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Contaminated data: a major threat to business intelligence

When you trust spoiled data, you make bad business decisions.

Contaminated data: a major threat to business intelligence

It has been a good year at your MSP. After such a successful year you're faced with several departments requesting bigger budgets and additional staff to continue to drive growth. The decision to approve these seems straightforward if the data is considered reliable. However, the risk of data contamination could significantly skew these decisions. Because there is an unsubstantiated belief in the accuracy of data collected from tickets and other operational tools across your tech stack, there's a need to investigate whether this data can be trusted.

Without effective project monitoring mechanisms, project data quickly becomes outdated.

The challenge of data integrity in MSP operations

Data from tickets is typically accurate when initially captured. However, the issue arises when scheduling data is sourced from disconnected tools, where it often becomes corrupted and stale. Projects plans are dynamic entities; they evolve rapidly, with schedules and resource allocations changing frequently. This fluidity makes maintaining data accuracy challenging. Without effective project monitoring mechanisms, like Autonomous Project Monitoring and Management (APMM), project data quickly becomes outdated, resulting in misguided decisions at various management levels.

Consequences of relying on stale data

Consider a situation where project decisions are based on data from disconnected project management tools. A decision such as accepting a customer’s request for expanded project scope, say adding an additional department to an in-process software roll out, if based on stale data can lead to project delays, budget overruns, and diminishing profit margins. That bad data can be as simple as not showing you three of the people working on that project were pulled off to work for another client and that delay pushed the actual delivery date back significantly, leaving no extra room for project expansion.  

Furthermore, using outdated data in customer interactions leads to misinformed planning for their assigned tasks as well, potentially overburdening them with unrealistic deliverables. In the end, these incorrect delivery dates and poor client communication can negatively impact customer satisfaction and the business relationship in a major way – all because both sides were working off poor project data!

Decisions regarding staffing,

budgeting, and other high-level management actions are at risk when based on tainted data.

The widespread impact of contaminated data

Contaminated data doesn't only impact your day-to-day operational decisions; it also flows upstream to ultimately distort strategic business insights. Decisions regarding staffing, budgeting, and other high-level management actions are at risk when based on tainted data. This jeopardizes the effectiveness of resource management, efficiency evaluations, and financial planning. Making it harder for you to handle common requests, like the end of year staffing requests mentioned earlier.

The need for APMM and AI in data management

In addition to the importance of integrated systems, it's essential to implement modern project management tools built with autonomous project monitoring and management technology for your MSP’s professional service delivery. A good APMM based tool tracks your entire project portfolio 24/7 to proactively warn you of potential operational concerns, with clear indicators of where the potential risks fall along the project plan. Such technologies, and particularly those driven by AI, will synchronize project data with other operational data sources in real time, ensuring accuracy and reliability across tools – so project technicians working in your PSA see the same information the project managers see within the project management tool. With APMM, high-level project data reflects the real-time accuracy of operational data, leading to more informed and strategic decision-making.

In summary, when clean, accurate project data is flowing into everyone’s area – including your high-level business analytics – the ability to make high-level decisions will be easier because they will be based on accurate, trustworthy, real-time information.  Ensuring data integrity through using advanced APMM based project tools (natively integrated with your existing tech stack) will be crucial for making informed, reliable decisions at all levels of your MSP in the coming years. This approach not only enhances operational efficiency but also can strengthen customer relationships and support strategic business growth – ultimately making the answers to next year’s department requests a no brainer.


To learn more about the emerging trend of APMM, check out our latest whitepaper. To see an APMM tool in action, consider taking a self-guided tour of our RPAX Engine, Moovila’s APMM based solution that allows MSPs to identify project delays, eliminate bottlenecks, and optimize resource allocation.


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