I often joke that when I started with Semantic Arts nearly two years ago, I had no idea a solution existed to a certain problem that I well understood. I had experienced many of the challenges and frustrations of an application-centric world but had always assumed it was just a reality of doing business. As an HR professional, I’ve heard over the years about companies having to pick the “best of the worst” technologies. Discussion boards are full of people dissatisfied with current solutions – and when they try new ones, they are usually dissatisfied with those too!
The more I have come to understand the data-centric paradigm, the more I have discovered its potential value in all areas of business, but especially in human resources. It came as no surprise to me when a recent podcast by Josh Bersin revealed that the average large company is using 80 to 100 different HR Technology systems (link). Depending on who you ask, HR is comprised of twelve to fifteen key functions – meaning that we have an average of six applications for each key function. Even more ridiculously, many HR leaders would admit that there are probably even more applications in use that they don’t know about. Looking beyond HR at all core business processes, larger companies are using more than two hundred applications, and the number is growing by 10% per year, according to research by Okta from earlier this year (link). From what we at Semantic Arts have seen, the problem is actually much greater than this research indicates.
Why Is This a Problem?
Most everyone has experienced the headaches of such application sprawl. Employees often have to crawl through multiple systems, wasting time and resources, either to find data they need or to recreate the analytics required for reporting. As more systems come online to try to address gaps, employees are growing weary of learning yet another system that carries big promises but usually fails to deliver (link). Let’s not forget the enormous amount of time spent by HR Tech and other IT resources to ensure everything is updated, patched and working properly. Then, there is the near daily barrage of emails and calls from yet another vendor promising some incremental improvement or ROI that you can’t afford to miss (“Can I have just 15 minutes of your time?”).
Bersin’s podcast used a great analogy for this: the kitchen drawer problem. We go out and procure some solution, but it gets thrown into the drawer with all the other legacy junk. When it comes time to look in the drawer, either it’s so disorganized or we are in such a hurry that it seems more worthwhile to just buy another app than to actually take the time to sort through the mess.
When it comes to legacy applications, companies don’t even know where to start. We don’t know who is even using which system, so we don’t dare to shut off or replace anything. So we end up with a mess of piecemeal integrations that may solve the immediate issue, but just kicks the technical debt down the road. Sure, there are a few ETL and other integration tools out there that can be helpful, but without a unified data model and a broad plan, these initiatives usually end up in the drawer with all the other “flavor of the month” solutions.
Another route is to simply put a nice interface over the top of everything, such as ServiceNow or other similar solutions. This can enhance the employee experience by providing a “one stop shop” for information, but it does nothing to address the underlying issues. These systems have gotten quite expensive, and can run $50,000-$100,000 per year (link). The systems begin to look like ERPs in terms of price and upkeep, and eventually they become legacy systems themselves.
Others go out and acquire a “core” solution such as SAP, Oracle, or another ERP system. They hope that these solutions, together with the available extensions, will provide the same interface benefits. A company can then buy or build apps that integrate. Ultimately, these solutions are also expensive and become “black boxes” where data and its related insights are not visible to the user due to the complexity of the system. (Intentional? You decide…). So now you go out and either pay experts in the system to help you manipulate it or settle for whatever off-the-shelf capabilities and reporting you can find. (For one example of how this can go, see link).
A Better Path Forward
Many of the purveyors of these “solutions” would have you believe there is no better way forward; but those familiar with data-centricity know better. To be clear, I’m not a practioner or technologist. I joined Semantic Arts in an HR role, and the ensuing two years have reshaped the way I see HR and especially HR information systems. I’ll give you a decent snapshot as I understand it, along with an offer that if your interested in the ins and outs of these things I’d be happy to introduce you to someone that can answer them in greater detail.
Fundamentally, a true solution requires a mindset shift away from application silos and integration, towards a single, simple model that defines the core elements of the business, together with a few key applications that are bound to that core and speak the same language. This can be built incrementally, starting with specific use cases and expanding as it makes sense. This approach means you don’t need to have it “all figured out” from the start. With the adoption of an existing ontology, this is made even easier … but more on that later.
Once a core model is established, an organization can begin to deal methodically with legacy applications. You will find that over time many organizations go from legacy avoidance to legacy erosion, and eventually to legacy replacement. (See post on Incremental Stealth Legacy Modernization). This allows a business to slowly clean out that junk drawer and avoid filling it back up in the future (and what’s more satisfying than a clean junk drawer?).
Is this harder in the short term than traditional solutions? It may appear so on the surface, but really it isn’t. When a decision is made to start slowly, companies discover that the flexibility of semantic knowledge graphs allows for quick gains. Application development is less expensive and applications more easily modified as requirements change. Early steps help pay for future steps, and company buy-in becomes easier as stakeholders see their data come to life and find key business insights with ease.
For those who may be unfamiliar with semantic knowledge graphs, let me try to give a brief introduction. A graph database is a fundamental shift away from the traditional relational structure. When combined with formal semantics, a knowledge graph provides a method of storing and querying information that is more flexible and functional (more detail at link or link). Starting from scratch would be rather difficult, but luckily there are starter models (ontologies) available, including one we’ve developed in-house called gist, which is both free and freely available. By building on an established structure, you can avoid re-inventing the wheel.
HR departments looking to leverage AI and large language models in the future will find this data-centric transformation even more essential, but that’s a topic for another time.
HR departments face unique challenges. They deal with large amounts of information and must justifying their spending as non-revenue producing departments. The proliferation of systems and applications is a drain on employee morale and productivity and represents a major source of budget drain.
By adopting data-centric principles and applying them intentionally in future purchasing and application development, HR departments can realize greater strategic insights while saving money and providing a richer employee experience.
Taken all the way to completion, adoption of these technologies and principles would mean business data stored in a single, secured location. Small apps or dashboards can be rapidly built and deployed as the business evolves. No more legacy systems, no more hidden data, no more frustration with systems that simply don’t work.
Maybe, just maybe, this model will provide a success story that leads the rest of the organization to adopt similar principles.
JT Metcalf is the Chief Administrative Officer at Semantic Arts, managing HR functions along with many other hats.