Data analytics journey: the process that takes data projects from abstract ideation to concrete success

There’s a wrong way and a right way for a company to solve a problem using data. Companies that jump into building a data solution without defining the “why” behind it likely will be disappointed in the results. An approach the moves from ideation to prototype to execution will more likely result in progressive and satisfying solutions.

The first article in our series highlighted that data maturity and organizational transformation go hand in hand. An organization that gets better at harnessing data will see transformation in its people, processes and overall business results.

The second article focused on the different roles and responsibilities an organization has to fill as it becomes better at understanding and using its data.

This article focuses on the process for a successful project and it applies to any company regardless of where they sit in the data maturity model. This is a simplified Agile model for a data analytics project; adopting this process within a company is the fastest way for a company to advance across the maturity model. The process will effectively move from an abstract process that helps a company understand the core problems limiting the business to a concrete project aimed at solving that problem.

An effective data analytics project has three parts: ideation, prototype and execution. These steps are similar to the roles and responsibilities discussed in the previous article. The ideation process includes the executive sponsor, program manager and project manager; in a $1 billion organization about 10 people would be involved in this step. The prototype stage is driven by the program and project managers and focuses on a specific group within a company, such as a singular business unit or functional area. The execution stage implements the work done in the first two stages throughout the entire organization, and features the work of the architect, business analyst, data analyst, data engineer and data scientist.

Ideation

Before a company jumps to building a solution, it has to identify the actual opportunity. The ideation process is what a company, often working with an outside advisor, will use to identify pain points and consider possible solutions. A client company often will say it needs access to all its data as part of the search for a solution. The ideation process will help a company identify its main pain points and opportunities, and then the necessary related data.