Wednesday, April 29, 2009

The future role of Business Intelligence within the global financial community...

The future role of Business Intelligence within the global financial community...

by Michael Brooks

Depending on who you ask, there are 195 countries in the world today. While the majority of economic resources are controlled by a small, yet very powerful subset of global corporations and sovereign nations, the impact of their actions is felt by the entire world community. That the recent economic crisis was triggered by a variety of factors including faulty assumptions, greed, malfeasance, ineptitude, lack of oversight and a host of other causes is not surprising in retrospect.

One of the most important revelations is how interconnected our world has become in the past 50 years. In his speech to the Council on Foreign Relations on March 10, 2009, FRB Chairman Ben Bernanke highlighted the significance of managing the systemic risk of the global financial system. Traditional assumptions around financial institutions once considered “too big to fail” or “too interconnected to fail” are being challenged in light of the enormous cost of the recent crisis to society as a whole. We are quickly understanding how difficult it is to apply traditional risk management practices, paradigms and assumptions to a highly interconnected and increasingly co-dependent world community view.

If the global financial system were characterized as a living organism, the recent crisis would be depicted as a series of rapidly forming blood clots that disrupted the life force and risked the mortality of the patient. The fact that such clots were triggered by a number of interconnected participants (government, banks, consumers, etc., etc.) illustrates the chain of ripple effects that not only disrupted the flow of capital but actually reversed the flow of capital out of the system.

Due to the complexity of this crisis, it’s extremely difficult to determine the triggering effects that caused the integrity of the system to degenerate and ultimately stall. In looking at the number of banks and businesses that failed, it is ironic that many of the participants had also invested millions of dollars in sophisticated business intelligence (BI) systems, processes and consulting firms. Decision makers at all levels were bombarded with conflicting and incomplete information from a wide variety of sources. Clearly the increased insights offered by these sources were insufficient to protect the patient from mortal danger.

So what happened? While the challenges of this crisis will be studied for decades to come, it is apparent that several common themes exist such as:

The majority of BI deployments was biased toward the internal operations of the individual institutions and did not adequately address the interactions between members of the value chain. Individuals, institutions and nations were focused on optimizing their own individual success at the expense of the system as a whole....

The assumptions used in building and implementing traditional BI systems were based on the world as we knew it, not on a holistic view of a global financial system of interconnected parts. Most BI systems were designed based on best practices and lessons learned from historical experience and repetitive practices. The interconnectedness of our world community view now requires a more open model that must factor in external data sources and analyze a variety of constantly changing future possibilities.

The majority of information yielded from BI deployments was based on hindsight and failed to deliver the foresight for management to make decisions based on a wide range of unfamiliar scenarios and risk threats. Most of the major financial institutions have easily invested over $10M in traditional reporting tools, data warehouses and applications that organize and report on historical results and current activity. While forward-looking analysis tools are used in specific departmental applications, they are often subject to latency and data quality limitations in up-stream systems.

There was a widespread presumption that as long as all participants engaged in shifting risk to each other, they would be smart enough to quickly react and cover their bets. The possibility that exposure of one firm would have ripple effect on other firms was not adequately factored in until the wheels of the crises were in motion.

As we begin to pick up the pieces and rebuild the global economy, it is clear that we cannot go back to doing business the old way, and yet we cannot take advantage of the situation by gaming the system through a combination of management practices, new business models, changes in accounting rules and other short-term quick fixes. As an industry, BI professionals and executives have a unique opportunity and responsibility to take a more systemic view of their business and how it relates to other participants in the global financial system.

So what is the role of business intelligence in transforming the global financial system?
In the short run, we must apply the best technology and methods we have to identify and exploit opportunities to revive the financial system while doing a better job of identifying and managing risk through efforts such as:

Increased emphasis on the use of high ROI applications: Applications such as risk management, fraud detection, asset valuation, customer segmentation and others offer enormous payback potential that can directly influence top- and bottom-line performance. Companies must take a balanced approach to managing both performance and risk to do a better job of addressing the potential factors that might quickly erode hard-earned gains.

Focused parallel deployments to reduce risk and achieve higher ROI: Traditional serial approaches to large BI projects can take years to achieve. By that that time, requirements, sponsors, technology platforms and market pressures have changed, resulting in scope creep, higher costs and unmet expectations. Quick-strike paralleled deployments offer faster payback, lower risk and much higher ROI than serial approaches.

Increased emphasis on the connection between the BI investment required and the business case in terms that relate to the ultimate stakeholder: Business intelligence professionals must develop new skills to justify, deploy and exploit new technologies based on a clear, significant and immediate business case.

Core support functions such as finance, IT and HR must use BI/analytics technology as a vehicle to transform their functions to become a more effective partner with line of business departments: Rather than serving as back-office administrative services, these departments must leverage their knowledge of resource management, corporate performance, resource utilization, analytics and other domains to address an array of evolving business models, alliances and product offerings.

In the long run, the private sector and government agencies must develop more effective ways to manage performance and risk at both the local level and between the various participants of the global financial community. To do this, the private sector and governments will need to consider steps such as:

Invest in technologies that are capable of harvesting information more efficiently for specific high value purposes: Information is growing at a faster rate than anything else on the planet, although the federal debt is making a good effort to catch up. As we shift from performing analysis at the local level to looking at more system-wide effectiveness, the potential for dynamically managing globally focused databases will require higher levels of performance and connectivity than exist today.

Develop new methodologies for managing system-wide performance and risk: The industry as a whole can no longer afford to operate with an impaired understanding of their risk profile and rely on gamesmanship to transfer risk to the next participant in the value chain. Better visibility is required at the customer, product, corporate and system-wide level. To do this, new processes, algorithms, management philosophies and business models will be required to operate in an interconnected global financial community.

Increase executive sponsorship to improve their knowledge-based skills and understand the relationship between their actions and the company’s financial performance within the context of the global financial community: As real-time analytics within a global financial system become more commonplace and embedded in the various information systems, it will be necessary for decision makers at all levels to develop new skills and adopt a more holistic awareness of their actions.

Increase funding for educational programs to enhance the knowledge-based skills in all disciplines, not just IT: Business intelligence as we know it is rapidly becoming a thing of the past. Just as IT and other technologies have permeated almost all roles within the company, knowledge-based skills and knowledge re-use are become more critical skills for future labor forces.

For business intelligence to move beyond data movement and reporting, it is important that the philosophy of knowledge utilization and optimization become embedded in our core processes. Decision makers within functions such as risk management, loan origination, application processing, management recruitment, sales and marketing, finance and strategy must all improve their ability to effectively utilize information to help avoid the clotting and sub-optimization of the global financial community. While we’ll find new controls that will provide some degree of comfort, we cannot depend on governments to come up with effective solutions that protect taxpayer dollars when we’ve seen billions in bailout funds disappear before our eyes. Even after spending millions on SOX, Basel II and other initiatives, individuals and institutions still found a way to exploit weaknesses in controls that resulted in the clotting of our global financial system. While it is likely that such problems will occur again in the future, we clearly cannot continue on the present course.

Years ago, Isaac Asimov wrote about a branch of mathematics called psychohistory in which it was possible to use sophisticated algorithms to predict the future course of human events on a massive scale. While we are far from his original concept, we are forced to come to grips with the fact that we must deploy systems and practices that look beyond the boundaries of individual organizations to manage the systemic risk factors that may trigger future clots in the global financial community. This will require a new generation of systems that go beyond the capabilities of traditional BI solutions; vendors must deliver the insight to operate effectively within a much larger, more dynamic system of interacting parts. Finally, we need a fundamental shift in the use of business intelligence that will allow us to react faster and anticipate changes that will occur rather than locking us into assumptions based on the way the world used to operate. We cannot manage the future by simply basing our decisions on the paradigms of the way the world is today or has been in the past – we must place a higher importance on using business intelligence and analytics to address the way the world will be in the future.

Michael Brooks
Michael is President of Checkmate Advisors LLC, a collaboration of strategy, business intelligence and analytics experts with experience in financial services, healthcare, information technology and management consulting.

His experience includes business strategy, process improvement, business development, sales, marketing and solutions development with organizations including Ernst & Young, Unica, Unisys, Theoris, Visual Mining and others. Representative technologies include predictive modeling, customer segmentation, performance management, data visualization, performance dashboards, scorecards, competitive intelligence, text analytics, risk management and data mining.

Michael is known for identifying high-value market opportunities, executing strategies to drive top- and bottom-line growth, building high-performing organizations, and leading business development strategies to acquire market share and increase profitability. He is a recognized thought leader that identifies industry trends and develops pragmatic strategies that move organizations to adopt new technologies and offerings. In addition, he has assisted several organizations in award winning projects, strategic alliances and other recognized programs...

In Advanced Analytic Techniques , we have been taking a quick look at a number of different intel analysis methods (with the results posted on our ADVAT blog) but we have also been examining some methods in a bit more detail.

One of the more interesting experiments was Jeff Welgan's attempt to do competitive intelligence analysis using free, online search engine optimization tools. Search Engine Optimization (SEO), for those of you new to the term, is basically about trying to make your website, blog, etc. easier to find.

Jeff noticed that there are many, many tools to help people do this on the cheap. His thought was that you could use these tools not to aid your own efforts but rather to gather data about a competitor company, organization or even a terrorist group through their web presence. This, in turn, might allow an analyst to gain insight into their strategies and, possibly, their next moves.

For his purposes, he focused on two competitors, Starbucks and Caribou Coffee for his case study. His site, however contains a good bit more data, however. He has included basic background material on SEO, a list of operational definitions, a fairly comprehensive list of online tools, and a concise section on how-to use these tools as an intelligence method.

I think Jeff would be the first to admit that relying on SEO analysis exclusively is kind of like relying on HUMINT exclusively when you have SIGINT and IMINT as well. That said, this approach certainly has the potential to add a rich, structured source of data to bounce off the anecdotal and unstructured stuff that makes up most of what is available to the intel analyst.

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