3 Things to Consider When Designing a Data Analytics Project

"Design is not just how something looks, design is how it works". These are the immortal words used by the late Steve Jobs, the co-founder of Apple Inc. These words are so true and yet so many individuals do not fully appreciate the value of design in their projects. There are no exceptions to this ideology, there is just lack of intention to implement good design in any project. Analytics projects are not very glamorous but they are extremely critical. Thus, it is critical that the overall process from raw data inputs always to final outputs are carefully designed. These are the main focus areas to focus on designing while conceptualizing an analytics project:

1. Scope – At its core, the goal of data analytics projects is to mainly provide answers to questions based on raw data to analyze the current trends in the market and anticipate future trends. If left to the imagination, any query can shoot of any number tangential questions. So it is important to define the baseline for any analytics project to keep the original goal in focus always. This would help design the initial data requirements and final outputs.

2. Workflow – Once the scope is clearly defined, the next step is to define the standard workflow ie the raw data files (specified with details of all required data points), intermediate files Ie the tables that created from the raw files, the final output tables ie the data sets that would have been extremely used to provide the final reports for the end clients. At this stage, the team must layout the specific details for all the tables that are expected to be created in the project. As the details of the intermediate and final tables are ironed out, the teams are also able to create standard scripts. SQL or Audit Command Language scripts could have been used to design such a workflow. These tools allows for data of a project to be insulated from other projects. A standard workflow also allows for an iterative feedback loop to be able to verify the tables created at each clearly defined stage of the process.

3. Infrastructure – At this point, it is matter executing the workflow. The team must then decide on the tools that are to be used to provide the desired results / reports. These decisions would involve factoring in the cost of resources infrastructural and personnel. Any decision should keep in mind future scalability so as to be able to accommodate further development of the solution.

Following these or similar guidelines to design an analytics solution is critical in achieving success by leveraging every resource to its complete and derive maximum value from the money invested. Design an efficient workflow is not an easy task but it is worth investing time and effort to reap the rewards.