Evidence-Based Workforce Management There Is More To It Than Data Analytics

Data analytics is all the rage. That comment is not to suggest that it is not critical for those who develop and execute workforce management strategies to move aggressively into analytics. In September, 2015 I did a LinkedIn post entitled “Evidence-Based Human Resource Management” and it went viral, with a few thousand hits. I realized the topic was not viewed as esoteric and that there were a lot of people that thought it to be important. Since then several of my posts have focused on some of the tools that are available to inform decision-makers when developing workforce management strategies.

Data analytics can find patterns in data, but only can deal with what is/has been, since there is as of yet no data on the future. Regression analysis can project trend lines into the future but for that to be useful the future must be like the present and the past. Machine learning and AI can make predictions based on models that are created, but they too are based on what is/has been. Again, this is not to minimize the usefulness of using data and the tools to analyze it.

Innovation is valued highly in the dynamic, competitive world of today. Most organizations need to leap ahead of others to gain competitive advantage. Creating a product that has never been or features that have not been available relies on innovation and creative design. And at best analyzing present/past data can provide some insight into what has worked in similar situations.

Another form of evidence is the knowledge possessed by those responsible for creation. The brain is capable of synthesizing past experiences into something that has not been, which suggests that using knowledge to augment data can contribute to projection into a unique future. The Toyota production system utilizes the “5 Whys” approach to explaining what has happened. So if an auto production line is producing a particular type of defect (easily identified using real time data) one can look at correlations and regression analysis to identify potential causes. If one factor seems to be perfectly correlated with the defect it is reasonable to suspect causation. Yet correlation does not establish causation. Even though A is correlated with B it may be that both are determined by a yet undiscovered C. Asking a subsequent “why” can divulge the cause of the cause and if that driving factor is not dealt with the problem may not have been resolved. Doing causal path explorations can be the job of a software model. But identifying the full range of possible contributors to the result takes human knowledge and intuition.

Software systems are available that promise to predict the probability of someone with critical talent leaving the organization. And they can be very helpful. But if a key person leaves because of (at least perceived) mistreatment by a manager and that person to that point had no intention of leaving these models are going to be inadequate. A resourceful analyst can begin a search for unique contributors that had not been present when the predictive model was used. And it takes experience with similar occurrences to know that people exit the rational world if a perceived wrong elevates their emotions beyond tolerable levels. To really explain the loss someone has to go the extra mile to do a wide exploration for contributing factors that were not build into the “likely to leave” model.

Organizations use employee surveys to identify dangerous conditions. A commonly asked question deals with employee satisfaction with their pay. If the survey shows that the vast majority of the population is somewhat dissatisfied with their pay it is reasonable to consider raising pay. But if care is not taken to determine whether this is normal the pay adjustments may do no good. If the analyst makes the effort to search for the normative response it may be discovered that this is exactly the result that could have been predicted (most providers of survey systems have databases with which to compare). Neuroscience and Behavioral Economics research into cognitive biases has shown that we all think we are better than we are, which will certainly impact how we feel about how we should be rewarded. That perception may be incorrect but a person’s perception is their reality. If the median performer in a group believes (s)he is in the 75th or 80th percentile (supported by research) there is apt to be dissatisfaction with the relationship of one’s pay increase to those received by peers. So some of the angst over pay rates may be something an organization has to live with.

Another reason a pay adjustment will not resolve the issue could be that someone feels the pay rate is too low compared to what that person should be paid for what (s)he could do, rather than to low for what is being done. If an analyst goes to the next level to identify that this is an under-utilization problem rather than a pay problem there will be a realization that a pay action will not address the issue. Of course the employee may have an inflated sense of capability but again, perception is reality.

So determining causes within complex contexts may require a Sherlock Holmes guiding the investigation. That detective would be wise to use analytics along the way to identify the cause(s) and perhaps the antidote. But when it is suggested that algorithms will replace people

I respond by suggesting that algorithms, AI, machine learning and big data are ideal for extending people’s reach, not replacing human judgment. It may be that with proper programming these systems could do the whole job but it is going to take skilled, knowledgeable people with extensive experience to guide that programming. People should be increasingly acting as Systems Analysts who turn over the programming to machine learning processes. And by the time we get smart enough to do all that we and probably our children are going to have plenty to keep us busy. And our grandchildren are probably going to be safe too… given the rate of change that is unlikely to slow down there will be work for them.

Bottom Line The awesome technology that is developing, when combined with human knowledge, offers the promise of better decision-making. Evidence-based management requires both.


An Organization’s People: It’s Most Important Asset

My favorite Dilbert cartoon has the pointy-head boss stating that he had thought the employees were the most important asset but was mistaken. When asked what their rank was now the response was “eighth… right after carbon paper.” I have experienced a similar mindset in organizations I have worked with, although carbon paper has dropped in criticality in most of them with the advent of new technology.

My book “The Most Important Asset: Valuing Human Capital,” which published in the Fall 2017, strives to make the case that nothing happens without the right workforce… all the money, technology, infrastructure and customer base will not result in organizational success without the people. Lester Thurow has pointed out that a competent and committed workforce is the only sustainable competitive advantage, since competitors can get everything else under similar terms. He also rightfully suggests that such a workforce cannot be purchased… it must be built and its viability sustained.

Some would argue the value of a competent and committed workforce cannot be determined. “The proportion of the market value of S&P 500 companies attributable to intangible assets rose from 20% to 80% in the 40 years from 1975 to 2015: from 4% of U.S. GDP in 1977 to 105 in 2006.” (Mayer, C., 2016, Reinventing the corporation. Journal of the British Academy, 4, 53-72). Not all intangible assets are attributable to workforce effectiveness (brands, intellectual property, patents and reputation count)but increasingly the market is valuing organizations based on things that skeptics think have no measurable value.

If employees do not perform well, individually and in aggregate, the organization will not do well. Obvious? Then why does it seem not to be universally recognized? The strategies and systems related to workforce management are often not viewed as being critical to success. Getting financial capital under the right terms, getting the best and latest technology and acquiring the state of the art infrastructure are all important. But if the strategies that enable an organization to staff and develop the right workforce are not in place organizational performance will suffer.

When I attempt to convince clients to do workforce planning the reception is often cool. If an organization is to have a respectable chance of successfully competing for top talent in today’s dynamic global labor markets it must utilize tools such as environmental scanning, scenario-base planning and data analytics. But I rarely see that investment being made. And when I contend that how effectively and appropriately an organization defines, measures and rewards performance will directly and significantly impact workforce effectiveness I get agreement in word but often not in deed. Performance management is typically the weakest link in workforce management processes. Today a debate rages about whether an organization should do performance evaluations or continuously measure results and provide feedback to employees. This “either-or” mindset is misguided… the need is for both to be in place. And rewards strategies often motivate destructive behavior. The banks that motivated employees to create and sell products they did not understand almost ordained their doom. Amazingly they paid out huge sums to make sure the employees did what they asked.

There does not need to be a debate to decide which type of capital (financial, operational, technological, customer or human) is most critical. They all are. The title of the book is intended to suggest that human capital should be given its appropriate place among the critical capitals. Success will be unlikely if attention is not paid to all of them. Batting .500 or .750 may put you in the Baseball Hall of Fame but will be inadequate in today’s competitive world when it comes to workforce management strategies. If something is not valued it will not receive adequate investment, and dramatic under-investments in the people that make everything happen are all too common.

Staffing Organizational Units/Functions

You wish to staff the Human Resources function within your organization. It is critical that you have both the depth and breadth of knowledge and skills required. Staffing, Development, Performance Management, Rewards Management and Employee Relations are sub-functions within HR and each has its own body of knowledge and skills. How the function is organized and staffed will determine if what is needed for HR to perform well is in place.
There are two dimensions to the knowledge and skill possessed by the human capital pool in the organization:

  1. breadth and
  2. depth

Specialists tend to have very deep knowledge and skill while Generalists tend to have broader skill and knowledge. Specialists know a lot about a single sub-function while Generalists know a little about multiple sub-functions. So like the person completing a jigsaw puzzle one must know what shape must the pieces have to cover the area. Staffing Compensation with all Specialists ensures incumbents are able to respond appropriately to Compensation problems. But they can be like the person with a tool box containing only a hammer… everything to be “fixed” must be treated as a nail. Generalists may be able to determine what the nature of a problem is, due to a broader range of knowledge, but may also lack the depth to be able to cope with it.

An example of how issues need to be dealt with would be an organization that does an employee attitudes survey and finds that employees feel they are somewhat underpaid. The obvious reflex response is to treat this as a Compensation issue, so the Generalist reviewing the survey results would call that department. But what if a deeper dive into the results is performed by going back to employees and asking them “underpaid for what?” If the response is that they are underpaid for the role they are playing in the organization it still seems like a Compensation issue. But if the response is that they are underpaid for what they are capable of doing the remedy lies outside Compensation’s wheelhouse. Based on the more substantive insight gained by the further exploration the Generalist will probably seek assistance from Specialists responsible for career management and employee utilization. So the Generalist’s breadth, fueled by sound evidence as to the cause of discontent, enables the person to be a traffic manager and to route the intelligence to the right party. But the Specialist role is also critical, since once the nature of an issue is discovered it still needs someone with the expertise to deal with it.

Cross-functional training can be an invaluable remedy for an employee’s knowledge and skill being only narrow or only deep. This does not mean every HR staff member has to have total breadth and total depth. That would take too long, be too expensive and be overkill. So a balance must be achieved. All Generalists would enable HR to diagnose the nature of an issue but lack the expertise to deal with it and all Specialists would result in tunnel vision and an inflated “we can handle any issue” arrogance. But how do the two breeds of practitioner talk to each other, if they lack overlapping knowledge? A third type of person is needed to remedy the Tower Of Babble condition. Perhaps referred to as “Integrators” these staff members have a knowledge and skill base that reaches across and down into multiple sub-functions. Cross-training people in both Compensation and Benefits not only enables them to adopt a total rewards perspective but also enables them to consider strategies that trade off between the two types of rewards and that achieve an optimal balance. Cross-training Staffing professionals and Development professionals enables incumbents to recognize alternative strategies:

  1. hire minimally qualified candidate (who may be less expensive) and invest in development, or
  2. hire fully-qualified candidates (who may be more expensive) but save what development would have cost.

The overall cost may be the same so the strategy should be based on the most expedient and effective approach.


Using a Human Resources function was been for illustrative purposes. The same principles apply to all functions. And for most organizations these principles apply across functions. Project Managers that supervise specialists from multiple disciplines must have an understanding of how to integrate the work of the staff. A Project Manager can be an accomplished Engineer and participate in deciding technical issues or a knowledgeable Administrator that leaves technical details to the Engineers and focuses on other issues. But to be successful there must be an adequate understanding of both technical and administrative issues. Often technical specialists want to make a product as good as it can possibly be, even though that makes it overly expensive in order to enjoy commercial success. But the financial folks may stop short of adequacy in pursuit of cost control. Obviously CEOs cannot know everything about everything so at this level it is obvious that integrating the work of Specialists across functions/units is a critical competence of management personnel. And within functions/units there must also be adequate perspective when identifying issues and dealing with them. You need enough breadth and depth within the talent pool but you also need to invest in the capacity to integrate knowledge. Cross-training is like Education… if you think it is expensive try costing out the alternatives.

Competency Models For Human Resource Management Professionals. Business, Technical and Personal Competencies

To request a copy of Dr. Greene’s competency models, email rewardsystems@yahoo.com


STRATEGIC PERSPECTIVE: analyzes trends and synthesizes information from all relevant sources; develops vision and works with others to realize; has long-term perspective Knows mission and strategy of organization; looks for ways to meet objectives; understands the need to frame decisions and actions in broad context Understands how HR strategy and programs fit into organization strategy; designs HR programs to support strategy; evaluates effectiveness of programs in facilitating success Assists in formulating HR strategy and plans; projects future objectives for programs and plans to replace or revise them so they will fit the objectives as they change
ORGANIZATIONAL KNOWLEDGE: knows the organization; (context, products, customers and financials); has understanding of functional roles; selects strategies/ plans based on clear objectives and their expected impact on organizational results Knows about organizational context, its culture and how it is organized; works to understand roles of functions and business units and their needs; learns about internal and external customers and suppliers and how HR strategies and programs impact them Understands how economic realities impact performance of the businesses and the overall organization; designs and administers HR programs in a manner that contributes to business success Assists in assessing the culture and the organization structure and in reshaping them to fit organizational needs and realities; evaluates the extent to which HR programs support the HR strategy and assists in modifying the strategy to be effective given external and internal realities
BUSINESS KNOWLEDGE: knows about industry and related industries; understands economic/ competitive forces; knows what is required for success; knows what knowledge/skills are critical and labor market realities for them Knows about the economics of the organization and its businesses; works to understand the human capital needs of the organization and the realities of the external environment/ labor markets Understands how economic realities impact performance of the businesses and the overall organization; designs and administers HR programs in a manner that contributes to business success Assists in evaluating HR strategies and programs to determine their business impact; ensures programs are cost-effective and based on sound business principles; evaluates strategy and programs continually to anticipate the need for change
CUSTOMER/SUPPLIER KNOWLEDGE: knows key customers (internal & external) and suppliers and understands their needs/ priorities; adopts strategies to meet their needs and uses programs and processes to meet them Knows about the needs of internal and external customers and how HR programs impact them; develops relationships with customers and works to understand how HR can make them more effective Understands what HR strategies/programs can do to satisfy customers and make them effective; designs and administers HR programs that satisfy customer needs while ensure they are cost-effective Assists in developing an HR service model that identifies needs of customers, suppliers and venture partners and that utilizes cost-effective processes; monitors HR’s performance; adjusts programs as required; recommends modifications to improve service
TECHNOLOGICAL KNOWLEDGE/SKILL: knows about what is available and adopts appropriate tools; searches for new applications of technology based on their probable fit to context and their cost-benefit balance Understands the commonly used tools and is proficient in using them; works to develop knowledge of emerging technologies and how they can be applied in HR Understands how technology impacts HR service levels and cost effectiveness; assists in recommending technology to improve service and/or lower costs Assists in planning the acquisition/ application of technology to increase HR effectiveness; directs implementation and evaluates the impact on service levels and costs


STAFFING: Recruiting; selection; placement; workplace/role design; workforce planning Understands staffing concepts, techniques and processes and develops competence in applying them in program design/ administration Administers staffing programs; makes recommendations on program revisions to improve effectiveness Evaluates effectiveness of staffing strategies/ programs; refines existing programs and develops new ones; directs implementation, communication and training
DEVELOPMENT: Human capital assessment; career planning/management; training; education Understands HRD concepts, techniques and processes and develops competence in applying them in program design/ administration Administers HRD programs; makes recommendations on program revisions to improve effectiveness Evaluates effectiveness of HRD strategies/programs; refines existing programs and develops new ones; directs implementation, communication and training
PERFORMANCE MANAGEMENT: Performance models at all levels; performance planning, measurement, feedback, development and contribution review (appraisal) Understands concepts, techniques and processes and develops competence in applying them in performance management program design/ administration Administers performance management programs; makes recommendations on program revisions to improve effectiveness Evaluates effectiveness of performance management strategies/ programs; refines existing programs and develops new ones; directs implementation, communication and training
REWARDS MANAGEMENT: Direct compensation ; employee benefits; recognition/non-financial rewards; employee ownership Understands rewards concepts, techniques and processes and develops competence in applying them in program design/ administration Administers rewards programs; makes recommendations on program revisions to improve effectiveness Evaluates effectiveness of rewards strategies/ programs; refines existing programs and develops new ones; directs implementation, communication and training
EMPLOYEE/LABOR RELATIONS: Employment policies; health, safety & security; ethics; communication; leadership; legal/ regulatory compliance Understands E/LR concepts, techniques and processes and develops competence in applying them in HR program design/ administration Administers E/LR programs; makes recommendations on program revisions to improve effectiveness Evaluates effectiveness of E/LR strategies/ programs; refines existing programs and develops new ones; directs implementation, communication and training


LEARNING AGILITY/CREATIVITY: Open to new concepts; observes, listens and absorbs new ideas; creates new approaches; adapts to new conditions Develops knowledge of ideas and concepts to create varied repertoire; is flexible in realizing, accepting and adapting to change Actively seeks new ideas and techniques; tries new approaches; accepts contextual change and attempts to adapt to new requirements Scans external sources for new ideas; leads others in search for better ways to design and administer programs.
CULTURAL UNDERSTANDING: Understands the similarities/differences between values and beliefs; open to different approaches; leverages benefits of diversity Develops knowledge of the perspectives of others; actively works to accommodate and respect differences when performing job Evaluates policies and programs to ensure they respect cultural differences; makes recommendations for changes Takes initiative to find approaches to work that will fit the beliefs and styles of others; evaluates policies to ensure they appropriately consider the impact on different cultures
FLEXIBILITY/ADAPTABILITY: Willing to consider new/ conflicting ideas; adjusts to different contexts and requirements; does not resist needed change Open to new ideas; adapts behavior to fit changes Open to new models; searches for behaviors and approaches that will better fit changes in context Open to new paradigms; anticipates need for change and proactively initiates actions to make necessary changes
INTEGRITY/HONEST: Represents beliefs, values and ideas candidly; shapes actions based on laws and principles rather than on expediency Adheres to legal and regulatory requirements and to values/policies; reports violations of laws/ regulations and of organizational values and policies Ensures programs are administered in a manner that is compatible with organizational values; identifies violations and takes appropriate action Acts as role model; helps others develop behaviors that enable them to maintain integrity; takes appropriate action when violations of laws, values
COMMUNICATION EFFECTIVENESS/ABILITY TO INFLUENCE OTHERS: Able to convey information in manner fitting audience; able to influence others to consider alternatives and to accept recommendations Effectively expresses self in manner understandable to target audience; receptive to views of others and exerts appropriate influence Evaluates how well programs have been communicated and recommends how employee acceptance and understanding can be improved Effectively dialogues with all internal and external parties; exerts influence on policies and strategies; develops communication strategies for new programs

Pay For Individual Performance: Pre-requisites For Success

Pay for performance is a concept that should be embraced by every organization. Research tells us that what you measure and reward you will get more of. But that puts the pressure on those who define performance must be sure it leads to the end result they desire. The banking industry severely damaged the economy with ill-conceived incentives… employees were richly rewarded for nearly destroying their own organizations and the investors who trusted them. Defining performance solely as maximizing physical output can result in shoddy quality. And attempting to use pay for performance to maximize individual motivation may result in employee discontent if their cultural orientation prefers group rewards.

Performance at the individual level can be defined in many ways. Productivity, sales, met objectives, quality and cost control are all measures that can be used to measure and reward performance. And there are some measures that are ill-conceived. The majority of public sector entities had historically used time-based step progression to administer base pay. But that rewarded longevity without ensuring the individual warranted a pay increase. Time-based systems and general increases will tend to drive high performers to other organizations, since they can do better where contribution is measured and rewarded. Paying for time spent sends the message that what is contributed does not matter. As a result many public sector entities have replaced time-based progression with merit pay.

There are pre-requisites for success using pay for performance. Readiness to implement pay for performance should be tested prior to implementation. The following assessment questions should be asked and honestly answered.

Assessment Of Readiness For Pay For Individual Performance

Base Pay

  1. Are jobs documented accurately and does the documentation reflect current duties, responsibilities and qualifications?
  2. Are jobs placed into a grade/classification structure in a manner that reflects internal equity?
  3. Are pay ranges assigned to grades that are competitive with prevailing rates in the relevant labor market(s), and do they reflect the organization’s desired posture relative to the market?
  4. Are performance standards established for jobs (in the form of criteria for assessing performance in the job and/or goals for the current period)? Are the standards reasonable?
  5. Is there a defined performance management policy that defines management’s responsibility to establish expectations at the start of the year, continuously measure performance during the year and appraise performance at the end of the year?
  6. Have managers been adequately trained in performance management?
  7. Have employees been informed of the role of their managers and themselves relative to performance management and do they understand how the process works?
  8. Are there policies mandating that performance appraisals be conducted for all employees annually according to an established schedule?
  9. Are there adequate corrective actions if appraisals are not done on schedule, if they are superficial or if the manager and the employee disagree on the rating?
  10. Is there an understood and agreed to model for linking pay to performance?

Variable Pay

  1. Is there a clear consensus on what constitutes organizational performance?
  2. Are there measurements in place for determining organization performance (including both criteria and standards)?
  3. Has organizational performance been cascaded down through levels?
  4. Are there measures in place for determining unit performance?
  5. Are performance criteria, standards and measures been defined for units?
  6. Have unit performance criteria, standards and measures been aligned with each other, to prevent conflicting objectives?
  7. Has a clear philosophy been agreed on relative to who should be considered for participation in variable pay plans?
  8. Is the organization able to commit to expenditures for variable compensation above and beyond base pay and benefits? Are there circumstances that could render financial commitments unaffordable?
  9. Has executive management committed to do the necessary communication/ training to provide employees with enough information about variable pay plans to determine whether they believe them to be equitable, competitive and appropriate?
  10. Have there been past experiences with variable pay that might make it difficult to gain the trust of employees?


Pay for performance can work. But only if pay is appropriately tied to performance. And implementation and administration should occur only after an organization ensures its readiness. Differentiating between employees can have undesirable results if the context is not right. Culturally diverse workforces are a reality for most organizations and that diversity can result in mixed reactions when rewards are based on performance. Whether cultural differences should be considered is a decision that should be made carefully. Doing the same thing for everyone can be administratively convenient but be viewed by everyone as wrong… there will just be different reasons for different people.

Organizational Memory: Undervalued Asset?

The claims that everything is new and that the “old ways” won’t work anymore are overstated. Water utilities strive to provide safe, reliable and affordable water and the way they do it is relatively stable, with the exception of technological innovation in some of the processes. Even software firms must rely on established technology when they develop innovative new products and they use processes that have been developed in the past. The excitement of doing something dramatically different and unique should be moderated by the recognition that another word for “new” is “unproven” and that what has been used successfully is known to be possible. Aspiring to do something new may result in failure. This is not a Luddite condemnation of innovation but rather a perspective that values knowledge and skills that are critical to organizational performance, no matter their age.

Water utilities have a wealth of knowledge resting in both procedure manuals and in the heads of their employees. Where the pipes are buried and how they are hooked up is very important to know and they make every effort to make this knowledge explicit, by writing it down and training employees to utilize it. And software firms utilize existing modules when creating new packages, which makes it necessary to know in detail how those modules work and what is necessary to effectively integrate them with revisions or new modules. For that reason they attempt to thoroughly document logic, coding and routines so that they are available to anyone using the software.

Knowledge comes in two forms: explicit and tacit. Explicit knowledge is algorithmic (there is an established process/set of rules that can be followed) and can be written down and conveyed to those needing it. But tacit knowledge resides in the heads of employees and they find it difficult to “tell all they know.” When I was with a large compensation consulting firm I tried to show other consultants how to develop a good fit salary structure utilizing relative internal job values and external market data. Yet I was apparently unaware of subroutines running in my head when I shot a structure. Simply using a regression tool often resulted in a poor result and I knew I did “something” to modify the formulaic result to make it better, but did not know just what. Several of the other consultants tired of me trying to show them exactly what I did and gave me a budget to do it. This was not an example of knowledge hoarding but a limitation in conveying knowledge that has been gained by doing something again and again. To be an expert you must have put in 10,000 hours of focused experience according to several research studies (see Outliers by Gladwell). We just cannot nail down why we cannot fully explain how we do some things… that is why the Blacksmith – Apprentice model has been used throughout history.

So if much of the critical know-how resides only in the head of employees and they are free to leave at the end of the day on Friday and not return on Monday how does an organization protect the tacit knowledge it so desperately needs? The field of Knowledge Management has become widely recognized as an important part of building and maintaining core competencies. There are many instances of organizations saying farewell to retirees and people leaving for other reasons without asking themselves “what is going out that door that we need and will not have?” Some organizations have developed sophisticated systems to capture knowledge as it is acquired and to make it available to those who might find it valuable. I have worked with organizations to set up simple mechanisms like internal Yellow Pages, which guide those needing knowledge about a system/topic to those who have that knowledge. To be listed is a tangible acknowledgement that someone is the possessor of expertise – it is flattering and can sometimes remind “veterans” they are still valued. Assigning them roles in conveying valuable knowledge can re-energize someone who has been doing the same work for a long time. And it is often the most efficient way to ensure the knowledge is not lost through people leaving.

I just attended the SHRM Foundation annual Thought Leaders conference and the theme was effectively dealing with a multi-generational workforce. Many of the presentations contained the message that bridging the “generational culture” gap was possible but that it took concerted effort and often required processes that differ from more traditional training and mentoring programs. In relay races losing or winning depends largely on the quality of the baton passes, and with five generations in the workforce it means four baton passes must be executed well.

We have become so enamored with big data, analytics and AI that we have forgotten these tools only work when explicit and tangible information is available. There is no way to put electrodes on the heads of employees and successfully retrieve tacit knowledge. Although explanatory models can be developed that seem to capture what went into specific behaviors we still should concern ourselves with knowledge management when we do workforce planning.

There are shelves of excellent books on Knowledge Management in my den and I have used many of the techniques to help organizations save, better use and retrieve critical knowledge. But when I am consulting with organizations on workforce planning (in those rare instances when I convince organizations not to wait until it is just too late to anticipate knowledge loss) I have trouble getting sufficient interest in knowledge management. Preventing tacit knowledge from being lost is much easier than trying to retrieve it or recreate it. But it requires recognition of its value and a willingness to invest in programs and processes to facilitate broad distribution of valuable knowledge and retention of that knowledge. This is not an AARP ad for retaining long service employees. It is an appeal to carefully evaluate what experienced employees know and to have a plan to prevent damaging losses of that which is needed.

The massive cohort we call “Boomers” are leaving and will continue to do so, even though many of them do not prefer an abrupt working/retired dichotomy. For those with valuable knowledge and skills that are difficult and time consuming to acquire organizations should attempt to render explicit as much of their tacit knowledge as possible. And handing the keys of the kingdom over should be a planned and controlled process. Recognizing that what you need is no longer on the premises is not a pleasant experience.

Artificial Intelligence & Human Intelligence: Complementary, Not Competitive

All of the attention being given to data analytics, artificial intelligence, machine learning and other emerging technology is largely warranted. But its usefulness is often over-rated. When a situation is unique there is no “big data.” When innovation requires invention of new outcomes historical data is of little use. Although technology is very often better than people at analyzing data it cannot help if there is no data. Technology can help to answer questions but it cannot decide what questions to ask.

This is not a Luddite condemnation of new technology. I marvel at what is being developed that can help us do analysis we either cannot do or cannot do very well. In graduate school I was forced to do Gauss Jordan pivots manually to solve Matrix Algebra problems. They took up reams of paper and if you made one mistake you were dead. I also had to find an optimal strategy for a hypothetical Admiral who was deploying an entire fleet, using a Simplex algorithm. Just formulating the objective function and the constraint equations came close to blowing a cognitive fuse. I got why I was being subjected to this torture… it made me understand what was going on inside software models and it illuminated relationships that would not have been apparent by just looking at the output of the model. MBA candidates do not warm easily to being shown just how limited their cognitive capacity is. But it is better to know one’s limitations.

So since these technology tools can do some things better than humans is everyone going to be replaced by computers? No, but a lot of people are going to have to cede activities making up some or even all of their jobs to technological tools. That means lifelong learning is mandated…to ensure that what people do to earn a living is still of value. People need to capitalize on their strengths and work around their weaknesses in order to remain viable. Story-telling and deciding what questions to ask are unique human strengths, as is inventing that which has never been. These are things data analytics can help with, but not initiate. This means a partnership between people and technology is the optimal approach. Years ago research into socio-technical systems theory told us technology and those who use it must be compatible for positive outcomes to result. A modern version of people-technology integration is blending inductive pattern recognition with deductive hypothesis-generated discovery. Algorithms and heuristics both need to be used as tools.

An example of how human judgement and technology can complement each other would be an analysis of what impacts employee pay rates. A multiple regression model grinds through data sets to look for correlations between an employee’s pay rate and factors such as education, experience, performance ratings, the grade assigned the job and the pay rate at entry/hire. The model calculates how well the factors used explain pay rate differences. If the r square number is .8 (80% of variance in rates is explained by the factors used) the analyst may conclude that things are working the way they should. However if a second analysis is done using age, race and gender as explanatory factors and the correlation is significant the news is not all good. In a discrimination case involving a large banking organization the statistician was told that an analysis had shown that females were paid equivalently to males in the same roles. The statistician took analysis a step further and using reverse regression found that the females were more qualified and that pay rate parity was not evidence that discrimination was not an issue. So in these cases asking the right questions was as important as calculating correlation and causation in discovering the extent to which discrimination was in evidence.

Given today’s realities it is apparent that humans need to do their part to become a competent partner with technology. This means acquiring an understanding of quantitative methods, no matter what one’s occupation is. An adequate knowledge of statistics, regression analysis, hypothesis formulation and testing, causal path analysis, predictive analytics, formal logic and other tools must be in one’s possession to take advantage of the technology that is available and developing. Going back to school may not require attendance at proms or competing with others to earn a high class standing. Online education has progressed rapidly and a wealth of knowledge is available free or affordably. People’s crowded lives may have to be restructured to replenish knowledge and skill sets and create new ones. It may feel like being on a treadmill… running at high speed just to stay in the same place. But it beats what happens if one stops running on a treadmill that will not stop.

Kahnemann and Tversky, pioneers in Behavioral Economics, identify System 1 and System 2 thinking. System 1 is fast but buggy. People are affected by cognitive bias and that recognition may lead problem-solvers to prefer the “perfectly rational” models created by data scientists. But aren’t data scientists human? Won’t their biases influence the models they create? Confirmation bias is the tendency to more readily seek and accept data that supports what one believes/wants to believe. If you only look for confirming data that is what you will find. There is also a bias that leads people to substitute a simpler rule of thumb to use in decision-making when facing a highly complex problem… the urgent need to find an answer may result in finding one that is not optimal. Oliver Wendel Holmes once said

“I would give nothing for simplicity this side of complexity, but everything the other side of complexity.”

Simplistic answers are worse than uncertainty.

We humans love to make predictions… we just are not very good at it. Evidence suggest we would improve decision quality by experimenting more… making limited tests rather than big bets. Rather than “just knowing customers would love a product like this” why not “let’s test reactions to some of the proposed features and benefits and use the information we get to increase likelihood that our expectations are realistic.” And forcing an algorithm to predict the future when the data used is not adequate to do so is a fool’s game.

Both artificial intelligence and human intelligence are necessary but not sufficient to give us the answers we need. They are both valuable and critical elements of sound decision-making. We must recognizing that they must be complementary and not competitive.

But as already pointed out humans have limitations as well. Both are necessary but not sufficient. They both are critical.