Managing Intellectual Capital Risks in Outsourcing Arrangements

Executive Summary

Current trends in technology and outsourcing are contributing to a substantial fragmen-tation of the “how to” capabilities that are core to any business. The simple fact is intellectual capital erosion is happening at your company, and what most organizations fail to realize is by how much. This essay presents an overview of knowledge management, in particular as it relates to intellectual capital, and the value it brings to organizations in stemming the loss. Intellectual capital is embedded in the thoughts, actions, processes, and capabilities of an organization that keep it viable. A complete knowledge management program will have objectives to identify knowledge assets that are at risk due to architecture evolution, outsourcing, or HR actions. You should consider any area within your organization where knowledge loss is potentially high, such as where systems integration happens outside of your control, where work is done by a 3rd party, or where knowledge workers are likely to leave the company, either voluntarily or involuntary. A key aspect of knowledge management is being able to evaluate knowledge in your organization and you will find knowledge is not easily categorized in black & white. As you contemplate your remediation plan you should give serious thought to the sourcing arrangements at your organization and does your strategy need to be adjusted

Challenges

Intellectual capital loss is a top concern within many IT organizations due in large part to systems architecture extensibility and the tsunami that is cloud computing. Add to this the strong outsourcing appetite companies possess and it means these trends contribute to a substantial fragmentation of the “how to” capabilities that are core to any business. New competencies are emerging and old ones are being deemphasized. The simple fact is intellectual capital erosion is happening at your company, and what most organizations fail to realize is by how much?  Intellectual capital, much like goodwill is a hidden soft asset that you won’t find as a budget line item, and as such intellectual capital is difficult to quantify and measure. We all know the fundamental necessity in properly managing a resource is the ability to measure it.

That said, a wholesale revamp of platforms doesn’t necessarily mean that all the old systems will be decommissioned. In fact as long as customers (and revenue) are reliant on your solutions, then they will be kept operational. It wasn’t long ago that IT organizations could carve out a maintenance team or contract with a 3^rd party to support their legacy systems with relative assurance the underlying architecture would remain stable; however, in today’s environment of platforms having a life span of three years or less, the very concept of what is legacy comes into question. Add to this a highly mobile contracted work force and your staff with core competencies, becomes stretch too thin to replicate their talents. Priorities shift to new platforms and legacy systems have to deal with reduced funding and support, further eroding intellectual capital.

Understanding the risk associated with intellectual capital loss is an important issue IT executives must face now, but more importantly they must go beyond just recognition and they must enact a program focused on implementing a comprehensive knowledge management. This paper presents an overview of knowledge management and the value it brings to organizations in stemming the loss of intellectual capital. I cover an introduction on how to measure intellectual capital and establish a baseline, prevailing mitigation strategies to turn around loss, building a comprehensive life cycle program to manage intellectual capital as a true asset. A critical aspect of knowledge management is that it must be a life cycle program that engages all stakeholders, including internal delivery owners and strategic sourcing vendors.

Definition

Intellectual Capital is a collection of all information sources that in aggregate are core to a business and which includes human, informational, and instructional capital. Intellectual capital is embedded in the thoughts, actions, processes, and capabilities of an organization that keep it viable. Intellectual Capital is an asset, just like other intangibles, such as goodwill and brand awareness, and like goodwill it makes up the sum of the parts that are the value of an entity.

Objectives

A complete knowledge management program will identify knowledge assets that are at risk due to enterprise architecture evolution, outsourcing, or HR actions.  Essentially, you should consider any area within your organization where knowledge loss is potentially high, such as where systems integration happens outside of your control, where work is done by a 3rd party, or where knowledge workers are likely to leave the company, either voluntarily or involuntary. With complex systems and scarce skills making up technology platforms, in order to maintain a high level of availability you must minimize the number of single points of failure. Another objective is to establish an intellectual capital baseline, whereby future assessments can be measured against it. By introducing Knowledge Management in a pilot program that is limited in scope and closely monitored, you will be able to determine the mitigation effectiveness, the required level of resource involvement, and the potential to expand the program to other areas. In addition, the pilot program will test the process and metrics used to score and rank areas of knowledge.

Knowledge management must be a life cycle program and not just a band-aid. A comprehensive program must evolve over time through a series of steps with incremental improvements. Knowledge management program objectives should include:

1.     Strong understanding of the cause and risks of intellectual capital erosion throughout the enterprise

2.     Identify and engage appropriate stakeholders

3.     Establish an intellectual capital baseline

4.     Development an assessment plan and tool

5.     Prepare for and prioritize remediation options

6.     Roll out a full knowledge management life cycle program

7.     Communicate ongoing status and progress

Scope

For purposes of this paper the majority of the discussion is focused on work outsourced to 3^rd parties, which is the area where intellectual capital erosion risk is the greatest. Not only do you find a large amount of intellectual capital erosion in outsourcing, but also because a high percentage of the work is done as Time & Material, you see a rise in the number of single points of failure (SPoF) both inside and outside your company. Having a large number of SPoF who are contractors adds yet another dimension to intellectual capital loss with even a higher level or risk to an organization.

A Knowledge Management program is neither about insourcing nor reclaiming work, but about risk recognition and management.  It’s about developing the discipline to recognize and react to risks to intellectual capital assets. In order to put it into perspective, think of this larger rectangle in the chart as representative all intellectual capital within your organization. The circles within the rectangle then represent particular domains, portfolios, or services.  The dashed line is intended to represent work outsourced to a 3^rd party.

When you take into consideration that today companies outsource on average 40-50% of application development and maintenance, it is inevitable that entire areas (dashed line,) including the associated intellectual capital, will lie within the remit of a vendor.  It’s also inevitable that larger portfolios, where both vendor and your company share services, will see a vendor providing the majority of the services.  It’s within these two scenarios that over time an evaporation of intellectual capital arises.

When defining your pilot program it is beneficial to choose a sample knowledge collection of a size that can adequately test all areas of interest, yet not so big that it overwhelms the effort. I’ve found using statements of work (SOW) is a good approach to evaluating intellectual capital because they are already containerized into a unique body of work and typically of like skills. I also recommend a SOW that is not wholly outsourced to ensure you get both internal and external perspectives when analyzing the data.

Knowledge Categories

A key aspect of knowledge management is being able to evaluate knowledge in your organization and then make informed decision on how it should best be managed. This process will involve certain levels of categorizing and grading knowledge.

By categorizing knowledge you will be able to visualize relationships across categories and see how knowledge reacts in aggregate as a result of change. Since you will be measuring intellectual capital to establish a baseline and to monitor over time, it is important to be able to differentiate knowledge types. Knowledge is a very broad concept, but for purposes of the this paper I categorize knowledge using these four widely accepted terms: 

Tacit, or unexpressed knowledge is exemplified by someone who does their job primarily through intuition and in a manner that is difficult to pass on to others.

Explicit, or highly structured, is the type of knowledge that may be gained through formal learning and certification and which is repeatable and adaptable to others.

Localized, or proprietary, is the type of knowledge that is limited to a small select group of people due in large part to complexity and or confidentiality.

Widespread, or highly diffused, is the type of knowledge that is common among organizations or even industries.

You’ll find knowledge is not easily categorized in black & white, as it can be a blend of any of the four. Having a means to categorize knowledge allows you to evaluate it in groups and you likely will find nuances that are an eye opener.

I-Space Model

The Information Space, or I-Space model, was developed by Max Boisot as a conceptual framework for dealing with knowledge properties by reflecting the relationship among the different categories and the effects of changes to knowledge structure and dispersion. I-Space is an important model because it supports categorization of knowledge between what is considered internalized learning driving actions, such as “gut feel”, and externalized knowledge that can be captured for future reference and more easily learned by other individuals. The value of this model is how it represents the knowledge types that are a blend of the categories introduced in the prior section:

·      Core competencies are tacit and localized knowledge

·      Patents & Copyrights are explicit and localized knowledge

·      Industry-wide principles are explicit and widespread knowledge

·      Conventional wisdom is tacit and widespread knowledge

This model demonstrates how knowledge utility rises as structure increases. For instance a patent has dramatically more utility than just gut instinct; however, scarcity also drives value up when the knowledge is very structured, which can give way to instability if the resource pool becomes dry. By using the I-Space model to plot a knowledge management assessment the aggregate will reflect the relative exposure of intellectual capital loss and areas at greatest risks.

Data Capture

In order to properly analyze knowledge you must establish a means to capture data about the knowledge in your enterprise in a concise high quality manner. For data collection I prefer to use short online surveys because they are non-intrusive and allow people to participate from any location and at a time that is convenient. I have used the survey that is integrated into SharePoint, but most online tools, e.g., Survey Monkey, will work fine. For analysis I export the survey data to Excel for sorting, grouping, and charting.

A survey can be a very simple and straightforward exercise taking as little as 5 minutes or less to complete. When targeting the participants you should ensure you engage front-line individuals with direct “hands on” experience. Ideally, when possible you should utilize both employees and contractors when knowledge spans both areas. An important aspect of a knowledge management program is the capability to harvest knowledge data and convert it to numeric values for purposes of evaluation over time. Data collection for the example is very straightforward and involves these simple steps:

1. Select applicable SOW from drop-down list (If you have multiple SOW’s, then repeat the process for each one.)
2. Identify the scenario that best describes the knowledge sharing level
   
   - Many people both inside and outside the industry have this knowledge
   - Many people in the industry have this knowledge
   - People throughout the organization have this knowledge
   - Many people in one part of the organization have this knowledge- A few people in this organization have this knowledge- Only one person in the organization has this knowledge
3. Identify the scenario that best describes the knowledge structure level
   
   - Relationships among task variables are so well know that outcomes are predictable and reliably delivered with precision
   - Possible to describe relationship among task variables so that general principles are clear and repeatable
   - Tasks performed relying on practical experience but fluctuating environmental changes present new challenges
   - Tasks performed by trial and error because task variable relationships are not clear
   - Tasks performed and the knowledge involved is discussed among peers but process is rarely documented
   - Tasks performed by specialists who do not articulate in a way that allows others to perform same
4. Identify a SPoF who contributes on any services for this SOW
5. Identify a 2nd SPoF (if applicable)
6. Comments for additional information or clarification

The output of the survey is such that it can assign a numeric value to both knowledge structure and dispersion and it is designed so that the selection options present a gradient of values from low to high. By using Excel to analyze the data you can average and plot the results on a two-dimensional grid that depicts where a particular body of knowledge lies on the I-Space model.  Adding yet a 3^rd dimension to the chart will increase the analysis exponentially. A beneficial dimension is to assign a value to a body of knowledge reflecting the magnitude or size relative to the other bodies being analyzed. For this analysis, a particular good addition is to use the dollar value of a SOW. Not only are you plotting the structure and dispersion, but also the risk exposure becomes magnified by plotting the size of each SOW.

Assessment

The rationale for assessing intellectual capital data is to take very subjective material and convert it to a form that can be plotted on the I-Space model. The best means to accomplish this is to engage the direct subject matter experts and stakeholders in a forum that allows them to express their ideas on a particular body of work and to differentiate the level of knowledge structure and dispersion that exist.

Once the survey concludes and the data is analyzed, a chart similar to the example shown will be generated using the I-Space model to represent the baseline Intellectual Capital assessment. Each SOW will then be ranked based on predetermined criteria for:

• Size of SOW
• How much of the work is within a vendor remit
• Single point of failure significance
• Desired movement along axis

With a baseline assessment and prioritized list of SOW’s, you then can reengage with the main stakeholders to define the remediation needs and necessary action plans for risk mitigation. Having a solid intellectual capital baseline will allow future assessments to determine the effectiveness of remediation actions and the overall results of your knowledge management program by documenting accumulative aggregate changes in the trend line. Also, you can judge if individual adjustments are effective for a particular SOW by gauging movement on the grid. (Did knowledge become more dispersed?)

Using this chart to analyze your intellectual capital you can begin to differentiate between core competencies and industry principle. By adding a trend line you can see macro level indicators, such as larger SOWs may be less structured (below trend line) and smaller SOWs may have more single points of failure within your knowledge repository. While this may seem like common sense, the visualization is important in defining remediation actions.

Single Point of Failure

When evaluating information systems there will be single points of failure that are hardware, software, process, and human related.  For purposes of this discussion I focus on the humans aspect and a single point of failure is thus any single person, who alone possesses the only knowledge to support a key application or system.  As with any complex system, the existence of a single point of failure severely limits the overall availability of the system. I believe single points of failure possess similar characteristics across all systems and single points of failure arise within areas where knowledge accumulates over many years on a single application and when firefighting methods of troubleshooting are common.  If you can profile these characteristics and create a recognizable persona, then you should be able to identify potential problem areas and mitigate exposure earlier.

It goes without saying that any single point of failure represents a significant risk to your systems and when these individuals are not employees, e.g., engaged through contracted services, this adds an extra level of risks into the equation.

The chart above represents SOWs from the knowledge baseline where there is one or more SPoF assigned.  When evaluating risks you should prioritize SOWs higher with the least structure and or least knowledge dispersion.

Remediation

As indicated by the I-Space model, knowledge value goes up as knowledge becomes more structured and diffused.  The remediation actions to address intellectual capital risk must entail managing how the knowledge moves along the two axis of the model.  There are a few generally accepted means to effect movement and some are described below.

For example, workshops and seminars are effective in spreading knowledge within organizations. While other means such as apprenticeship and bench strength also diffuse knowledge. There are more tactical approaches, such as rotation assignment and cross training that pinpoint specific exposures.

An effective means of ensuring knowledge dispersion is to require certification at some level. This can be as simple as having people sign-off that they have viewed or participated in a seminar to requiring test to prove some minimum level of proficiency.

As you can see, spreading or diffusing knowledge is somewhat fundamental and can be accomplished by rudimentary blocking and tackling.  On the other hand, adding structure to knowledge where it doesn’t already exist can prove more challenging.  Organizational level initiatives, e.g., CMMi, will add structure, but they can be expensive and lengthy to implement.  In today’s environment of significant outsourcing and an aging workforce, more immediate actions will need to be considered.

• Education
• Work assignments
• Tools
• Sourcing mix
• Incentives

Adding structure to knowledge where the work is outsourced to 3^rd parties can be accomplished by breaking up large SOWs and unbundling into multiple more strategic initiatives. This will also provide the opportunity to evaluate if certain core competencies should be brought back in-house and possible replace with less strategic work.

Sourcing Considerations

As you contemplate your remediation plan you should give serious thought to the sourcing arrangements at your organization and does your strategy need to be adjusted. For instance are quadrants more suitable for one delivery model over another?

• Fixed-bid
• Time & Material
• Hybrid
• In-sourced

Also, are quadrants more suitable for one vendor over another?  Does one vendor perform better with legacy systems? Does one vendor perform better at fixed bid work?

Conclusion

Intellectual capital loss is a top concern within many IT organizations. It is much like goodwill in that you won’t find as a budget line item, and as such it is difficult to quantify and measure. In this paper I discussed how to measure intellectual capital and establish a baseline, prevailing mitigation strategies to turn around loss, building a comprehensive life cycle program to manage intellectual capital as a true asset. A complete knowledge management program will have objectives to identify knowledge assets that are at risk due to enterprise architecture evolution, outsourcing, or HR actions and by introducing knowledge management in a pilot program you will be able to determine the mitigation effectiveness, the required level of resource involvement, and the potential to expand the program to other areas. It’s about developing the discipline to recognize and react to risks to intellectual capital assets. With a baseline assessment and prioritized list of SOW’s, you then can reengage with the main stakeholders to define the remediation needs and necessary action plans for risk mitigation. Knowledge management is a full life cycle program that evolves and experiences incremental gains with each iteration.

Work Cited

Ihrig, M., MacMillan, I, Managing Your Mission-Critical Knowledge, Harvard Business Review (Feb. 2015)