Data in Our Solutions

Data in Enterprise HR Solutions:

There are so many interesting things you could say about data in enterprise solutions; big data, operational data, stewardship, security, integration, design, semantics, modeling, governance, architecture… the list goes on and on…

Let’s talk for a minute about HR (who’s a big enterprise stakeholder in what we’ll call “people data”), and how we regularly see HR data leveraged in our solutions to deliver real value to the business and to business consumers…

We like to think about people data in some broad categories:

1. Personal and Demographic – these are data about people, their home address, their dependent information etc. These are typically highly-sensitive details that aren’t shared with other employees.
2. Job and Work – these are data that are related to current employment, and include job information, contact and work location, organizational information, and reporting relationship. These data are typically widely shared with other employees. These are the kinds of information you would find in a directory.
3. Functional Data – these are specific data as it relates to functional areas such as compensation or performance ratings. Access to these highly-sensitive data are typically restricted.
4. Status and Behavioral – these are data that communicate where a person (or, collection of people – like a manager’s organization) is relative to completing key business process. For example, a particular manager has completed performance rating recommendations for 50% of his/her team.

So how do we effectively leverage and capitalize on these classes of data in enterprise solutions?

One way you might think about it is to provide access to data centrally, and let people reference and report on these in different contexts of the employee lifecycle. In a model like this, a user needs to connect data to the context in which they are coming from, and may need to reason about relevant data in that context.

Although the idea of centralized user data in location sounds attractive from an engineering perspective, the burden on the user to rationalize what set of data to reference, or update under what conditions quickly becomes daunting – and it doesn’t really match the online behavior of most business consumers, most of the time. Some data, which may have relevancy across many different contexts, may be appropriate to provide centrally.

Another approach is to provide just the relevant set of data based on specific user situations / process contexts as the user is experiencing the online solution. Not only is this kind of distributed data in context simplifying the amount of rationalization the user has to do, these data could go beyond just displaying relevant data but also recommend specific actions that the user needs to take or consider in relation to the context. In this way we can influence behaviors! What power!

So what should business and data architects consider as they contemplate approaches like this?

Data Availability & Quality: In diverse and global organizations, data in each of the above mentioned categories may be highly fragmented due to variability in technology, processes, and local regulations. Enterprise HR solutions need to address data availability, consistency, quality and reliability and the likely coverage of these data to user populations before bringing select candidates for user consumption.

Entitlements: Complex rules may also drive who can see these data derived based on HR service delivery models (i.e., how clearly responsibilities for client organizations are defined) for HR access to these data, and how organizational responsibilities are delegated to people managers. These need to be well understood to appropriately factor related complexity to enabling these data to these important constituents (i.e., HR and Managers).

IT Strategy: Consider the larger HR ecosystem to validate that the data represented in the primary online channel is aligned and consistent with other properties in the ecosystem where a user may navigate to as a result of data displayed.

Readiness: Provide due considerations for technology readiness, capability and reliability to organize these complex data landscapes, enforce security and governance, to ensure that your business solution can consistently deliver a single source of truth, in real-time.

In diverse and global organizations, data in each of the above mentioned categories may be highly fragmented due to variability in technology, processes, and local regulations. Enterprise HR solutions need to address data availability, consistency, quality and reliability and the likely coverage of these data to user population before bringing selecting candidates for user consumption.

Complex rules may also drive who can see these data derived based on HR service delivery organization (i.e., how clearly responsibilities for client organizations are defined) for HR access to these data, and how organizational responsibilities are delegated to people managers. These need to be well understood to appropriately factor related complexity to enabling these data to these important constituents (i.e., HR and Managers).

Consider the larger HR ecosystem to validate that the data represented in the primary online channel is aligned and consistent with other properties in the ecosystem where a user may navigate to as a result of data displayed. Provide due considerations for technology readiness, capability and reliability to organize these complex data landscape, enforce security and governance, to ensure that your business solution can consistently deliver a single source of truth, in real-time.

So, as you can see, data is an important and complex idea in online enterprise solutions.  There are important nested decisions to solve the use of data to achieve business goals, related technology, and data governance. At LDS, we’re accustomed to working through this problem space to unlock new value in the experiences we imagine, design and realize.