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Writer's pictureDimitris Adamidis

Follow the Power of Data: Navigating the Journey from Data-Driven to Data-Informed Organizations.

Updated: Mar 14


Data-Driven to Data-Informed


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Article Summary: From data-driven to data-informed organizations: Unlock the power of data for better decision-making, increased creativity, and sustainable business success. Learn the key cultural distinctions and explore critical processes to adopt a data-informed culture. Discover the opportunities and challenges of this transformative shift, and harness the full potential of your data assets for long-term growth.


 

A 2021 survey by Gartner found that 82% of organizations are committed to becoming data-driven, but only 35% have achieved this goal. It suggests that many organizations are still in the early stages of developing a data-informed culture on their journey to data-driven organizations.


For many, that's the way to go, using data therefore. Remaining data-informed is making critical decisions without restraining the organization around the data organization possesses or is capable of gathering and analyzing. Having data-informed teams helps them to make crucial decisions based on the data, leaving room for creativity and the best operationalization of these decisions at the lower level. As you can imagine making vital decisions based on data is beneficial, and numbers are compelling enough. Data-informed organizations are 6x more likely to retain customers, 19x more likely to be profitable, and 3x more likely to report significant improvement in decision-making. On top of this, data-informed organizations are growing at an average of 30% each year. Here you go if you think about the ROI of that cultural shift. You can go right here.


It might need clarification for the readers when I distinguish between data-driven vs. data-informed organizations. A couple of weeks ago, I wrote about the data-decision organization and how to help the team transition to that model. Data-driven model is a robotic-like blueprint that assumes humans can't make perfect decisions. Clarifying the difference between these terms would be helpful. Both data-driven and data-informed organizations recognize the importance of data, but there are differences in their cultural approach:

  • Data-Driven Culture: places data at the forefront of decision-making processes. It relies on data to guide and validate decisions, often using quantitative metrics as the primary driver. Decisions are based on empirical evidence and statistical analysis. The focus is on using data to lead and drive the organization's direction.

  • Data-Informed culture: values data as a critical input in decision-making but also recognizes the importance of other factors, such as experience, expertise, and intuition. Data is used to inform and complement decision-making rather than solely driving it. While data plays a significant role, there is room for interpretation, context, and subjective judgment in decision-making processes.

Remember my analogy about selecting the t-shirt in the morning before leaving your home and applying for an A/B test? If done, you would only leave a house after midnight. The same is with your organization. You want to make sure that your data and time works for you. Otherwise, your competitor will take your spot. Using data is extremely important, but the fundamental skill is to ensure that you have a defined set of processes and that you do it in a disciplined way. Data-informed culture tells you how the critical statistical conclusions help you to guide the entire organization, align around the set targets and empower each team to stay focused on what matters. That approach is essential for the growth of smaller organizations that must move 10x faster than the incumbent. Decision-making agility is the fundament of their business existence.


I won't spend time discussing the inhibitors preventing your organization from shifting to better decision-making. These are the same as I listed in the previous article Building a Data-Driven Culture: Insights from the Frontlines of Corporate Decision-Making.


There are plenty of processes or areas where you can create data analysis to help you improve your organization. The most common examples are Strategic Planning, Performance Tracking, Customer Analytics, Process Improvement and Optimization, and Risk Management. These are just a few most popular, but plenty of more exists.


Conclusion: The real tricks start with how to do it so that you successfully adopt the newly introduced culture. It's easy to say, "Change it that or this way." I've often heard something must be done "because it is rational." That is not making things better or more accessible on the ground. And that's where the real problem exists. My recommendation for every leader transitioning to data-informed culture starts at the essential mid-level management. If you are unsuccessful with that group of people, you are set for failure and a very rocky, confusing, often detrimental process moving you away from your objectives. Antagonizing the approaches or points of view is good in the short term. However, you must be aligned and execute a cohesive plan for your organization in the long term. So pick your battles carefully before you step in that direction. I know it takes work when you have management or board expectations simultaneously. But ask yourself: Do I care for short-term gains or long-term objectives?


Below are a few ideas you can use as building blocks for your journey toward a more data-informed organization. Some of them will be parallel with the data-driven, but if you put this in the context of what I've elaborated above, you will get a better sense of where this is supposed to go:


Data Literacy and Training: Building a data-informed culture requires developing data literacy across the organization. Get your first-line managers onboard with your vision around the decisions they must make each quarter, month, and year around critical processes. This is the single epicenter of that fundamental change. Only move somewhere if you see a strong traction of these managers understanding why they do things and how this affects their results. Empowering is one, and understanding the data is second.


Collaboration and Cross-Functional Alignment: While selecting your specific processes as a primary suspect for this exercise, I recommend starting with Strategic Planning as an excellent organizational exercise avoiding disconnected objectives. At the bare minimum, get your GTM under one approach. Processes rarely begin and end in one function, often getting through multiple teams before they end in RevOps or Finance. Strategic Planning is a primary example of that inevitably in any organization.


Data Quality and Accuracy: The old-new problem. Data is always flawed; data scientists will tell you they are unhappy with the data quality. That's a good thing because you know that someone will take action to improve them over time. However, you need to decide what is good enough data quality. Often the risk is more significant when you don't settle; instead, I suggest you decide on "good enough" data quality. One way to overcome that problem is to have a master data management framework in your organization to help you identify the gaps and start filling them with better sources over time. It takes time to be done in this space, and you are just dealing with the risks.


Data Accessibility and Integration: Systems will come and will go. However, integrating them helps you save days or weeks in the decision-making process. Remember that data transfers are faster than your analyst can download, copy, paste, apply formulas, and upload them again somewhere else in the Excel spreadsheet. Having an adaptable system architecture and integrating them is significantly improving your analytical experience. It is not a purely administrative task but an activity that accelerates your decision-making process. Break down data silos, implement robust data management systems, and enable seamless data sharing across departments. Easy access to relevant data allows employees to make informed decisions.


Agile and Iterative Approach: Data-informed decision-making is an iterative process. Get your teams on a short cadence review process. There is plenty of mid-level management decisions that must be made each week. A structured review helps guide them based on the data they put together. Be careful to stay within the expectations around structural changes or big asks. These are not best for these forums, taking too much time or resources before you re-iterate next week. Pace matters, so be wise and think twice. Although this is a strategic initiative, the steps within are tactical or operational.


Ethical Data Use and Privacy: Data-informed equals data-respect approach. This is not the first thing that sales reps will think about, but organizational leadership must ensure that your teams know what is possible and what is not. Organizations must prioritize ethical data use and protect customer privacy. Compliance with data protection regulations and ethical data practices builds trust with customers and stakeholders. Respecting privacy rights and establishing transparent data governance frameworks are essential to a data-informed culture.


Executive Support and Leadership: Creating a data-informed culture requires strong executive support and leadership. Executives should know which processes are prioritized in this approach. You don't have to do anything unusual. You can attend the regular cadence meetings, champion the use of data, drive key processes like planning, and set an example by making data-informed decisions. At that point, you are like a basketball coach telling them how to play in that specific court situation. Communicate the importance of data-driven approaches throughout the organization. Leadership support helps drive the cultural shift and reinforces the value of data in decision-making. In the end, people follow believers, not manipulators.


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