Questions you need to consider before starting your data analytics project
To execute a data analytics project, organisations need to balance technical infrastructure with managerial finesse. However, most organisations neglect the managerial aspect of a project, expecting data analytics to solve all their problems with minimal effort. But data analytics is a tool, and like any other tool, it requires experience, focus and discipline to make full use of its potential. This is why management is crucial. It brings the necessary qualities for completing a project successfully, and a crucial component of management is asking the right questions. By asking the right questions, project managers can set proper budgets, realistic goals and set manageable deadlines for their analytics project.
Key questions to consider
There are several questions to consider, but asking these questions will make the process smoother.
What business problems are you trying to solve?
It might seem like an obvious question to ask, but you would be surprised to find out how many companies fail to consider this before moving forward with a data analytics project. Many companies are sold on the benefits of data analytics services and tend to rush right into a project. However, data analytics comes with different features and capabilities. Sometimes, a specific model has to be built. As such, data analytics can only generate value when they solve specific problems. This is why it is important to establish what the root cause is before asking questions. By having the root cause identified, it becomes much easier to set objectives and select the right analytics services.
What are the sources for the data?
Organisations collect a vast amount of data regularly, but what are the sources that feed the funnel for the data analytics project? Sources have huge implications on data accuracy and will affect findings in the project. If the source is off, then findings will be off, tanking the entire project. There are also legal implications to consider. Governing bodies like the EU are starting to get a legal grip on the collection and analysis of data (GDPR being the best example). Organisations also need to address ethical concerns because while they might be following the law, it does not change the fact that people will be comfortable with their information collected and repurposed. This is especially the case for industries, like retail and healthcare. Before embarking on the project, organisations need to consider their sources for accuracy, ethics and make sure they are staying within legal boundaries.
What is the quality of the data?
Alongside checking sources, organisations also need to check data quality. As the term implies, it refers to data that is clean, uncluttered and unambiguous. Organisations need to take a hard look at the quality of their data before embarking on the data analytics project because it has massive implications on their findings. The cleaner and more consistent the data set is, the more accurate and helpful the insights will be for the organisation. Organisations should examine their data schema and format for inconsistencies to ensure high-quality data to progress with the project.
What are the tools available for analysis?
While data quality is important, it is just raw, unrefined information. What organisations need for their data analytics project is the right toolset. Powerful data analysis tools like SAS Viya or SAS 9.4 are the type of tools organisations need. Why are these tools necessary? Because organisations collect petabytes of structured and unstructured data and basic tools cannot handle these large volumes. The right data analytics tools can perform several functions like generating visual findings, track changes, governance/transparency and protect data.
Do you have the right team to work these tools?
While data quality is important, it is just raw, unrefined information. What organisations need for their data analytics project is the right toolset. Powerful data analysis tools like SAS Viya or SAS 9.4 are the type of tools organisations need. Why are these tools necessary? Because organisations collect petabytes of structured and unstructured data and basic tools cannot handle these large volumes. The right data analytics tools can perform several functions like generating visual findings, track changes, enabling governance and securing data.
Questions, questions galore
Data analytics projects require the perfect combination of technical and managerial expertise. It is simply not possible to start up a project and expect it to generate value. Organisations need to give due consideration to several factors including data quality, sources and personnel. If hiring personnel for handling analytics tools is an issue, then organisations should consider consultants.
Furthermore, if the company does not have any prior experience, then hiring consultants with experience in data analytics projects is a smart move because they bring both expertise and experience to the technical and managerial aspects of a project.