When a tree falls: Change management key in becoming an IDO

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Change management is key in becoming an IDO

How to deliver value in your Insight Driven Organization?

If a tree falls in a forest and no one is around to hear it, does it make a sound? This well-known thought experiment teaches us that vibrations in air are meaningless unless registered and processed by a human being. This thought experiment is relevant for organizations that are transforming into an Insight Driven Organization (IDO). To become a successful IDO many organizations focus on selecting and implementing the technical (i.e. systems) and analytical (i.e. data & model building) capabilities. This is understandable as these capabilities are needed to generate insights. However, to realize the benefits of being an IDO it is key to apply the data-driven insights in organizational decision processes. This requires a different behavior from employees in the organization, a new way of thinking and working. A solid change management approach is essential to create the value of being an IDO.

Inge Kouijzer & Sander Buijsrogge - 5 april 2018

A slightly adjusted version of the earlier mentioned thought experiment highlights the importance of change management: "If an insight is generated within your IDO, but nobody uses or acts upon it, would it have any value?" The data-consumer is the employee within your organization who can transform data into value by acting upon the generated insights. For them to “hear the sound” they need to understand and learn the new way of thinking and working which will result in an adequate consumption of the insights that are produced.

For your employees transforming into an IDO consists of letting go of old habits and learning new ones. For example they will need to stop making key decisions only based on their intuition but instead actively seek and use data-driven insights that allow them to make decisions. Additionally, IDO’s require a solid cross-functional collaboration, meaning that employees will need to work closely with data experts and analysts during all stages of process/service, change or decision-making process. This will not happen by just implementing technical and analytical capabilities.

Based on experiences of transforming clients into an IDO, four Change Management factors have proven to be key in order to be successful:


1. Telling is not enough; employees must see & feel it
  • A clear vision and narrative for the IDO journey is the foundation. This will help to motivate employees to connect with a set of common objectives, steer employees in the right direction and motivate them into action. Employees must know what the purpose of the change is and why they must be part of the change. 
  • Additionally the vision must be translated into the impact on individual level. Make it specific, what does the new way of working mean for a person? These changes can be made tangible by co-create an employee journey of this new way of working. 

For example: create an experience lab where employees can see and experience what the new vision means for their profession, role and way of working.


2. Sitting together is not enough; employees must collaborate
  • Design multidisciplinary teams of 5 to 8 people consisting of data scientists, the insight-consumers and a purple person . A purple person is the linking pin between the sophisticated data analysis (red skills) and fluent communication skills, business acumen and political nous (blue skills). Together the team starts exploring the new way of working in a pilot (see point 3). The team must have mandate to prioritize analytic demands, high degrees of autonomy and opportunities to develop as subject matter experts. 
  • Make sure that there is a representative of every department that is involved in the development of a service or product. In this way the intended insight-consumer can learn to appreciate the value of data by experiencing every step in the process. Vice-versa, data-scientists will gain a better understanding of the business demands and reporting optimizations needed by the insight-consumers. 

For example: encourage cooperation between team members by making pairs responsible for a deliverable/result or actions. Team members need to work together as a team to achieve high performance and successfully accomplish their IDO task. Make sure that 1 or 2 days a week the multidisciplinary team can work together on the same location.


3. Knowing is not enough; employees must experience and learn
  • Start small by organizing a pilot set-up and run by a multidisciplinary team. A good first trial is key for the embedding process. By experiencing what data can do for all team members/departments it will enlarge the likelihood that this way of working embeds in the organization. Make sure there is a clear goal and assignment for the team where data insights can be used to optimize an existing process or product. 
  • Create a team feedback culture based on psychological safety in which team members feel free to share their experience – both good and bad - with their colleagues and with each other. Learning (on the job) during ‘reflection time’ can improve the collaboration between team members and usability of data. Design a process that allows to reflect on collaboration, personal development, and the process between team members and departments.

For example: place reflection time at the core of the way of working, introduce retrospective meetings after each phase or milestone. At this meeting, the team reflects on its own process, tools and their behavior and take action to adapt it for the future. By the end of the meeting, the team should have identified improvements that it will implement in the new way of working.


4. One delivery is not enough; employees must realize short cyclical results
  • There are two arguments to create short cyclical processes. One, team members can deliver quick results. This creates positively influences commitment and increase motivation to deliver. Two, the data scientist (disclosing and analyses data) and the end-users discuss an optimum between requirements and short term data supply possibilities. Team members therefore experience the added value of data right away and create strong ties between demand and supply of insights. 
  • Work with different phases or steps during a pilot, to avoid the risk of endless data follow-up questions. Try to make data-analyses part of every phase. Make data insight necessary for go/no go decisions or to determine the next steps is the last phase. 

For example: develop different phases in a pilot with specific actions and results. Make sure that every phase will end with a meeting where management can make a decision bases on data-driven advises and conclusions prepared by the multidisciplinary team.

Yes, the process of insight-driven decision making starts with generating insights, and yes for this you will need technical (i.e. systems) and analytical (i.e., data and model building) capabilities. But remember that once your forest produces relevant data-insights you need to have your people ready and prepared to transform them into value. Don’t fool yourself by thinking that merely presenting insights to the decision-makers or insight-consumers will result in them using the insights as intended, nor will it help them develop an appetite for more insights. A thorough Change Management approach is essential to go above and beyond the technical and analytical capabilities and successfully transform into an IDO and deliver value.

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