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Business intelligence (BI) is a computer based technique used in spotting, digging-out, and analyzing business data, such as sales revenue by products and/or departments, or by associated costs and incomes.

"Business Intelligence is a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making. Business intelligence also includes technologies such as data integration, data quality, data warehousing, master data management, text and content analytics, and many others.

BI technologies provide historical, current, and futuristic views of business operations. The common functions of business intelligence technologies are reporting, online analytical processing, analytics, data mining, business performance management, benchmarking, text mining, and predictive analytics.

Business intelligence aims to support better business decision-making. Thus a BI system can be called a decision support system (DSS). BI uses technologies, processes, and applications to analyze mostly internal, structured data and business processes while competitive intelligence gathers, analyzes and disseminates information with a topical focus on company competitors.

BI applications in an enterprise:

Business Intelligence can be applied to the following business purposes, in order to drive business value.

• Measurement – program that creates a hierarchy of Performance metrics and Benchmarking that informs top management about progress towards business goals

• Analytics – program that builds quantitative processes for a business to arrive at optimal decisions and to perform Business Knowledge Discovery. Frequently involves: data mining, statistical analysis, Predictive analytics, Predictive modeling, Business process modeling

• Reporting/Enterprise Reporting – program that builds infrastructure for Strategic Reporting to serve the Strategic management of a business, NOT Operational Reporting. Frequently involves: Data visualization, Executive information system, OLAP

• Collaboration/Collaboration platform – program that gets different areas (both inside and outside the business) to work together through Data sharing and Electronic Data Interchange.

• Knowledge Management – program to make the company data driven through strategies and practices to identify, create, represent, distribute, and enable adoption of insights and experiences that are true business knowledge. Knowledge Management leads to Learning Management and Regulatory compliance/Compliance

BI in banking:

BI in banking evolved through Manual Systems to management Information systems with Computerization. Banks had efficient transaction recording systems before computerization also. The manual systems too had effectively provided the necessary reports for management and regulatory requirements. These reports were manually consolidated at lower offices and final reports were presented at head office level. These manual systems worked well till the scale of operations of the banks were small.

As the banks grew in size and expanded geographically the number of branch network grew leaps and bounds and so the, the volume of transactions became quite large and manual operations became time consuming and error prone. To cater the load of operations from all bank branches spread across geographies the banks have started using computers and slowly banks have become fully automated.

The manual management information system (MIS) in the banks had the following drawbacks:

• The data is laying in different silos
• There was a Time lag in data collating.
• Data quality is poor.
• Unavailability of customer specific data
• Data granularity required for developing analytics (what if scenario, drill down)
• Was not available to decision makers.
• Reporting activity competed with business activity for resources at the branch.
• Data classification rules were not applied uniformly across the organization, and also varied with time.

Slowly, majority of the banks began using information technology for MIS. The inflexibility of Cobol programmes and batch processing was soon overcome by powerful

desktop systems with rudimentary database systems, which allowed banks to analyse data, once it has been received in manual form from branches, the same was transcribed into machine readable formats and validated. Quite a few of regulatory reports were also produced in this way. These earlier initiatives laid the foundations of BI in banking.

Uses of BI in banking:

Business Intelligence tools can be used by banks for historical analysis, performance budgeting, business performance analytics, employee performance measurement, executive dashboards, marketing and sales automation, product innovation, customer profitability, regulatory compliance and risk management.

Examples of these applications are;

Historical Analysis (time-series)

Banks can analyze their historical performance over time to be able to plan for the future. The key performance indicators include deposits, credit, profit, income, expenses; number of accounts, branches, employees etc. Absolute figures and growth rates (both in absolute and percentage terms) are required for this analysis. In addition to time dimension, which requires a granularity of years, half year, quarter, month and week; other critical dimensions are those of control structure (zones, regions, branches), geography (countries, states, districts, towns), area (rural, semi-urban, urban, metro), and products (time, savings, current, loan, overdrafts, cash credit). Income could be broken down in interest, treasury, and other income; while various break-ups for expenses are also possible. Other possible dimensions are customer types or segments. Derived indicators such as profitability, business per employee, product profitability etc are also evaluated over time. The existence of a number of business critical dimensions over which the same transaction data could be analyzed, makes this a fit case for multi-dimensional databases (hyper cube or ‘the cube’).

Analyzing, interpreting and acting upon on the information is a subjective exercise. Hence, the BI vendor shifted their focus to customer relationship management (CRM). CRM continues to be the centre of the attraction to banks today and risk management comes to second.

Customer Relationship Management (CRM):

CRM is at the centre stage of BI in banking. However, it is becoming difficult to assess whether it is driven by technology or business. Traditional or conservative banking business models of Indian banking industry relied heavily on personal relationships that the bankers of yesteryears had with their customers. If we look into the application of CRM in banking, more closely, CRM is an industry term for the set of methodologies and tools that help an enterprise manage customer relationships in an organized way. It includes all business processes in sales, marketing, and service that touch the customer. With CRM software tools, a bank can build a database about its customers that describes relationships with sufficient detail so that management, salespeople, service people, and even the customers can access information, match customers needs with product plans and offerings, remind customers of service requirements, check payment histories, and so on.

A CRM helps a bank with the following:

• Find customers
• Get to know them
• Communicate with them
• Ensure they get what they want (not what the bank offers)
• Retain them regardless of profitability
• Make them profitable through cross-sell and up-sell
• Covert them into influencers
• Strive continuously to increase their lifetime value for the bank.

The most crucial and daunting task before banks is to create an enterprise wide repository with ‘clean’ data of the existing customers. It is well established that the cost of acquiring a new customer is far greater than in retaining an existing one. Shifting the focus of the information from accounts tied to a branch, to unique customer identities requires a massive onetime effort. The task involves creating a unique customer identification number and removing the duplicates across products and branches. Technology can help here but only in a limited way.

The transition from a product-oriented business model to a customer-oriented one is not an easy task for the banking industry. This is true in case of all the banks of all the banks, Indian or otherwise.

For example, even today, in a tech savvy new generation private sector bank there is no 360 degree view of a customer details. They treat the same way a for a credit card applications to its existing customers as well the new ones.

A retail loan application does not take into account the existing relationship of the customer with the bank, his credit history in respect of earlier loans or deposit account relationship. And the private banks are the pioneers in setting up a data warehouse, and a world class CRM solution.

Most CRM solutions in Indian banks are, in reality, sales automation solutions. New customer acquisition takes priority over retention. That leads to the hypothesis that it is BI vendors that are driving CRM models in banks rather than banks themselves. Product silos have moved from manual ledgers to digital records. An implementation model of ‘relationship’ in Indian banking industry is hard to see as of today.

Most of the BI applications cater to the needs of the top management in banks. But, line managers have a different set of BI requirements, which differ from those of the top management. The line managers of banks require operational business intelligence.

Operational Business Intelligence:

Operational BI embeds analytical processes with the operational business structure to support near real-time decision making and collaboration. This characteristic fundamentally changes the way how data is used, where it exists and how it is accessed.

Thus ‘Operational BI merges analytical and operational processes into a unified whole’. This change is rapidly exposing the limitations of traditional analytical tools. Operational BI helps businesses make more informed decisions and take effective action in their daily business operations. It can be valuable in many areas of the business, including reducing fraud, decreasing loan processing times, and optimizing pricing.

Characteristics of Operational Business Intelligence:

Caters to middle management and frontline:

Operational BI delivers information and insights to those managers that are involved in operational or transactional processes. For example while serving a customer over the phone if a customer executive get a flash on his computer screen on the likely requirements of the customer based on his profile and past transaction behavior. This is an example of operational business intelligence.

Just-in-time delivery:

To manage time sensitive process the needed information should be delivered in near real-time i.e. within minutes or hours. Operational BI will help in reducing user reaction for a business issue. The reduced user reaction time with the help of operational BI can bring business benefits to the organization.

For instance, the ability to detect and react more quickly to the fraudulent use of a credit card is a good example of how operational BI can provide business value.

By analyzing the history of fraudulent situations, the BI system can be used to develop business rules that signify potential fraud, and operational BI can be used to apply those rules during daily business operations. The closer to real time the fraud can be detected; the less is the operational risk.

However, not all operational BI systems need to be near real-time. Reducing action times to close to zero are is beneficial only in specific types of business requirements such as the fraud example. In fact, operational BI can be classified into being demand-driven and event-driven, the latter being more automated. If the action time requirement is a few hours, business users or applications can use the BI system at on-demand analysis and evaluate the results manually to determine whether any action is required. In the demand-driven case, it is the user who drives the BI system.

But if the action time requirement is two seconds, then on-demand will not be suitable. In this scenario BI systems must track business operations continuously
and automatically run analyses to determine whether any action is required. If it is, the business user must be alerted about the situation and sent recommendations on potential courses of action. In case of a fraudulent credit card transaction, the BI system is expected to refuse authorisation. In event-driven BI, business operations and the BI system drive the user. It is obvious that the implementation of event driven operational BI is more complex than demand-driven BI.

Uses recent transactional data

Data used for operational analysis is frequently accessed before getting loaded into the data warehouse. The latency in a traditional data warehouse implementation results from the batch mode in which it is populated. It is more suited for strategic applications such as historical analysis, risk management, performance management etc. But a dashboard needs to be as close to transaction data as technically feasible.

Less aggregation, more granularities

In a sharp contrast to traditional BI in which pre-aggregation, with optional drill down to detail levels is a norm, operational BI normally requires more of data granularity to address the needs of the specific operational function it supports. Traditional BI aims at a holistic view of corporate performance, while operational BI is process and user specific. Yet, some operational BI requirements do require aggregated data, such as the lifetime value of a customer, which is required for a directed sales call.

Embedded into business processes

Operational BI is intricately connected to transactional business processes. The extent of this integration depends on the level of implementation. One could use it to generate operational reports to analyze processes, or monitor them using dashboards and scorecards. In these two levels there is not much of integration.

In the other two levels, where operation BI is embedded into business processes either to facilitate them (demand-driven) or to execute other processes (event-driven), it is embedded into the process.

Handles disparate sources and unstructured data

Traditional databases and data warehouses do not take into consideration the increasing use of unstructured data; such as emails, telephone calls, letters, internal notes etc, stored outside these systems, which are of critical value in an operational BI implementation. Another issue that it has to handle arises out of the disparate transaction systems in use in most of the banks. The variety of banking services makes it very complex and often impractical for a single software solution to handle all kinds of transactions. Extracting data from such disparate systems and making use of unstructured data is required to be handled by an operational BI system.

Availability is a concern

The high level of integration with transactional business processes demand the same level of availability from operational BI implementations that transaction processing systems have to provide. An outage of an operational BI application could have a direct impact on the organization’s ability to do business or to service its customers. Therefore, availability becomes a critical issue for operational BI applications.

Requires different architecture

Traditional BI vendors had built their products using proprietary architectures. While these architectures are ideal for strategic BI, they are not suited for operational BI. Because operational BI entails coupling BI applications with operation applications and operational processes, a component-based, service-oriented architecture (SOA) is necessary to fully support operational BI. Service-oriented architecture that lets users access real-time knowledge with a set of service feeds can maximize business agility while reducing complexity. For example, SOA flexibly and cost-effectively supports the midstream, on-the-fly data collection and analysis necessary for operational BI. Service orientation also supports operational BI throughout the business by pushing BI data out to the mobile workforce and enabling workers across the enterprise to incorporate this vital data into their workflow. The straight-through processing requirements in the banking industry necessitate immediate risk analysis, which in turn requires an online BI capability.


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