Strategic Workforce Planning, Technology, and Data
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In today's ever-evolving business landscape, where technology reigns supreme, understanding the marriage between strategic workforce planning and data is indispensable. At its core, strategic workforce planning is about ensuring that an organization has the right number of people with the right capabilities when they’re needed and where they’re needed. (There’s more to it than that, but that’s for another article). Think of it as building a dream team for your business's future. Sounds simple, right? Yet, its execution is anything but.
In the age of Generative AI, automation and digital transformation, businesses that fail to plan effectively are planning to fail. By not understanding the skills and roles needed in the future, companies risk falling behind competitors, leading to reduced profitability and, in some cases, obsolescence. While there is much more to strategic workforce planning than the software used to aggregate, analyze, and visualize the associated data, SWP technology has a huge role in facilitating the SWP process. Implementing that technology often comes with it’s own set of headaches.
Tech Implementations & The Role of Data
Imagine technology as the engine of a car and data as the fuel. One can't function optimally without the other. In the realm of strategic workforce planning, technology serves as the tool that processes, analyzes, and utilizes data in a way that enables business leaders to make informed decisions. In essence, data powers the tech, the tech facilitates the insights, the insights drive the decisions, and the decisions move businesses forward. Again, sounds simple. But in reality, there’s a lot that can go wrong.
Ever been excited about a new gadget, only to find it doesn't work as expected? This disappointment often mirrors what businesses feel when tech implementations don't deliver desired results. One primary culprit? Data-related issues.
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Recognizing Common Data Issues
To solve a problem, we must first recognize it. Here are some pesky data challenges businesses often face:
Inconsistent Data. Much like trying to mix oil and water, inconsistent data causes disruptions. When data from different sources doesn't align, it leads to inaccurate analysis and misguided decisions. Consider, for example, a scenario where in the old HR system, job titles were free-text fields, leading to multiple variations for the same role (e.g., "Sr. Developer", "Senior Dev", "Developer II"). When migrating to a new system with a standardized dropdown list for job titles, this inconsistency can cause issues. One way organizations address this: conduct a data "cleaning" or "scrubbing" process before migration. This involves standardizing and normalizing data. For the above scenario, a mapping exercise can be conducted where all variations are mapped to a single standardized title in the new system.
Data Loss. In the migration process, if there's a mismatch between the old system's data structure and the new one, some data fields might not be transferred, leading to data loss. Thoroughly mapping data fields between the old and new systems can help. Organizations also often perform a test migration first, comparing the results with the original to ensure no data is lost.
Data Duplication. If an employee has multiple entries in the old system due to historical errors (e.g., transferred departments), migrating without rectification might result in duplicate records in the new system, too. Before migration, organizations should run deduplication processes on the dataset to identify and merge or remove duplicate entries.
Data Corruption. During the transfer process, there might be interruptions or issues that can corrupt data, leading to unreadable or inaccurate records in the new system. Using reliable migration tools and ensuring a stable environment during migration can help. Additionally, always having a backup of the original data allows for a restart in case of corruption.
Historical Data Compatibility. The new system might not support certain data types or historical records from the old system. Organizations can opt to archive old data formats or convert them into a readable format in the new system. It's essential to decide which historical data is crucial to migrate and which can be archived.
Mismatched Data Validation Rules. The old system might have allowed for non-standard phone number formats, while the new system enforces a specific format. Migrating the old data can thus lead to validation errors. Before migration, a validation process can be run on the old data to identify non-conforming entries. These can then be corrected to match the new system's validation rules.
Data Security and Compliance. If sensitive employee data (e.g., Social Security numbers) is not encrypted or protected during migration, it could be vulnerable to breaches. Ensuring encrypted data transfer, using secure migration channels, and adhering to data protection regulations can address this issue.
Proven Strategies to Address These Challenges
Planning. Any organization undergoing a technology implementation should do so with a detailed migration plan including data mapping, validation checks, and timelines.
Backup and Testing. Always back up the original data before migration, and conduct test migrations on a subset of data to identify and rectify issues before full migration.
Change Management. The detailed migration plan should have a change management roadmap, which articulates roles and responsibilities of all stakeholders and details other timelines according to specific needs. (I’ve seen organizations express a preference for ADKAR, but I’ve also seen Kotter’s 8-step change model deployed successfully). Keep stakeholders informed about the migration process, potential downtimes, and changes.
Engaging Experts. Most software vendors will offer data migration specialists or consulting services to facilitate the migration process.
Post-Migration Audit. After migration, conduct an audit to ensure data integrity and accuracy in the newly implemented system. This can be done by a dedicated team, or (as I’ve seen in my career) by a pilot group of “super-users” who are data-curious and know what insights the SWP customers need when making decisions.
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A Note on Data Culture. It's one thing to have data, but another to use it effectively. Cultivating a culture where data drives decisions ensures that the entire organization is aligned and moving in the right direction. Think of it as giving everyone in your team a compass pointing to success.
If business success is a tapestry, strategic workforce planning and data are interwoven threads. By understanding their synergy and addressing challenges head-on, businesses can ensure that tech implementations not only succeed but also propel them into a more informed future.