Some Types Of Data Analytics To Improve Decision-Making

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Data Analytics

Data is an extremely powerful instrument that is available to companies in a vast array. When properly harnessed data has the potential to influence decision-making, improve the formulation of strategies, and boost the performance of an organization.

According to the Global State of Enterprise Analytics report released by the business intelligence company MicroStrategy 56 % of the respondents claimed that data analytics helped to achieve “faster, more effective decision-making” in their businesses.

What Is Data Analytics In Business?

The term “data analytics is the process of studying data to answer queries, find trends, and uncover information. When data analytics are used in business, it’s typically known as business analytics.

There are frameworks, tools, or software to analyze data like Microsoft Excel and Power BI, Google Charts, Data Wrapper, Infogram, Tableau along with Zoho Analytics. These tools can help you analyze the data from different perspectives and develop visuals that help tell the story you want to convey.

Machine learning and algorithms are part of the field of data analytics. They can be utilized to collect the, sort, and analyze data in more volume and speed speeds than humans. Writing algorithms is higher-level data analytics expertise however, you don’t require an extensive understanding of programming and statistical modeling to reap the benefits of decision-making based on data.

Who Needs Data Analytics?

Every professional in business who makes important decisions must have a foundation of information about data analytics. Data is now more prevalent than ever before. If you create strategies and take decisions without taking into consideration the information you can access it is possible to miss significant opportunities or the warnings the data reveals.

Marketers: who use the data of customers, trends in the industry and performance data from previous campaigns to formulate marketing strategies

Managers of product: who review industry, market, and data from customers to help improve their businesses’ products

Financial professionals: that use historical performance data as well as the latest trends in their industries to predict their company’s financial future.

Human resources, diversity equity and diversity professionals: who gain insight into employees’ attitudes of motivations, behaviors, and attitudes and combine it with data on trends in the industry to bring about significant changes in their companies.

To gain the most value from your data, you must familiarize your self with four main kinds of analytics for data. Below is a breakdown of these kinds of data analytics that can be used together or separately to maximize the value of the data of your business.

Some Key Types Of Data Analytics

Key Types Of Data Analytics

Descriptive Analytics

Descriptive analytics is the most basic kind of analytics, and is the basis that other types of analytics are built upon. It lets you draw conclusions from raw data and concisely explain what occurred or is happening at present.

As an example, suppose you’re looking over your company’s data and notice an annual increase in sales for one of your items such as an electronic game console. In this case, descriptive analytics could inform you that “This video game console experiences an increase in sales in October, November, and early December each year.”

Data visualization is an ideal choice for communicating descriptive data since graphs, charts, and maps are able to show patterns in data, as well as spikes and dips in simple, easy to understand manner.

Diagnostic Analytics

Moving the analysis further, this method involves the comparison of coexisting trends or movements in identifying the relationships between variables, as well as finding causal connections where it is possible.

In the same vein you could look through gamers’ demographics and discover that they’re between between eighteen and. The purchasers, however, are typically between the age of 35 and 55. The analysis of survey results shows that the primary reason for consumers to buy the console for video games is to give it for their kids. The increase in sales during the autumn and winter months could have to do with the upcoming holidays which include gifts.

Diagnostic analytics are useful for finding the root of an organizational problem.

Predictive Analytics

Predictive analytics helps forecast the future of events or trends and answer the question, “What might happen in the future?”

When you analyze past data and the trends in your industry, you can determine with confidence the potential future for your business.

For example the fact that sales of video game consoles have risen during the months of October, November and the beginning of December each year over the last decade gives you plenty of data to determine if similar trends will be observed the following year. In addition, with the upward trend for the gaming industry overall This is an enviable forecast to make.

Predictions about the future could aid your company in formulating strategies based upon likely scenarios.

Prescriptive Analytics

Prescriptive Analytics

Prescriptive analytics take into consideration the various variables that could be involved in the scenario and recommends practical takeaways. This kind of analysis can be particularly helpful when making data-driven choices.

The final video game scenario: What would your team do in light of the expected increase in seasonality owing to the gift-giving season in winter? Maybe you decide to conduct an A/B testing with two advertisements: one that is targeted to end-users of the product and another that is targeted towards customers. The information gathered from the test will help you decide how to make the most of the seasonal surge and its alleged cause more. Maybe you decide to boost your advertising efforts during September using themes of the holiday season to make the spike last another month.

While the manual process of prescriptive analysis is attainable and affordable machines-learning algorithms are typically used to assist in parsing massive amounts of data in order to determine the most appropriate next step. The algorithms employ “if” and “else” statements, which function as rules to parse data. If a particular combination of conditions is met an algorithm suggests the best course of action. There’s more to machine learning algorithms than only those three statements, they–along with mathematical equations serve as a fundamental component of the process of training algorithms.

Using Data To Drive Decision-Making

The four kinds of data analysis need to be combined to build a complete image of the story that the data tells, and then make educated choices. To comprehend your company’s current situation, you can use descriptive analytics. To determine how your business came to this point by using diagnostic analytics, you can use. Predictive analytics are useful for understanding the direction of a particular situation. How long will the current trend continue? In addition, predictive analytics can aid you in considering every aspect of the current as well as future scenarios and formulate effective strategies.

Based on the issue you’re attempting to solve as well as your objectives, you can decide to utilize one or three kinds of analytics or apply them all in order to get the most thorough knowledge of the story that the data tells.

The development of your analytics skills will enable you to make the most of the information your data provides and help you advance your company and professional career.