This chapter is from the book
This chapter is from the book
This chapter is from the book
An Overview of Business Analytics
Is there a difference between analytics and analysis? Even though these two terms are being used interchangeably, analytics is not exactly the same as analysis. In its basic definition, analysis refers to the process of separating a whole problem into its parts so that the parts can be critically examined at the granular level. It is often used for complex systems where the investigation of the complete system is not feasible or practical; therefore, the analysis definition needs to be simplified by decomposing it into its more descriptive/understandable components. Once the improvements at the granular level are realized and the examination of the parts is completed, the whole system (either a conceptual or a physical system) is then put together using a process called synthesis. Analytics, on the other hand, is the variety of methods, technologies, and associated tools used for creation of new knowledge/insight to solve complex problems and to make better and faster decisions. In essence, analytics is a multi-faceted and multi-disciplined approach to addressing complex situations. Analytics takes advantage of data and mathematical models to make sense of the ever-so-complicated world that we are living in. Even though analytics includes the act of analysis at different stages of the discovery process, it is not just analysis but analysis, synthesis, and everything else. More than anything else, analytics is a methodology that encompasses a multitude of methods and practices.
Why the Sudden Popularity of Analytics?
Analytics is the buzzword of business circles nowadays. No matter what business journal or magazine you look at, it is likely you will see articles about analytics and how it is changing the way managerial decisions are being made. It has become a new label for evidence-based management (evidence/data-driven decision-making). The question is why analytics has become so popular, and why now? The reasons (or forces) behind this popularity can be grouped into three categories: need, availability and affordability, and culture change.
Need. As we all know, business is anything but “as usual” today. Previously characterized progressively as local, then regional, then national, the competition is now global. Large to medium to small, every business is under the pressure of global competition. The barrier that sheltered companies in their respective geographic locations with tariffs and transportation costs are no longer as protective. In addition to—and perhaps because of—the global competition, customers have become more demanding. They want the highest quality of products and services with the lowest prices in the shortest possible time. Success or mere survival depends on businesses being agile and their managers making the best possible decisions in a timely manner to respond to market-driven forces (rapidly identifying and addressing problems and taking advantage of the opportunities). Therefore, the need for fact-based, better, and faster decisions is more critical now than ever before. In the midst of these unforgiving market conditions, analytics is promising to provide managers with the insight they need to make better and faster decisions, which would improve their competitive posture in the marketplace. Analytics nowadays is widely perceived as the savior of business managers from the complexities of global business practices.
Availability and affordability. Thanks to recent technological advances and the affordability of software and hardware, organizations are collecting tremendous amounts of data. Automated data collections systems—based on a variety of sensors/RFID—significantly increased the quantity and quality of organizational data. Coupled with the content-rich data collected from the Internet-based technologies including social network/media, businesses now can have more than they can handle. As the saying goes, “they are drowning in data but starving for knowledge.” In addition to the data-collection technologies, the data-processing technologies have improved significantly. The machines with numerous processors and large memory capacity make it possible to process considerable and complex data in a reasonable time frame, often in real time. These advances in hardware and software technology are also reflected in pricing, continuously reducing the cost of ownership for such systems. In addition to the ownership model came the software (or hardware) as a service (SaaS or HaaS) business model that allowed businesses—especially small to medium businesses with limited financial power—to rent analytics capabilities and pay only what they use of them.
Cultural change. At the organizational level, there is a shift from old-fashioned intuition-driven decision-making to newage fact/evidence-based decision-making. Most successful organizations are making a conscious effort toward shifting into a data/evidence-driven business practice. Because of the availability of data and supporting IT infrastructure, such a paradigm shift is taking place faster than many have thought. As the new generation of quantitatively savvy managers replaces the baby boomers, this evidence-based managerial paradigm shift will only intensify.
What Are the Application Areas of Analytics?
Even though the business analytics wave is somewhat new, there are numerous applications of analytics covering almost every aspect of business practices. For instance, there are numerous success stories in customer relationship management (CRM) where sophisticated models are developed to identify new customers and up-sell/cross-sell opportunities and customers who have a high propensity to attrite. Using social media analytics and sentiment analysis, businesses are trying to stay on top of what people are saying about their product/services and brands. Fraud detection, risk mitigation, product pricing, marketing campaign optimization, financial planning, employee retention, talent recruiting, and actuarial estimation are among the many business applications of analytics. It would be hard to find a business issue where a number of analytics applications cannot be identified. From business reporting to data warehousing, from data mining to optimization, analytics techniques are used widely in almost every facet of business.
What Are the Main Challenges of Analytics?
Even though the advantages as well as the enabling reasons for analytics are evident, there still are many businesses hesitant to jump on the analytics bandwagon. Even though they may all have their specific reasons, at the highest level, the main roadblocks/hurdles to analytics adaptation can be listed as follows:
Analytics talent. Data scientists, as many people today call the quantitative geniuses who can convert data into actionable insight, are scarce in the market, and the really good ones are difficult to find. Because analytics is relatively new, the talent for analytics is still in the process of development. Many colleges have started master’s and undergraduate programs to address the analytics talent gap. As the popularity of analytics increases, so will the need for people who have the knowledge and skills to convert “Big Data” into information and knowledge that managers and other decision-makers need to tackle complexities of the real world.
Culture. As the saying goes, “old habits die hard.” Changing from a traditional management style (which is often characterized with intuition and gut feelings as the basis of making decisions) to a contemporary management style (which is based on data and scientific models, to base managerial decisions, to data/evidence and collective organizational knowledge) is not an easy process to undertake for any organization. People do not like to change. Change means losing what you have learned or mastered in the past and having to learn how to do what you do all over again. It suggests that the knowledge, or power, you’ve accumulated over the years will disappear or partially will be lost. Cultural shift may be the most difficult part of adopting analytics as the new management paradigm.
Return on investment. Another factor behind analytics adoptions is the difficulty in clearly justifying its return on investment (ROI). Because analytics projects are complex and costly endeavors and their return is not clearly and immediately related, many executives are having a hard time investing in analytics, especially in large scales. One has to answer the question “Will, and if so when, will the value gained from analytics outweigh the investment?” It is hard to convert the value of analytics into justifiable numbers. Most of the value gained from analytics is somewhat intangible and holistic. If done properly, analytics could transform an organization to new and improved levels. A combination of tangible and intangible factors needs to be brought to bear to numerically rationalize investment and movement toward analytics and analytically savvy management practice.
Data. The media is taking about “Big Data” in a positive way, characterizing it as an invaluable asset for better business practices. That is mostly true, especially if the business understands and knows what to do with it. For the others, who have no clue, Big Data is a big challenge. As we will reiterate on the topic later in the book, Big Data is not just big; it is unstructured and is arriving at a speed that prohibits traditional means from collecting and processing it. Not to mention that it usually is messy and dirty. For organizations to succeed in analytics, they need to have a well-thought-out strategy for handling Big Data so that it can be converted to actionable insight.
Technology. Even though it is capable, available, and to some extent, affordable, technology adoption poses another challenge for traditionally less technical businesses. Despite being affordable, it still takes significant money to establish an analytics infrastructure. Without financial means and a clear ROI, management of those businesses may not be willing to invest in needed technology. For those, perhaps an analytics as a service (AaaS) model (which would include both software as well as infrastructure/hardware needed to implement analytics) would be less costly and easier to implement.
Security and privacy. One of the most commonly pronounced criticisms of data and analytics is security. As we often hear in the news about data bridges for sensitive information, there is no completely secured data infrastructure unless it is isolated and disconnected from all other networks (which would be something that goes against the very reason of having data and analytics). The importance of data security has made information assurance one of the most popular concentration areas in information systems departments all over the world. Although the techniques are increasing in sophistication to protect the information infrastructure, so are the methods and techniques used by the adversaries. In addition to security, there are concerns about personal privacy. Use of personal data about the customers (existing or prospect), even if it is within the legal boundaries, should be avoided or highly scrutinized to prevent the organization from bad publicity and public outcry.
Despite the hurdles in the way, analytics adoption is growing, and it is inevitable for today’s enterprises, regardless of the size and industry segment. As the complexity in conducting business increases, businesses are trying to find order in the midst of the chaotic behaviors. The ones that succeed in doing so will be the ones fully leveraging the capabilities of analytics.