Key Points
  • No two companies execute knowledge processes in quite the same way. They execute them in various forms, via three methods: internal roles, technology, and the use of third parties
  • Two primary positions that typically shoulder the bulk of an organization's analytic load are the departmental analyst and the “analytics superman”
  • When an analytics problem has a material impact on an organization's cost of doing business or its ability to compete, the technology is generally robust and sophisticated
  • The third method organizations employ to gain knowledge-based insights is outsourcing to a third party a task or series of tasks of which research and analytics is simply a component

No two companies execute knowledge processes – or research and analytics-based support for all aspects of corporate decision making – in quite the same way. Rather, in various shapes, flavors and forms, they execute them via three methods: internal roles, technology, and the use of third parties. 

1. Internal Roles

Many roles in the corporate roster contain at least some responsibility for analytics. We categorize the two primary positions which typically shoulder the bulk of an organization's analytic load as the Departmental Analyst and the “Analytic Superman.” They are worlds apart in terms of individual capability and how they deliver analytics to their company. 

The Departmental Analyst

Departmental analysts are typically hired from MBA programs. They possess good general business sense along with some level of proficiency with spreadsheet programs and above average math skills. While numerically adept, departmental analysts are unlikely to be specialists in any particular knowledge skill set such as statistics. 

Within a few years, the analytics work completed for a few specific business situations positions the departmental analyst for the next line job while his or her analytic models are inherited by the next recently hired MBA. In high-growth environments, the pace of rotation through analyst positions is raised to a speed which makes the institutionalization of knowledge practically impossible. In tough times, the first positions companies cut are these generalist analyst positions. As a result, it is difficult to embed a structure and skill set to turn a company's knowledge into insight. 

The “Analytics Superman”

The Analytics Superman is a math genius or statistics whiz who is generally long tenured within the organization. He or she knows the business inside and out, knows the source and dependability of the data and understands the behavior of the competition. And he or she is exceptionally fluent in statistics and math, the bedrocks of analytics. 

For all this knowledge, the Analytics Superman is highly compensated. Further, as this individual is in many cases the only in-house expert capable of performing the required analyses, requests for insight go in and domain-relevant insights eventually come out, but only in a workload-dictated timeframe. 

Thus, companies deploy the Analytics Superman to solve their important analytic problems. But they cannot afford to hire sufficient supermen – even if they could find them – on a global scale. As a result, this is clearly not a scalable model for a company aspiring to compete with knowledge. Supermen are rare, expensive and reputation savvy, so only first tier companies can retain them. And when they leave the organization for another opportunity it's extremely challenging to find talent to fill their shoes. 

The upshot? The Analytics Superman is a bottleneck in democratizing the consumption of actionable analytics across the organization. Only the largest geographies get attention. And the methodologies are not standardized nor communicated, which results in a black hole if the role is vacated. 

2. Technology

The technology tools companies deploy for analytics vary in their level of sophistication. When an analytics problem has a material impact on an organization's cost of doing business or its ability to compete, the technology is generally robust and sophisticated. These tools typically provide directional “markers” or clues in industry-specific situations such as identification of fraudulent insurance claims, or deal with functional challenges such as intelligent call routing in call center settings. In either case, these technologies can rarely be deployed straight out of the package as the business challenges they address are typically well-bound within a specific function or task. And while they codify the more mechanical aspects of analytics by providing filters and “what-if” scenarios, insight remains the domain of the analyst. The tools also require a high degree of customization to incorporate business-specific situations, processes, policies and rules. In some cases, their biggest value-add is the ability to create work-flows around knowledge processes, enabling diverse stakeholders to provide input and be alerted to tactical decisions being made. 

But the need for analytics extends well beyond these closed-loop environments in which repetitive, mechanistic decisions must be made. And tools, whether spreadsheets or robust packages, are not analytics in and of themselves; rather, they consume analytics. If anything, deploying tools only increases the need for a company to create analytics in the form of algorithms or quantitative business rules that feed and refresh them. So while tools are an important means by which a company can become a knowledge-based competitor, they in no way solve organizations' basic need to analyze their data. 

3. Use Of Third Parties

The third method organizations employ to gain knowledge-based insights is outsourcing to a third-party a task or series of tasks of which research and analytics is simply a component. For example, companies often outsource the analytics and execution supporting direct mail campaigns. 

A number of service providers will, based on the client's input, segment a universe of prospects, and design and distribute a direct mail campaign around a product or service. Another frequently outsourced task is collection analytics in which companies hand over pools of consumer receivables for cents on the dollar to third parties who then use analytics to figure out what they will be able to recover via call center agents. 

Other areas in which third-party outsourcers can assist with research and analytics support are helping clients identify which phase of the sales cycle their end customers are in, determining where R&D dollars are best spent for the probability of greatest commercial success, and understanding the factors that drive employee satisfaction within clients' internal organizations. 

Leveraging the analytics capabilities of third parties with expertise in specific functions, tasks and industries represents a highly viable and valuable option for many organizations. These third parties have the knowledge, specialists, bandwidth, best practices and technologies to deliver the knowledge-based insights companies require to compete as a knowledge player. 

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