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Turning Data Into Wisdom by Kevin Hanegan or Why Throwing Money at BI Tools Doesn't Work



written by human not by AI




Before taking the leap and launching myself into solopreneurship, I had multiple jobs in different industries. The first role where I could officially say I worked with data was in 2016 - but I had been dabbling in creating reports, dashboards and visualisation before that too. I have also had the unique opportunity to go through 3 BI stack implementations: one using SAP, one using Tableau and one using Power BI. They were all different processes. Each company was in a different stage in its analytics journey. Each company had a different technology stack and people with diverse skills available. But all of them had one thing in common: the (false) perception that investing in technology would single-handedly be enough to make the company better at data-informed decision-making.


I sat through countless vendor meetings, and the conversation usually goes like this:

  • Data is the new oil! There’s value! But you have to unlock it by refining it!

  • Our software does that for you (almost) effortlessly!

  • Look at all these fantastic, perfectly curated examples created with a perfect dataset for the perfect use case that doesn’t exist in real life!

  • See all the pretty charts?!! They’re interactive!!!

  • All you need is to spend this ridiculous amount of money and an even more ridiculous amount of effort, and everything should be seamless.

  • [excited grunts from executives hyped up on coffee]


The appeal of throwing money at technology stacks is that it is a tangible effort. It is easy to measure how much was invested, giving the false hope that it will be equally as easy to measure the return on that investment. It is often a couple of years into the process that everyone starts to recognise that technology alone will only take you so far. The true value of your data assets can only be realised through fostering a truly data-literate culture.


Sure, relying on a modern data stack can enable a lot of good stuff. But if you want any of it to last, all that good stuff needs to rest on the shoulders of the boring stuff - governance, a data strategy, data quality, building trust, a change management strategy, fostering a culture that promotes learning, diversity and psychological safety, and having a framework to guide data-informed decision-making.


Turning Data Into Wisdom by Kevin Hanegan is a book directed at business people looking for strategies, frameworks and processes to help their companies and teams do more with their data. If you ever found yourself reading about data literacy and wondering, “but how am I supposed to actually do it?!” this is the book for you.



The recipe: How to become a more data-literate organisation


Kevin Hanegan starts his book by explaining that data has the potential to inform us in driving incredible positive changes. But it can just as easily result in poor or unethical outcomes due to inaccurate, incomplete, misleading data analysis. But what sets apart good from bad decisions using data? How can we evaluate if our decisions will lead to positive outcomes?


According to Turning Data Into Wisdom, the secret sauce is a well-thought-out set of guidelines and frameworks that can help decision-makers navigate through complexity and structure the decision-making process.


The author explains that a genuinely data-literate organisation needs to figure out three core principles to ensure they have the tools to evolve how they consume and use data.


  • First, the organisation needs to define an overarching decision-making process;

  • Second, they need to define and embrace a methodology that clarifies the why and how of each step in the process;

  • and third, the organisation needs to invest in the right skills to apply this methodology correctly and successfully.


If this sounds too daunting, it doesn’t have to be. The book’s core walks us through a six-phase data-informed decision-making process, which the author further breaks down into a 12-step methodology.




This is a book about processes and frameworks. It reads like a textbook, and it is one I always come back to whenever I need some guidance on how to help leaders identify the bottlenecks in their decision-making processes. It is a pocket decision support consultant. It is a data literacy strategy cookbook.


But is the recipe any good? The answer is a resounding yes. The frameworks are not your typical bite-size steps program. They are well-detailed and supported by clear research, with references for more. The author offers two whole chapters of tools and what he calls “job aides” for the reader to choose from and adapt the frameworks to their specific use cases.


Alongside this resource-packed framework, the author also discusses the skills gap that needs to be addressed to enable better data cultures. He names ten data-informed decision-making skills.


This well-rounded set should be present at any organisation that wants to take data seriously. Not all individuals must be proficient in all ten skills, though - as long as they can collaborate with people with complementary skill sets. He splits them into hard skills and soft skills. The hard skills are required more heavily by those who work directly with acquiring and analysing data, while the soft skills are essential for leadership and strategic roles.


Top 5 hard skills for a data-literate culture:

  • Data extraction

  • Data transformation and standardisation

  • Basic match and understanding of data

  • Foundational statistics

  • Data Science


Top 5 soft skills for a data-literate culture:

  • Systems and enterprise thinking

  • Critical thinking

  • Active listening

  • Relationship building

  • Communicating with data


Apply, Announce and Assess - it isn't just about asking, acquiring and analysing.


If you have worked as a Data Analyst before, this scenario may look familiar: your stakeholder or manager has a question. It is often a generic question, and they ask you to investigate the data available to see if you can find the answer. You find an intriguing clue or pattern in the data, but with little time allowed for analysis and a business culture pressing for quick results, your stakeholder jumps into this first piece of insight and makes decisions almost organically, without much thought.


This happens when an organisation lacks a clearly defined process for how analytics should be embedded as part of their day-to-day activities: it taps into the data aimlessly, desperately looking for a way out of the pressures of constant performance.


Turning Data Into Wisdom makes it clear that it’s not enough to jump straight from analysing your data to making a decision. The framework presented in the book includes steps to review and orient ourselves to the data and information we’ve collected, apply our personal experiences and intuition to create a hypothesis, and then challenge the data and actively look for information that may disprove our mental models we may be applying to our analysis.


Leveraging strategies to become aware of and mitigate bias is also essential. The author presents us with a few options and frameworks that vary from trying to prove yourself wrong (or playing devil’s advocate) to creating groups of diverse stakeholders to scrutinise and review our analysis and decisions to ensure we have considered other points of view in our process. A company culture that promotes sharing knowledge and continuous learning makes this process easier. Time pressure should not justify carelessness. It should be factored into the process.


Once a decision has been made, the author argues the importance of communicating the decision to all parties involved to allow everyone to absorb the new information and adjust behaviour if necessary. As a data visualisation professional, this is an exciting part of the book because this is the first time Hanegan explores data viz a bit more in depth.


Visualisation is usually part of the exploration and analysis steps long before a decision is made. But rarely, it gets factored into the later communication stage. It is undoubtedly quite a nice use of a data visualiser’s skills, though: after a decision is made, how do we communicate it to everyone involved in the process or impacted directly or indirectly by it? How do we inform what was the hypothesis, the evidence taken into consideration and what has led the decision-makers to opt for one choice instead of another? It involves storytelling skills, audience engagement and a great deal of transparency, and it is a fantastic take on the role data visualisation can play in a data-informed culture and I love it.


Finally, after a decision is made and communicated, the author includes a step to assess how the decision has performed. He suggests a decision journal of sorts: take note of all decisions, whether they were chosen or not and evaluate the one you took against the other options. How did the selected decision perform? Could one of the other alternatives have been a better choice? What can be learned from this process and adjusted for the next decision-making cycle? After the process is done a few times and becomes a habit within the organisation, your decisions will inevitably increase in quality. And your process will inevitably increase its speed to insight.



Bias, groupthink and diversity


One of the most critical elements of a data-informed culture is how it acknowledges and deals with cognitive biases. One of the things I appreciate the most about Turning Data Into Wisdom is how it recognises and discusses the prevalence of bias in organisations and its impact on decision-making.


Kevin Hanegan discusses at length how biases play with our perceptions when interpreting and analysing data. He defines bias as an unconscious, systematic, and reproducible failure in information processing that gets in the way of logical thinking.

He classifies biases into four groups:

  1. Pattern recognition and salience bias: the tendency to make decisions based on previous experiences and looking for patterns that confirm existing beliefs. Confirmation bias is the most common bias in this category.

  2. Action-oriented bias: the tendency to feel pushed or overconfident when deciding on an action.

  3. Interest and social biases: Involves conflicting ideas or wanting to avoid conflict. Affinity bias is an example, where one tends to gravitate towards and develop relationships with people who are more like them and share similar interests and backgrounds.

  4. Stability bias: Tendency to be comfortable with the status quo, especially when there is little pressure to change.


He focuses on one particularly pervasive behaviour called groupthink.


Groupthink is defined as a psychological resistance to creativity or diverse perspectives when making decisions as a group. It is usually part of the company’s culture and particularly difficult to overcome unless a conscious effort is made to improve group diversity.


According to Hanegan, certain conditions increase the occurrence of groupthink:

  • High cohesiveness and homogeneity within the group, lacking cognitive diversity;

  • Insulation of the group, with limited contact with members outside of it

  • Lack of systematic processes for working with data and making decisions

  • Absence of disagreement, where everyone agrees with the conclusions and decisions even if they personally don’t.


The author makes it clear across all book chapters that we must acknowledge the potential for biases to creep in at each step of the data-informed decision-making process. Be it when we’re formulating business questions, collecting and sorting our data, analysing it, making assumptions or creating a hypothesis, we must always strive to minimise bias as much as we can.


Here again, going with its cookbook approach, the author offers a range of practical, research-based methods and processes that can be applied to the decision-making process to minimise the prevalence of bias at individual and organisational levels.



Should you read it:


Turning Data Into Wisdom is not so much a book concerned with defining or introducing Data Literacy as it is with providing the reader with a treasure trove of excellent resources, processes, frameworks and templates to guide us through our data journeys. The book is aimed at business people interested in better leveraging data in their jobs.


It is not a book you’ll read in one sitting, and if you’re anything like me, you’ll take months to digest its contents. But it is a book to have nearby if you are in a strategic or leadership role. You will surely get back to it multiple times, so I recommend keeping a pack of page markers nearby - you’ll surely use plenty of them.


I highly recommend it to anyone who has ever wondered what ingredient they’re missing in their data strategy and why those investments in new and shiny tools never seemed to have had the expected returns. You are missing the recipe for the secret sauce: data literacy. This is your cookbook.


Always check your local library first to see if any of the books I recommend are available. If they’re not, consider donating a copy!

 

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Written by human, not by AI


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