BI in Banking: Moving Towards a Customer-Centric Business Model

Banks are facing enormous amounts of changes today. Fundamentally, the business model is changing from a product-centric model to a customer-centric model. The customers are demanding more convenience and more interactive features from their banks so that their ease of interaction with the banks improves dramatically. These are leading to changes in the business operations of banks to deliver meaningful services to customers.

Effect on core-banking systems

Core-banking systems are at the heart of all these changes because a bank’s ability to deliver customer-centric products and services are dependent on the architecture of the core-banking system. Earlier, when the banks followed a product-centric business model, their core banking system was able to deliver the right products and services. As the business model is changing, it is become more and more challenging to the same core banking systems to manipulate and change in order to meet and deliver the customer-centric requirements.

Instead of completely changing the core system, it is about re-structuring the core system because the earlier core systems were designed as monolithic systems which had embedded business processes, logic and rules inside the system. The business is demanding that these embedded processes, rules and logic be exposed so that they have a faster, better and an agile way to combine them because the degree of change that is coming from customers is so rapid that the change is not able to get done inside the existing core system. So, re- structuring them is a fundamental way of increasing their agility and flexibility to deliver new products and services.

Role of DW/BI systems

Along with changing the core system, there is a need to implement technology solutions which provide a single version of truth across the business, deliver timely and trusted information and aid in efficient decision-making. This can be achieved by a DW/BI system. In the context of enabling a customer-centric business model, three aspects of a DW/BI system clearly stand out, and they are: [2]

• Enterprise-wide Customer Data Integration
• Data Quality and Data Governance
• Master Data Management

Data residing in different sources need to be combined and presented to the users in a unified manner. Customer data can originate from multiple channels, business lines, application systems and databases [3]. An accurate, timely and complete representation of this data needs to be maintained in the data warehouse. Customer Data Integration will support an efficient customer-centric business model which will lead to achieving banks’ ultimate goal of decreasing costs and increasing yield.

For huge volumes of data, internal consistency and correct representation of the real-world construct are extremely important. Both technology and business stakeholders must take responsibility of managing data quality issues. A bank’s data quality solution must detect errors, allow users to create their data quality rules and assist in implementation of these rules. The increase in demand for compliance has necessitated the need for careful handling of data. Before extraction of data from source systems, data of poor quality needs to be identified and handled. Data profiling will find out such data and this will be handled efficiently by the ETL process.

Effective customer data consolidation involves capturing the data accurately in the source systems along with data cleansing/merging tools in the ETL process. The Master Data Management tool provides a single point of reference by managing the critical customer data which will lead to effective management of customer relationships, retaining customers and growing the business. It helps banks to find unique customers, link them to accounts and stretch out such relationships to all the entities involved in the banking world.

There is a tremendous need to acquire and retain customers in banks, and in the financial services industry in general. Managing enterprise-wide customer data effectively leads to maximizing the bank’s growth.

References:

[1] The Data Warehouse Toolkit, 3rd Edition - by Ralph Kimball  & Margy Ross

[2] http://blogs.informatica.com/perspectives/2013/02/15/bankers-insurers-how-customer-centric-are-you/

[3] Wikipedia

BI in Healthcare

According to a 2013 study from Oracle, healthcare providers lose on average $70.2 million annually, or 15% of additional revenue per hospital due to their failure to fully leverage the information they collect[1]. The volume of healthcare data (from CRM, Electronic Medical Records, labs, billing, etc.) is humongous but ‘information’ is less.

Currently, about one-third of the approximately 5000 hospitals in the United States are operating at a loss and another third are just breaking even. Is this an indicator of poor financial health of the healthcare industry? To an extent, yes. However, healthcare organizations are undergoing a data revolution. They are adopting innovative techniques in order to stay ahead in the stiff competition. Although many organizations had recognized the importance of intelligent systems to aid in decision making many years ago, they have now begun to utilize the true power of Business Intelligence!

Why healthcare BI?

BI enables a healthcare organization to:

• Analyze operational data of hospitals
• Evaluate the operations against best practices in the industry
• Improve patient treatment and care
• Make well-informed decisions and provide predictive analytics
• Analyze decisions made in the past to make the necessary operational adjustments

Three significant categories of data for a healthcare organization are: operational, clinical and financial. A DW/BI system captures all of these data in one view to provide a single version of truth. It helps an organization in converting data to information by providing reports and dashboards. This in turn leads to increased effectiveness of operations, increased revenue and decreased expenses, and better quality of service. It also helps in reaching goals and establishing an edge over competitors by providing crucial business insights.

Healthcare DW/BI system Architecture

The above figure shows the process flow of an Enterprise Healthcare DW/BI system.

While designing such a system, some of the Key Performance Indicators (KPIs) [4] to be addressed are:

• Patient Care KPI - Patient wait time during admission, service timeliness, complaints, etc.
• Clinical Data KPI - Cost effectiveness, in-patient admission rates, response time, etc.
• Financial KPI - Net income, operating margin, capital expenses (%), etc. 

BI Adoption Challenges

Initial investment and budget allocation to a BI implementation project is a big obstacle. The one-time upfront costs which could be millions of dollars and the operational costs need to be carefully evaluated. Only a BI plan which involves generating significant tangible monetary returns can do justice to the huge investment. Some of the other key challenges of adopting BI in a healthcare establishment are:

• Heterogeneity and complexity of existing IT systems: Each health department will have its own type of IT systems and will vary in its operational methodologies.
• Resource shortage: The supply of expert BI Analysts and Implementation Consultants does not meet the overwhelming demand for precise decision making in the healthcare industry.
• Complex data: Capturing and analyzing data involving many-to-many relationships between several entities can be challenging. 

The process of adopting BI to create business value begins with defining a BI strategy and roadmap, and then creating the most suitable solution. BI is becoming an essential part of any organization. A healthcare BI solution should be designed by keeping two things in mind - creating immediate business value and providing flexibility to future needs.

References:

[1] http://www.informationweek.com/healthcare/clinical-information-systems/poor-data-management-costs-healthcare-providers/d/d-id/1105481?

[2] https://www.perficient.com/Industries/Healthcare/Business-Intelligence-Analytics

[3] http://www.cedarcrestone.com/blog/?p=504

[4] http://helicaltech.com/blogs/importance-of-business-intelligence-in-healthcare/

Need for Data Governance

According to Wikipedia, data governance is the discipline that embodies a convergence of data quality, data management, data policies, business process management and risk management surrounding the handling of data in an organization. [2]

What is the need for data governance?

In simple words, it is required to support the essence of a DW/BI system - presenting a single version of the truth.

The above image shows the process stages of data governance. “A data governance initiative must build competencies, assign roles and responsibilities and invest in technologies to enable these core processes no matter the scope and scale of your business objectives”. [4]

Let us get into the nitty-gritty of data governance.

Inconsistencies in dimension names and meanings: Reaching a common agreement on the names and meanings of dimensions across an organization having multiple OLTP systems and data marts is a big challenge. In an organization, it is common to have the same keywords, terms and codes meaning different things, or different terms meaning the same thing. For example, a ‘customer’ can refer to individuals in one data mart and organizations in another. The currency notation used in three different data marts (of three departments) can be ‘USD’, ‘US Dollars’ and ‘$’. All of these represent the same currency - US Dollars. Such inconsistencies lead to data quality issues which can have a direct impact on the organization’s revenue.

Begin with conforming dimensions: Conforming dimensions is not an easy task - getting senior managers from different areas of business to agree upon same dimension names, meanings and values especially in today’s ‘agile’ world definitely sounds like a task cut out! What is to be noted here is that everyone need not agree on having the same name for every attribute of every dimension table. Only the important dimensions such as customer, date, product category, etc. need to be identified and conformed. If nothing else, this will reduce the business user’s reconciliation effort and aid in making timely and effective decisions.

Do it NOW: Implementing data governance right away can have a great positive impact on an organization. Delaying the implementation can have many ill effects. For example, as the organization grows in size, it becomes more difficult to get people adjusted to the new data governance policies and standards. Also, if existing issues related to data governance re-occur in the future, the resource consumption for handling such issues increases, thus decreasing the profit margin.

By implementing the data governance business function, the DW/BI systems will provide consistent solutions, help the organization in improving efficiency of developing and implementing new products/features and provide a competitive advantage to the organization by converting the integrated enterprise-wide data to a strategic asset. [3]

References:

[1] The Data Warehouse Toolkit, 3rd Edition - by Ralph Kimball & Margy Ross

[2] http://en.wikipedia.org/wiki/Data_governance

[3] http://www.informatica.com/us/solutions/enterprise-data-integration-and-management/data-governance/

[4] http://blogs.informatica.com/perspectives/2012/10/15/the-process-stages-of-data-governance/
[5] http://www.primedataconsulting.com/blogPDC/data-governance/2010/12/13/value-proposition-why-do-i-need-to-do-data-governance-now/

Make the right decision using a data warehouse

In my previous blog, I discussed about the goals and benefits of a DW/BI system, the primary goal being aiding an organization in converting knowledge into profit through improved decision making. According to Ralph Kimball, ‘the real purpose of a data warehouse is to be the perfect platform for decision-making’. So, how do you make decisions using a data warehouse? How do you solve operational problems faced by organizations?

First, you need to compartmentalize the problem into segments each of which can be conveniently dealt with. Next, you need to identify the principal activities of the organization for which you are trying to solve the problem. Asking the business user questions like ‘What is it like to be a manager of a large company?’ and ‘What is it that you do when you do your job well?’ help in compiling a list of the principal activities and uncovering the Key Performance Indicators (KPIs) used to evaluate the success of an activity. Some examples of KPIs are revenue (marketing), equipment utilization (manufacturing), brand awareness (advertising), etc. KPIs are different wherever you go but it is important to identify them by developing the skill of listening even when you don’t know the user’s job well. These KPIs will give us a good start to designing the data warehouse.

The classic steps of bringing information and making it useful to the users for decision-making (as proposed by Bill Schmarzo) are:

• Publish data, publish reports
• Identify exceptions in the current business operations
• Determine the root causes for the identified exceptions
• Provide decision alternatives
• Track actions which follow the decision

Step-3 is the secret of data warehouse/decision support system: ‘what do the users want to do when they ask the question - Why?’ Suppose you tell the marketing manager (user) of a large retail firm, ‘the revenue (which is a KPI) for this month is low in North America’, he will answer ‘drill down and show me the specific regions where this exception has occurred’. Drilling down is the most fundamental response to step-3. It will reveal the specific regions where the revenue is low.

Another possible answer by the user will be: ‘drill across and show me the various factors that could have influenced this exception’. The possible factors include performance of the organization's competitors in that month, performance of the sales team, etc. Drilling across provides data from other data marts in the organization existing at the same point in time. 

While designing a data warehouse, you need to anticipate drilling down and drilling across multiple data sources beforehand. This is where conforming dimensions in your data warehouse will play a huge role - you will have an integrated view of your business and the ability to analyze data from different areas of your business. It will also help you in understanding what to do when a KPI is abnormal. All in all, you will be well-equipped to make the right decision!

Thanks for reading. We will explore another interesting DW/BI topic next time.


References:
1. The Data Warehouse Toolkit, 3rd Edition - by Ralph Kimball  & Margy Ross
2. http://www.kimballgroup.com
3. http://en.wikipedia.org/wiki/Key_performance_indicator

Analyze your data. Take well-informed decisions. Make money!

“Data really powers everything that we do” - Jeff Weiner

Majority of my grad courses at Eller College of Management have been driven by data: MIS-587 (Business Intelligence) more than any other course. In the past two weeks, I have learnt quite a lot of Data Warehousing concepts. Most importantly, I have started to feel confident about my understanding of DW basics.

Ralph Kimball and Margy Ross have explained and illustrated concepts such as facts and dimensions, star schema, OLAP cubes and various dimensional modeling techniques in their book 'The Data Warehouse Toolkit'. In this post, I would like to focus on the goals/responsibilities and benefits of DW/BI systems, and the DOs/DONTs of BI implementations.

A DW/BI system aids an organization in converting knowledge into profit through improved decision making. Apart from this, some of the goals of such a system are:
·   Providing simplicity and ease of use to the end (business) users
·   Improving the quality, timeliness and consistency of information
·   Analyzing the changing trends and adapting to changes

While reading about how BI solutions are implemented in organizations, I realized that Ralph Kimball and the likes have taught people how to make a lot of money. Or lose a lot! BI implementations are prone to failure if the IT world does not clearly understand the real needs of the Business world.

A few pointers on avoiding failures in BI implementations:

·   Establish clear business needs

·   Involve users and other stakeholders during the selection and implementation process

·   Choose a flexible BI product - helps in anticipating changes and handling them

·   Evaluate and select the least expensive and the most appropriate data-integration method

·   Provide sufficient business value to BI investments and avoid generic BI solutions

DW/BI systems in an organization are intended to increase productivity and decrease costs. Having a good understanding of the business, a well-documented BI strategy and the befitting tools will lead to a seamless BI implementation.

In conclusion,

Enough said.

Until next time!


References:
1. The Data Warehouse Toolkit, 3rd Edition - by Ralph Kimball  & Margy Ross
2. www.gartner.com
3. www.searchbusinessintelligence.techtarget.in