Good data governance is the key to leveraging AI in resource management. Get started with our guide to preparing your data for AI.
In the near future, generative AI could add between $2.6 trillion to $4.4 trillion annually across 63 use cases in resource management.
Impressive, right?
Except, getting to these results is a data-rich path — requiring extensive, efficient, and up-to-date data gathering and maintenance (collectively called data governance).
Essentially, data governance is an important precursor to leveraging AI in resource management. For instance, you’ll need data to (train and) use AI to find the right fit for different project roles.
So, where do you start? The short answer: data governance. The long answer? We'll dive into it in this guide, which will cover the current data governance challenges holding RMOs back, and potential solutions for tracking, reviewing, and using data to be able to effectively use AI
Let’s go!
Unstructured data spread across platforms is inconsistent, redundant, and unstandardized. This means that it won't be much use for training an AI - as it doesn't demonstrate clear and accurate trends and patterns, the AI will not be able to make feasible predictions, or recommend realistic next steps to take.
With AI rapidly growing in resource management, the only way you can make the most of it is with clean, structured data. With AI-ready data, you can:
Here’s a quick peep at ways AI can help you, before we dive into potential reasons your data isn’t AI-ready:
As 78% of organizations update their data weekly, the problem with effectively using AI to support resource management decision-making has less to do with data collection and more to do with data quality.
Here’s a full breakdown of all the challenges preventing resource management teams from effectively using AI in their workflows:
While better than no data collection, manual data entry isn’t free from its challenges as it:
To top that, if there’s no process regulating data collection, the gathered data ends up being inconsistent, leading to poor data hygiene and, subsequently, inaccessibility.
Paper-based documentation or spreadsheet-driven resource tracking are the leading causes of poor data integrity.
Besides increasing manual data entry work, both prevent you from creating an integrated system for collecting data, leading to version control issues, errors, and missing data. Not to mention, it’s challenging to track or update the data in real-time when your data systems rely on non-digitized or poorly integrated workflows.
Yet another issue here? Informal or decentralized data entry.
When employees, teams, or departments have their separate systems to track resources, you end up (unintentionally) creating data silos. Ad hoc approaches to tracking mean that while you do have an abundance of data, it’s all inconsistent or redundant — sprawled across different platforms and data sources.
Lastly, you have yet another problem to solve if data collection in your organization relies on elaborate, email-based (read: non-centralized) approvals.
For example, let's say your business manages critical resource management decisions like allocating or scheduling via email. You'll likely experience challenges like miscommunication and a lack of traceability that urgently need addressing. Plus, email-based communication is difficult to integrate into centralized systems.
In short, non-standardized, reactive, periodic, or poorly integrated data systems lead to errors and delays, making data less reliable and a poor fit for use within AI systems.
Siloed data is closely related to the challenge above. Siloes are created when different teams and departments use different methods or platforms to collect data.
If these systems don’t integrate well, they lock data away, making it inaccessible and, therefore, unable to be used. In turn, this limits AI’s ability to analyze data.
Different data-gathering systems also lead to misaligned data fields, interpretations, and formats — bringing data inconsistency back into the picture.
The lack of an integrated data gathering system, standardized data collection, and manual data collection snowball into data redundancy.
You’ll find multiple records for the same resource, leading to confusion and unstructured data. This lack of data cleanliness furthers inefficiency in AI-driven recommendations.
We’ve already touched on periodic data collection, which is preventing you from tracking data in real time.
These irregular updates also lead to outdated data, as do disjointed workflows that fail to sync records in real time or integrate mismatched records, leading to — say it with us — stale and inaccurate data.
The result? AI uses outdated information to forecast trends, resource allocation or demand, leading to accurate prediction and inefficient decision-making.
There's one core challenge sitting at the heart of non-standardized, inconsistent data collection: no one person or department taking ownership of data governance.
Ambiguity around who is responsible for maintaining data quality leads teams to build their own processes, bringing about data collection neglect, siloes, and inconsistency.
Poor resource data governance also increases the risk of data breaches, as no one oversees data security and privacy protocols. In the long term, this opens the door to issues with compliance as you fail to meet industry or legal standards for handling data.
Not to forget, a lack of data governance ownership can also prompt data overload. This occurs as a lack of effective data filtering and prioritization generates an excessive volume of resource data.
Given the stronghold of spreadsheets for data gathering in organizations, it can be hard for stakeholders to warm up to the idea of using a specific resource management software.
Yet, without buy-in, you’ll find yourself dealing with poor data centralization and data inaccessibility, among other issues outlined above, so beginning these conversations is imperative.
Once you get buy-in, there’s another challenge that needs addressing — your team’s adoption of the resource management system.
A simple plan of action is to use the challenges above as your guiding checklist for preparing your data for AI. Tackle those challenges and you'll be ready to go!
In this section, we'll dive into the solutions to countering data governance challenges highlighted above with tactical steps to take throughout.
Make sure you bookmark this guide to come back to these recommended steps so you can achieve them one by one this year.
To start, review your existing data pool and processes and ask yourself if there's a clear, known authority who owns and is responsible for data governance in our organization?
If your answer is yes, you'll need to look at:
If you don’t have a person/team responsible for data governance, determine who should be responsible for:
The goal here boils down to building data ownership to create a standardized and centralized data system that ensures high data quality.
Depending on how mature your organization’s data governance is, you’ll want to plan for getting stakeholder buy-in for different items, including:
For many businesses moving away from spreadsheets and towards centralized software is huge — requiring stakeholder convincing. Thankfully, this guide comparing spreadsheets versus reliable RM software (yep, that’s us!) can help you make your case and get buy-in.
Yet another essential point to get your data ducks in a row is to ensure all your data is in one place. Centralized resource management not only makes it easy to collect and track data but also makes it more accessible and, therefore, more usable.
Getting there means assigning clear ownership of data management — and using resource management software.
If your company isn’t ready to take these steps yet, consider adopting real-time data sync tools. This way, you can facilitate data adjustment across spreadsheets, tools, and applications in real time.
Another option is to use middleware solutions like Make, Zapier, and Workato to integrate incompatible systems and automate seamless data exchange.
Lastly, standardize data formats. For example, make sure all teams use the same templates and naming conventions to maintain data consistency.
When you’re ready to unlock next-level resource maturity, it's time to adopt resource management software.
This will reduce the work that comes with maintaining a cobweb of middleware and real-time data sync tools you use to create a makeshift central data repository.
Doing so will reduce related maintenance work, further streamline and automate data governance, and make it easy to leverage AI optimally to manage resources better.
Admittedly, finding the right resource management software is no easy feat. Here’s a full guide breaking down what to look for in RM software, but the gist is that an ideal platform:
Discover more about getting stakeholder buy-in: 9 Ways to Get Buy-in for New Resource Management Software ➡️
Last on the checklist: a change management strategy to counter objections as you improve data governance, centralize data management, and use AI to optimize resource management.
But what's the key to building an effective strategy? Never use a one-for-all change management action plan. Our advice is to tailor your strategy for a roadmap to bring about change in different types of employees.
To this end, you’ll want to review what motivates employees, and then center the change around how it will benefit them. This makes the changes about ‘them,’ not the organization, incentivizing them to take the needed steps (and build new workflows or adjust their existing processes).
Moreover, your change management blueprint should include data governance training as well as tool adoption workshops. The idea is simple: the more you educate folks on the ‘why’ and ‘how’ of needed changes, the better you’ll be able to make the implementation.
Here’s a full guide laying out 11 steps to create an effective change management process. Don’t have stakeholder buy-in for this? Go through these 12 reasons why you need change management to make your case.
Preparing data for AI in resource management takes thoughtful data governance. Frankly, you should be doing this whether or not you plan to use AI for resource optimization, as it helps with assessing and analyzing data through traditional methods, too.
With that in mind, here are our key take-homes:
Want to continue developing your understanding of the role high-quality data plays in resource management? Here’s a detailed dive into why the two are a match made in heaven.
