As a managed service provider grows and becomes more mature operationally, the way projects are managed becomes crucial for increasing revenue. A significant risk to this revenue growth, even for the most experienced MSPs, is the contamination of project data.
Addressing the issue of contaminated project data is vital for improving your financial health. By tackling problems like outdated information, scheduling challenges, and the need to improve service delivery, transforming contaminated project data into an accurate data pipeline becomes a critical step towards enhancing your business’s growth and financial success.
Diagnosing Data Dilemmas Before They Wreak Havoc
As an MSP takes on more and complex projects, closely monitoring and managing these projects becomes essential in tackling data contamination issues. This is directly attributed to the MSP business intelligence pipeline, which consists of three layers:
- The work layer, where you’re capturing and tracking the project and service ticket data
- The business layer, which includes communications, scheduling, scoping, and everything else that helps your business run
- The growth layer, where you track resource requirements, utilization, efficiency, and profitability
Contaminated project data can percolate through these layers, affecting internal and external communications, profitability calculations, and future resource requirements. All of these affect the growth of your business.
Two significant challenges that contribute to the contamination of project data are the inherent human difficulty in dealing with probability and the manual tracking of dynamic data without sufficient automation.
The Probability Problem
Mike Psenka
Project timelines often go astray because human intuition frequently misleads us when assessing the likelihood of on-time delivery.
For example, if a project has 50 tasks, and all team members complete their tasks on time nine times out of 10, we assume the project will be done on time without issue. But the actual chance of this project getting done without crashing the project is just 0.5%. This false sense of comfort derived from assuming all team members will complete their tasks mostly on time can lead to unexpected project failure.
To address this, leveraging improved processes and tools that provide accurate assessments of project timelines is crucial, which leads us to the problem of data volume.
The Data Volume Problem
Even a moderate portfolio of work includes tens of thousands of elements that change, affecting accuracy of schedules, delivery dates, and utilizations.
The sheer volume of dynamic data in common MSP projects underscores the impossibility of manually managing all the details accurately. This is when an autonomous function like autonomous project monitoring and management (APMM) becomes essential for efficient project data management.
Cleansing Strategies: How to Purify Your Project Data
APMM is an AI-driven, automated process designed to maintain data accuracy through the following features:
- Autonomous Diagnostics: Identifies problems in projects automatically, including structural flaws, dependencies, delays, and resource and capacity issues.
- Remediation and Recasting: Corrects errors, recasts projects, and updates timelines automatically, saving time and ensuring accuracy.
- Monitoring and Communication: Observes, diagnoses, and notifies stakeholders of relevant changes, ensuring transparency and customer satisfaction.
- Integrations: Effectively integrates with larger systems and data frameworks, facilitating bi-directional communication and eliminating double entry.
Because APMM monitors your projects around the clock, it identifies problems well before they cause any damage and reduces the overall management costs, keeping everything running smoothly for your customers and your team.
Pristine Projects Point to Profits
Improving operational efficiency and accuracy with APMM can lead to substantial revenue increases by optimizing project management and enhancing communication.
This results in predictable revenue capture, improved customer satisfaction, and referrals — all drivers of revenue growth for high-performing MSPs.
Addressing project data contamination is not just a technical matter or a project team issue; it’s a strategic imperative for MSPs that should be driven from the top down. By adopting cutting-edge processes and tools like APMM and embracing cleansing strategies, businesses can transform their project data into a valuable asset, ensuring accurate decision-making, improved customer satisfaction, and sustained profitability in the year to come.
Mike Psenka is CEO and founder of Moovila.
Image: iStock
