5 Ways Data-Driven Program Management Helps Your Business

Typical program management in a small business is a cascade of tasks—a flood of assignments, shrinking budgets, and never-ending deadlines. Under these overlapping pressures, teams tend to focus on the most urgent tasks, leaving the strategic ones for “the future.”

In such a setup, decisions are often based on the program manager’s expertise and intuition. While their experience is undeniably valuable, the lack of objectivity can lead to risks of losing time, opportunity, revenue or employee well-being. That’s why a data-driven approach is a must-have for program managers.

Data-driven program management is built on objective information and consistent use of metrics and analytics. It allows for time-efficient and impactful decision-making on program strategy, product design, team performance, risk management, stakeholder engagement and much more.

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Why choose data-driven program management for your business


Data analysis allows program managers to draw objective conclusions. Decisions might stem from opinions like ‘My mom is part of our target audience and she believes this website looks better in blue instead of green.’ Of course, this is an exaggeration, but if we design a business or product strategy without data, it will likely lead to wasted time and resources, potential product failure, or development delays.

Improved Team Performance

People are the most valuable resource for any business. Data analysis can help avoid team burnout and increase employee satisfaction while also meeting project schedules and deadlines. Moreover, as a program manager, you can use preliminary data to develop your team’s KPIs and track achievements.

For example, when I was a program manager at the United in Gaming esports platform, I was tracking team performance metrics non-stop. Are all our processes efficient enough? Do we need these weekly status meetings? How long should the meetings be? Should we redistribute the teams? Some colleagues saw me as a hawk watching over all the processes. But it worked. The team was happy and we were consistently meeting the program expectations.

Better Understanding of the Audience and Product Perception

Data analysis can also help to better understand product development and the target audience’s reactions. The most commonly used practice for this is A/B tests. Imagine that your online store is missing some potential clients because of the design choices for the product list on the website. So, you decide to add descriptions, change tile size, and add the “Bestseller” labels.

A/B tests can show you how users react to these changes, time spent on the page, navigation patterns, return visits, and many other metrics. As a program manager, I always advocate for the strategy of repeated A/B testing to analyze the results from multiple angles and to develop the best understanding of customers.

Adapting to Changes in Real-time

A data-driven approach allows businesses to make well-informed decisions based on metrics such as product usage and market conditions in real-time. Today, you simply can’t afford to wait a quarter or half a year to conduct proper market research and then introduce the necessary changes. Sometimes even waiting a few weeks might be too long since the landscape is highly dynamic and a product update can quickly become irrelevant.

Program managers can set up decision forums with data science, product, engineering, and leadership analysis regarding the ongoing value of metrics and whether there is still a strong ROI for the effort.

Predictive analysis

Data can be used for making performance forecasts and anticipating trends. For example, if you decide to invest money and resources in building a new product feature, analytical tools can give you a potential outcome overview. So, these insights can minimize the risks of errors. Any modern platform, such as Jira or Trello, provides statistics about workload distribution, time tracking, sprint burndown, team velocity, issue resolution time, quality metrics, etc.

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What data should be monitored?

  • Teamwork. Are team members efficient with their time? Here you can monitor factors like equitable distribution of work, sprint velocity, sprint burndown, bugs/defects, and their resolution time.
  • Project. Factors such as delivery time, quality, cycle time, and budget variance will help to ensure that the project is money and time efficient.
  • Product and strategy. To monitor user and customer engagement, use A/B test results, click-through, and retention metrics, alongside customer long-term value (LTV) and regional or global revenue information.
  • Stakeholders and clients. What are their expectations and how do they influence the business? Program management tools can track the status of stakeholder relationships and the level of their satisfaction.
  • Market. How is the market changing and what impact will it have on the business process? Keep track of geopolitical events, economic factors, trends, and emerging competitors.
  • Risks. What internal and external factors might negatively influence project development? Data can provide program managers with insights into potential risk triggers.

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How to encourage your team to adopt a data-driven approach

Drowning in everyday routine, teams can be very reluctant to adopt workflow updates such as new work tools or methods.

Here are some useful tips on how to make the transition to data-driven program management easier:

  1. Give access to analytical tools to as many team members as possible. I have worked in companies where only C-levels or a limited number of employees had access to data and metrics. The more people are immersed in data, even at a high level, the more likely everyone will understand the value of a data-driven approach. Program managers should hold regular meetings, drive discussions, and collect feedback on which metrics to retain or drop.
  2. Visualize data. Raw data can be difficult and time-consuming to decipher and apply to business decisions. Simple visualizations of raw data can go a long way. I recommend using a tool called “data storytelling” to transform overwhelming tables into engaging narratives.
  3. Advocate for the data-driven approach. As a program manager, you have the power to highlight the benefits of the data-driven approach. I recommend starting with a “pilot” to showcase real data analysis for business decisions, with a proposal to incorporate metrics into all decision-making.

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