity, and employee engagement. In this instance, data analytics is not only a tool for smarter and more opportune decision-making, but also a catalyst for increasing diversity and inclusion in the workplace.
A thorough analysis of data can illuminate workforce trends by region, company, type of position, gender, and diversity, among others. Reports based on internal company data describe what already happened, while insights from externally-available public data are often current or forward-looking: allowing companies and recruiters to make smarter and more opportune decisions that positively impact the future of their organizations.
While the first steps to building a data analysis program may seem daunting, innovative software companies have invented a process that is able to self-learn. By utilizing a system capable of continuously improving its ability to both aggregate and relate data as well as perform analysis, companies can make strategic decisions with remarkable speed and precision. These agile and more informed decisions, for example, can help companies create a more diverse and inclusive environment.
There are three main types of data analytics: descriptive, predictive, and prescriptive. Descriptive analytics are the base level of data processing: summarizing historical company data and providing a comprehensive picture of what is happening within a company.
Not only do descriptive analytics present a review of trends within a business (i.e. number of employees, employee retention, etc.), but they can also be helpful in understanding how those workforce trends might influence future outcomes.
However, while descriptive analytics collects and presents historical data, it does not extensively explore the roots, causes, and effects of the data as in-depth as predictive and prescriptive analytics. For instance, as its name suggests, predictive analytics utilizes data from descriptive analytic summaries to make predictions about the direction of a company, and prescriptive analytics “prescribes” solutions to the problems that arise from the data collected.
According to InformationWeek’s article, “Big Data Analytics: Descriptive Vs. Predictive Vs. Prescriptive”, it is “estimated that more than 80% of business analytics– most notably social analytics– are descriptive” (Bertolucci, 2013). Examples of descriptive data include social media posts (ex. LinkedIn status updates) and other publicly available data.
Among the three types of analytics, descriptive analytics are the most basic; they aim to summarize, and they lack actionable solutions to the insights they present. This is where the power of predictive analytics takes effect, breaking down the collected data to its most essential and telling parts.
Predictive analytics helps companies see their future through data, and prescriptive analytics formulate a plan of action based on the information collected and understood in the descriptive and predictive phases.
Data analytics has existed for a while, but the growing ubiquity of individuals with an online social presence has brought its importance to the forefront of strategic decision-making. Understanding the essence of data analytics is the first step toward utilizing its power for decision-making and recruitment.
References:
https://www.mastersindatascience.org/resources/what-is-data-analytics/
https://www.lotame.com/what-is-data-analytics/
https://www.dataversity.net/fundamentals-descriptive-analytics/
https://www.informationweek.com/big-data/big-data-analytics/big-data-analytics-descriptive-vs-predictive-vs-prescriptive/d/d-id/1113279
