Search queries, users’ locations, the ads that we click, and many other patterns of our behaviors are all types of data that businesses can use to boost their overall performance. Thanks to the constant developments in technology, data volume has seen a huge surge and continues to grow exponentially.
Between 2010 and 2020 alone, the amount of data created, captured, copied, and consumed grew from 1.2 trillion gigabytes to nearly 60 trillion gigabytes. This represents a 5000% growth rate for big data, which is forecast to continue growing at even bigger rates. Now, how do businesses make use of this all? Let us first define data analytics and its types in order to understand the ways in which businesses can implement them as well.
Data Analytics Defined
Data analytics is a science in itself, consisting of the analysis of groups of data with the intent of finding patterns, relationships, trends and drawing conclusions based on that information. As this field continues to develop, most of its processes are automated and conducted through specialized systems, software, and algorithms that process the raw data.
By creating structure, order, and finding meaning out of the vast collection of existing data, commercial industries can use data analytics to their advantage in making more informed decisions. It is no wonder, then, that the global big data and business analytics market is forecast to grow to $215.7 billion in 2021. With this amount of revenue, data analytics is a crucial aspect of business optimization.
Types of Data Analytics
Data analytics is categorized into four types, which can also be considered as levels of data analysis, seeing as they answer questions that target the past, present, and future of changes within a business. Let’s take a look at each step of the process.
Descriptive analytics deals with examining historical data and interpreting it in order to answer to “What happened?” in the series of decisions that were made within a business during a period of time. This type of analytics is characterized by traditional business intelligence (BI). Further, descriptive analytics visualizes the description of historical data and changes within a business through pie and bar charts, line graphs, scatter plots, tables, diagrams, generated narratives, and other visualization tools.
For example, if a company wants to look over its monthly sales growth and other financial metrics, this is made possible through descriptive analytics. Therefore, this type of analysis is very useful in highlighting various aspects of business performance.
Diagnostic analytics is a more advanced data examination method that aims to answer the question “Why did something happen?” through techniques such as data discovery, drill-down, data mining, and correlations. Diagnostic analytics targets a particular aspect of the data in order to determine insights that can uncover specific issues within the business. This type of analytics is the most abstract out of the four, as the factors that are accounted for in the analysis can vary greatly depending on the result’s goal.
For example, if a company was having trouble with high employee turnover rates, diagnostic analytics can help to identify the causes through analyzing factors such as compensation ratio, promotion waiting time, pay raises, tenure, performance, etc.
While descriptive and diagnostic look back to past events, predictive analytics does just what its name implies: it predicts. This type of analytics combines the outcomes from the “what” and “why” of the two other types of data analytics and interprets them in an attempt to forecast future events and actions within a business. This type of analysis is quite complex, therefore it relies on techniques such as data mining, artificial intelligence (AI), and machine learning.
Foreseeing future trends can be very beneficial for businesses and their profit. For example, retail companies can gather information on what time of the year a certain product sells the fastest or whether there is a particular pattern of sales for that product.
Different from predictive analytics, which gives a prediction of what is likely to happen based on data, prescriptive analytics uses data to suggest the best course of action. This type of analytics gathers information from past, present, and future (predicted) events and scenarios and suggests strategies that can substantially improve decision-making processes within a business.
Remember the predictive analysis example of a product’s sell-through rate? Prescriptive analytics is what businesses use to materialize those predictions into profitable business decisions. In this case, the analysis aids decision-making around stocking inventories and marketing strategies for that particular product.
How Can Data Analytics Help Businesses?
Through listing each of the types of data analytics, we have given concrete examples of how they are employed by businesses. Overall, there is a myriad of opportunities in which they can use data analytics to their advantage. Here is how their proper implementation can benefit businesses:
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Making better decisions
The four types of data analytics are one of the most helpful tools for effective decision-making. Through understanding each angle of your customer behavior and/or the inner workings of the business, you are equipped with information that can help you analyze various aspects of your business, such as human relations, inventory stocking, marketing campaigns, product development, and much more.
Better marketing strategies
Data analytics is the future of marketing for various reasons. Through it, your business can understand its audience and customers, their behaviors, preferences, and expectations. This eliminates the guesswork around marketing strategies and provides concrete data and solutions for this industry.
Improving customer experience
If you run a customer-based business, the four levels of data analysis will aid you in constantly improving your product or services. Through analyzing past performance, current trends, and future predictions with suggested strategies for the next course of action, you can tremendously improve the operations within your business. As a result, customers will get an improved experience, and their expectations will be met.
Mitigating business risks
Apart from its other benefits, data analytics additionally serves businesses in creating strategies that can help in preventing and mitigating risks. This is done through examining historical data of previous and current losses, for example, and finding patterns and solutions that can help in risk management.
Additionally, data analysis and management can improve fraud risk management processes, which is crucial for many businesses that suffer losses from fraudulent activity.
Seeing how it can improve their day-to-day operations, it is clear that businesses can thrive and gain a competitive advantage through the use of data analytics. This trend emphasizes the need and importance of data analysts with adequate education and training in the field. Considering all its benefits, this aspect of business operations is definitely worth investing in.