Figure 11.1 Technology options to support workforce analytics.
“When choosing technology, we suggest people start with, ‘What is it the business is asking you to do?’ Then ask yourself ‘Where would you like to go?’ This way, people can map out what role they want technology to play in the business.”
—Jackie Ryan
Director, Watson Talent Analytics, IBM
Technology in workforce analytics refers to the combination of hardware and software services (that is, tools, applications, platforms, and so on) that facilitate the activities of the workforce analytics team by increasing speed, accuracy, and the level of automation of work. Technology helps workforce analytics scale because it makes tasks more easily repeatable and also makes addressing compliance requirements more straightforward.
On one hand, discussions about workforce analytics technology can entail incredible complexity. This potential complexity is compounded by the rate at which new technologies emerge and existing technologies are rendered obsolete. On the other hand, the core elements of workforce analytics technology are straightforward because certain common requirements across organizations are unlikely to change for the foreseeable future.
This chapter covers the following points:
• Vision and mission as the starting points for technology discussions
• Components of workforce analytics technology
• Decisions about sharing, subscribing, or owning technology
An overarching principle is to understand your starting point with respect to technology and where you need to be to accomplish your workforce analytics goals. This starts with your workforce analytics vision and mission, described in Chapter 7, “Set Your Direction.” The potential range of technology options is wide, from spreadsheet software delivered through desktop computing to cognitive systems delivered in the cloud (see Figure 11.1).
Figure 11.1 Technology options to support workforce analytics.
For example, if your analytics mission is in its early stage and you have both a limited team and limited technology, you might be working mostly with spreadsheets to report workforce data. For relatively simple analyses, this could be fine, but spreadsheets struggle to cope as the volume of data and the demand for analytics across the business increase. Eventually, you should switch from basic spreadsheet software and consider what other technology you need to profile and interpret the data. Consider what tools and services enable better understanding and aggregation of the data than standard desktop spreadsheet software such as Microsoft Excel.
Other organizations that have been working with analytics for a long time have a very different starting point than those that rely mostly on Microsoft Excel. They might have already built a data warehouse and could be asking questions such as “How can we get more value from our data?” Considerations for these organizations depend on the business problems inspiring their analytics efforts. For example, they might be interested in sharing their data with the rest of the HR function through some kind of self-service provision. These organizations also need to understand their current challenges and the technology that will help them meet those challenges. Working with a partner on your technology strategy is worth considering; we discuss this later in this chapter in the section, “Technology Vendor Relationships.”
At ANZ Bank,1 Kanella Salapatas has had a great deal of success winning over people to the possibilities of data and analytics technology. Kanella is the HR Data Manager and Reporting Service Owner, and her trick has been to get people involved with the technology throughout the journey.
According to Kanella, the value in involving HR people with technology comes from the confidence they gain when they know what’s going on with the data and the analyses. “If a business leader asks why a certain number is 18 and not 20, for example, the HR Business Partner (HRPB) must have an answer and must be confident about it.” Kanella has found that some HRBPs are reluctant to use technology to really understand those answers.
To overcome such technological reluctance, Kanella has taken a two-fold approach:
• First, she helps train people in basic skills of using technology such as Microsoft Excel or business intelligence toolsets. Learning how to query and explore information for themselves builds confidence in the technology solution and people’s knowledge of where they can get an answer when put on the spot by a business leader.
• Second, Kanella gets people involved in creating the reports they receive. “Getting HRBPs involved in the construction of their reports means that they will know the formula for the metrics and the individual data elements used. In turn, this will allow them to more confidently have conversations with their business leaders, and this gives them additional credibility.”
Kanella’s advice is simple: Involve your HR leaders and HRBPs in your technology and take away the mystery.
In this section, we introduce the main elements of the technology required for undertaking purposeful analytics (see Chapter 4, “Purposeful Analytics,” for more detail on this area). Here we focus on the minimum level of detail everyone in the workforce analytics team should be aware of when it comes to technology. Data scientists with computer science skills will undoubtedly have deeper knowledge on the topic, but the level of detail here will enable all team members to have sensible conversations about technology with each other and with people outside the workforce analytics team. If you want more detail, Jackie Ryan and Hailey Herleman provide a comprehensive discussion of the elements of a modern HR analytics platform, including data integration and governance, in Chapter 2, “A Big Data Platform for Workforce Analytics” of the edited book Big Data at Work: The Data Science Revolution and Organizational Psychology (Routledge, 2015). The elements discussed in the following sections are Human Resources Information Systems, HR data warehouses, reporting, statistical analysis and machine learning, visualization, and cognitive technologies.
Human Resources Information System (HRIS) technology platforms store information about employees. Operational versions for the different HR subfunctions often exist (for example, payroll, recruitment, and learning), and some providers offer an integrated HRIS with modules that cover multiple HR subfunctions. These systems serve two main purposes. First, they formalize many of the organization’s HR policies and practices into a workflow that reflects the organization’s view of best practice in HR. Second, they capture all data generated about employees (and, in some cases, by employees) in the course of performing HR function responsibilities.
Most HRIS platforms from major providers are built around a vendor’s data warehouse technology. However, in many cases, organizations using these HRIS platforms find that they do not provide the flexibility to handle the variety of analytics they want to undertake. Organizations with multiple HRISs are also interested in linking their data across systems.
For these reasons, many organizations take regular data exports from proprietary vendor systems and store the data in a specially constructed HR data warehouse. These periodic data exports form the system of record used to populate all other downstream systems that need accurate data for either advanced analytics or HR reporting purposes. The data from the operational systems (for example, payroll) are prepared in staging areas where the data are cleansed and validated. Next, the data are transferred to an enterprise data warehouse. The data warehouse then becomes the master record and provides a longitudinal view of the workforce data of the organization.
Business intelligence tools can then be overlaid on top of this internal HR data warehouse to enable data exploration and reporting. Data marts, or views customized to specific business needs, can also be created. Figure 11.2 depicts the components in a standard data warehouse and associated systems.
Figure 11.2 Structure of typical data systems.
“On a monthly basis, we export selected parts of our HRIS data and store it in our wider data warehouse. We can overlay the definitions that we require for particular metrics and the specific hierarchies we need for data mapping rather than having to rely on how the HRIS defines things. We combine these data exports with such things as customer research data and can do all sorts of useful analytics on it.”
—Sally Dillon
Head of Business Intelligence, UK Life, Aviva
Reporting technology in workforce analytics refers to business intelligence (BI) software applications that sit over the top of the HRIS, the HR data warehouses, or both. The HRIS provider can supply this functionality in an integrated single system, but many third-party vendors also offer reporting technology to complement the large HRIS provider systems. Reporting applications allow users to undertake tasks such as querying the HRIS for information about each of the HR subfunctions. Types of reporting that can be carried out include generating payroll reports for tax reporting requirements, examining the average length of time taken to fill open job roles, and checking the level of engagement with online learning management systems.
BI technology can also be used to regularly examine how well the HR function is performing relative to a set of metrics or key performance indicators (KPIs). These tasks often involve using reporting technology to populate dashboards or generate scorecards with information on topics such as HR compliance violations, turnover rates in different parts of the business, and numerous other HR metrics.
Sally Dillon is Head of Business Intelligence at UK Life at Aviva,2 a large insurance company based in the United Kingdom. She has been the data lead on large systems implementation projects, such as Workday, to help HR get the most from their people systems. One of her key approaches is to make sure the technology allows for drillability, or the ability for users to thoroughly explore their data at increasingly fine levels of detail.
For most HR professionals who lack highly technical skills, drillability is possible only with a strong user interface to the technology. “We encourage people to understand their data, and that means having a very usable technology,” explains Sally. “The interface must be user friendly. The technology should also have strong mobile capabilities, since many HR professionals rely on their mobile device to access information. Finally, it must have connectors into the enterprise people systems and the data warehouse.”
Keeping her user’s hat on at all times has helped Sally ensure that Aviva’s technology choices are delivered for even the least technologically minded: “It’s not only about the user interface,” Sally reveals,“You also need to put the data in the language of the business, and that means knowing the specific audience. I talk to our finance team with numbers. With our business leaders, I start with a story about customer impact.”
Sally realizes that the technology is an enabler for business. She looks for great technology that helps people view their data and reports in a mobile-friendly and visually compelling way so that they want to drill down into their data. As she says, “Drillability is key.”
Advanced analytics technology takes the form of standalone software for carrying out many of the analytical techniques Chapter 5 describes. This is the technology that many machine learning and statistical modeling experts use to test hypotheses. It also includes advanced data management systems such as distributed computing used in Big Data platforms to share the computational burden of time-consuming data analysis across many different computers.
For these types of analyses, your data scientists will rely on a range of software programs. They are likely to take a best-of-breed approach, selecting a software package for the type of analysis they want to perform or else writing their own algorithms using open source or commercial software packages. Be aware that many packages might appear comprehensive in their functionality but do not incorporate the latest analytical techniques. Data scientists are the best people to guide the team in adopting the best technology.
Cognitive computing is a new area of technology based on artificial intelligence intended to help advise HR. This technology learns, reasons, and advises the user based on models that the system builds and inferences that it makes. The user can interact with the system through natural language and apply the models that the system builds. Cognitive computing technology is playing an increasing role in analytically enabling HR practitioners.
Visualization technology refers to software used to generate graphs and other visual representations of data. Visualization in analytics serves several purposes.
First, visualization can help analysts gain a clearer picture of the data they are considering. By their very nature, summary statistics such as means and standard deviations convey limited information about the data they summarize. For example, the mean does not tell you how spread out the data are for a particular variable. Even summary statistics developed to describe variation, such as the variance, will not readily spot outliers. Graphs can easily highlight the average and the variability in data. See Chapter 10, “Know Your Data,” for a discussion of why issues such as data variability and outliers matter in analytics.
Second, visualizations help those who have a hard time interpreting data understand the results of analyses. People who are less familiar with interpreting statistical information often find graphical representations more intuitively understandable. For this reason, visualization is an important part of the insight generation and communication processes in analytics.
Finally, visualization can reveal complex results. Important developments in statistical modeling and machine learning allow complex statistical relationships to be developed. These cannot easily be represented graphically using basic presentation software. Visualization software can bring these complex relationships to life.
In the past, HRISs comprised technology purchased and installed on the organization’s physical premises (this is known as on-premise). Occasionally, an alternative was to purchase technology but have a technology provider host it on its own premises (referred to as hosted). The expense involved in installing, maintaining, and updating technology in both these instances meant that only large organizations were able to invest the substantial financial resources needed. Since the early 2000s, HRIS platforms started to be developed “in the cloud” using a Software as a Service (SaaS) approach. The cloud refers to the delivery of computing on demand over the Internet on a pay-for-use basis (see Figure 11.3).
Cloud-based services enable widespread access to workforce analytics information and insights for organizations of all sizes. These systems will become even more prevalent except in areas where factors such as data privacy legislation or requirements for local data storage restrict their use. Hybrid cloud technologies are often used in such scenarios where data must be managed within the business but interact with cloud-based applications.
Figure 11.3 Cloud computing technology.
Technology vendor relationships encompass all forms of supplier relationships surrounding workforce analytics technology, whether your analytics function uses shared technology systems, purchases its hardware and software outright, or decides to lease it. The types of technology you might purchase or lease span the entire spectrum of technology components discussed earlier in this chapter—namely, the HRIS for essential HR administrative functions and reporting on the performance of those functions, an HR data warehouse that functions as the system of record for the organization’s workforce data, and reporting, analysis, and visualization technology. In general, the fundamental aspects of your workforce analytics technology architecture, such as the HRIS and data warehouse, are likely to be owned or accessed via subscription. Beyond these fundamental elements of workforce analytics architecture, you must decide whether you will share, subscribe to, or own your workforce analytics technology.
Before you decide whether to lease or own technology for workforce analytics, it is important to assess whether having exclusive access to the technology is necessary for your workforce analytics goals. If you need access to the technology on a more limited basis, you might be able to leverage existing technology elsewhere within the organization. An example might be access to high-powered computers for complex analyses or access to technologies that distribute processing tasks across multiple idle computers throughout the organization. If you do not need immediate access to such technology, you might be able to share technology—that is, access existing technology that other functions, such as marketing, use. However, if you require access to technology on demand, you will likely want to lease or own your own systems.
Many technology vendors now offer the option to subscribe to their software through Software as a Service (SaaS) licenses. Other service subscriptions include Infrastructure as a Service (IaaS) and Platform as a Service (PaaS). These cloud technologies have important implications for workforce analytics. First, cloud technology removes the need for significant upfront capital expenditure (CAPEX) by turning it into a monthly subscription (which is appealing for cash flow reasons as well as reduction in CAPEX). In addition, with cloud technology, HR typically does not need to involve the IT department in either the purchase decision or ongoing support.
Subscription approaches have the advantage of distributing the cost of the software over time. They also disperse some of the responsibility for maintaining the capability and performance of the hardware and software to the technology vendor. Technology vendors that lease technology often give service agreements, which means that while the lease is current, your organization has right of access to any updates to the technology that the vendor develops. This can include both bug fixes and capability enhancements. Typical technologies that it makes sense to lease include HRIS and data warehouse technology systems.
Leasing offers two important benefits. First, as long as the lease is not overly long, the organization has some freedom to exit a relationship if it is not sufficiently productive or if the organization finds a vendor that can better service its needs. Second, organizations are quickly developing their technology to ensure that their offerings are perceived as market leading, so the regular updates leasing arrangements provide can be highly advantageous. If you decide to lease, it is important to understand the nature of your lease—for instance, at the end of some leases, the leasing organization owns the technology.
If the function requires exclusive access and you are not concerned with the rate of renewal and refreshing of service capabilities, buying the technology outright might be a sound option. In general, buying technology makes sense if important new functionality is unlikely to emerge and if the technology will be needed longer than it takes the asset to depreciate financially.
Technology that typically falls into this category includes hardware for analytics, such as powerful desktop computers and servers. Although new and faster technology will certainly come out, the desktop machines and servers you purchase for your function will remain capable of performing all the tasks you require of them for the foreseeable future (albeit not as fast as newer, more powerful machines). Of course, ownership entails upfront capital expenditure, although the value depreciates over the life of the asset.
If your workforce analytics function determines that leasing or buying equipment is the best approach, you need to select the best vendor. Most organizations have technology policies dictating whether an open competitive tender is required. The key point is that workforce analytics professionals need to influence the direction of tender processes so that decisions support the goals of workforce analytics.
Involvement in the tender process will include writing the tender brief, answering requests for information, and selecting the vendor. At a minimum, workforce analytics professionals need a good working relationship with procurement teams because their expertise is needed to guide the tendering process for workforce analytics.
Decisions about technology are among the most important ones you will make in workforce analytics. To make the right decisions, you need good advice from Information Technology (IT) and HR Information Technology personnel and from your data scientists. To prepare for these discussions, take the following steps:
• Decide on the appropriate mix of technology by understanding what you have now and what you need to perform the required types of analyses in support of your workforce analytics vision and mission.
• Get familiar at a high level with the basic components of workforce analytics technology, including the benefits of cloud technology.
• Consider whether and when cloud-based services will work in your organization.
• Understand the breadth of technology required for a workforce analytics technology system: the HRIS, the HR data warehouse, reporting and business intelligence technology, analysis and data integration software for advanced analytics, cognitive solutions, and visualization software.
• Decide whether to share, subscribe to, or own your technology, based on ease of access, financial cost, and your degree of concern over how the technology ages.
1 ANZ Bank traces its origins to the Bank of Australasia in 1835. Today it is one of the five largest companies listed in Australia and is the biggest bank in New Zealand. As of April 3, 2017, it had over nine million customers and more than 50,000 employees. ANZ Bank is headquartered in Melbourne, Australia (www.anz.com).
2 Headquartered in London, U.K., Aviva is an insurance company with 33 million customers and approximately 28,000 employees in 16 markets in the United Kingdom and throughout Europe, Asia, and Canada. It has a rich heritage dating back to 1696; one of its most famous customers was Winston Churchill, who took out a policy in 1896. In its 2015 annual report, Aviva reported revenues in excess of £23 billion (www.aviva.com).