Remember the thick textbooks you had with tons of text to read and no interesting pictures? And, how much you relished reading comic books like Batman and Superman which had loads of images outdoing the script.
Indeed, a picture paints a thousand words.
Comic books always left a visual memory in our minds, unlike the topics in a general textbook. A flow chart in a life chain diagram was easy to remember. The bottom line is that a human mind can catch inferences from visual data much easily to connect the pieces of a story together.
Organizations these days gather a lot of data via different sources – projects delivery, Continuous Integration (CI) pipeline, project management tools, bug/issues management tools, productivity data, invoicing and payments. The data is sometimes spread among multiple tools and platforms that manage each aspect of data or it could be CRM systems on top of legacy systems that each individual has to monitor.
Take a look at the image below. Collaborating the data buckets could possibly give a singular view to derive actionable insights for the organization at each level, whether at CXO (C-Suite) or the Managerial one.
To understand more on the importance of Data Visualization, read our previous post: How Data Visualization Can Help You Take Better Decisions For Your Business.
At Net Solutions, data is a practice. As a result, we established a goal to develop a Data Dashboard to view insights from the data across multiple system environments used by the project and by the accounts/billing team in the organization. The idea was to make information available in a digestible format for each level – Team level, Managers/Lead level, and CXO level (the C-Suite).
We created a Data Visualization solution to leverage information in the best possible manner. This blog will give you a preview of the things you need to consider before you begin developing a data visualization dashboard solution of your own. Here’s how you need to go about it:
The aim for any data dashboard solution should be to gather data under one roof in a way that conveys a story that helps the audience derive insights from it. As you can see in the above image, three pillars of a data visualization solution are:
- Strategic Insights
In order to achieve a solution that is successful in covering all the above three aspects, you need to follow a certain process. Below are the steps you can go over to achieve a good data visualization solution for your organization or customers in relation to what we did to do same for our organization:
Step 1: Identify Your Audience
The foremost step is to identify your audience that will use the data. You can start doing this by picking up the data buckets. There are multiple ways to visualize data and each audience genre may need different types of visualizations. The purpose of data visualization is to make audience’s life easy by presenting data in a manner that makes the maximum sense to them. It is all about making sense of scattered data using techniques of tabulation, graphical representations, and logic.
How We Did It
We interviewed the stakeholders at each level of the organization. In our case, we had multiple user groups:
- Project Teams
What we were able to conclude from the interviews and discussions was that a project team will care for data related to their bug counts and project progress while the team managers will care more about the overall quality of the product being developed and whether the planning is right.
A CDO/CTO (Chief Delivery Officer/Chief Technology Officer) will be more concerned about whether the product delivery is going smoothly and if the planned delivery has all the elements promised to the client during the project planning. The CDO (Chief Delivery Officer) would also want to know the constraints and challenges in achieving a certain plan.
Step 2: Zero-In On Your Data Points
Once you are clear with who you will be serving the visualization with, it is now important to formulate what you will need to make sense out of it and present this to your audience. A best practice would be to start documenting the events that trigger certain data points which you will need to use in your solution. The next step will be to organize the data points and define which data will be used vs which data to create a set of data visualizations.
How We Did It
We started monitoring the CI pipeline used for the projects, project management tools like Jira and Taiga, bug tracking, CD pipeline. After having a decent idea of what process triggers what data, we were able to document the data points that were needed to plot a solution. Based on our interviews and discussions, we were able to understand what each type of data means for a stakeholder at each level of the organization starting from the delivery teams to the scrum masters and the CXOs.
Step 3: Define The Logics to Derive Insights
You now have the audience and you have the data. So, you should now start planning on how to filter raw data into meaningful insights for the audience using the data visualization toolkit you have chosen. This step will primarily involve defining what intelligence or data plots you can pick and then displaying them using charts and graphs.
Separate what logics you can reuse from the data sources and then list down the logics you will need to paint on top of the data you extract from those sources. Another list could be derived to track what all data points are currently missing and plan on how to induct them in the solution. You can do this by either modifying the data bucket or finding a way to extract that info by building a supporting data bucket.
How We Did It
Having known what kind of information a stakeholder wishes to have from each data bucket or in collaboration of multiple data buckets, we were able to understand what form of data will be an acceptable and consumable insight for each of them. We decided to create an accumulated stats section for the Macro-level feed and then separated into micro- ones for project specific feeds of plans, graphs, and stats.
The feeds were further filtered to tell a story about the project quality in terms of bug reporting and project progress in terms of the plan and milestones.
Step 4: Represent The Data
This is the stage when you use the strategic insights derived from data points and use them to weave a story around it for the defined audience. The solution may contain a lot of charts, graphs, and reports which may clutter the view sometimes.
It is advisable to define how the artifacts generated from data are segregated from each other and let user have an intuitive experience of where can he find what kind of visualization.
Ironing out the data points is an ongoing practice, it should be your recurring question – Will removing a data point or adding a data point elevate the sense of what inference I intend to pass to the client from it?
How We Did It
We faced many challenges while handling multiple types of data. The greatest challenge was to make sense of data logs which were independent of each other. Generating an insight out of multiple types of data was the key prime focus.
For instance, we first collected bug data from complex report and charts and then put together project management data pertaining to planning and status. The team worked out ways to devise a navigation and workflow of the solution in such a way that it made complete sense for the user.
As planned, we created accumulated graphs and data feed that provided an overview of project health and what the upcoming risks and challenges. We also developed a project-specific dashboard that includes all the planning involved in the project, the bug reports and project billing.
Having a visualization platform designed and integrated as a practice will let an organization unlock the hidden treasure that was already with them but there was no map to find it.
Having complete project status, reports and data on a single platform can do wonders for an organization by enabling them to:
- Track project well-being and progress (CXOs)
- Estimate Risks (Managers)
- Assess the approach (Project Teams)
A data visualization platform at the workplace results in better delivery rates, streamlined processes and smarter teams.
If you are looking for any help on building such or any other digital solution for a better customer or employee engagement, please contact us at [email protected]