Setting the foundation of a learning organization

In an age of BIG data, it seems strange the so many of our organizations are struggling to take advantage of the opportunities it offers.  In fact, the gap between the know’s and the know-not’s is growing wider and wider.  Even more, our organizations’ survival will soon depend on it. 

Through my consulting, I talk to a lot of different levels within an organization.  When I talk to the entry level staff, they wish that leadership was more data-driven in their decisions.  When I talk to the middle management, they wish that had more data to inform decision-making.  When I talk to leadership, they wish they had data in a format to make better decisions.  So where is the disconnect?  Here are my observations and tips:

 Leadership needs to value it AND commit to it

Whether it was from a recent TED talk, a Harvard Business Review Article, or just smart business practices, I hear leaders say they want to foster a learning organization.   However, they often fall short of supporting the steps needed to create that type of structure.  Sometimes they hire a Data Analyst or Knowledge Management Specialist then expect the learning to just happen.   This “check-the-box” scenario not only fails to solve the data issue but also puts high, often insurmountable, expectations on the individuals who hold those positions.  To establish a true road to being a learning organization, that leadership commitment needs to come in the form of financial resources, staff, and time to develop the necessary systems and process to support proper information management.  That commitment also needs to be visible to the organization.  The best way to show the value of evidence-based decisions is to demonstrate when and how you actually made the decision using quality data and sound reasoning.

Organize your data for scale

We are busy people.  When there isn’t a demand we prioritize our “need to have” deliverables much higher than our “nice to have’s”.  This is not lazy, it is being lean.  But small changes in structures can lead to huge gains down the road.  Most times in my consulting, I see people produce templates and tools that are just…fine.  They work for the specific purpose and for the specific context.  Yes, your MS Word table works fine for your small project with 2 deliverables, but what if you were managing 8 projects with 100 deliverables?  Would this format work?  Most of the time, the answer is no.  A foundational obstacle to building a learning organization is poor quality data sets.  It is often in multiple formats or file types, missing information, categorized inconsistently, or just plain hard to find.   So, here are my tips for good data structuring:

  1. Think tables – every observation should be a row and fill in all the blanks

  2. Separate your data – numbers and text should never be together.  And, categorize / code your qualitative data

  3. Learn pivot tables – not only is this a great skill but it will teach you what data structures are easiest to analyze

  4. Think scale – ask yourself if this format could still be used if you had 10 times the data

  5. Share – your data needs to be accessible if it will be useful.

Stop trusting your gut

For most of human history, we were forced to make decisions with very little information.  Is it safe to leave the cave?  Will it rain tomorrow to water my crops? What time will the bus arrive?  These questions are now a thing of the past.  The accessibility of data and the predictions they provide have erased a lot of uncertainty of the world.  However, after millennia of dealing with the lack of information, we have gotten used to trusting our intuition.  But our intuition is easily misled.  So, it’s worth questioning.  The most disruptive and successful innovations have defied conventional wisdom.  Those who asked questions like, “What if you could access the internet from your phone?” “What if everyone could become a taxi driver?” or “What if we people could drive a sportscar that is powered by electricity?” have fostered amazingly successful businesses like Apple, Uber, and Tesla.  Each of these companies have committed to quality data and incorporated it not just in their strategies but in everyday decisions.  By examining the data like these industry leaders, we can uncover the reality and rely less on perception or opinion to drive our performance.  Cash transfer programs for humanitarian aid, opt-out approaches vs opt-in for small business savings, and sustaining the environment by using more land to rotate cattle herds are all examples of people questioning the status quo and using data to create more effective programming.

A flashy system won’t solve your issue

I have a lot of experience in technology and systems implementation.  Too often, organizations think an information system or fancy tool will solve their problem.  Don’t get me wrong, a well-designed system is part of the solution but it is a lot like buying a Range Rover before you’ve taken your first driving lesson.  Before these pricey investments, organizations need to consider the behavior and practices that have prevented good data management practices in the first place.  What are the incentives, or lack thereof, of good data analysis? What are the key questions you want answered?  What is the flow of data, i.e. collection to aggregation to analysis to decision-making?  There are a lot of fancy dashboards created for management that are answering questions no one asked.  And, if your organization buys a system, the vendor will have to answer these questions anyway (and on your dime!).


Ryan LaPrairie is the Director of Systems and Innovation for Mission Critical Development International.  He has a Bachelor's degree in Cultural Anthropology, Master's in Public Administration and holds a Project Management Professional (PMP) certification and is a Certified Change Management Practitioner. As a project manager and consultant, he has been working with individuals and teams to improve group dynamics, processes and procedures, and decision-making methodologies. He routinely conducts data analyses for organizations using advanced statistical software. As a trainer, Ryan builds capacity for individuals and organizations around topics such as risk management, indicator targeting, applied project management tools, and Project Management for Development Professionals 1.  He can be reached at