The Concept of Data Analytics Explained

July 27, 2023
Data analytics is scientific knowledge of examining raw data to conclude it. Data analytics may assist a firm in optimizing its performance, performing more effectively, maximizing profit, or making more strategic decisions. Data analytics approaches and procedures have been mechanized into algorithms and mechanical processes that operate over raw data for human consumption. Examining what occurred (descriptive analytics), why something happened (diagnostic analytics), what will happen (predictive analytics), or what ought to be done next (prescriptive analytics) are all methods of data analytics. For most data manipulation, data analytics relies on several software tools like spreadsheets, data visualization and reporting tools, data mining applications, and open-source languages.

What is data analytics?

Data analytics (DA) is the technique of analyzing data sets to discover trends and develop conclusions about the data contained within them. Data analytics is increasingly being performed with specialist tools and software. Data analytics technology and methodologies are widely employed in commercial industries to help businesses make better choices. Researchers often use analytic tools to validate or reject scientific models, ideas, and hypotheses. Most data analytics processes and techniques have been automated into mechanical processes and algorithms that operate on raw data for human use.

Perception of data analytics

Data analytics, as a phrase, primarily refers to various applications, ranging from essential business intelligence (BI), reporting, and online analytical processing (OLAP) to different types of advanced analytics. In that way, it is akin to business analytics, another umbrella word encompassing techniques for data analysis. The distinction is that the latter is geared toward commercial applications, whereas data analytics has a broader scope. However, this broad interpretation is not universal: in certain instances, people use data analytics specifically to refer to advanced analytics, addressing BI as a separate category.

Data analytics is a broad terminology that incorporates a wide range of data analysis techniques. Data analytics techniques may be used to any information to get the knowledge that can be utilized to improve things. Data analytics approaches can uncover trends and indicators that otherwise get lost in a sea of data. This data may streamline procedures and boost a company's or system's efficiency. Manufacturing firms, for example, frequently document the runtime, downtime, and work queue for different equipment and then evaluate the data to schedule workloads better so that the machines perform closer to peak capacity.

Data analytics can accomplish significantly more than identifying production bottlenecks. Gaming firms use data analytics to create incentive schedules for players that keep most players engaged in the game. Many of the same data analytics are used by content companies to keep you clicking, watching, or re-organizing content to get another view or click.

Data analytics is critical since it allows firms to improve their performance. Companies that include it in their business models can help cut costs by developing more efficient business methods and storing massive volumes of data. Data analytics may also help a firm make better business decisions and assess consumer patterns and satisfaction, resulting in innovative and better products and services.

SQL was crucial in the early days of contemporary data analytics. This programming language, developed in 1979, allows relational databases to be searched and generated data sets to be more readily evaluated. Today, SQL remains widely used.

Steps of data analysis

Data analysis is a multi-step process that includes numerous steps:

i.          The first stage is to identify the data needs or how the data is organized. Age, demographic, economic, and gender data may all be segregated. Data values might be numerical or grouped into categories.

ii.          The process of gathering data is the second phase in data analytics. This may be accomplished through various means, including computers, the internet, cameras, environmental sources, and humans.

iii.          After data collection, it must be organized and analyzed. This might happen on a spreadsheet or any program that can handle statistical data.

iv.          The data is then cleansed before being analyzed. This implies that it has been cleaned and examined to ensure there is no duplicate or error and that it is not incomplete. This phase aids in correcting inaccuracies before the data is sent to a data analyst for analysis.

Data Analytics Categories

There are four primary forms of data analytics.

i.     Descriptive analytics: These outline what happened over a specific period. Has the number of views increased? Are sales up this month compared to last?

ii.    Diagnostic analytics is more concerned with why something happened. This requires more diversified data sources and some hypothesis testing. Was the weather a factor in beer sales? Did the most recent marketing campaign affect sales?

iii.   Predictive analytics: This focuses on what is expected to occur soon. What happened to sales over the past scorching summer? How many weather forecasts call for a scorching summer this year?

iv.   Prescriptive analytics: This type of analytics advises a course of action. Suppose the average of these five weather models predicts a hot summer; we should add a night shift to the brewing facilities and lease another tank to improve output.

Several quality control systems in the financial world, which include the ever-popular Six Sigma program, rely on data analytics. It is practically hard to optimize something if it is not adequately measured, whether your weight or the number of errors per million in a production line.

Some industries that have embraced data analytics include travel and hospitality, where turnaround times can be short. This industry may collect client data and determine where problems exist and how to resolve them.

Healthcare makes use of large amounts of organized and unstructured data, as well as data analytics, to make timely choices. Similarly, the retail business extensively uses data to fulfill customers' ever-changing needs. Retailers may use the data they gather and analyze to spot trends, promote items, and enhance profitability. 

Methods of data analytics

Data analysts can analyze data and extract information using a variety of analytical methodologies and techniques. Below are some of the most prevalent ways.

·       Factor analysis requires reducing a considerable amount of data collection to a smaller data set. The purpose of this move is to try to uncover hidden trends that would otherwise be difficult to spot.

·       The process of assessing the relationship between dependent variables to discover how a change in one may impact the change in another is known as regression analysis.

·       Monte Carlo simulations simulate the likelihood of various events occurring. Frequently used for risk reduction and loss prevention, these simulations integrate many values and variables and generally have stronger predicting skills than other data analytics methodologies.

·       Time series analysis follows data across time and establishes a link between the value of a data point and its recurrence. This data analysis approach is commonly used to identify cyclical tendencies or to anticipate financial outcomes.

·       Cohort analysis divides a collection into groups of comparable data, frequently split into consumer demographics. This enables data analysts and other data analytics users to delve deeper into the figures about a specific subset of data.

Data analysis software tools

Data analytics has rapidly grown in technology capabilities, in addition to a wide range of mathematical and statistical ways to crunch statistics. Data analysts now have many software tools to assist them in obtaining data, storing information, analyzing data, and presenting conclusions.

Data analytics has long had a tenuous relationship with spreadsheets and Microsoft Excel. Data analysts frequently engage with primary programming languages to adapt and change databases. Open-source languages like Python are often used. More specialized data analytics programs like R can be employed in statistical analysis or graphical modeling.

Data analysts might also get assistance when reporting or discussing results. Tableau and Power BI are data visualization and analysis applications for gathering information, doing data analytics, and disseminating results through dashboards and reports.

Other technologies to aid data analysts are also evolving. SAS is an analytics platform that can help with data mining, whereas Apache Spark is an open-source platform that can analyze enormous amounts of data. Data analysts today have a wide range of technology skills to increase the value they provide to their organizations.

Applications of different kinds of data analytics

Data analytics may also be divided into quantitative and qualitative data analysis. The former entails the examination of numerical data, including measurable factors. These factors can be statistically compared or quantified. The qualitative method is more interpretative, focusing on comprehending the subject matter of non-numerical data such as text, photos, audio, and video, along with ordinary words, themes, and points of view.

At the application level, business intelligence (BI) and reporting give entrepreneurs and corporate employees actionable information on key performance metrics, business operations, customers, and other topics. BI developers who worked in IT used to build data queries and reports for end users. Self-service BI systems, which allow executives, business analysts, and operational personnel to perform ad hoc queries and produce reports independently, are becoming increasingly popular.

Data analytics programs enable a wide range of corporate applications. For example, banks and credit card firms evaluate withdrawal and spending habits to avoid fraud and identity theft. E-commerce enterprises and marketing service providers use clickstream analysis to identify website users likely to buy a specific product or service based on navigation and page-viewing behaviors. Healthcare institutions mine patient data to assess the efficacy of cancer and other illness therapies.

Mobile network providers analyze customer data to estimate churn, allowing them to take preventative measures to keep consumers from defecting to rival suppliers. Companies use CRM analytics to segment consumers for marketing campaigns and provide call center staff with up-to-date information about callers to improve customer relationship management efforts.

Drawbacks of data analytics

·       Inconsistency among teams

A small group of team members may perform data analytics, and the results can be shared with a small group of executives. Nevertheless, the knowledge created by these teams is either insignificant or has a minor influence on organizational metrics. This might be due to a "silos" approach to working, with each team using their methods and unconnected from other departments. Analytics should be focused on providing the correct answers to business questions. The results produced by data analytics teams ought to be accurately conveyed to the right employees to power the suitable set of actions and behaviors that can benefit the company.

·       Bias & Complexity

Some of the analytics tools businesses create are more akin to a black box paradigm. What is inside the black box is unclear, as is the logic the system employs to learn from data and develop a model. Although using these tools is simple, no company understands the logic behind decisions. Suppose firms are not cautious, and a low-quality information set can be used to train the model. In that case, hidden biases in these systems' conclusions may not be readily apparent. Organizations may breach the law by discriminating against race, gender, sex, age, etc.

·       Lack of dedication and patience

Analytics solutions are not difficult to install, yet, they are expensive, and the return on investment is not quick. It may take time to put systems and procedures in place to collect data, especially if previous data is not accessible. Analytics models, by definition, grow in accuracy over time and need the commitment to apply the solution. Because business users do not see immediate results, they may lose interest, resulting in a loss of credibility and the models failing.

Data science vs data analytics

As automation advances, data scientists will increasingly focus on business requirements, strategic supervision, and deep learning. Business intelligence data analysts will increasingly focus on model generation and other regular duties. Generally, data scientists spend their efforts generating broad insights, whereas data analysts concentrate on addressing particular queries. Data experts, in the future, will need to concentrate on the machine learning operations process, known as MLOps, regarding technical capabilities.


In a world that is more reliant on information and statistics, data analytics assists individuals and companies in ensuring the accuracy of their data. A set of raw statistics may be converted into instructive, instructional insights that drive decision-making and deliberate management using various tools and methodologies.