In fact, if your organization is just getting started in the world of big data, it makes sense to find a few smaller problems to try to solve. These small problems allow you to tweak your systems and processes to make sure you are gathering, storing and analyzing data correctly. These small problems also let you build up the appropriate skills within your teams to ensure when the big problems come along, your teams are ready to handle them.
Wouldn’t you feel better about your big data initiatives if you could ‘prove’ that the systems, processes and people were working effectively and giving output that can be believed? Most people would…and that’s why most organizations should start with these small projects. There’s nothing worse than getting 6 months (or a year) down the big data road and realize your data has been collected and stored in a way that makes it difficult to believe whether that data is correct and ‘clean’.
As important as making sure your systems, processes and people are working effectively is, it is just as important to make sure your organization is ready to accept the outcomes of any big data analysis. There’s nothing worse than spending time and money and realizing that your organization (or certain people within your organization) aren’t willing or able to accept the outcome of work performed analyzing the company’s data.
Starting small with big data lets you and your organization get comfortable with the entire process of collecting, storing, analyzing and reporting. Big data doesn’t have to require big projects, big budgets or big teams…especially when starting out.