People Analytics as a service

Becoming data driven in HR is no easy feat. It requires a deep understanding of data collection, analysis, and interpretation, as well as matters like Privacy and Ethics. One of the most common challenges organizations face when building People Analytics capabilities is a suboptimal data infrastructure.

Organizations often find themselves struggling with systems and processes that are ill-equipped to handle the volume and complexity of people data. HR systems were primarily designed to execute HR processes and lack the necessary capabilities to effectively collect, store, visualize and analyze the vast amount of data that is now available.

Furthermore, obtaining the required skill sets can be a significant challenge. Individuals with expertise in data analytics are in high demand, and the competition for talent is fierce. Organizations often find themselves in a race against time to attract and retain top data analytics professionals who can help build and strengthen their People Analytics capabilities.

Even if an organization manages to overcome the hurdles of data infrastructure, talent acquisition and talent retention, they still face another obstacle - resistance to change. Implementing new ways of working and leveraging data-driven insights requires a cultural shift within the organization. Many employees may be resistant to change, clinging to traditional methods and sceptical of the value that People Analytics can bring. This especially the case in HR where many people went into the trade because of their focus on interaction with individuals not data driven insights.

Change management becomes crucial in this scenario, as organizations need to effectively communicate the benefits of People Analytics and address any concerns or fears that employees may have. It is essential to create a supportive environment that encourages experimentation and learning, where employees feel empowered to embrace data-driven decision-making.

In addition to these challenges, organizations must also navigate the ethical considerations that come with collecting and analyzing people data. Privacy concerns and the potential for bias in data analysis are important factors that need to be carefully addressed and managed.

Despite the challenges, building People Analytics muscle can be a game-changer for organizations. It enables them to make more informed and strategic decisions about their workforce, leading to improved employee engagement, productivity, and overall business performance.

By investing in the right data infrastructure, attracting and retaining People Analytics talent, and fostering a culture of data-driven decision-making, organizations can unlock the full potential of People Analytics.

Effectively building People Analytics muscle can bring great benefits to an organization. In addition, it is important to consider People Analytics is not a one-time project; it is an ongoing process that requires continuous monitoring, analysis, and improvement.

Because of the immense value that can be unlocked a People Analytics transformation must succeed and organizations should evaluate whether they have the resources and capabilities to sustain and evolve their People Analytics capabilities over time.

Within this frame one of the crucial considerations to make before commencing a People Analytics transformation is whether to Buy or Build People Analytics capability.

By making a conscious People Analytics sourcing decision, organizations can ensure that they are investing in a model that meets their unique needs, aligns with their overall business strategy, can be sustained over a longer period of time and is ultimately going to take them further faster.

Building People Analytics capability in-house requires subject matter expertise both on a functional and technical level, a significant investment of time, resources and money, as well as serious stamina. Some companies can fill in all these prerequisites, for instance large tech companies like Google. They have deep pockets, substantial size and their workforce naturally relates to data and data driven insights. Unfortunately, many organizations do not tick these boxes.

Buying People Analytics capability can be a fitting option for organizations that don’t have time/patience to build a full blown People Analytics team, technology stack and data driven culture from scratch, don’t want a full blown People Analytics team on the payroll, and need to go through a cultural shift, but do want to remain flexible and have the size and budgets to make a pivot towards data driven HR.

The decision to buy or build People Analytics capability is not a one-size-fits-all approach. It depends on the specific needs, resources, maturity and goals of each organization. Organizations must carefully evaluate their options and consider the long-term implications before making a decision.

• Flexibility

The managed service budget can be periodically adjusted. This allows the organization to tune People Analytics budget to the rhythm of the organization and the economy.

Over time different HR topics, e.g. Talent Acquisition, Diversity &a Inclusion, Workforce Planning, different technical domains (e.g. Data Engineering, Data Science, Business Intelligence) and various enabling items (e.g. Privacy, Ethics, Security, Operating Model), will feature on the People Analytics DevOps portfolio. Different topics and domains require different skills. In a managed service partnership the supplier fades the right people in and out at the right time.

• Stability

Often times organizations cannot afford more than a hand full of People Analytics professionals on their payroll, especially not at the start of their People Analytics transformation. Being the only person, or one of a few, on a topic that the rest of the HR team has little affinity with drives employee turnover. For Managed Service providers People Analytics is there bread and butter. They have large teams in which people find peers who speak their language, they can bounce ideas of and can learn from. An environment with likeminded people spurs retention and stability.

• Speed

Organizations typically deal with very similar topics at the same time. E.g. when GDPR came into play privacy had to be dealt with. With the pending AI and Equal Pay Act two new topics are in vogue. People Analytics agencies can create synergies between clients (e.g. interpretation of laws, establishing good-practice methodology, writing code, designing and developing a big data architecture) that enable them to perform the same work in fewer hours, at a faster pace while offering solid quality.

• Privacy

For employees of the managed service provider employee identification numbers are just a number and if they have names, which is often not the case, those names don’t say much to them. Putting the People Analytics team at bay can be an additional Privacy control measure.

• Focus

The client organization can focus on the first and last mile of People Analytics. I.e. demand identification and elaboration (first mile) and turning insights into action (last mile). The process of data integration, data transformation, data science, data visualization and everything that comes with it from a tech and compliance perspective can be left to the partner.

• Knowledge

In a buy scenario there is a risk that the purchasing organization builds up limited knowledge about their data and their technology stack which makes the organization dependent on the partner. Vendor management is of the utmost importance.

• Budget

In a buy scenario the investment of building People Analytics muscle becomes an out-of-pocket cost

• Legacy

Many organizations already have some resources working on People Analytics. Are they to be retained, do they join the People Analytics partner, or they not back-filled or actively let go of.

When it comes to buying or building People Analytics capability, organizations have a couple of consideration to make.

The first consideration is how People Analytics can be broken down. Different approaches can be taken. For instance looking at roles, looking at activities and/or looking through a technology lense. Below is an example of a high-level activity based break down of Analytics.

• Strategic Direction
• Portfolio Management
• Data Engineering
• Data Science
• Business Intelligence
• Employee Research & Psychometrics
• Insight implementation/adoption

The second consideration is which of these elements could be considered for outsourcing. The result of this consideration should be based on a careful consideration of factors like strategic importance, flexibility required, available knowledge and bandwidth available, ability to build substantial bandwidth and expertise internally, availability of managed partners that can offer the required scope. The result of this consideration could look like the visual below.

Each set-up has its own set of advantages and disadvantages, and organizations must carefully evaluate their “lenses” and options before making a strategic sourcing decision. Moreover, a strategic sourcing decision should be periodically calibrated and can change over time.

FMCG

Sourcing model characteristic

•  15K employees
•  Hybrid model
•  Client hosted technology stack
•  Work executed on client infrastructure

Activity allocation

• Strategic Direction (Build)
• Portfolio Management (Build)
• Data Engineering (Buy - Partner contracted by IT)
• Business Intelligence (Dev: Buy - People Analytics partner. Ops: Build)
• Data Science (Buy - People Analytics agency)
• Research (Buy -SaaS provider)
• Insight implementation (Buy - People Analytics agency responsible for development program. Follow up: Build

Financial Services

Sourcing model characteristics

• 16K employees
• Hybrid model 
• Client hosted technology stack
• Work executed on client infrastructure

Activity allocation

• Strategic Direction (Build)
• Portfolio Management (Build)
• Data Engineering (Build)
• Business Intelligence (Buy and Build)
• Data Science (Buy and Build)
• Research (Buy - SaaS provider) 
• Insight implementation (Build)

Retail

Sourcing model characteristics

• 100K employees
• Buy, unless…
• Client hosted technology stack
• Work executed on client infrastructure

Activity allocation

• Strategic Direction (Build)
• Portfolio Management (Build)
• Data Engineering (People Analytics partner)
• Business Intelligence (People Analytics partner)
• Data Science (People Analytics partner)
• Research (SaaS provider & People Analytics partner)
• Insight implementation (Buy - People Analytics agency responsible for development program. Follow up: Build)

Healthcare

Sourcing model characteristics

• 25K employees
• Buy, unless..
• Partner hosted technology stack
• Work executed on partner infrastructure

Activity allocation

• Strategic Direction (Build)
• Portfolio Management (Buy - People Analytics partner)
• Data Engineering (Buy - People Analytics partner)
• Business Intelligence (Buy - People Analytics partner)
• Data Science (Buy - People Analytics partner)
• Research (Buy - People Analytics partner)
• Insight implementation (T.B.D)

In conclusion, People Analytics holds tremendous promise for organizations looking to optimize their workforce and gain a competitive advantage. Whether an organization chooses to buy or build People Analytics capabilities, it is crucial to make a conscious decision that aligns with its strategic objectives. While both approaches have their pros and cons, organizations must carefully evaluate their options, considering factors such as data infrastructure, skill sets, customization needs, and available resources. By investing in People Analytics, organizations can unlock insights that will enable them to make more informed decisions, leading to improved performance and productivity.

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