Corporate transparency, flat organization and open book policies are terms executives and entrepreneurs hear about all the time. As the corporate world shifts towards a more open culture, the demand for open data and insights has increased. This shift has helped the overall corporate strategic planning and management process, easing the alignment of business activities towards a series of goals. Being top-down transparent aligns your culture. (For more on this, EO founder Verne Harnish wrote a book called Scaling Up, which has a section about the company mathematics and creating scoreboards).
The growth of corporate transparency is not only important internally, but externally as well. Corporate certifications like B Corporations require companies to provide a transparent view on their social consciousness efforts to the general public. Achieving the certification is one step of the process — the true goal is to show the world how and why the certification is deserved.
From my time running a data visualization firm, I’ve seen the positive role data visualization can play here. For example, it can help reveal insights and patterns that aren’t immediately visible in the raw data. To help out your own efforts, here’s the process on how to get it done:
Step 1: Data Discovery and Determining the Story
Before this step, it’s easy to underestimate the effort it takes to pull the best insights from your data. Data manipulation products like Tableau, Domo, Pentaho, IBM’s Many Eyes and R, among others, make insight extraction easier. They help users gain an understanding of data using a visual medium.
The key is to start with a simple portion of your data and pull basic insights to visualize and correlate with each other. This process leads towards a compound series of questions, which helps provide an overall vision for your end product. We see the effect during our own discovery process, which leads to unforeseen avenues for data intelligence.
Step 2: Data Infrastructure Setup
Data infrastructures can be simple or complex, depending on the end goal. Many of our clients prefer complete data integration in order to centralize their data repositories. Technologies such as Hadoop have helped by unifying disparate data sources, but other options such as data cloud environments can help produce API’s for future product deployments. Why is this important? Data accessibility is an important foundation not only within the context of dashboards, but also for the possibility of branching out to other products.
Step 3: Product Design and Development
Wireframing, prototyping and application development are the main engines to transform an idea into a final product. Products can range from static presentations and reports to full interactive applications. Mobile, tablet, TV and workstation platforms can all be mediums to help deliver the final product. The secret to a great end product is how well the data story is conceptualized. If the story is weak, the end product will also suffer.
Step 4: Product Release
The best part of any project is getting it finalized and released for all to see. Verify your data for accuracy, run some functionality testing and application flow if applicable, test your design, and make sure whatever remaining items are all completed. The end result is an engaging visual product for all intended audiences to see and use.
The word transparency is a legacy from the early years of the social media marketing movement. Our belief is that transparency is playing a larger role in all industries, and the available data assists. The biggest challenge in revealing insights from this data is to ensure that the story is thought out and the end product is truly successful in delivering that story.
An earlier version of this post appeared on the author’s blog, here.