Mastering data analytics is a feat every organization aims to accomplish. Yet, some companies get data analytics projects right …and others get them all wrong. Project failures are usually the result of moving full steam ahead without having a solid plan.
Data analytics projects are only as good as the process you surround them with. We want to make sure your future data analytics projects are triumphs, so here are some actionable steps to help you.
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At Drivatic, we pride ourselves in providing our customers with the best service possible.
We are dedicated to present our current and prospective customers with the necessary tools surrounding the data of business intelligence, to furthermore promote their existing platform in business.
CUSTOMER AND PRODUCT ANALYTICS
To set your data analytics project up for success, you need to align teams, set responsibilities, and ensure a free-flow of communication throughout the project. Efficient teams utilize modern data collaboartion tools that keep everyone on the same page while notifying all parties of the project’s status.
At Drivatic, we use Microsoft Teams – as well as a custom Project Management Business App (Yes we also build Buisness Apps) and Power BI reports—so both the client and Drivatic are aligned with project tasks. Utilizing these tools keeps the project’s progress transparent and serves as a great way to track deliverables. We all know who is responsible and where the project is in the pipeline.
Giving your team members the resources needed to collaborate and communicate with one another will boost positive project outcomes. Three words can make or break the project outcome…
Requirements gathering is one of the most critical aspects of the project. This session is meant to gather as much upfront detail about the project as possible. It allows us to understand what we are trying to accomplish with the project so we can set reasonable and achievable project goals and deadlines.
If we are building a single report, we’ll also use this time to gather all information needed about the report, so we can begin the build process immediately. This includes criteria like:
Other key aspects include identifying data sources and their associated location and access restrictions.
At Drivatic, we utilize a standardized template of key items we must cover during each requirements gathering session. Before the session ends, we ensure we’ve checked off all items before we sign off on the client call. This way we can be certain to avoid any items that could result in scope creep.
To meet client expectations, a three-part process of reviewing, reconciliation, and iteration must be executed well.
After you’ve gathered all project requirements, it’s time to go to work on deliverables outlined in your various sprints. Maintaining constant communication with the client during this time is crucial. We set detail deliverable for each department, from Modelling, Dax and then the Visual department…
If there’s any doubt, uncertainty, or further information needed on a specific task, we immediately schedule a meeting with the client to avoid creating any re-work or issues later on in the project timeline. The client’s feedback allows you to reiterate on the task and reconvene again for another client review.
The reconciling phase is more or less the validation of data. Validation should be a mutual activity between you and the client. Oftentimes, we’ll request past data sets or reports to validate against and ensure the data ties-out.
Once you’ve validated the data in your project, it’s still important your client verifies this validation and/or has performed the task. This way your client can be certain the project is portraying the correct information.
Otherwise, the next thing you know, the client gets two months down the road and says, “Hey, we’ve been reporting against our financial data, and it’s all wrong. We’ve been missing X amount of dollars on our cost because it wasn’t pulled into the actual report.” No good.
And finally, iteration is often where the client has the first review of the report. If the client says, “You know we’d like to see XYZ change,” then we gather the information, pull in extra data (presuming it’s in our scope), make the iteration, and present new changes/additions to the client.
Hopefully at that point, the client is satisfied and feels the build represents what was portrayed during the requirements gathering session. If the report satisfies the client’s needs, we move onto the next phase of the project. Otherwise, we gather further requirements and perform another iteration until the desired end result is achieved.
Always ensure your initial requirements gathering is as thorough and descriptive as possible. This, in essence, will eliminate the need for additional requirements sessions and get your project completed in a more timely manner. Incorporating requirement gathering sign-offs by the client is also a best practice to eliminate scope creep and keep the project on pace.
Iteration and review dovetail with User Acceptance Testing. This is where we show the end-user how to use the final result of the report we have built. The client should take the report or other project deliverable and make sure it satisfies all requirements outlined at the beginning of the project. The build should be at a point where your client says, “Yes, this what we wanted.”
At the end of this phase, the client will sign-off on the deliverable(s) and move to schedule knowledge transfer and training events.
The end of each project should always consist of some form of knowledge transfer and training. You want to make sure your client fully understands what you’ve delivered for them. This is where training needs to occur in order to ensure the client achieves success with the project you’ve completed.
Not only will it make your client happy, but you’ll walk away with a sense of pride knowing you’ve successfully empowered your client with the analytics project fully implemented. This includes documentation we have captured throughout the project, so items can be referenced—and nothing is lost in translation.
Once this has been accomplished, we hand over the project, close it out, and move onto the next one. Along the way, we ensure continuous improvement is accomplished on the Power BI Report, Power Apps, or whatever data analytics software we have implemented. We also setup the schedule refreshes to make sure this report is all automated
According to a recent PMI management survey, 52% of projects reported in the last 12 months experienced scope creep. Data analytics projects are a lot like climbing Mount Everest. You’re almost to the summit, and then, uh oh—there is a client request for a big value add. Get to the summit first, wrap up the project, and then tackle the new request afterward. This makes a lot more sense than rerouting your trajectory before reaching the finish line.
Communicate between your team members and your client by developing a true partnership versus a transitory communication style. If something comes up over the course of the project that you feel may negatively impact the project, inform the client ASAP. It’s better to head off issues immediately, rather than attempting to cover them up, which normally results in bigger issues down the project timeline.
You need to have a point person for each side of the project. Who’s leading the build and who is delivering the build? A point person (aka Project Manager) driving the team is the one who will make sure the project won’t stall. Even if it hits a few bumps, the operational technical manager will feel compelled to ensure the proper information and support is there to deliver the build.
Certain projects require the support of executive sponsorship. Having this type of support Also keeps the project in priority and prevents corporate strategy stalls.
Organizations today work with an unprecedented volume of data. Between 60-73% of data is neglected that could otherwise help drive business decisions. Some organizations take a “shoot from the hip” approach when managing data analytics projects, while others follow a cohesive data analytics process. Take a guess which strategy reaches the summit first.
When you sit down with your teams, understand what you’re trying to accomplish. If you follow these six actionable steps, all your data analytics projects are sure to be winners.
The Drivatic team is here to help you achieve your data analytics project goals and craft a data analytics process that works every time. Book a free online analytics visioning session with us, to find out how we can help.