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Data Analytics Explained

“Data Analytics is the process of examining data sets in order to find trends and draw conclusions about the information they contain.”

Most companies are collecting loads of data all the time—but, in its raw form, this data doesn’t really mean anything. This is where Data Analytics comes in.

A Data Analyst will extract raw data, organize it, and then analyze it, transforming it from incomprehensible numbers into coherent, intelligible information. Having interpreted the data, the data analyst will then pass on their findings in the form of suggestions or recommendations about what the company’s next steps should be.

You can think of Data Analytics as a form of Business Intelligence, used to solve specific problems and challenges within an organization. It’s all about finding patterns in a dataset which can tell you something useful and relevant about a particular area of the business—how certain customer groups behave, for example, or how employees engage with a particular tool.

Data Analytics helps you to make sense of the past and to predict future trends and behaviors; rather than basing your decisions and strategies on guesswork, you’re making informed choices based on what the data is telling you. Armed with the insights drawn from the data, businesses and organizations are able to develop a much deeper understanding of their audience, their industry, and their company as a whole—and, as a result, are much better equipped to make decisions and plan ahead.

What Is Data Analytics

Data Analytics is the science of analyzing raw data to make conclusions about that information. Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption.

Data Analytics is a broad term that encompasses many diverse types of data analysis. Any type of information can be subjected to data analytics techniques to get insight that can be used to improve things. Data Analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of information. This information can then be used to optimize processes to increase the overall efficiency of a business or system.

For example, manufacturing companies often record the runtime, downtime, and work queue for various machines and then analyze the data to better plan the workloads so the machines operate closer to peak capacity.

Data Analytics can do much more than point out bottlenecks in production. Gaming companies use Data Analytics to set reward schedules for players that keep the majority of players active in the game. Content companies use many of the same data analytics to keep you clicking, watching, or re-organizing content to get another view or another click.

Data Analytics is important because it helps businesses optimize their performances. Implementing it into the business model means companies can help reduce costs by identifying more efficient ways of doing business and by storing large amounts of data. A company can also use Data Analytics to make better business decisions and help analyze customer trends and satisfaction, which can lead to new—and better—products and services.

Types Of Data Analytics

Data Analytics is a broad field. There are 4 primary types of data analytics: descriptive, diagnostic, predictive and prescriptive analytics. Each type has a different goal and a different place in the data analysis process. These are also the primary data analytics applications in business.

Types Of Data Analytics
  • Descriptive Analytics helps answer questions about what happened. These techniques summarize large datasets to describe outcomes to stakeholders. By developing key performance indicators (KPIs,) these strategies can help track successes or failures. Metrics such as return on investment (ROI) are used in many industries. Specialized metrics are developed to track performance in specific industries. This process requires the collection of relevant data, processing of the data, data analysis and data visualization. This process provides essential insight into past performance.
  • Diagnostic Analytics helps answer questions about why things happened. These techniques supplement more basic descriptive analytics. They take the findings from descriptive analytics and dig deeper to find the cause. The performance indicators are further investigated to discover why they got better or worse. This generally occurs in three steps:
    • Identify anomalies in the data. These may be unexpected changes in a metric or a particular market.
    • Data that is related to these anomalies is collected.
    • Statistical techniques are used to find relationships and trends that explain these anomalies.
  • Predictive Analytics helps answer questions about what will happen in the future. These techniques use historical data to identify trends and determine if they are likely to recur. Predictive analytical tools provide valuable insight into what may happen in the future and its techniques include a variety of statistical and machine learning techniques, such as: neural networks, decision trees, and regression.
  • Prescriptive Analytics helps answer questions about what should be done. By using insights from predictive analytics, data-driven decisions can be made. This allows businesses to make informed decisions in the face of uncertainty. Prescriptive analytics techniques rely on machine learning strategies that can find patterns in large datasets. By analyzing past decisions and events, the likelihood of different outcomes can be estimated.

These types of Data Analytics provide the insight that businesses need to make effective and efficient decisions. Used in combination they provide a well-rounded understanding of a company’s needs and opportunities.

Data Analytics Techniques

There are several different analytical methods and techniques Data Analysts can use to process data and extract information. Some of the most popular methods are listed below.

  • Regression Analysis entails analyzing the relationship between dependent variables to determine how a change in one may affect the change in another.
  • Factor Analysis entails taking a large data set and shrinking it to a smaller data set. The goal of this maneuver is to attempt to discover hidden trends that would otherwise have been more difficult to see.
  • Cohort Analysis is the process of breaking a data set into groups of similar data, often broken into a customer demographic. This allows data analysts and other users of data analytics to further dive into the numbers relating to a specific subset of data.
  • Cluster Analysis is a class of techniques that are used to classify objects or cases into relative groups called clusters.  It can be used to reveal structures in data — insurance firms might use cluster analysis to investigate why certain locations are associated with particular insurance claims, for instance.
  • Monte Carlo Simulations model the probability of different outcomes happening. Often used for risk mitigation and loss prevention, these simulations incorporate multiple values and variables and often have greater forecasting capabilities than other data analytics approaches.
  • Time Series Analysis tracks data over time and solidifies the relationship between the value of a data point and the occurrence of the data point. This data analysis technique is usually used to spot cyclical trends or to project financial forecasts.
  • Sentiment Analysis: Sentiment analysis uses tools such as natural language processing, text analysis, computational linguistics, and so on, to understand the feelings expressed in the data. While the previous six methods seek to analyze quantitative data (data that can be measured), sentiment analysis seeks to interpret and classify qualitative data by organizing it into themes. It is often used to understand how customers feel about a brand, product, or service.

Importance Of Data Analytics

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:

Importance Of Data Analytics

1. Reduce the cost of operation

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, ensure data 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.

2. Predict future trends

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.

3. Monitor product performance

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. 

4. Strengthen security

Businesses use Data Analytics to examine past security breaches and diagnose the vulnerabilities that 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.

5. Manage risks

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. 

6. Improve decision-making

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.

7. Improved business performance

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. 

Data Analytics  Use Cases

Organizations across all industries leverage Data Analytics to improve operations, increase revenue, and facilitate digital transformations. Here are three examples:

UPS delivers resilience, flexibility with predictive analytics: Multinational shipping company UPS has created the Harmonized Enterprise Analytics Tool (HEAT) to help it capture and analyze customer data, operational data, and planning data to track the real-time status of every package as it moves across its network. The tool helps it keep track of the roughly 21 million packages it delivers every day.

Predictive analytics helps Owens Corning develop turbine blades: Manufacturer Owens Corning, with the help of its analytics center of excellence, has used predictive analytics to streamline the process of testing the binders used in the creation of glass fabrics for wind turbine blades. Analytics has helped the company reduce the testing time for any given new material from 10 days to about two hours.

Kaiser Permanente reduces waiting times with analytics: Kaiser Permanente has been using a combination of analytics, machine learning, and AI to  overhaul the data operations of its 39 hospitals and more than 700 medical offices in the US since 2015. It uses analytics to better anticipate and resolve potential bottlenecks, enabling it to provide better patient care while improving the efficiency of daily operations.

Final Thoughts

In a world increasingly becoming reliant on information and gathering statistics, Data Analytics helps individuals and organizations make sure of their data. Using a variety of tools and techniques, a set of raw numbers can be transformed into informative, educational insights that drive decision-making and thoughtful management.

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