What Is Data Analytics? Definition, Types, and Applications | Spiceworks

What Is Data Analytics? Definition, Types, and Applications | Spiceworks

Data analytics is defined as a set of tools and technologies that help manage qualitative and quantitative data with the object of enabling discovery, simplifying organization, supporting governance, and generating insights for a business. This article explains the meaning of data analytics, its different types, and top use cases for an organization. 

Data analytics is defined as a set of processes, tools, and technologies that help manage qualitative and quantitative data to enable discovery, simplify organization, support governance, and generate insights for a business. 

Data analytics is a discipline that involves analyzing data sets to get information that would help solve problems in different sectors. It employs several disciplines like computer programming, statistics, and mathematics, to give accurate data analysis.

The goal of data analytics can either be to describe, predict, or improve organizational performance. They achieve this using advanced data management techniques like data modeling, data mining, data transformation, etc., to describe, predict and solve present and future problems. 

These goals differentiate data analysis from similar disciplines like business analytics and data science. Business analytics is a form of data analytics that is only used by businesses.Data scienceand analytics solve problems through deeper learning and strategic oversight.

Data analytics involves a series of steps to give an accurate analysis. While performing these steps, data analysts include data scientists and data engineers to create data pipelines or help set up models. We discuss the steps involved in data analytics in this article:

There are two ways to practice data collection. The first approach is to identify the data you need for the analyses and assemble it for use. If the data are from different source systems, the data analyst would have to combine the different data using data integration routines.

But in some cases, the data needed might just be a subset of a data set. The data analyst would include a series of steps to extract the relevant subset and move it to a separate compartment in the system. Doing this allows one to analyze the subset without affecting the overall data set easily.

The next step is finding and correcting data quality problems in the collected data. It also entails setting up the data for the analytical model according to corporate standards. Data quality problems include inconsistencies, errors, and duplicate entries. They are resolved by running data profiling and data cleansing tasks.

The data analysts also manipulate and organize the data according to the requirements of the analytical model he intends to use. The final task in data quality is implementing data governance policies. These policies ensure the data is used correctly and is according to corporate standards.

Moving forward, the data analyst works with data scientists to build analytical models that would run accurate analyses. These models are built using analytical software, like predictive modeling tools, and programming languages like Python, Scala, R, andStructured Query Language (SQL).

After building, the model is tested with an experimental data set. The results from the test are reviewed, and changes are made to the model. The model is tested over and over until the model works as intended. Finally, the model runs against the intended data set in production mode.

The final step in data analytics is presenting the models’ results to the end-users and business executives. It is best practice to use tools like charts and infographics for presentations. They are easy to understand and communicate results. 

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There are five types of data analytics. One can employ all of them in making a complete data analysis depending on the problem, but that is often unnecessary. However, it is essential to know each type of data analysis. 

This type of data analytics examines past data to explain what had happened. It is the most straightforward data analytics technique. Depending on the scenario, some data analysts use descriptive analytics as a summary to support investigations and analysis from other types of analytics. This can be tagged as “best practice” because it explains the results from other analytics regarding historical data. 

Companies use statistical analysis techniques to perform descriptive data analysis. This type of data analytics helps them compare past results, identify anomalies, distinguish strengths and weaknesses, etc. Companies use descriptive analysis to identify a problem.

Diagnostic data analytics examines past data to explain the cause of an anomaly. This type of analytics aims to answer “why did this happen?” from a descriptive analytics result.

The techniques used for diagnostic data analytics are drill-down, data discovery, data mining, and correlations. Data analysts use the data discovery technique to find sources that might help them deduce reasons from a result. Data mining involves getting information from a large set of raw data by automated processes. Results from diagnostic analytics are obtained by finding correlations or patterns between different data.

Results from this type of analytics help companies draft accurate solutions to problems instead of relying on guesswork.

Predictive data analytics involves using current or historical data to predict future actions. Individuals and companies conduct predictive analysis by combining historical data with machine learning, data mining techniques, and statistical modeling. These help them to quickly locate patterns and predict risks and opportunities in the future.

This type of analytics works with algorithms and methodologies (like alinear or logistic regression model). Different algorithms exist for different scenarios, and a mismatch would result in erroneous results. The chunks of data derived from customers and external sources are useless until they are used to solve a problem. Without predictive analysis, companies would be prone to making mistakes in the future that they might never recover from.

Prescriptive data analytics involves selecting the best solution for a problem from available options. This type of data analytics examines results from other analytics and gives guidance on how to reach a specific answer.

Prescriptive data analytics is used in recommendation engines, loan approval engines, dynamic pricing models, machine repair scheduling, and similar tools to examine any decision options and to personalize the process. These options could be in a yes/no or a list. These tools illustrate the consequence of every option, and also provide better options. Companies can use prescriptive analysis to automate decision-making and hasten complex approvals.

Real-time data analytics involves using data immediately when entered into the database. Unlike other types of data analytics that use data from past events (historical data), this type analyses new data from customers or external sources on the go.

Some technologies employed in real-time data analytics includeedge computing,in-database analytics, in-memory analytics, data warehouse appliances, parallel programming, etc. This type of data analytics would be most helpful if it is used in high availability and low-response time applications. Companies use it to identify trends and benchmarks faster than their competitors. They can also track and analyze their competitor’s operations instantaneously.

Augmented analytics usesmachine language (ML)and natural language processing (NLP) to analyze data. Incorporating machine learning into analytics helps automate the tedious task of code-based data exploration and make it available to business users. This reduces the chances of errors and enables the data analyst more time to do other actionable tasks. Most analytics software integrates augmented data analytics tools to leverage the machine language and other outstanding features.

Companies include this type of data analytics in their analysis process to interact with data organically – i.e., through English or any other natural language – and identify trends.

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These days, most departments in organizations use data analytics to examine present situations and predict future scenarios. The results of these actions can bring many benefits and advantages to an organization. These benefits include:

Although paying a data analyst might be expensive, it is cheaper in the long run compared to its benefits. With good data analysis, you can prevent financial risk, ensuredata security,and perform other actions that might save you a fortune. Also, organizations use data analytics to check for functions that use more finances than they should and others that need more financing. This helps cut costs – especially operations and production–and ultimately replaces manual activities with technology.

Organizations can predict future trends and innovations with data analytics. Using predictive analysis tools, organizations can develop future-focused products and services and stay at the top of their market. Using good marketing, these organizations can create demand for these offerings and capture a larger market share. They can even obtain patents for futuristic inventions to maintain an advantage over competitors and maximize profits.

Data analytics is used in tracking customers’ behavior towards products or services. You can use it to identify why sales are low, what products people buy, why they are buying them, how much they are spending on these products, how you can sell your products better, and many other queries. Studying audience behavior helps enterprises make financial decisions like changing the prices of products or finding a niche to target. 

Businesses use data analytics to examine past security breaches and diagnose thevulnerabilitiesthat led to these breaches. Analytics applications help IT experts to parse, process, and visualize audit logs to discern the origin and path of security breaches. They can also prevent future attacks using analytical models that detect unusual or abnormal behavioral patterns. These models can be set up with monitoring and alerting systems to identify breach attempts and notify security pros.

Risks in business range from theft by customers or employees to legal liability or an excessively high number of inventory goods. Data analytics help organizations prevent and manage risks. For example, a retail chain can use a propensity model to determine which stores are more liable to theft. This would help decide whether to change store location or improve security. 

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Organizations can use data analytics to prevent financial losses. Predictive analysis can detect future actions of customers if a change is made, and prescriptive analysis would suggest how to react to these changes to maximize profit. For instance, let us say a company wishes to increase the prices of its products. They can build a model to determine whether this change would affect customer demand. Results from this model can be confirmed by testing. This would prevent terrible financial decisions.

Collecting and examining data about the supply chain can help detect production delays, bottlenecks, and future problems. In the case of inventory levels, data analytics can help in defining optimal supply for all products of an enterprise. This makes it easy for businesses to identify and resolve issues quickly. 

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Over the years, data analytics applications have improved due to advancements in the IT sector. The rise of new technology trends like big data and theinternet of things (IoT)has led to new and innovative applications for data analytics. This includes:

One can use data analytics to solve traffic congestion and improve travel by improving transportation systems and intelligence. It works by obtaining enormous volumes of data to build alternative routes to solve traffic congestion. This would reduce traffic congestion and, in turn, reduce road accidents.

Likewise, travel companies can obtain buyers’ preferences from social media and other sources to improve their packages. This would improve the travel experiences of buyers and companies’ customer base. For example, data analytics was used to solve the transportation problem of 18 million people in London city during the 2012 Olympics.

Policymakers can use data analytics to improve learning curricula and management decisions. These applications would improve both learning experiences and administrative management. 

To improve the curriculum, we can collect preference data from each student to build curricula. This would create a better system where students use different ways to learn the same content. Also, quality data obtained from students can help better resource allocation and sustainable management decisions. For example, data analytics can let admins know what facilities students use less or subjects they are barely interested in.

Search engines like Google, Amazon e-commerce search, Bing, etc., use analytics to arrange data and deliver the best search results. This implies that data analytics is used in most search engine operations. When storing web data, data analytics gathers massive volumes of data submitted by different pages and groups them according to keywords. In each group, analytics also helps rank web pages according to relevance.

Likewise, every word the searcher enters is a keyword in delivering search results. Data analytics is again used to search a particular group of web pages to provide the one that matches the keyword intent best. 

Marketers use data analytics to understand the audience and get high conversion rates. There are different activities in these two sub-applications, which are done using data analytics. To understand the audience, digital ad experts use analytics to know the intended audience’s likes, dislikes, age, race, gender, and other features. They also use this technology to segment their audience according to behaviors and preferences.

Furthermore, to obtain high conversion rates, experts use data analytics to identify trends and produce relevant content for long-term engagement. They do this by studying buying habits and frequency via analytics trends.

Data analytics is used for productive workflow and better delivery processes in the logistics industry. This has yielded improved industry performance and, in turn, a broader customer base. It increases productivity by enabling real-time data sharing of the company insights between partners. These insights show customer demand fluctuations and the performance of the company’s workforce.

In improving the delivery process, logistics companies use data analytics for route optimization. This enables companies to select the best routes and time usingGlobal Positioning System (GPS)data, weather data, road maintenance data, and personal schedules.

Security personnel use data analytics (especially predictive analytics) to find future cases of crimes or security breaches. They can also investigate past or ongoing attacks. Analytics makes it possible to analyze how IT systems were breached during an attack, other plausible weaknesses, and the behavior of end-users or devices involved in a security breach.

Some cities use data analytics to monitor areas with high crime rates. They monitor crime patterns and predict future crime possibilities from these patterns. This helps maintain a safe city without risking police officers’ lives.

Many organizations in different industries use data analytics to detect fraudulent activities. These industries include pharmaceutical, banking, finance, tax, retail, etc. When identifying tax fraud, predictive analysis is used to assess the reliability of tax returns for individuals. The Internal Revenue Service (IRS) uses this type of analytics to predict future fraudulent activities.

It is also used to identify bank fraud by analyzing communication. Banks use data analytics for constant communication with customers. They can leverage data analyzing algorithms to detect fraudulent activities based on previous communication data with a particular customer.

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Data analytics is among the top areas of research and investment today. Gartner predicts that by 2025, new forms of analytics like context-driven analysis and artificial intelligence will replace existing technologies. Connected governance, data sharing, and the rise ofdata fabricsare among the other critical trends anticipated by Gartner. 

To leverage the power of these technologies, companies need to know all about data analytics, its types, and applications. Implementing analytics properly makes it possible to drive business success and accelerate outcomes, even in a challenging market. 

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