The best way to ensure analytics success is to make sure you take an iterative approach to development and design. Starting small and building up an analytics environment is the best way to make sure that you are getting requirements and applications right, without having to go back and redesign parts of your solution, wasting time and money. Organizations that get ahead of themselves and try to take on more than they can handle, end up getting overwhelmed and suffer from scope creep and potential project failure.

 I’m sure you’ve heard this time and time again – it’s important to start small with your BI deployments. Pick something small, develop a simple dashboard, or set of KPIs, and make sure that everything, from data collection and storage to delivery is done right. Once you have a successful implementation, it becomes easier to sell the value of analytics and expand the overall environment.

Many organizations want to do everything at once, and in some ways it makes sense, especially where data is concerned. There is no sense taking a piecemeal approach to data management. Based on the fact that many of the data environments that exist are already prone to silos and the inability to gain insights automatically, the way to achieve the “start small, get quick wins” approach, really does take more planning. With strong planning and project management, organizations can do both. Here’s how on a high level as each organization will have a different maturity level or corporate culture which will change the way projects are developed and managed.

Overall, analytics projects can be quite complex. With increasing data volumes and complexities, the number of moving parts requires a lot of planning and data management to ensure that overall analytics make sense. Therefore, organizations need an analytics strategy that looks at how data will be managed. In many cases, business users only care about the end product, which involves how they will interact with apps and leverage the data for business. The data only becomes important itself when it isn’t validated or if its quality does not meet end user standards.

In essence, this project approach involves making a plan for overall data management that leads to a consolidated and holistic approach to data access, while delivering small and quick value to business users. For instance, the first resolution discusses tying analytics to business strategy. Since we are focusing on what drives business and what will provide quantifiable business benefit that is where initial delivery of analytics should start. Identifying key performance indicators, ensuring that key metrics are delivered in a self-service way, helps provide those quick wins. On the back end, organizations may be planning for large BI project implementations or changes to infrastructure. Although equally as important, to ensure business buy-in, delivery of applications need to be consistent and provide value that is tied to overall business needs.