There are many important elements to completing a successful analytics project. But after decades of working on projects big and small, Mustansir Saifuddin, co-founder of Innovative Solution Partners, goes back to his 5 Rules, with the last and most important being alignment on the important factors: approach, scope and timeline.
“If there’s one thing I’ve learned in more than 20 years of managing large-scale projects it is that there will be challenges,” Saifuddin says. “Keeping to the timeline and ensuring everyone knows ahead of time how important it is to stay on track is critical.”
The key, Saifuddin says, is ensuring that the project sponsors agree ahead of time on the project’s approach, so they can keep stakeholders informed.
Here’s more on how to to keep faithful to time, scope and project approach, the last — and arguably most important — of the 5 Things to Know Before Starting a Data Analytics Project.
Laying the Foundation for Success
Large-scale analytics projects involve intricate data landscapes, overarching organizational objectives and varied personalities among project team members, sponsors and other stakeholders. The importance of establishing a clear and agreed-upon approach to the project cannot be overstated.
Use an analytics approach to delineate methodologies, tools, technologies and resources. Ensure project sponsors are on board: Their perspective and strategic insights are important early on.
Make sure you understand stakeholder needs as well; failing to do so can lead to project failure.
In addition, open and transparent communication sets the stage for continuing informed decision-making throughout the project lifecycle. Set the tone early on for this approach, and it will make it easier when there are hiccups – which are inevitable in any large-scale project.
Challenges are inevitable, and being ready to meet them is essential. Identifying, addressing and developing mitigation strategies are the responsibility of a strong and proactive project manager.
Engage the project team and sponsors in a collaborative problem-solving exercise. Identify the root cause of the issue, whether it pertains to data quality, technical restraints or unforeseen stumbling blocks. Once you have a clear understanding of the challenge, determine the right mitigation strategy.
If adjustment to scope is called for, do whatever is possible to keep to the timeline. This is where consensus ahead of time comes into play: If everyone knows going into the project that staying on track is critical, finding solutions that keep to that timeline will help avoid cost and time overruns.
Why is this critical? According to a McKinsey study done in collaboration with Oxford University, half of all large IT projects – defined as those with initial price tags exceeding $15 million – go over budget. The average project went 45% over budget and 7% over time, based on a study of 5,400 IT projects.
Mitigation strategies may involve reallocation of resources or re-evalution of scope. Recalibration of timelines should be considered as a last resort. The key? Address the setbacks promptly so as to maintain project momentum and to prevent bottlenecks from becoming insurmountable barriers.
Adhere to the Timeline
Adhere to the timeline.
It bears repeating.
In the realm of a large-scale analytics project, time is both an important resource and a critical success factor. Adherence to the timeline ensures the project stays on track and aligned with the organizational goals.
When adjustments are necessary, communicate their importance and implications to the stakeholders.
The importance of adhering to the timeline must be deeply ingrained in the consciousness of all affected individuals inside and outside the team so everyone internalizes the direct correlation between timely execution and optimal outcomes.
This becomes even more critical for large-scale projects over $1 million in budget, which have failure rates that are 50% higher than projects with budgets under $350,000.
While the agreed-upon approach serves as a guideline for the project, the evolving nature of data analytics in particular may necessitate recalibration. In such instances, it’s up to the project manager and their team to ensure all stakeholders understand the why behind the adjustments.
Actively engage them in the decision-making process. When projects experience delays or deviations from the original plan, stakeholders may begin to question the team’s ability to deliver. This can lead to reduced confidence in the success of the project, which may result in reduced support, resources and stakeholder engagement.
Foster Trust to Increase Chances of Success
From planning through execution and delivery, a project’s success balances on the element of trust.
Stakeholders, sponsors and team members must be able to rely on one another’s commitment and expertise.
Consensus on timeline and dedication to the timeline signify commitment to a project’s success.
Conversely, lack of these critical tenets can erode trust, leading to cost overruns, missed deadlines and risk of project failure.
According to one study in the International Journal of Managing Projects in Business, project success becomes more likely as collaboration improves which, in turn, is influenced by an increase in the level of trust between team members.
Despite the dire statistics about the chances of projects going sour, there are things project managers and their teams can do to avoid being part of the “failed projects” statistics. It all come down to alignment beforehand; communication and transparency throughout the duration of the project; and faithfulness to the agreed-upon project plan and timeline.
5 Things to Know Before Starting a Data Analytics Project: A summary of the five things, with links to more detailed explanations.
Delivering IT Projects On Time, On Budget and On Value: A McKinsey report