The Future

The Future

INTRODUCTION

Big data has taken the human services profession by storm and it is not one that will end soon. Data will continue to grow and predict the future for years to come. There is a saying, you don’t know what you don’t know until you discover it. That is the future of big data. No one knows what or where it will take the world but we will enjoy the ride while we can.

Think of it this way, more data means more potential for organizations. Make sure you can trust the data. Keep it clean, solid, and error free. Remember if you want more from your data, know what to ask. As organizations learn how to use and interpret data, they will demand more from their data analytics. Organizations will want immediate insights that will help drive their decisions.

As a new data analyst, you might need to adopt new technologies as they emerge and push data farther than we can imagine. Make sure you are current with trends, confidentiality issues, and privacy and remember to always think outside of the box when looking at data. Big doesn’t necessarily mean it is actionable or fast, but rather knowing what to do with the data is ultimately the goal in the end.

Required Readings

  • Salganik, M. J. (2018). Bit by bit: Social research in the digital age. Princeton, NJ: Princeton University Press.
    • Chapter 7, “The Future,” pages 355–360.
  • Guenole, N., Ferrar, J., & Feinzig, S. (2017). The power of people: Learn how successful organizations use workforce analytics to improve business performance. Upper Saddle River, NJ: Pearson FT Press.
    • Chapter 18, “The Road Ahead,” pages 277–286.

Assignments

Stakeholder Decision

You have worked hard on this report for this quarter: you should be proud of your accomplishments. You presented your PowerPoint presentation, and the stakeholders are excited and decided to fund your proposal.  

Unfortunately, new data has emerged since your presentation and therefore the stakeholders are willing to only give you a percentage of what is needed. Present the new data that you feel may help with the decision of the stakeholders to fund your project. Discuss in detail what could have changed in your data sets (increased or decreased) and why.

How will you respond to the stakeholders?

Response Guidelines

Take the role of the main stakeholder and let the organization (peer) know what you will do and how much funding they will receive from you. Explain the main points that swayed you to make that decision. Provide your approval based on the updated recommendations. Include the percentage you will approve for the project in your feedback. Be as specific as possible. 

Learning Components

This activity will help you achieve the following learning components:

  • Analyze how one would utilize different methods, technology, et cetera to use data in the future in a chosen organization.

What a student from the class wrote

Student 1

U10D1 – Budget cut – Patty Gross

COLLAPSE

Top of Form

You have worked hard on this report for this quarter: you should be proud of your accomplishments. You presented your PowerPoint presentation, and the stakeholders are excited and decided to fund your proposal.  

Unfortunately, new data has emerged since your presentation and therefore the stakeholders are willing to only give you a percentage of what is needed. Present the new data that you feel may help with the decision of the stakeholders to fund your project. Discuss in detail what could have changed in your data sets (increased or decreased) and why.

How will you respond to the stakeholders?

I honestly had difficulty understanding the issue as presented. How one would respond to the new data would highly reflect:

  • The applicability of the data to the project design as well as the data difference specifically:
    • Response – Data not applicable: Reassess the original data to determine whether or not there were any biases or flaws in the analysis first. Then respond to the stakeholder(s), if it is not specified in the notification of the funding cut, requesting clarification on why they believe it to be applicable. If the data is not applicable this generally represents that the original data was not sufficiently explained. I would then revisit the original explanations to see where things could have been made more clear and/or explain my reasons why I do not believe the new data to be applicable.
    • Response – Data is applicable: I wish I could say that this never happens if a project is fully researched, but I know that is not the case. We have experienced this significantly in the past few months with the regulations regarding the wearing of face coverings to limit the spread of COVID-19. When something comes in (perhaps new regulations) that is unanticipated that would derail the implementation and/or the funding of a project an amount of regrouping is required. This is where being nimble as an organization to change within the environment in which you work becomes crucial. Exploring adjustments to the project definition, timeline, and deliverables will undoubtedly become necessary. When (a) stakeholder(s) realizes that the deliverables of the project are cut by the same or a larger percentage to the funding cut, they might have a change of heart.

The amount/percentage that is being cut from the budget

  • Response to a small percentage: The response would be to make quantitative adjustments in the project. A proposed budget generally aims “high” (but not “too high”) when considering things like sample sizes, salaries, the cost of supplies and equipment, etc. Small percentages (less than 15-20%) can probably be accounted for in the way the project is staffed and/or supplied.
    • Response to a large percentage: This would be much like the “data is applicable” response above. This would require a redefinition of the scope, timing, and deliverables of the project, and if those would impact the reliability (of a research project) or integrity of a service that is being provided, then it might require scrapping the project altogether. Part of the “buy-in” to the project is “buy-in” to the budget as well.
    • Response to corporate-wide budget cuts: This might be a little different than the other two, in that it is something that likely cannot be negotiated, therefore project adjustments (where appropriate) will be required. It is important that the stakeholders invested in this project are kept apprised of the changes in timing and/or deliverables (when applicable).
  • Whether or not the new data represents errors in the original hypotheses and/or the conclusions drawn from the original data
    • Response: This can be a very tricky proposition, as it can show that there was bias within the hypotheses and/or the conclusions, which can damage the credibility of the researcher. It is important to determine the credibility of the new data first. If it uncovers a hidden bias, acknowledge it, make adjustments to the project to account for it, and then communicate safeguards that you will deploy to keep this particular bias from reoccurring.
  • Required Reading
  • Chapter 7, “The Future,” pages 355–360.

7.1 Looking forward As I said in chapter 1, social researchers are in the process of making a transition like that from photography to cinematography. In this book, we’ve seen how researchers have started using the capabilities of the digital age to observe behavior (chapter 2), ask questions (chapter 3), run experiments (chapter 4), and collaborate (chapter 5) in ways that were simply impossible in the recent past. Researchers who take advantage of these opportunities will also have to confront difficult, ambiguous ethical decisions (chapter 6). In this last chapter, I’d like to highlight three themes that run through these chapters and that will be important for the future of social research. 7.2 Themes of the future 7.2.1 The blending of readymades and custommades Neither a pure readymade strategy nor a pure custommade strategy fully utilizes the capabilities of the digital age. In the future, we are going to create hybrids. In the introduction, I contrasted the readymade style of Marcel Duchamp with the custommade style of Michelangelo. This contrast also captures a difference between data scientists, who tend to work with readymades, and social scientists, who tend to work with custommades. In the future, however, I expect that we will see more hybrids because each of these pure approaches is limited. Researchers who want to use only readymades are going to struggle because there are not many beautiful readymades in the world. Researchers who want to use only custommades, on the other hand, are going to sacrifice.Bottom of Form

scale. Hybrid approaches, however, can combine the scale that comes with readymades with the tight fit between question and data that comes from custommades. We saw examples of these hybrids in each of the four empirical chapters. In chapter 2, we saw howGoogle Flu Trends combined an always-on big data system (search queries) with a probability-based traditional measurement system (the CDC influenza surveillance system) to produce faster estimates (Ginsberg et al. 2009). In chapter 3, we saw how Stephen Ansolabehere and Eitan Hersh (2012) combined custommade survey data with ready-made government administrative data in order to learn more about the characteristics of the people who actually vote. In chapter 4, we saw how the Opower experiments combined the readymade electricity measurement infrastructure with a custommade treatment to study the effects of social norms on the behavior ofmillions ofpeople (Allcott 2015). Finally, in chapter 5, we saw how Kenneth Benoit and colleagues (2016) applied a custommade crowd-coding process to a readymade set of manifestos created by political parties in order to create data that researchers can use to study the dynamics ofpolicy debates. These four examples all show that a powerful strategy in the future will be to enrich big data sources, which are not created for research, with additional information that makes them more suitable for research (Groves 2011). Whether it starts with the custommade or the readymade, this hybrid style holds great promise for many research problems. 7.2.2 Participant-centered data collection Data collection approaches of the past, which are researcher-centered, are not going to work as well in the digital age. In the future, we will take a participant-centered approach. If you want to collect data in the digital age, you need to realize that you are competing for people’s time and attention. The time and attention of your participants is incredibly valuable to you; it is the raw material of your research. Many social scientists are accustomed to designing research for relatively captive populations, such as undergraduates in campus labs. In these settings, the needs of the researcher dominate, and the enjoyment of participants is not a high priority. In digital-age research, this approach is not sustainable. Participants are often physically distant from researchers, and the interaction between the two is often mediated by a computer. This setting means that researchers are competing for participants’ attention and therefore must create a more enjoyable participant experience. That is why in each chapter that involved interacting with participants, we saw examples of studies that took a participant-centered approach to data collection. For example, in chapter 3, we saw how Sharad Goel, Winter Mason, and Duncan Watts (2010) created a game called Friendsense that was actually a clever frame around an attitude survey. In chapter 4, we saw how you can create zero variable cost data by designing experiments that people actually want to be in, such as the music downloading experiment that I created with Peter Dodds and Duncan Watts (Salganik, Dodds, and Watts 2006). Finally, in chapter 5, we saw how Kevin Schawinski, Chris Lintott, and the Galaxy Zoo team created a mass collaboration that motivated more than 100,000 people to participate in an astronomical (in both senses of the word) image labeling task (Lintott et al. 2011). In each of these cases, researchers focused on creating a good experience for participants, and in each case, this participant-centered approach enabled new kinds of research. I expect that in the future, researchers will continue to develop approaches to data collection that strive to create a good user experience. Remember that in the digital age, your participants are one click away from a video of a skateboarding dog. 7.2.3 Ethics in research design Ethics will move from a peripheral concern to a central concern and therefore will become a topic of research. In the digital age, ethics will become an increasingly central issue shaping research. That is, in the future, we will struggle less with what can be done and more with what should be done. As that happens, I expect that the rules-based approach of social scientists and the ad hoc approach of data scientists will evolve toward something like the principles-based approached described in chapter 6. I also expect that as ethics becomes increasingly central it will grow as a topic ofmethodological research. In much the same way that social researchers now devote time and energy to developing new methods that enable cheaper and more accurate estimates, I expect that we will also work to develop methods that are more ethically responsible. This change will happen not just because researchers care about ethics as an end, but also because they care about ethics as a means to conducting social research. An example of this trend is the research on differential privacy (Dwork 2008). Imagine that, for example, a hospital has detailed health records and that researchers want to understand the patterns in these data. Differentially private algorithms enable researchers to learn about aggregate patterns (e.g., people who smoke are more likely to have cancer) while minimizing the risk of learning anything about the characteristics of any particular individual. The development of these privacy-preserving algorithms has become an active area of research; see Dwork and Roth (2014) for a book-length treatment. Differential privacy is an example of the research community taking an ethical challenge, turning it into a research project, and then making progress on it. This is a pattern that I think we will increasingly see in other areas of social research. As the power of researchers, often in collaboration with companies and governments, continues to grow, it will become increasingly difficult to avoid complex ethical issues. It has been my experience that many social scientists and data scientists view these ethical issues as a swamp to be avoided. But I think that avoidance will become increasingly untenable as a strategy. We, as a community, can address these problems ifwe jump in and tackle them with the creativity and effort that we apply to other research problems. 7.3 Back to the beginning The future of social research will be a combination of social science and data science. At the end of our journey, let’s return to the study described on the very first page of the first chapter of this book. Joshua Blumenstock, Gabriel Cadamuro, and Robert On (2015) combined detailed phone call data from about 1.5 million people with survey data from about 1,000 people in order to estimate the geographic distribution ofwealth in Rwanda. Their estimates were similar to those from the Demographic and Health Survey, the gold standard of surveys in developing countries, but their method was about 10 times faster and 50 times cheaper. These dramatically faster and cheaper estimates are not an end in themselves, they are a means to end, creating new possibilities for researchers, governments, and companies. At the beginning of the book, I described this study as a window into the future of social research, and now I hope you see why.

  • Chapter 18, “The Road Ahead,” pages 277–286.

18 The Road Ahead “With all the technology like wearables, it would be nice to get these ideas into our company. We thought about using wearable analytics on our factory staff, with the goal of preventing health issues. That would be a long-term aspiration.” —Ralf Buechsenschuss Global HR Manager, People Analytics & Transformation, Nestlé Throughout this book, we have presented the recommendations and experiences of practitioners, academics, and thought leaders as advice for organizations to develop their workforce analytics capability. With the input of analytics experts, we have painted a picture of the discipline of workforce analytics as it is today and how it can be used to create business value. In doing so, we have explained, to a large degree, why it is important to human resources (HR) and to businesses and organizations as a whole. Although notable exceptions exist, the overall discipline is in its infancy, and practitioners are focused on building their organizations’ foundations. This chapter forecasts what we believe will happen next in workforce analytics. Predictions about the future are not likely to be 100 percent accurate; however, the trajectories of existing trends should continue, unless unimaginable events disrupt those trends. In this chapter, we cover the following: • Ways to meet business challenges analytically • Emerging data sources • Evolving technology • The evolution of the workforce analytics function Analytics Provides New Opportunities for HR Chapter 1, “Why Workforce Analytics?”, describes a pervasive demand for competitive advantage that transcends industries and geographies. The demand is driven by global competition for business in a labor market that is internationally mobile and globally connected. Faced with these pressures, organizations have a stronger need than ever to acquire, develop, and retain the best talent. Employees also have much more choice about who they will work for, when they will do the work, and where they will work. “HR can no longer rely on an old road map to meet the ever-changing demands it faces. The world of analytics opens up new opportunities that will enable HR to have different conversations and implement new solutions that drive better results for the business.” —Dave Millner Executive Consulting Partner, IBM Resolving these challenges satisfactorily requires addressing a variety of complex psychological and logistical issues. This is because the factors that make a worker a good fit for a role (a strong performer, someone who is unlikely to leave, and so on) include a mix of psychological attributes and technical capabilities. These features of workers need to be aligned with the demands of job roles in international markets that span geographies and cultures as well as time zones. Anecdotal evidence suggests that highly capable professionals who were once attracted to roles in disciplines such as finance and economics are now being drawn to HR because of its turn toward analytics. Until this point, workforce analytics projects have commonly been ad hoc efforts and have been deployed on a case-by-case basis to solve localized problems. Increased levels of capability, coupled with the ability to leverage technology, mean that in the future we should see more integrated solutions to solve problems for greater business impact. These solutions will be able to match workers to opportunities, develop worker capabilities, and optimize work environments more quickly and effectively than ever. Taken together, these factors make it an exciting time to be working in the area of workforce analytics and in the profession of HR. Emerging Data Sources Data sources that we could not have imagined five years ago are becoming common (for instance, text-based analysis of a worker’s digital footprint). New data sources will continue to emerge in the future, and those that scientists successfully demonstrate utility for are likely to make their way into applications of workforce analytics. Sources of emerging data include sensors, wearables, disappearables, and the Internet of Things. Whereas much traditional data in workforce analytics is structured (easy to store and analyze using traditional databases and spreadsheet software), data from emerging sources are often unstructured. Examples of unstructured data include data used to create reputational assessments from digital footprints (such as text, images, and video). Data-management technology for large-scale storage and analysis of unstructured data has only recently evolved to the point that analysis of such data is feasible in workforce analytics. Connected Devices One major anticipated development is that the data sources that once told us something about workers at a single point in time (for example, via an employment survey) will increasingly be replaced by systems that stream data in real time from technology that is perceptive (sensors), mobile (wearables), and small (disappearables). Many of these devices are relevant to workforce analytics and are part of the Internet of Things, an interconnected world linking electronic devices to others—clocks, refrigerators, cameras and so on. This means that devices useful for workforce analytics will be linked to devices used in other areas of the business, providing greater opportunity for workforce analytics practitioners. Recent developments in sensor technology will begin to be deployed live in organizations. These sensors will permit digital badges to monitor all social interactions (virtual and in person), including how, where, and when the interactions occur. The effectiveness of worker interactions can even be assessed with facial monitoring while people are having conversations. The information from these devices will see physical office architectures optimized for collaboration and productivity. The communication patterns of workers via electronic mobile devices will be examined for ways in which the digital work environments of employees can be altered to facilitate productivity, in the same way that sensor data can be used to optimize the physical environment. For example, the heart rates of workers in highly stressful occupations can be monitored to identify when work becomes too stressful, and the delivery routes of couriers can be optimized with global positioning data. Digital Footprints Standardized testing of ability or personality scores is common. Our ability to measure these attributes on job candidates was previously limited to the people we could encourage to complete a test, either in person or remotely. Now, however, nontraditional data sources on candidates abound that do not require the applicants to expend any effort or even know they are being assessed. Biographical data, or a candidate’s personal history (for example, educational attainment), can be found, and psychological profiles of candidates can be derived from digital footprints they leave via their use of the World Wide Web. Based on this information, businesses can proactively approach candidates instead of having to wait for the candidates to seek out opportunities. Digital footprints can be analyzed to help identify change management influencers. The data can come from sources such as emails, instant messaging, or social networks. Text-based analysis of candidates, using information from online digital footprints, will become an established approach to assessing worker suitability for jobs in the context of high-volume candidate screening. This will require overcoming numerous barriers related to employee privacy. Whether workers have a right to privacy when on the Internet depends on setting and circumstance; the question is not easily answered with a clear yes or no. Text-based analysis of digital footprints also presents challenges related to inclusivity. Although digital information likely exists for most individuals living in developed countries, those without Internet access will not have as complete a digital profile, nor will they be able to influence it. For these reasons (and others we turn to later in this chapter, such as validity), standardized testing should remain the primary approach in high-stakes testing until we understand more about the appropriate use of text mining for personnel selection. In other words, if today you have the chance to assess a candidate with a good personality questionnaire or a text profile, go for the standardized personality test. Genetic Testing Another new development involves molecular genetic analysis of suitability for jobs. Recent developments have seen researchers establish links between genetic profiles and work outcomes. For instance, a Journal of Applied Psychology article by Chi Wei and colleagues from the National University of Singapore showed a link between genes associated with extraversion and job-hopping tendencies. Importantly, the practical significance of these relationships has not yet been determined, and the sizes of the relationships so far appear small. Many other aspects of work behavior are caused by one’s environment, not one’s genetic profile. Moreover, genetic influences can be switched on or off by one’s environment. The implications of using these methods need to be better understood before they are adopted in workforce analytics. Considering New Data Sources The availability of new data sources does not mean that their use is scientifically justified, legally defensible, or socially appropriate. As this section emphasizes, careful consideration of each of these issues is recommended before using new data sources in workforce analytics. Validity New data sources come with as many questions as answers about people at work. The biggest question of all relates to exactly what constitutes appropriate use. An important factor to consider is whether the methods can be scientifically demonstrated as effective. For example, although scientific journals have related social media and text profiling of personality to standardized personality questionnaires, these relationships are typically modest: Social media and text-based profiles of personality measure something similar to standardized questionnaires, but they do not measure the same concepts. Scientifically speaking, these approaches are likely adequate for high-volume pre-employment screening, but right now there are better approaches to use when selecting among the final few candidates. Legal Appropriateness Along with considering the scientific merit of utilizing new data sources, organizations must closely consider the legal appropriateness of doing so. Legislation generally reacts and follows developments in technology. This is because the technology that yields the data workforce analytics professionals might use is evolving quickly and in ways that legislators cannot predict or imagine. New applications of emerging data sources can be scientifically validated before they are even considered legal. In other words, science and technology are often ahead of the law. Deloitte’s 2017 Human Capital Trends Report explores the rate of change of technology and public policy, including legislation, in more detail. Different countries also have different laws regarding the use of technology in workforce analytics. Employment lawyers should be consulted before new data sources are used in workforce analytics for employee-related decision making. Social Impact Industrial psychologists have developed a thorough understanding of the social consequences of using different forms of employee-related information. Using some characteristics in personnel-related decision making will lead to adverse impact (across socially, legally, or politically salient groups). However, this understanding is either nonexistent or in its infancy when it comes to many of the new data sources we have discussed. Organizations therefore need to carefully consider the social consequences of the workforce analytics decisions they make based on new data sources. Just because the science says an application of a new data source is effective and the law doesn’t say it is inappropriate does not mean that it won’t have undesirable social consequences.

Evolving Technology Chapter 11, “Know Your Technology,” outlines the requirements for establishing an analytics function today. But what technological developments are on the horizon? This section discusses open standards, cognitive technology, real-time analytics, and self-service technologies. Open Standards The approach of completing time-consuming large technology implementations before undertaking analytics will fall out of favor as technology based on open standards becomes the norm. Open standards mean that HR analytics can begin with the technology on hand today, together with skilled computer scientists who can integrate the data. We will continue to see the adoption of cloud technology except where legislation restricts its use. Artificial Intelligence and Cognitive Technology Recent developments in artificial intelligence and cognitive computing, which have incorporated sophisticated analytical techniques that were once available only to highly trained data scientists, will continue. Because the technology permits it, HR practitioners will be expected to have a stronger analytical perspective. Insights will be at the fingertips of those who know how to access them. The acceleration toward evidence-based HR is most likely to happen with ready access to cognitive computing—that is, systems that understand, learn, and reason as they interact with humans using natural language. Cognitive technology does this by taking advantage of some of the machine learning technologies referenced in Chapter 5, “Basics of Data Analysis,” and combining these with other technologies such as natural language processing. Cognitive technology promises large benefits for business because it puts all forms of data (structured and unstructured) to work in analytics, whether numerical, text based, or rich media such as audio and video files. Artificial intelligence also holds great promise for business and workforce analytics. Its applications are numerous, for example with bots and digital agents, where their introduction will change the nature of the workforce, and hence workforce analytics. This and other emerging technologies bring great opportunity as discussed in PwC’s 2017 Global Digital IQ® Survey. Self-service Technology Expect to see a much wider distribution of analytics capability to workers, managers, and executives through the use of self-service technology in HR. This analytical empowerment of workers will become increasingly app driven and enabled via mobile devices. Numerous benefits follow a shift to self-service HR environments. First, workers can obtain the information they need immediately instead of having to wait for a response from HR business partners. Second, managers will have access to information that allows them to make insight-driven decisions in real time. Finally, advances in self-service technology will put information at the fingertips of HR business partners, enabling them to become more insight driven and strategically influential. Real-Time Model Updating and Reporting Practitioners can expect to see much wider adoption in the future of real-time reporting and model updating. This technology means that when recurring events happen (for example, employees resign and exit the business at the end of any particular day), predictive models and reports are updated automatically and immediately. The latest information can then be passed to managers using intranet technology, a mobile app, or a text alert. Managers will be able to take advantage of the outcomes of real-time analytics immediately instead of having to wait for the results of new models to be disseminated. These real-time analytics will deliver the information leaders in organizations need to manage change in increasingly dynamic operating environments. The Workforce Analytics Function The continued pressure for workforce efficiency, coupled with new data sources and the technology to manage them, will require workforce analytics functions to look different in the future. This difference will be evident in terms of the function’s structure and reporting, the types of skills required, and an enhanced need for collaboration. Functional Reporting Line Today the most common structural approach for the workforce analytics function is to locate the team within HR, often several levels down from the chief human resources officer (CHRO). More team leaders are beginning to report either directly to the CHRO (see Chapter 3, “The Workforce Analytics Leader,” and Chapter 14, “Establish an Operating Model”) or to a direct report of that person, such as the head of organizational development or talent. This provides the advantage of a sponsor who is hierarchically close to the CEO and is focused on workforce-related business issues. Nevertheless, for many organizations, reporting to the CHRO might be an interim structure before workforce analytics is integrated into an enterprise-wide analytics function. The benefit of this approach is that the business develops a quantitative understanding of how all functions interact with one another to impact business performance. A centralized model could have a chief analytics officer or chief insights officer, with the function of workforce analytics becoming part of that team. Such a structure makes access to the information required to accurately link workforce measurements to business performance more readily available, and projects will likely become more complex to tackle much larger business issues. For other organizations, such as those that are highly federated, a better workforce analytics model might be one in which responsibility for workforce analytics is dispersed into the lines of business, with an HR analytics partner role established to complement the now common HR business partner role. Either way, having the workforce analytics function remain solely within HR will become less common in the future. Skills of the Future Chapter 12, “Build the Analytics Team,” detailed the Six Skills for Success, the knowledge, skills, and abilities that a workforce analytics function should have access to in order to succeed. The demand for these skills will increase and the mix and importance of each skill will evolve. First, the workforce analytics leader will be required to manage greater complexity and prioritize the ever-growing volume of information. This ability will become critical as projects become more integrated into the broader analytics work of businesses. Analytics leaders will also need to become adept at leading highly complex projects in unfamiliar areas and for a broader range of stakeholders. Second, availability of new and increasingly personalized data sources, together with machine learning technology and the algorithms it generates, will make managing privacy more complicated. Workforce analytics projects and activities will require more sophisticated privacy and ethical skills to manage multiple considerations: behavioral (what people like and how they will behave), legal (what is permitted), and ethical (what is the right thing to do with people’s data). Dawn Klinghoffer, General Manager of HR Business Insights at Microsoft, already has someone on her team fulfilling this behavioral, compliance, and ethical role. Dawn believes such a role will become more common in the world of workforce analytics: “With all the new data emerging, such as metadata, network behavior, and email traffic data, we have to get a handle on what this means and how we will use this for decision making.” Third, as workforce analytics technology evolves and new data formats become available, data scientists will need to keep up with the latest techniques and technologies. Workforce analytics team members will need to continuously learn about new data sources and analytical techniques to ensure that they can contribute effectively to solving business challenges. Finally, as people data sources become more varied, larger, and more complex, there will be greater demand for people trained in the nuances of workforce-related data, such as data scientists and industrial-organizational psychologists. Extreme Collaboration In the future, managing workers will require applying analytics to see the way HR policies and practices interact with the approaches being applied in other functions, such as finance, marketing, and legal departments. The new approach will view HR practices and processes as just one factor that impacts the way organizations turn inputs (for example, materials and labor) into outputs (that is, products and services) for customers. The workforce analytics function therefore needs to embrace the approaches and language of other functions to fully understand the likely complex ways that HR policies and practices impact business performance. These demands will see the workforce analytics function work more collaboratively, and with a broader range of colleagues and stakeholders, than it has to date. Expect to see a greater number of partnerships as projects become more complex and require abilities beyond the immediate capabilities of the team. This integration will also see the data that once flowed only within silos in businesses become available in real time for authorized users across the organization. Maturity Models Perhaps notable due to its absence in this book is the notion of analytics maturity models. Maturity models in analytics essentially specify a roadmap that organizations can choose to follow to develop their workforce analytics capability. In the early days of workforce analytics, maturity models were useful because they highlighted the possibilities for workforce analytics at a time when the discipline was finding its feet. They spotlighted the usefulness of data warehouses (the reporting level in many frameworks) and the potential to predict employee events with a useful degree of accuracy (the predictive level in many maturity frameworks). In the future, the implied need to follow a linear progression through seemingly more sophisticated levels of analytics capability is a misrepresentation of what is needed and what is possible for most organizations. In fact, all forms of workforce analytics are likely to be needed concurrently. Organizations do not need to surmount the hurdles associated with earlier levels of an analytics maturity framework before reaching the desired stage of workforce analytics capability. Instead, organizations should simply focus on the analytics capability they require to solve their most pressing business challenges. In other words, don’t focus on the maturity model which is an inside-out view of workforce analytics. Instead focus on the business issues and the stakeholders that are served, which gives an outside-in emphasis. Summary Workforce analytics is an essential organizational capability and experts believe its impact on business performance will only increase. By taking the following steps, you can position your function well to capitalize on the opportunities: • Familiarize yourself with emerging data sources in workforce analytics, such as worker digital footprints, sensors, and wearable data. • Ensure that your function is ready to utilize new and emerging technology that blends reporting and predictive analytics using open source technology, all deployed with artificial intelligence and cognitive technology. • Decide on the appropriateness of data sources for particular purposes based on the scientific evidence, legal context, and social implications of using the data. • Prepare your team to evolve its skills, particularly the ability to lead in a complex environment, with privacy as a specialty and using data scientists who can learn new methods and techniques quickly. • Learn why workforce analytics is important for tomorrow’s business and prepare to embrace the road ahead.

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