Agile Workshop – Pre-work

3L 5Why Analysis

3L 5Why Analysis is synonymous with Root Cause Analysis nowadays with most organizations having knowledge and know-how to perform 5 Why analysis. Most leaders encourage their leaders to use 5 why analysis to identify the root cause. That’s the good news!

Now the bad news 🙁 Most of the 5 Why analysis are incomplete and provide only tactical means to the current situation. They hardly look at underlying system level causes. If you don’t agree, list down all those problems in your organization that have been occurring for more than 5 years now, manifesting itself in some form or the other, in spite of a series of efforts year on year to arrest it. I have noticed that after some time, there is an innate acceptance in the organization that this problem is like the seasonal flu, it will keep coming again and again, we can’t do much other than taking precautions and facing it’s aftermath. In corporate, such perennial problems start off a mill of finger pointing rituals targeting other functions, individuals, company policy, customers, and even competition.

A comprehensive 5 Why Analysis should identify the systemic root cause of any problem. Some problems don’t need deep analysis at system level. Depending on the severity and occurrence of a problem, it should be possible to prioritize energies in identification of System level root causes wherever required.

3L 5Why analysis, represents 3 Level 5 Why analysis performed around the same symptom.
The 3 levels of the 5 Why analysis implies, performing 3 different 5 why analysis on the same problem from 3 different perspectives – 

• Specific to the problem
• Specific to the detection mechanism
• Specific to the systemic issue

Here’s is how I use this method with my client projects and for coaching teams during my engagements (and it’s a little different from what others do with 3L 5 Why):

1. Level 1 – Specific to the problem – Perform 5 Why analysis for the issue on hand. It can be related to customers, regulators, safety, quality , productivity or any other significant failure. Usually this is done by the people closest to the processes in question, essentially the doers and immediate team leaders. The purpose of this level 1 5 why analysis is to identify the root cause for why the problem on hand occurred. For ex: Lets say, your regulator has fined you for non-conformance in a standard laid out by them. Here, our focus will be on : Why did we get fined now?
2. Level  2- Specific to the detection mechanism – Ideally this level of analysis has to be done by managers. They have laid out processes, controls, checks and balances to ensure critical issues are identified before it goes outside the organization. So the focus of this level of 5 why root cause analysis will be to identify the root causes for why the detection system allowed defects to pass downstream. Hence addressing this area will strengthen the control and governance system. For ex: Why did the transactions escape the automated checks, human checkers, approvers, auditors, process owners, etc
3. Level 3 – Specific to the systemic issue – The last and highest level aims to look for systemic deficiencies that lead to the problem. From my experience, this requires a very good understanding of the entire value chain. Actually it also requires a candid acceptance of flaws in the enterprise such as culture issues, process deficiencies, silos, resourcing gap, ineffective leadership, etc. Ideal for senior leaders to deep dive to identify system level root causes. From my experience, this is best done at periodic intervals by combining the study of several similar L1 and L2 failures. Most of them converge to a handful of common system level root causes. Unfortunately, the hard hitting reality is that these are conveniently ignored. For ex, in the case of compliance issue we touched earlier, an example of system level cause is, employees are held accountable for their mistakes and it directly impacts their growth and C&B; And so most of them wish to cover up anything that occured, even if it was inadvertent. 

There are several practical strategies that need to be developed to improve the effectiveness of 3L 5 Why analysis depending on culture, know-how, sector, etc. 

Another important missing link to 3L 5Why analysis is the data based validation. It is almost impossible to get to the root cause by merely brainstorming. An ideal approach would be to use a triangulation method.

#nilakantasrinivasan-j #canopus-business-management-group #B2B-client-centric-growth #3L-5Why-Analysis #5Why-Analysis

How to measure the ROI and effectiveness of Learning Programs?

Many learning interventions, whether they are simple training programs or complex workshops involving multiple touchpoints, fall short of getting sufficient endorsement from business leaders. There can be several reasons & those might vary case to case, but one of the biggest issues with learning engagements is lack of data and supporting analytics to back the effectiveness of intervention.

Most large enterprises allocate more than 25% of the enterprise’s L&D budget for Technical skills development employees and partners (field sales & service), that is, annual technical training spend per employee (including hidden costs) varies anywhere between INR50,000 to INR4,00,000 depending on nature of business (technical or non-technical).  

Usually technical skills have a high direct impact on top line performance and customer’s perception about brand, product & service quality. Luckily, they are also one of the easiest candidates to assess ROI.

Some of the common questions that business leaders ask are:

• How has the intervention helped in meeting business priorities? & How to isolate the effect of training?
• What is the Return on Investment (ROI) on L&D Interventions?
• How to measure the effectiveness of a training program?
• What is the retention of knowledge after a few months of interventions?
• Are we doing the right type of interventions? Should we design our interventions differently?

To this means, L&D leaders stumble on the following:

• Link Learning Goals to Business Goals?
• What to measure? How to analyze
• How to measure effectiveness in the field?
• How to differentiate between evaluating the learning interventions and the participants?

Kirk Patrick model for evaluating L&D interventions is a popular go-to technique for this program:

• Kirk Patrick Model Level 1 – Measure Reaction – Gather training feedback
• Kirk Patrick Model Level 2 – Learning – Evaluate participant knowledge and skills through post training assessment
• Kirk Patrick Model Level 3 – Behaviors – New skills that participant gaining in training that is being used in field
• Kirk Patrick Model Level 4 – Results or effectiveness – Business results directly relatable to the learning intervention

Obviously, all of them have their own pros and cons.

It is not possible to measure at Kirk Patrick Model L4 for all interventions and not necessary too.

As we live in a digital-data world, it lends itself to applying innovative methods and techniques to perform deep analysis and gain insights about the effectiveness of learning interventions and it improve learner’s engagement.

Here are few technology & analytics driven techniques:

• Learning Pulse measurement
• Descriptive Analytics & EDA to identify adoption issues
• Reaction & Feedback Analytics
• Verbatim Analytics using NLP models
• A/B Testing Experiment design
• Behavioral Analytics 
• Analytics on sustenance 
• Diagnostic Analytics  
• Performance Modelling
• Predictive Modelling learning
• AR based learning assessments

And key steps to assessing ROI for L&D interventions are as follows:

• Decision on evaluation method across learning life cycle (training design, during & after training)
• Collecting Post-Training Data
• Isolating the Effects of Training
• Computing Monetary Value (Tangible, Preventive, etc) of training results
• Computing Learning Cost
• ROI Computation

Using a structured and well-thought through approach, L&D effectiveness and ROI can be improved drastically.

#nilakantasrinivasan-j #canopus-business-management-group #B2B-client-centric-growth #L&D

Data Based Mindsets – Maturity Levels

As of 2021, organizations have heavily invested in data infrastructure and supporting resources. But it’s grim to hear that nearly 85% of all data and analytics based transformation projects fail to meet their objectives.

There can be a long list of reasons for this state of affairs, but an undeniable contributor and a systematic issue is the organization’s culture – A lack of a Culture of Data Based Mindset.

Many times when we don’t know what is the problem with the organization, we tend to brand it as a cultural issue. So to say, the problem is attributed to Data Based Culture or lack of it, is a biased statement without going deeper into the issue.

The willingness of employees to accept & adapt to the new way of working. ie., taking decisions or actions based on data, is in fact the real barrier and reason for low adoption of data based transformation projects.

This is one of the new pillars of professional excellence – being fluent with data and making it an integral part of day to day actions.

Data Fluency, Data Literacy, Data based Mindset – whatever name we want to give, it boils down to how comfortable we are with data.

Like any other new technology – mobile, web applications, smart devices, social media, etc., we have a continuum of users of different maturity and proficiency in Data Based Mindset or Data Literacy too

If you wish to honestly self-assess or evaluate your team on where they stand when it comes to using data for decision making, a Data Based Mindset Maturity Model that covers data literacy maturity levels such as the one below will be very helpful. We’ll now look into the levels of Data Based Mindsets:

Resist – confused beginners

At this level, at heart, an individual is a disbeliever in data based decision making. Such individuals would hardly look into data but when they do so, they try to run a variety of logical counter-arguments to discredit data and its insights. One can say that they have a strong belief system that is anti-data or facts, which is based on biases, preconceived ideas of the situation, hear-say facts, anecdotal evidence, etc. Folks at this level offer maximum resistance to change. At the deepest level, it can be on account of fear of some kind. When an organization has many key stakeholders and king-pins in this bucket, the organization would more or less be a data-laggard. Ultimately they will have to take off from this level due to peer pressure and so ‘confused beginners’ is a fitting description for this group. 

Access – social drinkers

At this second level, individuals are limited users of data, only based on necessity. I’m tempted to compare them with ‘social drinkers’. Situations and environment forces them to consider data and they do so with reluctance. Naturally, they are users with limited proficiency in data handling and are uncomfortable when placed in data rich discussions.

User – consumers and cheerleaders

Bulk of employees in organizations that are doing well today, fall into this group. They believe in data based decisions. So there is no mental block with this group. But they have skill limitations. I’m tempted to compare them with the audience and cheerleaders in a football match. They love the game, they enjoy and relish talking about it in great detail but they’ll always be in the stands and never put their foot on the playground. Either they don’t have the skill to play or they are too old or not physically fit for the sport. But being here is not that bad from an organization’s perspective. They are willing to consume data and inferences given to them and believe in it.

Understand – power users

They are individuals who have good expertise and are strong believers in data. They lead with data, whether it is about analysis, framing scenarios, statistical and probabilistic analysis, considering divergent views from data, they are very proficient. You can take them as ‘power users’. At extreme, they may tend to be ‘annoying experts’ too :-). 

Create – prosumer

Standing at the highest maturity, this group may not be really senior in organizational hierarchy, but they definitely are good at synthesizing deep insights from data, applying unconventional & innovative methods to generate unique value propositions for business and customers that can disrupt the current state. They look through the data beyond the obvious. You can call them the ‘prosumer’. They are both the producers of insights and its consumers. As a result, they are the trend setters. Obviously there are going to be only a few fitting into this group. 

This understanding of the Data Based Mindsets & Maturity Levels can be applied to evaluate its relevance to your industry, specific roles & departments and their ideal maturity states, skills needed and critical mass needed at each level to build a Data Based Mindset Culture, etc. 

If you wish to learn more about evaluating your organization on Data Based Mindsets & Maturity Levels or create a roadmap, feel free to contact us. 

#nilakantasrinivasan-j #canopus-business-management-group #B2B-client-centric-growth #Data-Based-Mindset #Maturity-models