What is Data Analytics?

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Definition:

Data analytics is the process of converting raw data into useful insights, which can improve an analyst’s ability to locate improvements and opportunities in business or finance.

🤔 Understanding Data Analytics

Data analytics is a process that uses computer programs to discover information otherwise hidden within a sea of data. Many companies use data analytics to improve sales, optimize operations, and locate opportunities. In the information age, data is quickly becoming one of the most valuable resources at a business owner’s disposal. It is collected in numerous ways, and many companies have more data than they know what to do with. Analysts can employ data analytics to turn information into valuable insights, which help businesses improve. In most cases, a company that embraces data analytics develops a competitive advantage over competitors who don’t.

Example

Google is currently one of the biggest names on the internet and in the stock market. As of March 2020, the company had a market capitalization of over $700B. Google’s entire business model is based on data analytics. The company collects and organizes information from the internet. Then, it uses data analytics to match what you put in the search bar to information that might answer your request. But the process doesn’t stop there. The company watches what you do next and records that data, too. For example, if you hit the back button after clicking on a search result, it might assume it gave you the wrong answer and update accordingly.

Takeaway

Data analytics is like hunting for easter eggs…

Every Spring, many children learn that eggs are hidden somewhere among the grass, trees, and shrubs. They leave the house with a basket trying to track down the treats. Some kids might have a systematic approach to locating their goals and eliminating possibilities (data analytics). Others might wonder around almost randomly, hoping to stumble upon their objective.

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What is data analytics?

The term data analytics refers to anything that uses data to gain insight. In some cases, companies use data analytics to parse large amounts of information for clues about what customers want. It is often applied in conjunction with terms like Big Data, cloud computing, machine learning, artificial intelligence, and business analytics. But the concept generally means that you are using a computer program to dissect and digest a bunch of numbers, turning them into something that is more meaningful to decision makers.

What are the types of data analytics?

You can think of data analytics as a four-part process that helps you answer some of the critical questions in business. The process is continuous, as you evaluate the business environment and adjust to changing circumstances. Here are some brief explanations of the data analytic process that starts after you form a plan.

Descriptive Analytics

Combing through your company’s finances to find trends in sales is an example of descriptive analytics. It’s anything that you do with data to get a better understanding of what happened in the past. You might use descriptive analytics to gain insight into how business operations are changing. Most of the standard metrics used in business are outputs from descriptive analytics. For example, year-over-year sales or month-over-month expenditures are descriptive analytics. So are things like average customer acquisition costs and average order value.

Descriptive analytics give context to information by comparing it to or combining it with other information. For instance, knowing that a company’s gross income is $5M doesn’t mean a lot on its own. But knowing that revenues in the same quarter last year were $4M provides the context that it is an improvement. And knowing that the costs of goods sold (COGS) are also $1M higher than last year gives further clarity about how the quarter went.

Business executives use descriptive analytics to get a better appreciation for the health of the company and the success of past efforts. They include many key performance indicators (KPIs), and several pieces of information presented in a format that’s easy to understand. From this information, company leaders can adjust their strategy moving forward.

Diagnostic Analytics

While descriptive analytics tell you what happened, they don’t tell you why it happened. That’s where diagnostic analytics come into play. Using diagnostic analytics, you can dig deeper into the data and look for the root cause of changes. While descriptive analytics might indicate that your total company sales increased, perhaps there is a decrease in sales in one region being masked by increases everywhere else. An analyst can dive deeper into the data and pinpoint the stores, products, and maybe even the lost customers that led to the decline. Such a diagnostic analysis could flag a problem or opportunity that is hidden in the broader picture.

Predictive Analytics

Descriptive and diagnostic analytics help business leaders evaluate the effectiveness of their strategy. They’re backward-looking, comparing results to their planned outcomes. From there, they need to determine if any deviations were caused by poor execution or unrealistic expectations. Predictive analytics sets the stage for what to expect. Analysts use regression methods and data mining to uncover relationships between one factor and another. Those relationships could be correlations (like hot weather increasing sales of lemonade), or they could be causal (like increased marketing leading to higher sales).

A well-tuned predictive model can help executives and company leaders prepare for what’s about to happen. A poorly constructed predictive model can lead to unexpected results, which could result in lost sales (if you sell out of product) or excessive waste (if you over-prepare perishable products).

Good predictive analytics are essential to maximizing profits. But predictions must match reality to be useful. Therefore, continuously evaluating the performance of forecasts and adjusting the models as new data arrives is a critical component of data analytics. If predictive models are well-calibrated and trustworthy, deviations from expected results are more likely to come from problems in execution, which diagnostic analytics can help identify.

Prescriptive Analytics

Most of the data analytics world focuses on explaining how things work. Even predictive analytics tell you what to expect, but not what to do about it. Prescriptive analytics, however, is used to provide advice. In many cases, prescriptive analytics solves optimization problems and determines the best approach to address a situation.

For example, predictive analytics might suggest that sales of lemonade will increase next week. Prescriptive analytics would use that forecast, combine it with inventory management guidelines and supply chain information, and adjust purchase orders to maximize profits.

What is the data analytics process?

Useful data analytics require good data. If the information you have isn’t what you need, no amount of analysis will yield meaningful insight. Therefore, the data analytics process starts with the data.

First, you need to figure out what you want to know. Collecting wide-ranging data might end up being useful, but it can become cumbersome. Instead, it’s better to determine what you’re trying to achieve. If you want to manage your inventory better, keeping track of product counts is essential. If you want to reduce labor costs, tracking the volume of customers during each hour of the day is critical.

Second, you need to collect the data. Many point-of-sale systems can store information about purchases. Indicators at the entrances and exits can record traffic patterns. Website plug-ins can capture all sorts of information about online visitors.

Third, you must organize the data in a meaningful way. There are many types of database software out there these days, so storing data is easier now than ever before. However, it is critical to design a database in a way that allows extracting and analyzing data to be seamless. If your data is spread across many tables, it could become impossible to connect one dataset to another in a relational database.

Fourth, you have to clean your data. If information gets put in the wrong place, gets entered incorrectly, or is missing, it can lead to incorrect results. Before doing any analysis, make sure that the data is correctly aligned, complete, and free of apparent errors or duplications. Finally, you can start your analysis. Feed your clean data into your favorite software program, or conduct your analysis manually in Excel. Glean what you can with descriptive analytics, dive deeper with diagnostics, and do some predictive analysis. Then you will be ready to make recommendations, create a strategy, and turn it over to your managers for execution.

What is the difference between data analytics and data science?

To understand the difference between data analytics and data science, consider the different mental images you get when you think of an analyst versus a scientist. Data analytics is the use of well-constructed data to discover information. One of the primary jobs of a data analyst is to communicate that insight to non-technical business leaders. Therefore, data analytics is heavily focused on creating visualizations of data and presenting findings to decision makers.

A data scientist usually goes beyond analyzing the information at hand. They create algorithms and statistical models to use on unstructured data. In other words, a data scientist usually has more experience and education than an analyst — Especially in computer programming. Therefore, data science is typically viewed as a step beyond data analytics.

What are the top data analytics tools?

Every analyst will answer this question differently. The specifics of your needs, data, and budget will push you toward one tool or another. But there are a few universally respected products out there.

For one, Microsoft Excel is a low-cost option for doing a lot of different things with data. It can store small datasets. Its pivot tables are great for organizing information, and the program is flexible enough to do some sophisticated model building. Plus, the data analysis add-in, along with the built-in tools, can do some meaningful statistical work.

For statistical analysis, R is a popular open-source option. It’s free to use and robust, with a relatively easy learning curve. Other statistical software packages, such as SAS, SPSS, Stata, and MATLAB, are also frequently used by data analysts. Of course, programming languages, like Python and Hadoop, are also widely used to manage and evaluate data. If you’re looking to communicate information, several products can create beautiful and impactful data visualizations. One of the most popular products on the market today is called Tableau. Meanwhile, the landscape is continually shifting, and programs such as PowerBI, Qlik, and Domo are quickly gaining market share.

If you need to manage a lot of information, there are Big Data analytics companies that provide cloud computing services. Amazon, IBM, Microsoft, Google, and others offer some compelling data analytics over the internet. With machine learning and artificial intelligence, these services can conduct advanced analytics and deliver some impressive business intelligence.

Why is data analytics important?

Data analytics gives business leaders the information and actionable insights they need to make quality business decisions. With a better understanding of how well the business is operating, managers can use descriptive and diagnostic analytics to pinpoint and solve operation issues they may not have noticed otherwise.

Executives can set strategy based on the outlooks provided by predictive analytics. Project managers can locate and resolve scheduling issues and supply chain bottlenecks. Marketing teams can discover trends ahead of their competition, getting in front of the next fad and positioning the company to profit from it.

With data analytics, a business can get continuous feedback on the perception of its brand, how well its product line is selling, and how its competition is positioning itself. A business can see trends, spot problems, and gain a competitive advantage over anyone that is not leveraging the power of data analytics.

How are data analytics being used?

Most businesses in the marketplace today are collecting data, analyzing data, and adjusting their strategy in response to data analytics. In the increasingly digital world, online activity is exceptionally easy to capture and evaluate. A business can track the number of visitors, where they came from, and how long they stay. It can record when someone almost makes a purchase but doesn’t complete the transaction, which can become a lead for the sales team.

In some cases, a business can even record how long a person stops to look at an ad before moving on. And companies can conduct low-cost experiments in real time to learn which advertisements get the best responses. All of this information can improve marketing campaigns, which results in higher quality customer acquisition efforts and lower costs.

Companies from Uber to UPS use data analytics to optimize delivery routes. Businesses from Microsoft to McDonald’s use business analytics to streamline workflows. And corporations from Amazon to Apple use data analytics to build brand loyalty and develop customer relationships that maximize the lifetime value of each customer. The use of data analytics is far-reaching, and the possibilities are endless.

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This information is educational, and is not an offer to sell or a solicitation of an offer to buy any security. This information is not a recommendation to buy, hold, or sell an investment or financial product, or take any action. This information is neither individualized nor a research report, and must not serve as the basis for any investment decision. All investments involve risk, including the possible loss of capital. Past performance does not guarantee future results or returns. Before making decisions with legal, tax, or accounting effects, you should consult appropriate professionals. Information is from sources deemed reliable on the date of publication, but Robinhood does not guarantee its accuracy.

Robinhood Financial LLC (member SIPC), is a registered broker dealer. Robinhood Securities, LLC (member SIPC), provides brokerage clearing services. Robinhood Crypto, LLC provides crypto currency trading. All are subsidiaries of Robinhood Markets, Inc. (‘Robinhood’).

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© 2022 Robinhood. All rights reserved.