Business Analytics And Business Intelligence Business Essay

Business Analytics And Business Intelligence Business Essay Business Intelligence (BI) is all about getting usable intelligence from data. Using BI you can provide right information to right people at the right time and through right channels to make a competitive advantage by informed decision making. Almost in every organization information is available in abundance as structured data is getting accumulated over the years that is resulting from a variety of operational information systems. Traditional models of operations might not be able to fully utilize the new opportunities presented by this abundance of information. BI systems today can organize, analyze, store and retrieve huge amount of information. We can now work on new organizational models that are agile, and better adaptable to constant change in order to beat the competition and discover new business opportunities. The techniques like Business Analytics (BA) and Business Intelligence are bringing just that to our door steps. What Is Business Analytics Business Analytics deals with the methodologies employed by organizations to enhance their business by making optimized decisions with the use of statistical techniques that might involve data collection and analysis. Business analytics might require many complex techniques that need advanced statistics. Applying Business Analytics, it may be possible to find how a territory or a region reacts to certain product variations or added features. This information can be very useful in devising new product line with features that are likely to maximize sales in a particular region for a set of target audiences. A proper analysis of data might also tell about things like recurring customer support issues and thereby proactive steps can be taken before it grows out of proportion. Business Analytics is often used by marketing folks in predicting and analyzing consumer behavior. This is done by applying statistical analytical techniques on historical data of customer transactions. Without high-quality data and statistics, business analytics can have little or no meaning to any organization. The Difference Between Business Analytics and Business Intelligence Business analysis employs statistical methods and analysis on past business performance to develop new business insights and drive business planning. Business Analysis may use a combination of technologies, skills, practices and applications in its continuous and iterative investigations. Business intelligence on the other hand uses a consistent set of matrices on the past data to measure performance and drive the business planning. BI can also employ statistical techniques like BA. Business intelligence is more of reporting, querying, OLAP and alerts. BA can be used as an input for human decisions or it can fully automate the decision making process. BI can answer what happened in the past, in what numbers, the frequency of occurance, location of the problem, and what corrective actions are needed. BA can tell us why this is happening, what will happen next, what if the trend continues, and more on optimization. What You Can Do With Analytics Analytics can answer information oriented questions like what happened, say, in past two years with sales in a particular region. What is happening now in different regions? And what is likely to happen in near future? Digging dipper may take us to answer questions with more insights like how and why did this happen? Answering this question may require some mathematical modeling and/ or some experimental design. Other insightful questions that can be answered are what is the next best thing to do; and predictions like what worst or best that can happen in a particular scenario? Analytics aims to move towards more insightful answers (to the problems or questions) that an organization needs to know, in order to stay healthy and stay ahead of competition. Its all about informed decision making over intuitive. Its also about giving competitive advantage to managers and reduced risks in decision making. Today Analytics is applied almost everywhere predicting consumer behavior, what will sell, determining price range for a particular market, in supply chain and operations, logistics and finding the best routes for transportation fleet, determining what factors are really driving the financial performance in a company, risk management, investments, issuing credit cards, maximizing sales and profits are just a few examples. Caution While Using Analytics // when not is the time to apply analytics Some decisions have to be made when there is little or no time for data collection; like war or in a sports field. You need to go by your already gathered expertise, intuition and by previous experience. Analytics has a very little role to play in such situations. Some situations have never occurred before and data is simply not available for Analytics to work upon. In some cases like stock markets plenty of data is available but its misleading and cant be used to do any analysis for the future or current actions. There are times when the experience of managers is more valuable over any kind of analysis like valuating plant and machinery for insurance purposes. Off course nothing can replace intuition while selecting a life partner or selecting a gift for the spouse, for that matter.. Almost all results based on Analytics have to be used along with human wisdom. Analytics is based on models, assumptions and data. Anything can go wrong there and blindly applying results might lead to disasters. Analytic models need to be tested carefully first with a controlled set of data that has known results. When tests are successful and results are consistent under test conditions; the models can be used in real time situations. Pre-Conditions To Apply Analytics An organization might have a process orientation with some degree of perfection; be it Six Sigma, Lean, or reengineering or a combination. The key is to identify which attributes in the process are associated with satisfaction and value from the standpoint of the end customer. If you have a comprehensive measurement system for these process attributes, you can perform statistical analysis on your value tree and determine the correlations among business drivers. The classic DELTA framework captures five conditions that are must for any analytical initiative to succeed. Applied to process analytics, these are: Data: This deals with data sufficiency and data quality requirements for analytics process. Sufficient data should be there for experimenting, modeling and testing as analytics demands. This also concerns with technology and management of data used in the whole Analytics process. Enterprise: An enterprise view point is necessary to for effective Analytics. Without that Analytics initiatives may be localized and it will be very difficult to draw any significant benefits at the enterprise level. The process under any Analytics initiative should have the cross-functional or cross-boundary scope to make a difference in overall business performance. Leadership: This calls for cross functional and capable leaders that have strong executive management support. The leadership focus is much more than just one project. Leadership strives for bringing an Analytics based culture in the organization. Targets: Metrics to track these results of Analytics projects. It can be strong customer loyalty, increased performance in supply chain, hiring better matching to skill set requirements, better quantitative risk management and so on. Analysts: They must have capabilities to build the models and derive results. They also bring Analytics culture to the organization by enabling business managers to appreciate and apply it in day-to-day decision making. The readiness of processes in an organization needs to be assessed in all the areas represented by DELTA. All the gaps need to be fulfilled for any meaningful Analytics project to happen in the company. A Typical Responsibility Tree In An Analytics Project The executive management needs to create the overall strategy and choose an information strategy. Middle management and operations managers design business processes and decide how information and knowledge is to be used. Business analysts are responsible for creating reports and Analytics; they are the persons who are responsible for information and knowledge. There is a set of people, who are responsible for data warehouses, data collection. IT infrastructure people are responsible for maintaining databases and technology infrastructure. Analytics Vendors Currently prominent BI and Business Analytics tools vendors are IBM (Cognos), SAP (Business Objects), SAAS Institute (SAAS), R and SPSS (taken over by IBM). All these vendors periodically publish white papers that can be an important source of updates on latest happenings and capabilities of their respective tools. Spatial Analytics Basics of Geographic Information Systems (GIS) Today, GIS is one of the fastest growing fields in Information Technology arena. It has applications in banking, natural resource management, defense, utilities, and government, and many other areas. Maps are fundamental tools used to depict spatial or geographic data. In GIS, we use digital maps for patterns, linkages, or relationships in data in either a single map or multiple maps simultaneously. In its simpler version, GIS provides an automated version of traditional map analysis. A GIS is used to access an integrated, location referenced database of maps that can be superimposed, combined, and analyzed as per user specifications. GIS has advantages over other data management systems in its ability to present geographical relationships in a digital map form where its easy to visualize and comprehend. Creating accurate data sets for a GIS system involves exploring satellite images to identify outlines geographical entities like roads, lakes, rivers, and other landmarks. It is then followed by a ground survey in order to capture names of these entries in the area of interest. There are four components of GIS: (1) data, (2) hardware, (3) software, and (4) users. These components must work in an integrated fashion for any usable inferences to be drawn from a GIS analysis. Some Applications of Spatial Analytics in Banking The GIS mapping of a bank branch involves defining a trade area around a bank branch including profiling of customers in that area. The banks can know about the particulars of the products that are being purchased by specific socio-demographic groups and plan new features/ products accordingly. GIS analysis can examine the interrelationships between land-use, infrastructure capacities, proximities of major civic amenities, and economic growth. Banks can utilize these GIS simulations to take strategic decisions like opening new branches, branch relocation or closure etc. The location dimension that is provided by GIS along with routine data used by classical asset management systems can be very useful in asset maintenance activities like location inspection and monitoring of a class of assets. GIS based solutions for the management and replenishment of ATMs, work in real time and they may provide effective ways to the cash management. GIS based application can show the ATMs on a map with their cash status. Cash requirement for each day for a Bank/ATM can be predicted with the help of historical cash transactions and other demographic data such as population density, economic status For ATM cash replenishment, GPS loaded, GIS based Fleet Management Systems can monitor the real time locations of cash vans in a very cost effective manner. Business Case: Personal Loan Customer Analytics This project involved integrating various ORACLE databases and their analysis in combination with digital maps of Bangalore. It required to plot around 40,000 loan locations and to analyze spatial spreads and correlations. This was done for an associate of a MNC bank. Some output screens from the analysis are included here. SIMS is a Spatial Analytics System developed by SPINFO.