“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 very true, and yet many people do not fully appreciate the value of design in their projects. There are no exceptions to this ideology, only the intention to implement good design in any project is missing. Analysis projects are not very glamorous but they are extremely critical. Therefore, it is crucial that the overall process, from raw data inputs to final results, is carefully designed. Here are the main focus areas to focus on design while conceptualizing an analysis project:
1. Scope – In essence, the goal of data analytics projects is primarily to provide answers to questions based on raw data to analyze current trends in the market and anticipate future trends. If left to the imagination, any query can trigger any number of tangential queries. Therefore, it is important to define the baseline for any analysis project to always keep the original goal in focus. This would help design the initial data requirements and final products.
two. workflow – Once the scope is clearly defined, the next step is to define the standard workflow, i.e. the raw data files (specified with details of all required data points), the intermediate files, i.e. the tables that were created from the raw files, the final output tables, ie. the data sets that would ultimately be used to provide the final reports for end customers. At this stage, the team must design the specifics of all the tables expected to be created in the project. As the details of the middle and final tables are worked out, teams can also create standard scripts. SQL scripts or Audit Command Language could be used to design such a workflow. These tools allow you to isolate the data of a project from other projects. A standard workflow also allows for an iterative feedback loop so that tables created can be verified at each clearly defined stage of the process.
3. Infrastructure – At this point, it’s a matter of running the workflow. The team must then decide which tools will be used to provide the desired results/reports. These decisions would imply taking into account the cost of infrastructure and personnel resources. Any decision must take into account future scalability in order to accommodate further development of the solution.
Following these or similar guidelines when designing an analytics solution is critical to your success in getting the most out of every resource and getting the most value for your money. Designing an efficient workflow is not an easy task, but it is worth investing time and effort to reap the rewards.
