Part 3 of 3 – Implementing a Data Strategy in your Organization
This is the third in a series of three articles on the differences between “data driven”, “data informed”, and “data inspired” approaches to working with data, and when to choose among these approaches.
Everybody recognizes that effective data analysis is a huge part of business and government strategy, and organizations are spending more than ever on data and analytics platforms. But many are struggling to effectively leverage data within their organization. In a recent survey published by Forbes, less than ⅓ of the companies surveyed have either a “data-driven organization” or a “data culture” despite this increase in investment. Why is this? As observed by the Forbes article, “It seems to be widely assumed that if data-driven insights and recommendations are available for the taking, managers and employees will seize upon them.” And that’s just not the case. As we explored in our first two blogs in this series (see part 1 and part 2), we need a flexible, useful framework to ensure we ask good questions, use data effectively, and pay attention to what is most meaningful. We see this framework as a continuum in which, depending on the situation and what you’re trying to achieve, you can choose one of three approaches:
No matter your situation, the following strategies will help your organization embrace a data culture:
- Identify which data strategy you need to answer your question. The key here is that determining which strategy you need should be determined based on your particular questions and challenges, not the data you have in front of you. Consider the following to help guide your approach:
- Can your question be answered with a definitive number to inform a decision? If so, you are probably in need of a data-driven approach to provide concrete answers.
- Are you looking to understand past performance (good and bad) to help guide your next steps? If so, consider a data-informed approach where you combine KPIs with your own knowledge and expertise.
- Do you simply want to observe general patterns or commonalities as part of a planning or strategy phase? If so, perhaps you need a more informal data-inspired approach to allow for some general exploration to help spark creative thinking.
- Understand your end user and understand what they really need to know to make a decision. Don’t include information that they DON’T need as this can be distracting and cause unnecessary confusion. In other words, just because you have the data, and you have the capability to put it out there, that doesn’t mean you always need to. Extraneous information will detract from the decision making process. Further, because we rarely get it perfect on the first try, create a two-way conversation to allow the end-user to provide feedback for continual improvements. This will help to ensure the end-user is getting what they need, and that the data and approach can evolve over time as needs evolve.
- Consider the questions you are asking. It’s not always about getting more data. More often, it’s forming effective questions that really get at the heart of the problem you’re trying to solve. For example, what’s really driving student performance? Is it always classroom instruction or are there other important factors? Perhaps, in addition to asking “Did this student get the math question right?”, administrators also need to also ask “Did this student eat breakfast today?” Missing the key questions may mean you are missing the real story. Remaining open to surprising results can lead to better questions. What’s the real cause behind that surprising result?
- Create a holistic strategy that encompasses both the technical and human sides of this challenge. Investments in data and technology alone don’t always lead to success or change. Your people are your greatest asset and you need to include them in the process. You need to identify the behaviors and workflows that will need to change to leverage data within your organization such as shifting the focus to outcomes, not data outputs, and really being honest about data biases (whether optimistic or pessimistic). You need to be realistic about your organization, both the opportunities and the challenges.
While these strategies can apply to any organization, we tend to look at things through a geospatial “lens”. Below are a handful of ways these strategies apply to geospatial organizations as they plan, create, integrate and publish data and tools for their constituents.
- You may leverage data and outcomes from similar organizations as part of a data-inspired goal-setting strategic planning process.
- While creating, updating, and maintaining your geospatial data you are likely also collecting QA/QC metrics to perform data-informed quality assessments.
- A large system integration project will likely need a data-driven approach to determine where the integration points are feasible and where they will create the most value.
- Application Development efforts require some combination of strategies: data-inspiration for design; a data-informed approach for tracking and analyzing end-user habits and site interaction to help improve functionality; and use a data-driven approach for tracking scalability and uptime.
We’ve also developed a data-informed approach to bring clarity to what can be a very emotional process — school redistricting. Data and scenarios help guide the conversation, but ultimately social emotional needs really impact the decision. There is fear that changing district boundaries will break up neighborhoods, destroy adolescent friendships, and create long bus rides. And these are all very real potential outcomes of any redistricting process. But a data-informed approach can help mitigate these fears. In these situations, data-informed visualization provides an effective communication tool enabling angry, fearful parents to see the challenges and contribute to an optimal scenario. It has changed the conversation from “this is destroying my neighborhood” to “here’s a better place to draw the line”.
Successfully employing the right data strategy can help your organization generate more value from the data you have and use. Regardless of whether you are a small or large organization, just beginning your journey on creating a data culture, or you’re well-versed in data analysis and data-driven techniques, there is likely room for improvement in your approach to using data to make better decisions and improve our organization’s outcomes.
Previous post (Part 2 of 3) – What it means to be Data-informed and Data-inspired. Click here to view the previous blog in this series, exploring what it means to be “Data-informed” and “Data-inspired” and how to use these different approaches within your organization.
