Strategic IT-related decisions, like adopting an enterprise-wide best practice or selecting an IT vendor, can have a lasting impact on a firm’s competitive edge. This can be particularly true for areas like supply chain, customer relationship management enterprise-scale business planning and decision support systems. Unfortunately however, no one set of decision criteria fits all firms, even within a business vertical. Industry analysts can provide generic recommendations about vendor capabilities and consult on best practices, but it is the executive who has full visibility to the requirements of the firm and the characteristics of the employees.
How can IT executives translate their qualitative knowledge into improved strategic decisions? How can they justify these decisions? How can IT vendors factor in the relevant decision criteria into their own product strategies?
The difficult choices faced by an executive during strategic IT decision making can be illustrated in the context of the vendor selection problem. The choices include suite vendors who claim to provide enterprise-wide solutions (e.g. SAP, Peoplesoft, Oracle), best of breed vendors who claim to rise above the mediocre in specific high-priority domains (e.g. i2, Manugistics, Hyperion, Seibel), as well as vendors that are said to achieve “extreme specialization” in focused areas (e.g. Demantra, Optiant, Syncra, Cognos, Salesforce.com, Roadmap Technologies).
The executive needs to balance criteria like maturity of product functionality versus sales and implementation cycle times, customized versus out-of-the- box integration, and expected vendor life versus competitive or cost/revenue advantages from cutting-edge functionality. From the IT vendor’s perspective, the decision criteria are useful for product strategies; especially in light of rapid software commoditization in recent years a phenomenon that has driven the need for scale economies and specialization or “verticalization.”
Buck stops at the IT exec’s desk
The decision to adopt enterprise-wide “best practices” was not enforceable even a few years back, in spite of best intentions. However, the Internet and the e-business models have changed much. The IT executive has greater powers to control decision-making processes even at micro-levels, and consequently have increased accountability, with horror stories like a Nike or a Cisco.
There are several challenging questions and little objective guidance in the marketplace. The marketing literature and guidance from IT vendors might be biased, while an excessive focus on “performance management” (suggested by leading industry analysts) might churn out misleading or inconsequential metrics. Even vendor references, often considered an objective yardstick, might be misleading, as success in software implementation need not correlate with return on investment or key business solutions.
While industry analysts and management consultants can offer guidance, they might have limited insights on the specific business processes and resources of a given enterprise. The final decision and the accountability lie with the IT executive.
Significant impact
The decisions by IT executives, when considered collectively, have far-reaching consequences, spanning industry and academia. The impacts range from business best practices and analyst guidance to product strategies of IT vendors, ultimately reflecting on the interests of academicians and the perceived value of an entire field of study.
As an example, consider the perceived value of “analytics”, broadly construed, within the e-business enabled enterprise. The confusion created by vendor hype has led to not so successful but costly implementations, which in turn has resulted in a perception that advanced analytics is marginally useful for “real-world” scenarios. This has led to a collective de-emphasis of analytics in sales cycles and hiring decisions, influenced industry analysts and IT vendors to sideline these approaches, which in turn has further reduced the perceived value of these areas.
As a consequence, quantitative departments in all but the very top-tier business schools need to justify their existence, caused to a great extent by lack of corporate sponsorships and student interests. This phenomenon, in this case a “vicious cycle” of the adoption of analytical methodologies, has potentially impacted the ability of businesses and managers to harness the power of these approaches in their planning and execution strategies.
While the impact of strategic IT decisions might be easily appreciated, executives rarely have the time or the resources to consider these effects during the crucial decision-making process. At the very least, the best interests of the firm and the executives require that their decisions be guided by their corresponding short-term and long-term objectives. However, the immensity of the challenge often leads to overly simplified solutions, with a reliance on “quick and dirty” evaluations and “gut feel” decisions.
Current processes and pitfalls
The process of e-business vendor selection and adoption of best practices usually proceeds along seemingly well-defined steps. The request for proposal (RFP), sales and pre-sales cycles, implementation pilots, buying decisions and “go-live” cycles are routine and well documented. Problems caused by enforcing enterprise-wide best practices, especially when these result from explicit or perceived recommendations by IT vendors, can cause spectacular failures and case statistics. These, in turn, lead to questions about vendor selection, often resulting in vendor substitution. Introspection and “root cause” analyses can result in additional dollars spent toward analyst and consulting services. These “routine” processes hide significant complexities, as well as the rather subjective and ad hoc nature of the decision-making process.
Too much reliance on subjectivity, not objectivity
Anecdotal evidences suggest the ad hoc nature of decision-making processes. This author, as the manager of strategic products for large and niche IT vendors, has experienced situations where buying decisions have been made without product demonstrations or evaluations, and/or based on subjective criteria ranging from the like-ability or appeal of a salesperson or a senior executive to unjustified focus on the latest buzzwords made popular by analyst firms and vendor marketing literature.
Considerations like product feature/functionality or core competencies on the one hand, and alignment with the “strategic vision” of an enterprise on the other, are often dealt with in a summary and highly subjective fashion.
IT executives need to make quick decisions to remain competitive, for maintaining profitability, and for enhancing their top and bottom lines. The luxury of 20/20 hindsight, available to researchers, is not guaranteed. The proposed framework provides a guideline for better strategic decision-making by executives under these constraints.
A proposed framework
In many ways, strategic IT decision making resembles the science and the art of forecasting. While much might depend on key decisions, the value of seemingly incremental advances in the decision-making process is difficult to quantity, except through their benefits, or (sometimes spectacular) failures. Even these might not be adequate indicators however. A forecaster or strategic decision-maker can only make the best choice given the information available, along with the uncertainty that invariably accompanies the information and the involved processes.
A fair evaluation might not necessarily be how well the end results turned out to be, for these could be caused by external factors unknowable or beyond the control of the forecaster or strategic decision-maker. These factors make the decision-making process rather difficult to evaluate, even with 20/20 hindsight. However, the value of better decisions is that these yield better returns on the average. In the context of an e-business enabled enterprise, this can make the difference between profitability and growth or despair and decay.
This perceived analogy between the processes of forecasting and strategic decision making suggests taking a closer look at the established wisdom of the former to see if improvements can be made in the latter. This author turned to the works of Allan Hunt Murphy, a brilliant forecaster and statistician who fundamentally influenced his fields of study. An essay by Murphy appeared particularly relevant, where he suggested a strategy for evaluating forecasts. He proposed the following three kinds of “measures”, and called these “Type I”, “Type II” and “Type III”:
- Type I: Do the forecasters utilize the best available information and skills?
- Type II: Do the forecasts agree statistically with the observations?
- Type III: Do the forecasts provide benefits to the end-users?
Of these, the Type II measure is the easiest to quantify and track on an ongoing basis. These can be statistical measures of skill that (for example) compares forecasts with actual observations on an ongoing basis, for successive forecast lead times.
Type I, however, is somewhat subjective, reflecting the nature of the forecasting process. However, the measure, albeit qualitative, is well defined.
Type III rests on Murphy’s belief that forecasts have no intrinsic value and are only useful in the context of their end use. This measure requires a definition of relevant utility metrics. Depicts Murphy’s evaluation measures as a three-dimensional “decision matrix”.
From Murphy to best practices for e-business
Strategic decisions about enterprise-wide adoption of e-business best practices are motivated by the need to understand the past, measure the present, anticipate the future, and react quickly to change. The analogous nature of forecasting and strategic IT decision making will be leveraged to extend Murphy’s formulations, and the decision matrix presented in Figure 1. The forecaster’s best knowledge and skills (Murphy’s Type I) translates to creating, managing and retaining knowledge across the enterprise. The need to measure and track accuracy metrics (Type II) is analogous to measuring the health of the business on an ongoing basis, for management by objectives and by exceptions, both for diagnosis and prognosis.
Finally, the utility of the forecasts in the contexts of their end-use (Type III) translates to strategic management, which equates to the aggregate state of the business in terms of stakeholder and market value, as well as the ability to respond quickly to change.
Adoption of best practices for e-business
Type I, “Knowledge management”
Global information visibility as well as knowledge creation, retention and sharing across and among organizations or trading partners, combined with information analysis processes for prediction and change detection. “Knowledge” in this context is broadly defined to include analysis of archived data and best practices, as well as prediction and anticipation of change by human experts, automated analytic tools, and human-computer interaction. These are the core processes that utilize and build the value, character and philosophy of an enterprise.
Type II, “Performance management”
Continuous performance measurement using key performance indicators (KPIs) and evaluation of business processes or decisions, these include monitoring pre-defined metrics that indicate the state of the business both in manual and automated modes, as well as having a process for defining new metrics in anticipation of, or as a response to, change. The performance measures need to be at sufficiently granular levels to enable tactical decisions by business line managers and at sufficiently aggregate levels for C-level executives to be able to feel the pulse of the enterprise as a whole. Measures at different levels of aggregation need to be adequately linked through automated allocation and consolidation mechanisms. These are diagnostic processes that measure the performance of the core knowledge management practices and could be useful as prognostic guidelines for change management.
Type III, “Strategic management”
Processes and mechanisms for controlling the overall health and direction of the enterprise, in a way that maximizes its value to the market and to the shareholder, these encompass the utilization of aggregate level knowledge and performance measures, but include the ability to provide strategic guidance that is quickly adopted throughout the enterprise. Examples of strategic decisions could be changes in the relative emphasis on cost-cutting efforts, revenue generation, profitability, or service levels. These decisions are caused by emerging market or business conditions, or anticipations thereof, and thus need quick translation to tactical if they are to be effective. Adopting best practices that facilitate strategic decisions as well as near real-time enterprise-wide implementation is a key requirement.
The IT executive needs to consider three orthogonal decision variables during the adoption of best practices for e-business: knowledge management, performance management, and strategic management.
From best practices to the most suitable IT vendor
The decision matrix for e-business translates logically to the one for IT vendor selection. The relevant decision criteria are the technologies, tools and application logic that support or facilitate the concepts and business processes discussed earlier. The ability of enabling technologies to support business processes, the degree of support, and the importance of the human factor, have been the topic of much discussion and will not be repeated here.
Type I, “Collaboration and analytics”
E-business technologies that support knowledge management can be broadly categorized into two groups: those that support collaboration within and among organizations or enterprises (i.e. information acquisition, management, visibility and transfer); and those that support information reconciliation and knowledge creation (e.g. analytical tools). Note that “analytics” is broadly construed in this context to include planner or human driven analysis as well as mathematical modeling. The former includes tools for decision support like spreadsheet analysis and OLAP while the latter includes advanced mathematical approaches like data mining and optimization.
Type II, “Metrics and reports”
The ability to define and measure the state of a business through e-business technologies requires the generation of pre-defined and ad hoc metrics, creation of presentation ready reports, continuous monitoring and tracking of key performance indicators at detailed and aggregate levels for business line managers and executives, as well as exceptions and alert mechanisms for handling special cases. The tools should facilitate performance management by fact and by exceptions, in the context of specific business verticals and within the constraints of the enterprise.
Type III, “Breadth of footprint”
The size of a vendor’s footprint encapsulates key decision considerations like the ability to provide a 360° view of the enterprise (for example, integration of back office and front office components), longevity amidst possible vendor consolidation, and the ability to execute and allocate the results of strategic decisions. While larger vendors tend to have an edge in these areas, the ability of smaller vendors to sustain niche positions and provide holistic solutions in their specific areas of expertise can be key considerations. The tradeoff between customized integration of best of breed solutions and “out of the box” integration by suite vendors need to be carefully balanced.
Vendors of e-business applications can be judged on the basis of three broad criteria: collaboration and analytics, metrics and reports, and breadth of footprint.
Managerial insights
The adoption of best practices for e-business, and corresponding IT vendor selection, needs to balance three orthogonal decision variables. Limited resources, opportunities and choices may force an executive to assign relative weights to each criterion. Unfortunately, there can be no one guideline that fits all enterprises and business requirements. However, it is useful to remember that strategic management as defined here is the end-goal, knowledge management is the means, and performance management provides a mechanism to check whether the means are sufficient and well aligned with the end. The executive needs to ultimately decide where in the decision matrix his or her organization fits best, in the context of the required business solutions.
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