How Data-Driven Project Management Improve Project Outcomes?

Zain Ahmed
How Data-Driven Project Management Improve Project Outcomes

Our average IQ scores increase by nearly three points a decade. An average individual today would score 130 on an IQ test from 1910 – higher than 98% of the human population at that time!

This is a global phenomenon and is known as the Flynn effect, brought on by our constantly increasing exposure to technology.

But what actually happens during this exposure to technology? Data processing. Exchange of information.

The same data, when applied to decision making, acts as a game changer. It enables us to make educated and informed decisions instead of traditional trial-and-error methods.

In simpler words, the more data you crunch, the better your decision making process becomes.

Despite speaking highly of data, it is important to take note here that data itself is nothing more than multidimensional, multivariate bits or dots of information. It only makes sense when someone connects these dots together.

Project Management Offices (PMOs) are incessantly involved with decision making activities. Be it something as subtle as making a small UX change to something as significant as rolling a big feature out at a certain time of the year, it all banks on the Project/Product Manager’s decision-making acumen.

And it isn’t easy!

PMOs cannot be whimsical. Not only do they have defined and elaborate targets, they are also super accountable for them. They have to base their decisions on top of something. And what would be better than to base their decisions on rock-solid, immovable data!

This notion of basing decisions heavily upon data-driven insights is called data-driven project/product management.

The two prime means through which a Project/Product Manager can get data’s help are:

  • Business intelligence, and
  • Business analytics

Business Intelligence

Business intelligence (BI) is the act of leveraging data to make well informed business decisions.

Data is extracted using both software- and process-based mechanisms. It is collected, recorded, analyzed, and then converted into meaningful information.

BI is considered as a descriptive analysis of data and gives PMs hands-on access to real-time metrics, thus improving visibility into projects, processes, and outcomes.

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Business Analytics

Business analytics provide relevant metrics and KPIs which can be leveraged to deduce what may happen in the future, when, and why, based on historical data.

It is not so easy and not so accurate either. BA is mainly concerned with predictive analyses as compared to BI’s descriptive analyses.

Summing it up, BI is explanatory whereas BA is speculative. BI gives you insight whereas BA gives you foresight.

BI and BA collectively enable modern-day Project/Product Managers to:

  • Avoid project failures,
  • Predict better outcomes,
  • Decrease decision downtimes, and
  • Make relevant decisions

Any project fundamentally comprises the following three crucial aspects:

  • Schedules
  • Risks
  • Controls

Baseline scheduling outlines the frame of reference against which planning is conducted and comparisons are drawn during the project cycle, risk assessments are made with reference to the baseline schedule and project nature, whereas controls govern how the project is executed in run-time.

In data-driven project/product management, these three aspects are heavily reliant on data.

Resource allocation and scheduling

Resource scheduling is a tough job! You either don’t know enough or you know too much to be able to allocate resources effectively.

Data provides actual information regarding resource allocation – what resource works in which manner and what pace – hence improving overall relevance and practicality of processes. Schedules are drawn on the basis of average and peak team performance and technical limitations, based upon previously recorded work history.

Risk analysis and mitigation

PMOs are all about risk management. This whole department in any organization exists to basically manage crisis situations and lead chaotic teams through them.

That bit of the job is not very data-driven, given the fact that crisis management or damage control is a more human-oriented area to excel at.

Data actually enters the scene to help you avoid these crisis situations. One way to do so is setting just the right deadline margins – what process needs what margins to avoid setbacks, delays, and liabilities. This information may be extracted from immediate and distant organizational performance records, independent case studies and researches, and surprisingly cost-benefit analyses as well.

How come cost-benefit analyses?

Regardless of the extent to which you mitigate a risk, it will remain a risk, and you only take a risk when its cost-benefit analysis even in the worst case scenario adds up.

A manager cannot gamble!

Increasing efficiencies, visibility, and control

Amusingly enough, I am mentioning it last whereas this is generally considered the foremost advantage of data-driven management.

Data enables a project/product manager (and everyone else too) to look with a bird’s eye view and a worm’s eye view simultaneously. This provides new ranges of visibility to everyone making plans regardless of nature – even vacation plans!

Data also tells you where the system’s inefficiencies lie. Real-time organizational metrics and KPIs can predict the organization’s immediate future – be it uphill, downhill, or stagnant.

Based on these indicators, specialized remedies can be devised that increase efficiencies and control of the project/product’s performance per se.

The theory mentioned above manifests in the following step-by-step manner:

  • Data is extracted from disparate silos and brought together.
  • Data is transformed into meaningful information.
  • Meaningful information is presented in human-readable forms, through data tabulation and data visualization.
  • Human-readable information is incorporated into decision making.

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Even though the notion of data-driven project management is theoretically as old as early 10,000 BC farmers cultivating their crops using weather, rainfall, and flooding patterns; the whole data-driven project management scene is fairly new to the world, even more so in tech.

People do claim to have authored ‘cookbooks’ for data-driven project/product management but things get very gray and speculative in real life.

In its entirety, data-driven management is rather an approach than a set of protocols with every organization and every manager cooking their own mix of tricks and techniques.

All-in-all, data-driven management is absolutely worth investing one’s time and energies in! Its short term benefits are sweet – its long term advantages are extraordinary!




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