As a profession, there is no question that business analytics is a vital area of expertise in increasing demand. Information has always been crucial in business, of course, but it is probably fair to say that in the past, many companies and leaders were only interested in the most visible and more obvious data found in the sales figures and the balance sheet – the bottom line – with precious little attention paid to the subtler data that can ultimately make a huge difference to the success of a company. Today, however, all forward-looking businesses are aware of the power of data and are searching for any information that can give them an edge over their competitors. Unsurprisingly, this has led to a boom in business analytics powered by powerful software, widespread data-collecting tools, and brilliant minds.
In this article, we will examine four of the main methods of business analysis: descriptive analytics, diagnostic analytics, prescriptive analytics, and predictive analytics. We will try to understand how they work, what tools they use, and various areas of application. In addition, we will also look at the best way to acquire the skills and knowledge needed to become an expert in this fast-paced and rapidly-developing field.
Widespread Application
Generally speaking, we can say that business analytics is the use of quantitative (or in some cases qualitative) data to gain a broader or more sophisticated understanding of a given area of business. In most cases, it is carried out with an express, pre-defined purpose, such as to increase efficiency, improve decision-making, or gain a competitive advantage in a promising or challenging part of a business. Of course, because this profession relates to the world of business as a whole, there is a wide range of applications, tools, and approaches available to achieve the given objectives or goals.
Naturally, the approach employed depends largely on the aims or objectives in question. Though many people think of analytics as relating predominantly to marketing and advertising, it is actually used in all area of business, including analyzing revenue or sales figures, working with customers or staff members to improve products, shaping manufacturing processes in the production cycle, and identifying the best way to reduce costs. In addition, methods employed in business analytics can also be extended to other areas such as HR, where they can be used to monitor employee satisfaction or distribution, where the focus is likely to be on achieving greater efficiency. So what are the four main types of business analytics that can be applied in these areas, and where are they typically used?
Descriptive Analytics
Gartner describes descriptive analytics as “the examination of data or content, usually manually, to answer the question ‘What happened?’ (or What is happening?).” This is arguably the simplest – or most familiar – form of business analytics, as it typically uses conventional data and employs more basic software such as Microsoft Excel and data visualization tools, mostly to examine current and historical data and to present trends and relationships between different variables. This method is particularly useful for analyzing trends over time. If deemed significant or noteworthy, any identified trends can be examined in detail to help with decision-making. In many cases, further investigation will be recommended using other methods in order to gain further insight.
One of the most crucial and also most recognizable forms of business analysis relates to financial statements, where the company examines the balance sheet, income statement, and cash flow statement to gain an overview of key financial information. Naturally, this is an important step for anyone looking to understand the current situation and historical context of a given company. Business analysts working in this area might also prepare traffic and engagement reports that show the company the development of key metrics – such as web traffic and social media engagement – in current and historical terms. This might help provide decision-makers with an understanding of advertising revenue or the potential for further sales.
Descriptive analytics can also be used to analyze trends related to customer behavior and preferences – for example by monitoring which products are selling particularly well at a certain time of year and promoting them more heavily as a result. In addition, it can also be utilized in conducing market research in order to gain greater insight into consumer choices and how they make decisions, as well as monitoring progress in a particular area.
Diagnostic Analytics
Diagnostic analytics is the next logical step after completing initial descriptive analytics. It is a form of more advanced analytics which takes data or other information to answer why something happened. Diagnostic analytics is based around the concept of hypothesis testing, which refers to the statistical process of either proving or disproving an assumption. It also examines the difference between correlation and causation – identifying whether two seemingly related phenomena are actually connected by causality or are simply statistical coincidences. It also typically employs diagnostic regression analysis, a technique that is used to determine the relationship between two variables (single linear regression) or three or more variables (multiple regression).
Though there are exceptions, diagnostics analytics is usually applied when a company has experienced or identified an unexpected or unexplained positive trend or issue and wants to gain a greater understanding of why it has occurred. For example, diagnostic analytics can be used to examine the reasons behind product demand. Analysts will examine data such as geographic location, demographic data, and consumer preferences to see which variables are most influential and subsequently how better to target the right products to the right people at the right time.
Diagnostic analytics is used to examine not only what customers do but also why they do it. If, for example, people keep cancelling their subscriptions to a product, it pays to know the reasons behind it so that the company can react accordingly. Similarly, if a particular product is selling very well, it can be useful for the company to know exactly why that might be, as this can potentially help them to replicate that success with other products. Diagnostic analytics can also be used to identify technology issues, typically with the help of a software program or algorithm – something that is useful in many areas of business, from consumer sales to manufacturing. In addition, it can also be used to collect insights into the thoughts and ideas of employees, something that can help the HR department in particular to improve company culture and boost employee satisfaction, safety levels, and retention rates.
Prescriptive Analytics
With prescriptive analytics, experts use data or other information to answer the questions “What should we do?” or “What can we do to make ‘XY’ happen?” Prescriptive analytics has been called the future of data analytics because it moves beyond simple recommendations and predictions to use the relevant data to recommend the right course of action based on informed decision making. Though there are many potential tools available, some of the most typical include graph analysis, complex event processing, neural networks, recommendation engines, heuristics, and machine learning.
Though prescriptive analytics can be applied in a wide range of cases, it is most commonly used when a company is faced with two or more possible courses of action and wants to decide which path to take. Typically, it uses data-driven analysis to examine a wide range of data and different factors related to the decision. For example, prescriptive analytics is frequently applied in sales. One example is lead scoring, or lead ranking, where a point is assigned to various actions along a sales funnel whenever they are successfully completed. This enables analysts to demonstrate how likely it is that a particular action will help the company achieve a conversion into a new customer or a sale.
Similarly, algorithms can also be used to help curate content based on customer or user engagement history. Prescriptive analytics is also often applied by venture capitals when making investment decisions, where they weigh all the relevant data they can find with the help of sophisticated algorithms to understand the true potential of a company or idea. In addition, it also finds application in banking, where it can be used to analyze spending patterns and identify anomalous behavior from customers, something that it vital in detecting credit card frauds. It is also popular in project management, where analysts can use a wide range of data from areas such as customer surveys, beta testing, and behavioral user data to identify trends and the reasons for them. Naturally, this helps decision makers to work out the right future direction of a project or particular aspect of a project, while it can also be used to help identify where drastic changes might be necessary.
Predictive Analytics
Predictive analytics describes any approach to data mining that focuses on prediction, entails rapid analysis measured in hours or days, focuses on the business relevance of the insights, and in many cases also emphasizes ease of use or accessibility. Tools employed in this area can include machine learning, AI-based analysis and a range of other sophisticated software, much of which is able to collect and analyze business data in real time.
With predictive analytics, we can say that companies are typically trying to understand possible future scenarios that could occur in order to make better strategic decisions. It can be carried out either manually or with the help of machine-learning algorithms. Regression analysis is once again a useful method for extracting and understanding the data. Predictive analytics is particularly useful in finance, where it can help companies to have a far more detailed understand about their predicted future cash flows, as well as any potential areas of difficulty. Here, historical data from previous financial statements and wider information from the given industry can be used to help project sales, revenues, and costs – an essential part of the decision-making process for any responsible and sustainable company.
In addition, predictive analytics can also be applied in the entertainment and hospitality industry to calculate staff needs; it is particularly useful here, given that far more workers are often required in the peak season, and wild fluctuations are extremely common in general. Other potential areas of application include behavioral targeting in marketing, where consumer data is used to shape and form content, advertisements, and strategies. It can also be used in manufacturing, where predictive analytics can help decision makers understand where and when to take action based on certain scenarios, such as when malfunctions are likely to occur in the lifecycle of a given piece of equipment. Finally, predictive analytics can be absolutely essential in healthcare, where it can be used to not only calculate staffing requirements but also predict health scenarios in both the short and long term, enabling companies and institutions the time to prepare the right level of care in exactly the right areas.
Getting the Right Training
From the above, it is clear that business analytics is a wide-ranging and complicated profession. It is no surprise, therefore, that it has now become an increasingly specialized and competitive position: the best people employed in this area are typically eager to learn, extremely proficient, and very well trained. While there are various online training courses and various learning materials available, many people interested in working in business analytics choose seek out a higher education course that can help them acquire the knowledge and skills required to build a successful career in their chosen field. Though many undergraduate courses do provide a more basic introduction to the world of business analytics, most people who want to excel in the field are more likely to choose to complete a post-graduate degree such as an online business analytics masters. This program from St. Bonaventure University is fully online, and anyone with an undergraduate degree is eligible to apply.
Fortunately, there are now a wide range of courses available in this field. An online Master of Science in Business Analytics from St. Bonaventure University in the US, for example, provides students with insight into the world of descriptive and diagnostic analytics, with even more focus on the cutting-edge fields of predictive and prescriptive analytics. In this way, prospective business analysts are able to develop the skills required to prosper in a wide array of industries by providing vital information to help their companies survive and thrive in challenging business environments. In addition to different forms of analytics, students also have the chance to learn about the fundamentals of analytical programming and the key innovative strategies behind data warehousing. They also gain practical experience in data visualization to ensure they are capable of effective communication of vital information and ideas. The flexible nature of the course means that it is perfect for students in part- or full-time employment, while the fact that it is online means there is no need to relocate for your study.
The Right Path Forward
As we have said, the world of business analytics is multifaceted, with countless tools available, many different approaches, and a huge range of potential applications in all areas of industry. It is also extremely fast-paced. The rate of change is phenomenal, in fact, which means that there can be no guarantee that what is effective and useful now will also work just as well ten or fifteen years down the line. In fact, we can be fairly sure that it won’t.
In order to build a long-term career in business analytics, it is essential to have a genuinely analytical mind and a passion for data. You also need a clear understanding of the bigger picture and high level communication skills to ensure you are capable of getting your ideas across. In addition, both initial in-depth education and also lifelong learning are key. In this field, genuine expertise is truly essential if you want to add a real competitive advantage to your company, and being at the cutting-edge is often the difference between success and failure. For anyone interested in working in this profession, then, it is essential to gain a full understanding of the various branches of analytics, their application, and how we can expect them to develop and evolve in the coming years. Only then will you be ready to step forward and help shape the future.
