These are the 4 data quality checks that PMOs must consider before AI implementation to maximize their success rate and ROI.
A recent survey by Informatica found that 42% of data leaders identified data quality as a hurdle to adopting generative artificial intelligence (AI). This makes sense when you understand that, with AI, the quality of your data correlates with its performance. That’s because AI can only learn from the data you provide. So, if the quality of your data is poor, you risk training your AI-enabled tool with flawed and inaccurate information.
As more of us turn to AI to fuel business decision-making, growth, and efficiency, measuring its impact becomes crucial. But if you’re not investing in the standard and volume of your data, you will compromise your ROI from AI.
So, how can you ensure your business’s data is ready for AI-enhanced technology? You need to assess its quality. We’ve identified 4 data quality checks that project or portfolio management offices (PMOs) must consider when implementing AI to avoid inaccurate forecasting, poor project performance, inconsistency, and more. Let’s explore.
1. Ensure Your Data Is Accurate And Complete
Accurate data is trustworthy and error-free. For PMOs, this means ensuring project data does not include outdated information or human errors created during the collection process.
Complete data refers to data that contains all required information and variables to form a whole dataset. In project portfolio management (PPM), for example, incomplete datasets might be missing task statuses, due dates, or assignees.
If your data is accurate and complete, you can start training your AI tool with confidence. You’ll know it’s basing its computations on relevant and reliable data, which helps you avoid misleading insights, inaccurate forecasts, and misguided decisions.
At Planisware, we always suggest starting at the source. Consider streamlining the data entry process. This might include reducing the number of required fields for simplicity or making specific fields mandatory so team members know how important they are. Regular data cleansing and auditing will ensure you remove outdated or irrelevant data. And opting for a single source of truth (SSOT) platform will help end data siloes and repetition.
2. Check that the data is consistent
Once you are confident your data is accurate and complete, it’s time to assess its consistency. Your data must be in a standardized format across all sources and entry points for AI to read and interpret it accurately. To ensure the consistency, accessibility, security, and trustworthiness of your data, you must invest in data governance — the process of managing an organization’s data.
Without robust data governance, teams across the business will collect, format, and store data differently. This impacts the performance, reliability, and learning capabilities of AI tools, and leads to integration, operational, and reporting issues. Your first line of defense should always be standard processes for data collection and storage, and holding teams accountable to them.
Some AI-enabled tools will use large language models (LLMs). These models can interpret unstructured data, like text, images, and audio. Planisware, for example, uses LLM via our AI chatbot, Oscar. Oscar can provide users with 24/7 support for any aspect of a project, from risk assessment to meeting schedules.
3. Assess your data volume and availability
The first two checks ensure the accuracy, completeness, and consistency of your data. But, the volume of data you have access to can also impact your AI success. Some PMOs will have less data than others. For example, a pharmaceutical company will run fewer projects per year than a large, food and drink manufacturer. This means less project data, making it harder for AI to learn, make accurate predictions, or assess risks.
It becomes even more complex when the types of projects you deliver greatly differ. Let’s say you work as a PM in the life sciences sector. Implementing an electronic lab notebook (ELN) system and rolling out a new medical device are both important projects. But they are vastly different. There may be very little overlap — or no overlap at all — when it comes to stakeholders, actions, budgets, outcomes, timelines, and team members involved. This makes it very difficult for AI to compare project data and draw relevant, accurate conclusions.
Additionally, when very little data is available, PMOs risk training AI on misrepresentative samples that don’t reflect project goals, team diversity, or favorable outcomes. This can lead to AI biases, which can cause problems for you, your team, and your business, such as non-compliance and negative brand perception, if they aren’t addressed.
Ask yourself: do we have enough historical data, and can we access it? AI-enabled PPM success relies on a supply of available business data. If the answer is no, don’t worry. There are alternative solutions you can consider. For example, Planisware’s PPM software employs parametric estimation techniques that enable users to populate project plans with one just click. Even with limited historical data, parametric estimations can provide users with basic insights such as estimated project timelines, assumed patterns, and potential risks.
4. Relevance, timeliness, and trustworthiness of data
Now, you have assessed that your data is accurate and complete. You’ve ensured consistent formatting so AI-enhanced technology can read it. You may also be opting for an AI tool that leverages LLM to interpret unstructured data. And, you’ve identified whether you have enough historical data for AI to analyze and deliver helpful insights.
As projects, teams, goals, and even technology evolve, the PMO must ensure they have a regular measure of data quality. Continuous monitoring and updates are key. This will include:
- Updating AI data sources to reflect your organization’s current strategic goals
- Ensuring data collection, storage, and sharing meet the current privacy and security standards
- Staying up-to-date with the latest AI capabilities
Reaping The Rewards of Data Quality and AI
To ensure the best results when working with an AI-enhanced PPM tool, consider implementing solutions that continuously monitor data quality and detect anomalies or shifts in data patterns. At Planisware, our AI-enabled PPM software helps businesses transform their project management. From a centralized, single source of truth to predictive analysis, forecasting, and prioritization capabilities, project management professionals can elevate their project success.
- Transform your PMO with AI-enhanced PPM technology that automates tasks, create rules, and delivers accurate predictions and forecasts
- Learn from historical projects to elevate future project success
- Identify data gaps with Planisware’s business intelligence (BI) tool
- Generate intelligent reports for key stakeholders
