First, gather the people – get a team together. Find people who are onboard with your idea and are willing to put in a little extra effort to build an efficient workflow. Recruit people who understand the work processes and are willing to champion the movement towards achieving better data and better quality. For example, members on your team could include a subject matter expert, a data scientist, developer, and IT/Business Analyst.
Second, gather the data – collect data from spreadsheets, databases, and in its raw form. Perform an analytics audit to understand the data sources and structure. This would also be a good time to determine the best technology for capturing and visualizing the data. Ask questions like what sources are being used most often and what teams are using their data effectively.
Third, wireframe the dashboard – meet with the key stakeholders, power users, and champions. Talk about current processes in place. Hammer out the details one nail at a time, if you must. Taking time to understand the parts of the process that need improvement will add tremendous value. Storyboard the to-be process. Most corporate companies will have Vizio, but I recommend using Lucidchart.
Fourth, integrate the data – work closely with IT to develop the best data model for the dashboard. Considerations include the amount of data and load time. Think about flow of data from input to output, both into the analytics platform and into the analytics model. Look for templates that can ease you into development. Some sites, like Exchange.ai, offer extensions that expand the capabilities of the analytics tool you are using.
Fifth, promote it – share your work within the organization. Market the concepts, algorithms, and effectiveness of the tool you’ve just built. Creating a community of practice is a great way to start sharing knowledge on company best practices for analytics. True value from analytics is realized when buy-in exists and others are ready to use your tool or motivated to develop their own.
Last year, I attended a workshop where the speakers talked about the steps they took to build a data analytics culture at their company in just nine months. When I heard this, I was very intrigued because as a consultant I have seen companies take up to a year just to pilot an idea let alone build an enterprise culture around it. As I listened to their presentation, I realized that these steps are the exact steps that I take with every client I have worked with on a Spotfire implementation. I also realized that there are those who must discover this journey the long, hard way due to a lack of resources. I am optimistic that, where organizations lack resources to kick start analytics, templates from Exchange.ai will provide value.