What Is Data Analytics? How Is It Used?

By StepUpwards Team, 29th August 2022

Have you ever thought of why most businesses and enterprises fail to meet expectations or profit margins? Would you have thought it's because they have little ability to make strategic decisions? 

Yes! Many organizational decisions are based on gut instinct rather than data and facts. As a result, most have lost their favorite customers, made mistakes that put the firm in debt, or even closed down, among other things.

Are you in any of these categories, or do you not want to be? Great news! Data analytics is the key to improving your company's performance, maximizing earnings, and making more strategic decisions. Let's start with what data analytics entails and then go on to how you can apply it.

What is Data Analytics?

Data analytics is the method of assessing data sets to discover trends and develop findings about the information provided. Data analytics refers to studying raw data to conclude it. In layman's terms, data analytics is a broad word that comprises various types of data analysis. 

Data analytics methods enable the identification of patterns in raw data and the development of valuable conclusions. Data analytics is carried out using specialized tools and software.

Both small and large-scale firms utilize data analytics to help them make decisions. Besides, Data scientists use it to do research. Companies build products, analyze their advertising efforts, customize content, and create strategies with the aid of data analysis. Data analytics can ultimately help firms increase performance and boost their bottom line. Data analytics also places a firm over rival firms by reacting swiftly to new market trends. However, the primary aim of data analytics is to improve corporate performance.

For example, Call of Duty and most gaming firms employ data analytics to create reward systems for players. The reward, in turn, keeps most of them engaged in the game. More so, data analytics are used by content firms to keep you clicking, streaming, or re-organizing information in order to acquire more views or clicks.

Vital Steps in Data Analytics

Step 1: Identification of Data

The first stage is identifying the data sets or how the input is organized. Age, demographic, economic, and gender data can all be segregated. The data could be numerical or categorical.

Step 2: Gathering of Data

Next comes the stage of gathering data. Collecting data can accomplish it through various means, including software, internet sources, video, environmental sources, and personnel.

Step 3: Organization of Data

Once the data is acquired, it must be arranged in order to be examined. The arrangement can be made on a spreadsheet or any program that can handle statistical data.

Step 4: Data verification

The data, at this stage, is then thoroughly checked before being analyzed. It implies that it has been well examined to ensure that there is no duplicate or inaccuracy. The phase aids in the correction of any inaccuracies before the data is sent to a data analyst for analysis.

Step 5: Interpretation of Data

Lastly, you interpret your results, which is the most crucial stage. It aids you in reaching a logical conclusion. Data Analytics help create action plans based on the results at this level. For example, you'd learn whether your customers prefer red or green wrapping, plastic, or paper. Furthermore, at this time, any restrictions can be identified and addressed.

Now that we understand data analytics, let us look at some analytic data forms and how you can use them.

Types of Data Analytics

Data analytics can also be divided into quantitative and qualitative data analytics. The first entails the analysis of numeric values(numbers) containing quantifiable variables. These factors can be statistically compared or measured. The qualitative is more interpretive, focusing on the content of non-numerical data such as text, photographs, audio, and video, as well as key phrases, themes, and points of view (declarative). However, there are four primary forms of data analytics.

  1. Descriptive Analytics: This type describes what has occurred over a specific time. Has there been an increase in product sales? Are sales this month better than last?

  2. Diagnostic Analytics: It is concerned with the causes of events. It requires more mixed data inputs as well as some speculation. Did the weather affect our tomato sale? Did the most recent marketing initiative affect sales?

  3. Predictive Analytics: It focuses on what will likely occur soon. When was the last time we seemed to have heavy rain? What happened to sales? How many forecast models anticipate heavy rainfall this year?

  4. Prescriptive Analytics: This type of analytics advises on action to take. If the average of all these five weather forecasts predicts a hot summer, we should add a late shift to the farm and hire an extra tank to improve output.

Hypothetical Illustrations on the Use and Application of Data Analytics

Let's take a quick look at a few illustrations of how you might gather and analyze data to help your business perform better.

Illustration 1: Assume your firm is running an online advertising promotion for a new vacuum cleaner. You can begin by targeting the advert to customers who purchased the previous model of the brand. You employ data analytics techniques to filter through the data collected when individuals click on the advert as your campaign runs. If you analyze data about these users' interests, you can realize that many of them are interested in durability. Perhaps this is due to the improved battery life of the new over the previous model. 

Using this data, you can fine-tune your advert to target users who purchased the previous model and liked its durability. You may also find new audiences among people who did not buy the previous model but are interested in a long-lasting battery.

Illustration 2: Suppose your firm runs a website that features sports videos. You could gather information about which videos different viewers watch and how highly the viewers rate the videos. You can also check which ones they comment on and how often they visit your site. You could also collect data about each user's demographics. Data analytics tools can ascertain which viewer groups are most likely to view specific videos. You can then recommend videos to users based on the sections in which they fit best. For example, older men may be more interested in golf. In contrast, younger men may be more likely to be drawn to basketball.

Conclusion

Data Analytics assists organizations and people in ensuring that their data is accurate. It helps convert raw data into actionable insights that can help an organization strengthen decision-making.

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