Creating a Culture of Analysis
The importance of analytics and access to the "right information at the right time by the right people" has been largely heralded as a business imperative and essential to enabling the execution of successful business strategy. Consequently, more and more departmental and enterprise-wide data aggregation and analysis projects have come into existence with applications deployed in hopes to more efficiently and effectively understand "the business."
Despite these nominal developments that might otherwise be taken as presaging a new "culture of analysis" for business management, there is a widening gap between the ability of the new technologies to deliver information beyond management expectations and the actual use of that data by management to make improved business decisions. This article will discuss the technology gap, building a culture of analysis and risk management data application.
The Technology Gap
Companies frequently encounter the widening gap between technology and decisions because as businesses rapidly change with external factors (i.e., the competitive landscape) and internal factors (i.e., mergers and acquisitions, product and business strategy), there remains an inability of associated data structures to keep pace. While different technology vendors and data incompatibilities are often to blame, lapses in defined processes commonly lead to loss of important data. For example, during a company merger, the history and meaning of a company's data, resident only in human repositories, may be lost due to changes in the workforce.
Other times, companies don't have a strategy for managing and using that data, limiting the ability to successfully leverage data analysis into a viable business strategy. Successful analytics are not simply about having access to information, but also taking that information and framing it into a context with which to make decisions. In other words, the best technology in the world is useless if the right people and processes are not in place to leverage that technology.
As companies become increasingly adept at extracting and analyzing data, ironically, they often become slower to act. This "analysis paralysis" is a result of too great a focus on getting as much data as possible and not enough focus on developing strategies based on using the right data. The successful enterprise effectively extracts only the data required to make a decision and quickly turns that data into action. While the importance of analyzing the data cannot be underestimated and undervalued, it must be balanced by processes that quickly impart information to key decision-makers and allow for swift execution of strategy changes when the data uncovers the need. Although a fine line, there is a distinct difference in the results garnered from a "technology dependent" organization and a "technology-enabled" organization.
Building a Culture of Analysis
Technology-enabled organizations have a common set of attributes that allow them to successfully integrate their people, process and technology. These include:
- Executive and top-down commitment to analytics and their applications.
- Flexible, efficient processes to execute new rules.
- Technology that is appropriate and properly utilized throughout the organization.
A commitment to analysis means that a consistent, deliberate and replicable approach to decision making is undertaken when possible. In order for this to happen, there must be an almost reflexive understanding of the value that data provides, the ability to turn that data into actionable analysis and the commitment to follow through on those actions. The elimination of strategies devised on "gut feel" allows an organization to truly assess its successes and failures and define clear-course corrections in a structured manner, resulting in more efficient operations.
Additionally, establishing at least one position of responsibility to be the "human repository" for analysis of data issues will ensure a consistent view of data and allow the analytical culture to permeate the organization. The person in this role communicates and assists in all data-related issues within the organization and also facilitates data analysis externally with consultants, vendors and suppliers. While there are a number of areas for which a business can outsource analysis, a competitive organization must internally commit and retain some analysis faculty.
The development of an analysis culture involves a number of change management practices that will not be discussed in this article; however, the hiring process is one area that cannot be overlooked as an organization moves forward. The hiring of analytic talent should be reflective of the desire for an analytical culture, with an emphasis on acquiring individuals who have developed their analysis skill sets by solving real-world problems.
The natural result of an analytic culture is frequent changes in strategies as new information is uncovered. As the environment changes, the strategy and rules used to make decisions must be modified accordingly. A decision environment and the processes that exist within an organization must allow these shifts to be executed swiftly and frequently. Frequent strategy and associated time-sensitive system rule changes can lead to costly mistakes; therefore, it is imperative that the processes be as automated and error-free as possible. Frequent and routine process review combined with intense quality control and documentation is imperative in creating an environment where analysis can flourish.
Technology for technology's sake is not a healthy strategy for an organization. Simply having the best data or the fastest hardware will not lead to the right decisions. The technology that is used must be able to provide the right information at the right time - no more, no less. Before undertaking any technology project, a thorough scoping of needs and identification of goals is essential. This allows the correct technology level to be identified and implemented, ensuring that the organization is neither saddled with unused capabilities nor hindered by inadequate capabilities.
Improving Risk Management with Process and Technology
Exploring the importance of process with technology, consider a common example in the financial services world. The use of predictive analytics and customer scoring to anticipate financial loss rates at both the account and portfolio level is a standard practice. Countless models have been developed and refined over time to help banks estimate charge-off rates and bankruptcies. While these models are successful at predicting loss rates at the aggregate level, they often do not provide any insight or strategies as to how to reduce these losses or even the information necessary to identify the root cause as to why loss rates change over time.
This is a prime example of how too much reliance on the technology side of analytics can lead to suboptimal results. Simply relying on the output of a model may be adequate for managing the decisions that affect the majority of credit card customers; however, understanding customers on margin - those who exist in a unique state or have experienced a unique event - can help in understanding how to better serve those customers in the future.
This understanding is only achieved when a true analytical culture is embedded within an organization. To illustrate, consider an example where a credit card issuer reviews customers as they move through the late payment cycles; the identification of false positives by the institution's analytic process is essential. The ability to quickly identify accounts that do not fall neatly into a model's framework can make all the difference in treating a customer correctly. While the technology that an organization relies on can make this identification easier, the next logical step of executing a strategy for properly treating the customer is not something that can come from technology. As the users within an organization become more competent with the technology tools available to them, they begin to understand how to leverage that technology to explore the possible outcomes of different strategies as an input for making decisions, rather than as the decision-maker itself.
Advanced analytical capabilities are a necessary but not exclusive characteristic of successful organizations. The push toward data mining and analysis across industries has made using data a more critical activity for success than merely accessing a large quantity of data. While underlying technologies make analysis of data possible, that is only one necessary component of a successful analytic organization. The technology must be integrated as a tool for the making of and acting on results of business decisions. The degree to which an analytic culture is ingrained in an organization determines the practical value of its data, and it also determines how efficiently and effectively that organization can make timely, consistent and strategic decisions based on available data. To derive the highest value from analytic technology implementations, enlightened organizations understand and balance the importance of people as the final users of technology and the business goal-oriented processes that connect technology to users.
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