Unsticking Your Data Analytics Project


AllCloud Blog:
Cloud Insights and Innovation

The success of data analytics projects hinges on a range of key factors – and as with any highly technical initiative, there are countless opportunities for a project to stall, ranging from a lack of a clear end-goal, to inadequate processes, to not having a well-defined use case. Companies of all sizes know that they need to do more with their data, but oftentimes not knowing the best practices for how that process should be run can lead to a project getting “stuck.” We’ve seen it happen to hundreds of companies, and it’s simply because when dealing with data analytics, you need to know how to build a process for success. Thankfully, when a project does get “stuck,” there are simple measures that can be taken to help get it moving again and back on track. 

1. Define (or redefine) the Goal

Successful data analytics projects start by identifying the objective of the work and defining clear criteria to track progress and measure outcomes. Ultimately, projects should yield either a process, system, or behavioral change that aligns with the organization’s goals. By identifying your critical use cases and building a fit-for-purpose analytics engine, you’ll ensure that you’re creating a blueprint for clearly-defined success. One of the most important aspects of this stage is partnering with an internal champion who will not only support the work, but also has the ability to transition results into actionable items that will create meaningful change. When we see projects that are ‘stuck,’ often there isn’t a clearly defined goal or a clearly appointed champion.

2. Create Your Data Operations Process 

A comprehensive, detailed data ops process is the backbone of your initiative and will ensure that your project moves efficiently. There should be a system in place for every aspect of the project’s lifespan, from how to take-in requests, to testing, to production. Identify the full life-cycle of your project – including source controls, automating testing, quality controls, monitoring, data validation, security, and access controls – and build a process that accounts for each. Any break in that process chain presents an opportunity for a project to get off course and off schedule. If that happens, rebuild the process to account for the gap.

3. Find Your Data Nexus 

Many analytics projects don’t garner impactful results because they pull in too many data sources. Start small and think strategically. By selecting 2-3 sources, the data can be blended but clarity remains around the results and the story it tells. The sources also matter – picking data sources that can be leveraged for other use cases beyond this project will drive down the cost to implement and turn results into a strategic asset. 

Complex but not Complicated

Data analytics projects require ample time, attention, and resources, and one successful project can provide a foundation for future growth, profitability, and efficiency. By simplifying the steps, from a clearly defined goal, to the process itself, to the right data sets, you can ensure that your project moves efficiently from kick-off to completion. Beyond just the project itself, the true value in a successful data analytics effort is that it builds the foundation for your continued data journey. By defining use cases and data sets, you’ve paved the way for the expansion into other aspects for your business; it’s an investment that will surely pay dividends.

We know that figuring out how to reignite a project is never easy. AllCloud’s data experts can help you get your team and project back on track.  Learn more about our data and analytics services and contact an expert today

Dave Taddei

SVP, Data Analytics North American Practice Lead

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