Who is in, and Who is Out

Imagine attending a meeting, and all the participants but you, are contributing ideas to a discussion. You are clueless. Maybe you missed a conversation, or you did not read an email? There could be numerous reasons why you could not contribute during the meeting. One instance could be that you were not included in an email chain or not invited to a meeting.

Leaving recipients off unintentionally (or intentionally) from any form of communication can lead to confusion and misunderstanding between the team members. A little bit of proactive questioning can help avoid hits and misses. Ask these questions first:

  1. Who should and should not be on a meeting invitation?
  2. Who should and should not be on an email chain?

The obvious answer is: It depends

Next, ask these additional questions to finalize the list of recipients. Evaluate the responses before hitting the send button:

  1. What is the email or meeting topic?
  2. Would skipping a team member in an email or meeting lead to miscommunication?
  3. Will including all the team members make a few of them feel that the meeting was irrelevant to them?

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You can answer the above questions by leveraging these options:

  • RACI (Responsible, Accountable, Consulted, and Informed) matrix: RACI matrix can be a great source of stakeholder information for global projects.

For example: List the Responsible parties under the To list or Required Attendees. List the Informed parties under the Cc list or Optional Attendees.

  • Working Agreement: No RACI matrix? An Agile team working agreement can come to the rescue. Define who are the core team members. Refer to this list when sending out any email communications or meeting invites.

Tip: Core team can be cross-functional with stakeholders across the organization.

  • Email distribution list: Say the team size is small (4 to 6 members) and there is no RACI matrix or working agreement, then create a distribution list that includes the email IDs of all the team members. It is less effort and error-proof when selecting a list instead of individual email IDs for sending any form of communication.
  • Instant messaging group chats: Most instant messaging tools allow the setup of groups. Create one for your team and post a message in the team chat. Plus, there are options for the recipients to acknowledge the chat message (emojis such as like, happy, celebrate, and such).

In conclusion, despite the ideas mentioned above, there are chances that someone is still left off an email chain or a meeting. Be a team player, reach out, and get them caught up. The crucial element is that the entire team is on the same page.

“We need to be on the same page and not play the blame game” – Nate Heying.

The Philosophical Data Analyst: Some Variables are from Extremistan

Most organizations understand data as an asset, providing a rich resource that can be analyzed to unearth both descriptive and predictive insights. Data-driven decision-making is promoted by many organizations. More and more, organizations are processing and analyzing data in real-time, automating operations based on results, making them reliant not only on the data but on the methods used to analyze it.

Many organizations invest heavily in their data operations to ensure the ongoing completeness, integrity, and accuracy of collected data. However, regardless of how complete, correct, and/or unbiased collected data may be, there are limits as to what insights can be gleaned from a given dataset. Acting on analytical insights outside these limits introduces risk.

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This article describes risks posed by data variables that are susceptible to seemingly improbable or extreme values. It explains the characteristics of susceptible data variables and outlines a simple technique for identifying them.  Once identified, Business Data Analysts can assess the impact of using these variables in the analysis, allowing any risks to be quantified and mitigated and/or alternatives analytical approaches explored.

Mediocristan vs. Extremistan

It is common to describe the range and frequency of variable values using statistical distribution patterns. The ability to describe data variables using a particular distribution pattern is a prerequisite for many data modeling methods. The most common statistical distribution pattern is Normal Distribution or the bell curve.

Image credit: MathsIsFun.com

For a normally distributed data variable, the frequency of values is symmetrically clustered around a mean – the further away from the mean, the less likely the data variable will take on that value. However, in some cases, data variables may appear to hold a certain statistical distribution pattern when, in fact, they may legitimately take on values that (given the distribution pattern) are deemed improbable or extreme – particularly in cases where a variable is subject to complex and/or unknown external influences.

In The Black Swan, Taleb introduces the idea of Mediocristan and Extremistan. Taleb defines Mediocristan as subject to the routine, obvious and predicted, while Extremistan is subject to the singular, accidental, and unseen (Taleb, pg. 35).  Applied to data analysis, data variables from Extremistan are susceptible to extreme and/or unpredictable values, while those from Mediocristan are not. The argument is that data from Extremistan cannot be accurately described using common statistical distribution patterns – and certainly cannot be described using normal distribution as value frequency is not symmetrical.

For example, take a sample of 1000 randomly selected human beings. If you were to calculate the average height of the group, what would you expect the answer to be? Now add the tallest human on earth to the sample (which will now comprise 1001 humans) and recalculate the average height of the group. How much would you expect the result to change? The answer is not a lot as there is a limit as to how tall a human can be. While adding the tallest human on earth to the sample would cause the overall average to rise, the impact would be minor. Human height is an example of a variable from Mediocristan.

Now perform a similar thought experiment, except this time use net worth instead of height. What would you expect to happen to the calculated average net worth of a randomly selected group of 1000 human beings if you were to add the richest person in the world to the group? In 2021, Forbes identified Jeff Bazos as having the highest net worth in the world, estimated to be around US$177 billion. You would expect the average from the sample that included Jeff Bazos to be dramatically higher compared to one that didn’t. Net worth is an example of a variable from Extremistan.

The problem is that if you were to take a random sample of 1000 humans from earth, what is the likelihood that it would include Jeff Bazos? Or Bill Gates? Or Jay-Z? Or the Queen? Or anyone else with a much higher net worth than average? And if the sample did happen to include one of these individuals, what would you do with the offending value? Would you treat it as a true representation of the entity being described? Or would you discard it as an outlier?

Extreme values can be mistaken for outliers when they are in fact indicative of the behavior of the entity they are representing. Analysis that does not account for Extremistan variables properly may prove unreliable – particularly when accurate but extreme values enter the underlying data. Extremistan variables may also be subject to extreme changes in value as a result of seemingly unlikely or improbable events (for example, a sudden stock market shock impacting the net worth of some individuals more than others).

What to do in Extremistan?

Taleb proposes a method for modeling Extremistan variables based on the work of the mathematician Mandelbrot, the pioneer of factual geometry. However, mathematics is complicated and beyond the capabilities of most organizations. (As most Business Data Analysts would know, explaining analysis based on simple mathematics to stakeholders can be a struggle – let alone analysis that uses more complex mathematical modeling techniques). Understanding Extremistan variables, how they contribute to the analysis, and mitigating any risks their use poses is a more realistic goal for most organizations.

Start by classifying data variables into the categories ‘Mediocristan’ and ‘Extremistan’. The table below provides some guidance on the characteristics of Mediocristan and Extremistan variables.

Mediocristan

Extremistan

Non-scalable

Scalable

The most typical member is mediocre

There is no ‘typical’ member

Winner gets a small segment of the pie

Winner takes all

Impervious to Black Swan (seemingly improbable) events

Vulnerable to Black Swan (seemingly improbable) events

Often corresponds to physical quantities with limits

Often corresponds to numbers with no limits

Physical, naturally occurring phenomena are often from Mediocristan

Variables that describe social, man-made aspects of human society are often from Extremistan

Examples include height, weight, age, calorie consumption, IQ, mortality rates…

Examples include income, house prices, number of social media followers, financial markets, book sales by author, damage caused by natural disasters…

 (Adapted from Taleb, pg. 35, 36)

Once classified, Business Data Analysts can identify where and when Extremistan variables are used in the analysis, and whether they pose any risk to the accuracy/reliability of analytical outputs. In many cases, this can be done simply by identifying or estimating extreme data points (such as the Jess Bazos in the example above), adding them to the underlying data, and assessing their impact on the analysis.

Note that using Extremistan variables in the analysis is not necessarily a problem – it depends on how they have been analyzed and the insights that are drawn from the analysis. Some analytical and modeling techniques will be able to deal with Extremistan variables without introducing much risk. However, be wary of analysis the assumes Extremistan variables to be normally distributed and/or simply treats legitimate extreme values as outliers.

When classifying variables, it is also important to consider the scope of the data collection, and the context in which it is being analyzed. Take for example a sample of bakers who live in a certain region. You may want to use data collected from this sample to predict the income of other bakers in the same region. Assuming there are no issues with data quality and/or data collection, baker income is likely to be a variable in Mediocristan as there is a limit to how much bread a baker can bake in a day, and price/demand variability for baked goods is usually low.  On the other hand, take the same example and replace ‘baker’ with ‘social media influencer’. A social media influencers income is subject to a more complex and ephemeral range of factors, such as numbers of clicks, ‘fame’, the popularity of social media platforms, etc. As such, social media influencer income is more likely to be from Extremistan.

Conclusion

Data is an asset. Data-driven decision-making can help increase efficiency, drive innovation, and reduce bias in decision-making. However, it is important to understand that there are limits to the insights that can be drawn from a given dataset. By identifying variables that may be subject to extremes, analysts can ensure these variables are appropriately accounted for in analysis by assessing any risks, and ensuring analytical insights are considered in context.

But know this – variables from Extremistan are anything but normal!

References:

  1. Taleb, Nassim Nicholas, The Black Swan: The Impact of the Highly Improbable, Random House, 2007.
  2. Guide to Business Data Analytics: Getting Better Insights. Guide Better Informed Decision Making. IIBA, 2020.
  3. Normal Distribution, MathsIsFun.com, 2021. (Last accessed Jan 2022).
  4. Dolan, Kerry A., Forbes 35th Annual World’s Billionaires List: Facts and Figures 2021, Forbes, Apr 2021. (Last accessed Jan 2022).
  5. Penn, Amanda, Extremistan: Why Improbable Events Have a Huge Impact, Nov 2019. (Blog last accessed Jan 2022).

Techniques for prioritizing requirements

One of the major challenges that Business Analysts face is getting stakeholders to prioritize requirements. Everyone is used to high syndrome – where the stakeholders say everything is a high priority.

The key to dealing with this problem is for BAs to understand the drivers of the project and then create priority evaluation criteria to assess each requirement. This is a key step that is often overlooked when starting the business analysis deliverables of the project.

Let’s say the objective of a project is to optimize a business process for an operations group. As a BA it’s important to understand what constitutes an optimized process. It’s a simple upfront question that will surface criteria that the BA can use to help the business prioritize the requirements. For example, the stakeholders may indicate that the optimal process would be one in which the To-Be process has fewer manual hand-offs, reducing the amount of paper that flows through the process, reducing the number of steps requiring manual entry of data, etc.

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These criteria can then be used to develop a framework with which to evaluate and prioritize requirements. My preferred approach is what I call the scale approach. With the scale approach, you get the stakeholders to call out how well each requirement aligns with the assessment criteria on a scale of 1,3 and 5 (where 1 means the requirement is poorly aligned with the criteria, 3 somewhat aligned, and 5 highly aligned). This gives a numeric priority assessment of the requirement and a quantitative method of comparing requirements. This helps to get stakeholders out of the “high” trap mode.

Scale Approach:

Here is an example of the scale approach. The scaling approach uses a 1,3,5 scale where 1 is poor alignment, 3 is some alignment (in the middle) and 5 is highly aligned. The max score for any requirement is 15 and the minimum is 3. I’ve tried more granular approaches like 1 to 10…but it just causes a lot more sitting on the fence when you have that many values to apply to a criterion…and it’s not as “distinct” an outcome as the 1,3,5 scale. I specifically avoid using numbers instead of High, Medium, and Low because I find that a numeric approach makes things more subjective than a High, Medium Low evaluation approach.

# Requirement Evaluation Criteria 1 (Reduce Manual steps) Evaluation Criteria 2 (Reduce paper through the process) Evaluation Criteria 3 (Reduce number of manual steps) Total Criteria Score
1 Requirement 1 3 5 1

9

2 Requirement 2 5 5 3

13

3 Requirement 3 1 3 1

5

4 Requirement 4 3 3 3

9

 

Typically, with the above scoring system I would then map it to the more traditional High, Medium, and Low using the following ranges:

Low – score equal to 5 or lower

Medium – score of 6 to 10

High – score of equal to 11 or higher

Based on the above you would then prioritize the requirements as follows:

# Requirement

Priority

1

Requirement 1

Medium

2

Requirement 2

High

3

Requirement 3

Low

4 Requirement 4

Medium

Heat Map Variation:

Most stakeholders are very visual. So sometimes I’ll combine this with a heat map approach. With the heat map approach, each score on the scale is associated with a color.

Score of 1 – shade the cell red

Score of 3 – shade the cell yellow

Score of 5 – shade the cell green

So, if we take the above example and add colors, it will look like:

#

Requirement Evaluation Criteria 1 (Reduce Manual steps) Evaluation Criteria 2 (Reduce paper through the process) Evaluation Criteria 3 (Reduce number of manual steps) Total Criteria Score

1

Requirement 1 3 5 1

9

2 Requirement 2 5 5 3

13

3 Requirement 3 1 3 1

5

4 Requirement 4 3 3 3

9

As you can see above – it really jumps out at you that there is a “strong” fit with requirement 2 and a poor fit with requirement 3. I find the heat map approach helpful when there are a lot of requirements because it’s a lot easier to gauge and compare requirements based on colors than numbers. Also, a lot of times with a heat map you don’t need a total criterion score at the end. It just jumps out at you.

You can further simplify things by just using the colors instead of the numbers.

Does it take more time?

The simple answer is no it doesn’t take more time. All it takes is a little bit of prep, a prioritization meeting and then you have prioritized requirements. When you compare that to having numerous emails, meetings, and discussions about what the requirements priorities are you’ll see it’s a much simpler and effective approach. It also forces stakeholders to really think about the requirements and how they want to achieve their objectives – so less second-guessing of requirements in the future.

One final note:

When I use this approach, I put the actual scoring matrix into the appendix of the requirements document and not in the main body to enable a wider audience to more easily read the requirements document.

Practicing Practical Optimism

What we believe is pragmatism can be perceived as pessimism. Is it time for BAs to start practicing practical optimism instead?

The Problem With Pragmatism

There are many words that BAs hold dear – objective, holistic, pragmatic. They guide our approach. We want to consider all factors and all perspectives, avoid bias and ensure appropriate action is taken in light of all relevant information. Pragmatism should mean planning for the worst, but hoping for the best. We are skilled at identifying the worst-case scenario, highlighting gaps and risks, and getting to root causes; have we become so focused on being ready for the worst outcome, that we have forgotten to hope for the best outcome? Has our pragmatism turned to pessimism?

We really do want to move forward, learn lessons, and avoid re-making past mistakes. It can feel like a way to achieve that is to focus on everything that has gone wrong previously. This past-focussed pragmatism nudges us closer to negativity.

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 The Problem With Optimism

 Many BAs see optimism as naivety. We believe that if people really understood the issues (as we do) then they wouldn’t be quite so positive! We think that the role of analysis is to surface and clarify needs, issues, and problems, and it’s very hard to talk about these topics in a positive way. We also know that over-optimism in planning and delivery causes many projects and products to fail.

Optimism has become synonymous with unrealistic and uniformed.

The Benefits Of Optimism

There are wide-ranging benefits, observed in comprehensive research from all around the globe.

Optimists are healthy and live longer. They are more likely to achieve their goals. They are more resilient and less stressed. They are more productive and have better relationships. Optimism increases the likelihood of success.

The good news is optimism is a skill and mindset we can all practice and improve at, whatever we consider our ‘natural’ disposition.

Practical Optimism

Optimism does not mean naively hoping for the best, denying reality or failing to prepare. The phrase “practical optimism” acknowledges the unspoken accusation of “blind optimism” and provides a path to taking sensible steps towards the best possible outcome. Genuinely understanding the best-case scenario and always keeping it in mind makes that outcome much more likely to occur!

Risk identification and problem-solving seem to get much more airtime than benefits and drivers. Reminding ourselves of why we are doing something, who benefits and how is a great motivator. Reflecting on how far we have come, highlighting successes, and celebrating milestones all contribute to future-focused thinking. This creates the right climate for practical optimism to thrive.

Conclusion

Pragmatism seems like the perfect balance between uninformed optimism and immobilizing pessimism. In reality what feels like pragmatism can easily look like pessimism. Striving for an approach of practical optimism rather than pragmatism can lighten our mental load, improve our relationships and lead to better personal and business outcomes.

Being realistic can be about striving for the best possible reality. It’s time for business analysis to look on the bright side.

Further Reading:

[1] When BAs Go Bad, C Lovelock, BA Times, 2019 https://www.batimes.com/articles/when-bas-go-bad/

[2] How To Incorporate Realistic Optimism Into Your Life, Forbes, 2021 https://www.forbes.com/sites/forbescoachescouncil/2021/01/07/how-to-incorporate-realistic-optimism-into-your-life/?sh=465ae45476f0

Developing a “Sense of Purpose” for a Business Analysis Initiative

Βusiness analysts can contribute in delivering the sense of purpose and worth concerning a business analysis initiative. This sense of purpose will contribute to the better effectiveness of the work that is performed between the BA team and the different stakeholders. As the business analysts are continuously communicating with different stakeholders and deal directly with their needs, they are the best source to contribute to the capturing and the diffusion of a common purpose that may also serve as a success criterion for the initiative.

The capacity to effectively lead a business analysis initiative is directly related to the pursuit of a worthy purpose. The purpose may be the most powerful link to join people and processes in a common effort. General/ Organizational purpose can be transformed and decomposed into more specific and detailed initiative purposes. The degree to which we pursue an ennobling purpose is the degree to which we attract others.

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Purpose attracts and therefore serves as a unifying force. There is unity of effort and energy to the degree of shared purpose. Our level of satisfaction and our level of energy is directly related not only to our understanding of our own purpose but also to whether the organization and specific project to which we contribute, share that same purpose.

Below you can find four considerations for effectively managing the sense of purpose as a business analyst:

  1. Big Picture

Being able to see the things holistically and the long-term value and effects of any task can help you embrace a worthy purpose that will give you energy and motivation but also distribute this sense of purpose to the other stakeholders

  1. Respond to “Why”

In order to successfully spread a sense of purpose, you need to instill a sense of worthy purpose. It is to answer the why question, why should work overtime for this project? Why should I sacrifice? Why should I dedicate my time to achieving high-quality deliverables? The answer has to be something that is worthy, something that is ennobling.

  1. Focus on the Perception

You may feel you have communicated effectively the purpose to the other stakeholders but do the others perceive the purpose as something worthy and important? Perception is reality. What people think they hear is the truth according to them.  So, we have to think through our communications in a very deliberate manner, in a planned manner, thinking through how it’s going to be received on the other end and making sure that people are receiving the message that we want them to receive.

  1. Align with the Organization Purpose

The organization’s purpose and the core values of your organization should be aligned with the project-specific purpose. Projects or initiative specific purpose may be derived and be a more detailed and case-specific purpose of your general organization purpose.

Effective execution of business analysis tasks requires convincing key stakeholders (both internal and external) that your analysis and your conclusions are valid so that you can transition from your analysis to implementation. As such, you must be able to summarize your findings in a message that makes a persuasive argument that aligns with the sense of purpose. An argument that mirrors progress towards the realization of this purpose. Therefore, defusing a sense of purpose and then communicating results towards achieving this purpose is an integral part of your effort in any business analysis task you are engaged with. One that is worthy of careful consideration.



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