IN recent times Bangladesh has experienced a plethora of internationally accredited banking software being utilised by a few leading banks. Two obvious objectives drove the banks in such mission which involved cohesive strategy of aligning business units, stakeholders and technology group. The perceived success was driven by the vision of stakeholders and the zeal of senior management team to reap benefits from technology in maximising early return on investment (ROI).
On the one hand, stakeholders wanted to enjoy a feel-good factor by the introduction of new technology, which, after all, was promised to bring in change -- a change in banking culture with the introduction of new policies and operational procedures by reducing duplications through the effective identification and implementation of change management. Senior management within the banks, on the other hand, wanted to benefit from the savvy introduction of technology by the development and introduction of new banking products and services. The perception was that the customers will be able to get better service. Hence the Customer Relationship Management (CRM) portfolio embarked on new Key Performance Indicator (KPI) metrics for satisfaction or dissatisfaction measurement. Some banks also wanted to identify operational pitfalls and to wipe out average performers.
The massive piling of operational data usually creates havoc and requires costly capacity planning for storage. Technology Management Group (TMG) within banks should consult the situation with senior management, hoping to get a sponsorship of a project to ensure that the information asset was utilised adequately. The key business case for embarking on a Corporate Data Warehousing (CDW) and Business Intelligence (BI) project will be to ensure that operational data from banking system and other ancillary solutions reside on a single platform, is characterised, has a time series cascading of presentation, allows aggregation, and overall, ensures consistency, accuracy and 3600 insightful information for improved decision making.
Information is knowledge: Traditional analysis demanded raw data helping to ascertain business trends and profitability analysis while business intelligence emphasised the nature and life cycle of raw data.
The obvious question in a data-driven institution is not 'how much I will accumulate from my newly launched deposit pension scheme (DPS) product for low cost of fund (COF)?' but 'what do I have to do to ensure that my low cost liability product gives me an astounding figure of 220 million in seven months?' The iterative process starts by looking at the knowledge stage of data and goes back to the raw form to provide an assumption. The ability of this predictive analysis yields better results for business.
A recent survey by the PMP Research in the UK revealed the top two reasons for using Data Warehousing or Business Intelligence tools, based on a scale of 1 to 5 -- where 1 indicates 'not important' and 5 'very important -- are to (i) improve the quality of decision making (4.5) and (ii) to increase the accuracy and integrity of their data (4.3).
Today technology has evolved further and given rise to the idea of pervasive BI, a framework that provides all recipients with the access to information that is relevant to their roles and activities within the organisation, delivered over different channels. In today's highly competitive marketplace, including financial institutions and banks in Bangladesh, one must strive to make the best use of the key assets available to them. One such asset is the information used to drive the decision making process. That is why information management has been raised to the top of the CEO agenda and, is not just an issue for CIOs/CTOs or IT Heads. It perhaps then makes sense why a high proportion of users ranked quality of decision making, which is based on correctness of data and timeliness.
But why build a data warehouse?: Banks can increase their competitive advantage and profit by harnessing corporate data as a strategic and tactical tool. One objective should be now to introduce users how a new BI system can be used for them to meet their business goals - in particular how it can make their working life easier and more productive.
In doing so, banks should now focus on identifying information stewards across the business that will align information needs, meta data, representing the thoughts of BI sponsors, power users or super users, expert users and technology group. Such a group then is able to identify how much data is needed from business intelligence and how much from content management - making a new analytical entity called Content Intelligence (CI), as predicted by PMP.
So what are the key KPI areas resulting from a data warehouse? It is very important for banks to clearly define the metrics that are to be used by the enterprise in measuring its performance. Banks should then build departmental data warehouses commonly known as Data Marts that should feed specific relational online analytical processing (ROLAP) capabilities for business analytics within each business units. For example, Finance may want to have a dedicated Data Mart facilitating the population of pre-defined reports, as well as analytics required by senior management and members of the board. Such data mart can also be used for hypothetical scenario building leading to 'what-if' analysis. This, then, provides the ability to predict an unknown future, or new business organisational structure, with current data masking. This exercise is particularly handy while reacting to changes occurring outside in a fiercely competitive marketplace. Jack Welch, the flamboyant ex-CEO of GE once said, "If the rate of change outside of your organisation is ever greater than the rate of change inside your organisation - it's over".
The list of items can be exhaustive but the following is a short catalogue of areas banking management can embark on technology projects in defining a corporate information portal.
Customer Relationship Management (CRM): The objectives for CRM initiatives are to provide business intelligence to achieve bank's objectives in attracting, servicing and retaining customers. So the first item would be to define a CRM Strategy. CRM strategy should not be confused with information catalogue like customer satisfaction, or customer loyalty or a single '360 degree view' of the customer. The key areas to focus by banks while building a CRM Decision Support System (DSS) are:
i. How many customers do you have and who are they?
ii. Who are the most profitable customers?
iii. Which customer segment delivers the largest revenue? (Pareto rule: 80/20 analysis).
iv. How many different product ranges (e.g., back2back LC, corporate loan, BG etc) does the customer buy from you?
v. How loyal is your customer base?
vi. What is your customer churn rate?
vii. What proportion of the customer's wallet do they spend with you? (Making a correlation between CRG data and customer performance report).
viii. How many customers repeat availing funded or non-funded services?
ix. What is the external perception of your bank? (Here some market information is required).
x. Which business area generates highest customer complaints?
xi. Do you know how to generate cross-selling, or up-selling or down-selling with your selected customers?
The above questionnaires then provide a basis for CRM KPIs for effective analysis of information, leading to operational efficiency and profitability measurement.
The author is the former head of IT, Dhaka Bank Limited and
can be contacted at muhammad_kafi@yahoo.co.uk.
