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Our lives today are defined by digital advancements. Be it popular tech gadgets or latest apps, we can’t evade technology. Amid all this progress, there’s the very essential data revolution. From project management to revenue predictions to generative AI powering granular personalization, data has become a crucial element. According to a report by McKinsey, companies that effectively implement business intelligence and data analytics are more likely to deliver higher returns on investment. They are 23 times more likely to acquire customers and 19 times more likely to be profitable!
By leveraging data, it’s easy to make informed decisions, predict outcomes, and ultimately, achieve better results. For project teams, data analytics techniques enable project managers to break down complex project requirements and predict behavior and outcomes in real-time, enabling better data-driven decision making, keeping projects on schedule and within approved budgets.
Embrace good management as a project manager. Your life will be so much easier. Imagine how accurate, reliable and relevant data collection will be! But without this best practice, the collected data may not be useful or may even mislead the project team. And you don’t want that.
Hence, the responsibility lies on project managers and leads for ensuring that project-related data including hours spent, revenue generated, variance scheduled, and resource planning is correct and reported in time so that any bottlenecks can be dealt with immediately. It is also imperative to ensure that this data is updated regularly so project management teams always have the latest information available to create the best solutions.
According to McKinsey, 5,000 different projects incurred a loss of a collective $66 billion due to poor planning, exceeded their lifecycle, and saw unnecessary expenditure on irrelevant talent and resources.
Furthermore, good management also includes having a clear understanding of how data should be used, which can help make well-informed decisions that contribute to the project's success.
Keep data close! As a project manager, you can turn a guessing game into a strategic, informed process.
Data transformed into valuable insights fuels the project's performance, helps in identifying potential risks, and guides decision-making. For instance, data can highlight if a project is on track to meet its goals, if there are any delays or issues that need to be addressed, and if the project is within budget. This information can assist in taking corrective actions in a timely manner, thereby improving the chances of successfully completing the project.
Data plays a significant role in planning and executing successful projects. It helps in estimating the time and resources required for the project, predicting the completion date, and determining the budget. Moreover, data can help identify the skills needed for the project, assign tasks to team members based on their strengths, and monitor the project's progress.
There are numerous case studies of successful projects that achieved phenomenal results, thanks to data.
Walmart is a great example of a company leveraging data for better management and planning. Walmart started as a simple discount retailer and grew into a global giant with operations in 24 countries, 10,500 stores, and 2.2 million employees (about the population of New Mexico).
With total revenue reaching $611 billion in the fiscal year ending January 31, 2023, Walmart attributes much of its success to being a data-driven company. Walmart Labs, its research and development arm, manages the world's largest private cloud, capable of handling 2.5 petabytes of data per hour. The company heavily invests in technology, including cloud, data science, and analytics, to enhance its operations.
Walmart's data-driven approach is evident in its personalized customer shopping experience, where we see that data analysis guides merchandise stocking and display decisions. The company also relies on big data for order sourcing and on-time delivery promises, utilizing algorithms to estimate delivery dates based on various factors. Walmart also employs packing optimization, a system that recommends the best-sized box for packing ordered items efficiently, addressing the classic NP-Hard Bin Packing Problem.
To showcase the practical applications of data science, Walmart has implemented projects like the Walmart Sales Forecasting Project, which uses historical sales data to predict sales for each department in its stores. These initiatives exemplify Walmart's commitment to leveraging data and analytics for enhancing customer experience and optimizing various aspects of its supply chain management processes.
Another interesting case study is LinkedIn, where the data science team works with a massive pool of data to generate insights to build strategies, apply algorithms and statistical inferences to optimize engineering solutions, and help the company achieve its goals. For instance, LinkedIn Recruiter uses Gradient Boosted decision trees to handle complex queries and filters on a constantly growing large dataset, improving the relevance and specificity of the results. As a result, recruiters now rely primarily on LinkedIn for hiring top-quality talent for their companies. Almost 72% recruiters use LinkedIn for recruitment needs.
Source: https://financesonline.com/linkedin-statistics/
Utilizing data for project management is a method that most major corporations already practice. Ford has incorporated data analytics into its project management for product development. By analyzing consumer preferences, market trends, and production data, Ford can make informed decisions about designing and producing vehicles that meet customer demands. As a result, Ford was able to achieve 11.19% increase in revenue in the quarter ending in September 2023 from the previous year.
Source: AlphaStreet
Setting clear project goals is a smart move for successful data-driven projects. Having clear goals gives direction to the project and makes it easier to measure the project's success using data. It also helps in aligning the project with the organization's strategic objectives.
Having a strong system for data collection and management is another great move. This includes setting up systems for collecting data, storing it securely, and making it accessible to those who need it. A strong data system ensures that the data collected is trustworthy and usable.
Using numbers to make good choices is a crucial aspect of data-driven projects. Some would say that's all data is about, anyway. By analyzing data, project managers can gain insights into the project's performance and make data-driven decisions that contribute to the project's success. This includes choosing the right resources, setting the right timeline, and allocating the budget wisely.
Keeping an eye on things and being adaptable to change is already an integral part of a project manager’s job. Coupled with a data-driven strategy, it becomes increasingly urgent to monitor and track the data for high level insights. This involves monitoring the project's progress regularly, analyzing the data to identify any deviations from the plan, and being flexible enough to make changes when necessary.
For some items, you’ll need to monitor continuously; for others, a regular check-in is appropriate. Most projects include major milestones or phases that serve as a prime opportunity for monitoring important indicators.
Once you start collecting data and analyzing it, confident decisions about virtually any business challenge are easy to make. Data is logical and concrete, unlike gut instinct and intuition, which can be subjective. This confidence allows your organization to commit fully to a particular vision or strategy without being overly concerned that the wrong decision has been made.
As you become more familiar with data-driven decision-making, you can become more proactive. Given enough practice and the right types and quantities of data, you can leverage it more proactively, for example, by identifying business opportunities before your competition does, or by detecting threats before they become too serious.
Data-driven projects can lead to cost savings. According to a recent survey of Fortune 1,000 executives conducted by NewVantage Partners for the Harvard Business Review, using data to decrease expenses was one of the most impactful initiatives, with more than 49% of organizations seeing value from their projects.
Managers need data. Data analytics can enhance collaboration, improve project processes, optimally use resources, and make accurate project timelines and budget forecasts. By studying data, project managers can identify trends in their team’s performance and project completion, which leads to better project and resource planning. Predictive analytics can help catch early signs of slips in project budgets, costs, and timelines.
That’s our motto at Data Pilot: making projects succeed by employing data. And that thinking drives us to provide prompt and seamless user experiences.
By adopting a data-driven approach, we can transform projects from being merely task-oriented to becoming strategic, efficient, and profitable. With data, we can make faster decisions, predict outcomes, and ultimately, fulfill all project goals.
Whether it's identifying potential risks early on, improving resource utilization, or making informed decisions about resources, timelines, and budgets, data can play a pivotal role in the success of our projects. And the best part? It's not just about the technology or the tools. It's about changing the mindset, fostering a culture of data literacy, and pushing for better project management practices.
By Zainab Fatima
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