Introduction to Business Analytics
Business Analytics is the process through which organizations analyze data using statistical techniques and technologies to gather knowledge and enhance their strategic decision-making.
Businesses rely on four different forms of analytics to help them make decisions: descriptive analytics, which explains what has occurred; predictive analytics, which shows us what might happen; prescriptive analytics, which explains what ought to occur going forward; and Diagnostic which means help to refocus attention from past performance to the present circumstances and identify the variables impacting trends. While many of these strategies can be employed on their own, when combined, they are highly effective.
To answer the very basic questions like what is Business Analytics? What are the various types of Business Analytics? We have curated this write-up for your best understanding. Let’s go!
Decoding The 4 Different Types Of Business Analytics
Today, it would be difficult to find a company that doesn’t employ analytics in some capacity to guide choices and assess performance. By 2022, global spending on Big Data analytics solutions will be expected to exceed $274.3 billion, with small businesses also participating. According to research, approximately 70% of small businesses invest more than $10,000 annually on analytics to assist them in understanding their markets, clients, and operational procedures.
Most executives claim that big data and Artificial Intelligence have helped their company achieve successful results. Data can also significantly affect the bottom line; companies that use big data see an average 8–10% increase in profitability. According to reports, Netflix saves $1 billion annually by enhancing its client retention strategy with data analytics. The next question arises in our inquisitive minds. What data analysis techniques are companies using to produce these great results?
Why Should Organizations Have Business Analytics Teams In-house?
Organizations might opt to use a business analyst or go without one regarding business processes. A cost-benefit analysis must be done before choosing to engage an analyst.
Business analysts support the organization’s understanding of its change needs, evaluation of the effects of the changes, capture, analysis, and documentation of requirements for the organization’s improvement and achievement of particular objectives. A business analyst helps and facilitates communication between stakeholders and the project team in addition to introducing, managing, and making important changes to the organization’s business model.
Certain firms sometimes see business analysts as an extra expense. This couldn’t be further from the truth; in reality, the business analyst role helps cut expenses for the organization and improves the company’s bottom line in various ways.
- Help in Increasing Return on Investment
Businesses use a variety of strategies in an effort to maximize profits while minimizing costs. ROI refers to the value a business receives from a solution it implements after deducting the solution’s cost. Your ROI, as a result, gauges how effective a business solution is. Moreover, the higher the ROI, the more beneficial an investment is. A business analyst influences both of the major ROI determinants. They can make adjustments that boost the value your company derives from the investment solution while also actively lowering the implementation costs by assisting the company in finding cost-effective solutions. The business finally receives a considerably higher ROI with a solution that has a higher return on investment and reduced installation costs.
- Help in Reducing Additional Cost
Business analysts do more than just looking for your company’s most affordable solutions. Their work aids in improving comprehension of project requirements. Focusing project teams on the appropriate criteria decreases project rework.
Types of Business and Data Analytics
Let’s go through the types of Business Analytics in detail:
- Descriptive Analytics
Descriptive analytics is focused on describing the past rather than inferring conclusions or making predictions from its findings. Data aggregation and data mining are two essential techniques used in descriptive analytics to analyze historical data and find patterns and trends. Although they provide insightful information on their own, line, bar, and pie charts are frequently used to illustrate descriptive analytics and frequently serve as the starting point for additional analysis. Any conclusions should be clear for the larger business audience to understand because descriptive analytics use very straightforward analysis methodologies. More than 90% of firms employ it as their most fundamental form of analytics. This is the foundation of all analytics and accounts for 80% of Business Analytics on its own. The goal is to comprehend what is happening and to summarise the outcomes. A company can learn from its historical behavior and how it will affect the future with the use of descriptive analytics. It offers data that makes it easier to comprehend how the company is doing on a global scale. Additionally, it is crucial to present the various stakeholders with the current raw data.
- Diagnostic Analytics
This kind of analytics aids in refocusing attention from past performance to present occurrences and identifies the variables impacting trends. Drill-down, data mining, and other techniques are used to find the underlying cause of occurrences. Probabilities and likelihoods are used in diagnostic analytics to comprehend the potential causes of events. Methods like sensitivity analysis and training algorithms are used for classification and regression. This kind of data also aids in the detection of anomalies and in establishing the causal connection for causes and effects that are present in the data. Data mining, data discovery, and correlations are their defining characteristics. Feature importance, principal component analysis, sensitivity analysis, and conjoint analysis are some of the approaches used at this stage. Probabilities, likelihoods, and the distribution of results are mostly used in the analysis. This sort of Business Analytics, which is the most sophisticated, combines several technologies, including Artificial Intelligence, semantics, Machine Learning, and deep learning algorithms, to apply human intelligence to specific tasks. The objective is to comprehend how a human brain makes decisions and to imitate those processes in a system or computer. Cognitive analytics can be used for a variety of activities, including chatbots, virtual assistants, object recognition in photos, and image segmentation.
- Predictive Analysis
Probabilities are used to estimate what might occur in the future in a more sophisticated form of data analysis called predictive analytics. Prescriptive analytics, like descriptive analytics, uses data mining to determine the likelihood of future events based on historical data. However, it also uses statistical modeling and Machine Learning techniques. Machine Learning algorithms use pre-existing data to form predictions and make an effort to fill in any gaps with the most accurate hypotheses. These forecasts can then be utilized to resolve issues and spot development prospects. For instance, businesses are using predictive analytics to optimize marketing campaigns by spotting cross-selling opportunities, preventing fraud by looking for patterns in criminal behavior, and lowering risk by identifying which customers are most likely to fall behind on payments based on past behavior. Deep learning is a subset of predictive analytics that imitates human decision-making to provide ever more accurate predictions. Deep learning is being used to sort digital medical images like MRI scans and X-rays to provide an automated prediction for doctors to use in diagnosing patients and more accurately predict credit scores through multiple levels of social and environmental analysis.
- Prescriptive Analytics
Prescriptive analytics identifies the optimum course of action for businesses. It employs several statistical techniques and largely draws inspiration from computer science and mathematics. Prescriptive analytics emphasizes practical insights rather than data monitoring, although being closely related to both descriptive and predictive analytics. This is accomplished by compiling information from a variety of descriptive and predictive sources and using it to inform decision-making. Then, algorithms construct and recreate potential choice patterns that might affect an organization differently. Prescriptive analytics are particularly useful since they can assess the effects of a choice based on several future possibilities and then suggest the best course of action to pursue to meet a company’s objectives. The use of prescriptive analytics has significant business advantages. Before making decisions, it enables teams to see the optimum path of action, saving time and money and ensuring the greatest possible outcomes.
- Cognitive Analytics
Artificial Intelligence and data analytics are used to create cognitive analytics. It looks up answers to the questions posed by sifting through various pieces of information in the knowledge base. It is a technology encompassing several analytical methods to analyze huge data volumes and provide unstructured data structure. It is employed to keep an eye on new and changing consumer behavior trends. It can also be applied to corporate intelligence and decision-making. Cognitive analytics searches the whole “knowledge base” at their disposal to find real-time data. It heavily relies on Artificial Intelligence techniques, Machine Learning, deep learning, neural networks, and semantics and frequently integrates all of these. It uses Artificial Intelligence to examine and learn from the given data to uncover useful information buried in data patterns. It gathers and provides these analytics tools with real-time data sources for decision-making, including text, photos, audio, and video. This type of Business Analytics is the most advanced and combines several technologies, including Artificial Intelligence, semantics, Machine Learning, and Deep Learning algorithms, to apply human intelligence to specific tasks.
Business Analytics vs. Data Analysis
Business Analytics focuses on the data’s broader business implications and the appropriate responses, such as whether a corporation should create a new product line or give a certain project priority. Business Analytics refers to a set of abilities, resources, and software that enables organizations to assess and enhance the efficiency of key business operations like marketing, customer support, sales, or IT.
In order to identify patterns and trends, reach conclusions about hypotheses, and support business choices with data-based insights, data analysts must sift through enormous databases. Data analysis aims to answer questions like “What effect does region or season have on consumer preferences?” or “What are the chances that a customer would switch to a competitor?” The term “Big Data Analytics” is also widely used to refer to the practice of data analytics, which includes various methodologies and approaches.
Data analytics is the driving force behind the advancement of forecasts and the adoption of suggested future actions. Knowing when to use the appropriate kind of analytics contributes to the creation of the best business solutions and provides a competitive edge. If you’re looking to start a career in this domain, we’d strongly suggest that you check out Integrated Program In Business Analytics offered by UNext Jigsaw. This expert-curated robust program helps you gain a hands-on learning experience along with obtaining the prestigious IIM Indore certification.