The analytical conundrum is on every organisation’s mind. With increased need of making quick and accurate decisions, the concept of decision analytic is becoming prominent. Here is an overview of the new phenomenon.By Jasmine Desai
Business analytics seems to be composed of many layers, like an onion that needs to be peeled one at a time. Decision Analytic seems to be one more such layer and it makes sense to understand it in detail if your organisation is readying to adopt analytic solutions. To begin with, let’s understand what is the difference between real-time analytics and Decision Analytic. Just a thin line separates these two. As per Shekhar Iyer, Executive Global Manager, SAS India, “Decision analytics is a higher level in analytics, wherein real time is an attribute, implying the speed with which you make a decision. One may have various streams of real time data, where there is a combination of events that needs to be clustered to make a decision.” For example, whenever a person enters a country they have to go through a passport check. However if a record shows that two people have checked through a counter almost simultaneously or in a very short time lag, it needs to be checked on suspicion for security reasons. You relate that with some other Interpol information or information from other varied sources. There should be integration between sources of information, which needs processing power to make the process seamless.
According to Prashant Tewari, Country Manager, Cognos & Business Analytics, IBM Software Group India/South Asia, “Decision analysis is the discipline comprising the philosophy, theory, methodology, and professional practice necessary to address important decisions in a formal manner. The objective of a decision analysis is to discover the most advantageous alternative under given circumstances.” Decision analysis includes many procedures, methods, and tools for assessing important aspects of a decision. On the other hand, real-time analytics is the use of or the capacity to use, all available enterprise data and resources when they are needed. It consists of dynamic analysis and reporting, based on data entered into a system less than one minute before the actual time of use. Real-time analytics is also known as real-time data analytics, real-time data integration, and real-time intelligence.
Bhavish Sood, Principal Analyst, Gartner, delves into more details of it, “Decision making is of three types in any organisation: operational, transactional and strategic. Operational would be how many shipments of product do I need to send to the client. Strategic will be what is the kind of possibility for a company in a particular investment. Operational decisions many a times might not require human intervention.” A point in case would be a bookstore, where if you buy a book, loyalty points are updated into the system. Thus, depending on the type of decision, there are various tools and technologies that exist which can be leveraged by organisations.
According to Keith Budge, VP & Managing Director, APJ, Progress Software, “Firstly, the amount of data is increasing very significantly. There is constant pressure on organisations to respond to information more quickly – whether it is to identify an opportunity or a risk, responding quickly gives an advantage. Thirdly, the world is increasingly going mobile.” These three trends have a significant influence on the way organisations need to make decisions. Mobile data is doubling each year, social media provides new streams of information for organisations to manage and monitor, and sensor-based information, whether in logistics, energy, health care, retail and other industries is beginning to proliferate. This changes the way people expect to interact with information but also increases the demand to view and react to information quickly. Organisations needs to make smart, real-time, decisions on big data information. This will be a vital part of decision analytic.
The practice of decision analytic can be integrated in situations across verticals. For instance, Euro Disney has various rides. They have no way to predict that on a particular day which ride will be more popular. However, they have a model built into their systems that gauges how many people are on how many rides with concentration of how many people per ride. Similarly, in Telecom sector, there are real-time decision offers.
Technologies such as Complex Event Processing (CEP) and Business Rules Management Systems (BRMS) are key. According to Budge of Progress Software, “CEP allows patterns in real-time data to be spotted and acted on immediately. The use of BRMS increases the intelligence of these decisions.” E.g. Has a stock price gone up? Has a mobile telco user experienced 5 dropped calls within a 20 minute period? Are the two ATM withdrawals suspicious and should be checked for fraud?
Verticals that have high transactional incidents and the proportion of those can be automated will be adopters of this technology. In Indian context the challenge lies that organisations do not understand that beyond traditional BI and reporting there is a lot that can be done. However, it becoming a part of core infrastructure is bound to happen. Mentions Sood of Gartner, “The stack companies like IBM and Microsoft have better chance at succeeding. BI vendors have started having this capability. The only roadblock seems to be that there are not enough use case to justify the buying of it.”
Traditionally, analytics in growth markets like India has been limited to Research & Development departments of government & corporates. The primary focus for using tools and technologies supporting decision analytics has been data analysis for research purposes. According to Tewari of IBM, “Companies are using analytics to get closer to customers. This has lead to customers getting customized services, their preferences being taken more seriously by the industry.” However, there is also a danger of invading individual’s privacy with Social Media analytical tools.
Today, analytics is being increasingly used and adopted by companies for decision making. On the face of it, application and adoption of analytics is mush faster in industry that is focused on end consumers e.g. BFSI, Telecom and Retail. These industries are also characterized by increasing competition and better customer services for future growth. However, decision analytics is soon making in roads in manufacturing as well. For instance, auto with applications like Predictive Maintenance, warranty analytics etc.
Every organisation is being challenged through having to deal with more information and the pressure to make the right decision faster. So where does one start with? Tewari of IBM sums up well by mentioning, “In case one wishes to start small, Decision analytics can even start from a single laptop or desktop. The user just needs to have clear objective and he must understand the industry well.”
