Evaluation and Control Program Warning Flag 1 – The Illusion of Accuracy

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Evaluation and Control Program Warning Flag 1 - The Illusion of Accuracy | StrategyDriven Evaluation and Control Article | Warning Flag“Measure with a micrometer, mark with a crayon, and cut with a chainsaw”
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Evaluation and control programs provide executives and managers with the critical information they need to make effective business decisions. However, an equally critical component of the decision-making process is the understanding that no data-set is a perfect reflection of reality. Therefore, it is important for business leaders to recognize the potential inaccuracies associated with their data in order to fully assess the risks these flaws pose to the achievement of desired outcomes.[wcm_restrict plans=”25541, 25542, 25653″]

The purpose of every evaluation and control program is to accurately represent the business conditions being monitored. Because of assumptions, averages, and/or approximations applied by evaluation processes and measurement systems, reality can never be perfectly represented. Leaders having an errant understanding of data accuracy will either over or underestimate the risk associated with decision options. Therefore, it is important for decision-makers to understand the accuracy of the data presented to them. Only with this information can the risks associated with each decision option be properly assessed and the decision-maker afforded the opportunity to select the best solution alternative.

The illusion of accuracy created by an evaluation method or measurement mechanism is a result of either the measurement process itself or the way in which the process is executed. While not all inclusive, the four lists below, Process-Based Warning Flags, Process Execution Warning Flags – Behaviors, Potential, Observable Results, and Potential Causes, are designed to help organization leaders recognize whether their evaluation processes and measurement systems unduly create the appearance of accuracy where less, little, or none exists. Only after a problem is recognized and its causes identified can the needed action be taken to move the organization toward improved performance.

Process-Based Warning Flags

  • analytical processes don’t reinforce the application of mathematical principles for the use of decimals
  • documents are not screened for either the use of absolute terms and/or non-observable adjectives
  • procedures direct measurement beyond the limitations of prescribed measurement equipment, either out of range or less than one-half measurement increment
  • lack of independent information verification through the use of alternate measurement devices

Process Execution Warning Flags – Behaviors

  • conclusions stated as facts
  • assignment of emotional labels
  • lack of leadership challenge to the use of absolution terms or excessive numeric accuracy

Potential, Observable Results

  • distorted perception of actual circumstances
  • division between team members; often leading to deadlocks and infighting
  • faulted decisions, either overly conservative or aggressive
  • excessive use of decimal places
  • use of absolutes, such as all, none, always, and never

Potential Causes

  • undue desire for the feeling of security provided by having ‘hard’ data
  • misunderstanding or lack of knowledge and/or experience in the application of sound mathematic principles
  • lack of relevant situational experience resulting in excessive data focus
  • inability to relate past experience with current circumstances

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[/wcm_nonmember]Additional Information

The following StrategyDriven recommended best practices are designed to reduce the likelihood leaders will receive data presented with an exaggerated accuracy.

How Impactful Is Machine Learning In Today’s Business World?

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Since 2012, with the proliferation of Python in general software development, Machine Learning has become the biggest trend in the technology world. Because of the many applications that ML could have within every business, it’s quite easy to understand why the topic is so heavily looked after. With applications ranging from the mobile world to the automotive industry, let’s break down Machine Learning in its complexity.

Why Automation Is Important

Machine learning as a matter it’s appealing because it proposes as an automated form of management for both infrastructures and digital tools. The brightest example would surely be related to warehouse management and production, where robots are the majority of the entire chain. In this case, a Machine Learning coded program could definitely be implemented in terms of management: the tool, installed in the central brain, could heavily optimise the entire production chain.

On the other hand, automation is as fragile as it could be: a simple error in the mainframe could cause a series of cascade malfunctions, naturally leading the entire production process to the end.

StrategyDriven Evaluation and Control Article
 
The Mobile Sector

Smartphones have been taking over the world since the launch of the first iPhone. Many mobile app development companies, in fact, are heavily working on different Machine Learning algorithms, in particular, the ones that are UX (User Experience) focused.

Applications like Alexa’s voice search, for example, have been incredibly popular from a development point of view, given the fact that Voice Search Optimisation will be potentially the biggest thing in the near future.

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Service Providers

When it comes to personal finance and mortgages, there is a variety of tools available online which are calculating requirements, status and projections. An interesting case study would be related to bridging loans, given the fact that fast-paced finance is heavily looked after by many clients. There are reasons to believe that in the near future most of the calculators, online tools and projections will automatically be generated by Python coded applications. This is incredibly important because it states the fact that the current Machine Learning development level is at a point in which we are able to create and monitor complex finance matters.

To Conclude

With dozens of applications available, machine learning is definitely going to be the biggest focus in the near future, given the fact that automating certain sides of a business can definitely be a very impactful thing.

About the Author

StrategyDriven ContributorPaul Matthews is a Manchester based business writer who writes in order to better inform business owners on how to run a successful business. You can usually find him at the local library or browsing Forbes’ latest pieces.

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Surf your data!

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Is your strategy built on received wisdom or analysis of performance data? – management rhetoric or business reality?

Are you building your business strategy on received wisdom or real data? Corporate strategies are often based on assumptions about what drives business performance rather than data from the company itself. J.W. Marriott (founder of Marriott Hotels) is famous for saying “You’ve got to make your employees happy. If the employees are happy, they are going to make the customers happy”. TNT Express promotes the slogan “Take care of your people, let them take care of your customers and the rest will take care of itself”. The implication is that happy employees make happy customers, which drive profits. But does this really happen in your organisation?

The problem is that often some drivers of performance aren’t measured at all; let alone the correlations between them. For example, you may believe that loyal employees create satisfied, loyal customers, but do you have data which demonstrates that your longest serving staff create the highest levels of customer loyalty? Another assumption is that loyal customers are the most profitable; we’re often told ‘it is five times more profitable to serve existing customers than loyal customers’. It makes sense. The better we know our customers the better we are likely to serve them. And because customer spend tends to increase over time, it may well be cheaper to serve long-term customers than keep attracting new ones. But, can you prove this is the case in your organisation?

Performance topology mapping is a tool that can help with this analysis. The first step is making sure that you’re measuring the right thing. So if your business is built on the assumption that employee loyalty is necessary to create loyal customers, collect loyalty data. Identify your key performance indicators, and then measure the correlations between them in order to build a map of business drivers.

The findings can be astonishing. For example, the link between customer loyalty and financial performance is often regarded as a basic principle of retail management. However when they came to explore the data in their own organisation, the management of one home improvement retail chain discovered that there was no such correlation. They could not prove that the stores with the most loyal customers were the most profitable.

Analysis of the performance topology map of one of the UK’s big four grocery superstore chains also revealed counter-intuitive results. Its management bought into the idea that satisfied employees created customer satisfaction which drove store profitability. But the data revealed negative correlations! In fact the stores with the highest levels of employee satisfaction were the least profitable. The explanation for this lay in the value proposition: customers in these stores did not value contact with staff so much as product availability, price and checkout speed. Therefore their shopping experience did not hinge on the quality of their interaction with employees.

In other businesses, of course, the interactions between staff and customers are likely to be much more critical. Take, for example, the professional services of clinicians or lawyers. Their services are based on more sophisticated interactions between staff and clients, and long-term business relationships may well be an essential part of the value proposition. Therefore employee engagement is likely to be a more important driver of profitability in professional services.

Understanding the performance drivers is crucial. Because failing to understand what drives profitability is to fail to understand why your company has succeeded… or indeed failed. The reality is that your business strategy is based on all sorts of assumptions about what investments will yield increased market share, revenue growth or profitability. To get the strategy right, better start testing those assumptions… surf the data wave!

About the Author

Dr. Rhian SilvestroDr. Rhian Silvestro is Associate Professor of Operations Management at Warwick Business School. Rhian has conducted service management research in a number of large, leading edge organisations including retail companies, banks, transport companies, health services and call centres. She has publications in over ten international journals in the fields of service design, performance improvement and supply chain integration.