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Making E�ective Human Capital, Data-Driven DecisionsIdentifying Potential Issues and Best Practices to

Mitigate Risks Along the Decision Chain

Thomas Psota | 2019

Making E�ective Human Capital, Data Driven Decisions: Identifying Potential Issues and Best Practices to Mitigate Risk Along the ‘Decision Chain’

This publication contains general information only and Reveille Group is not, by means of this publication, rendering any professional advice or services. This publication is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may a�ect your business. Before making any decision or action that may a�ect your business, you should consult a qualified professional advisor.

Reveille Group shall not be responsible for any loss sustained by any person who relies on this publication.

As used in this document, “Reveille” refers to Reveille Group, LLC. Please see www.reveille-group.com for additional information about Reveille.


Today, organizations are under heavy amounts of pressure to leverage their workforce in the most e�cient and cost-e�ective manner to meet strategic goals. With the rapid evolution of technology over recent time, many are using data analytics platforms, techniques, and methods to develop meaningful insights on their current workforce, and the workforce that will be needed to help execute strategic initiatives in the future. In fact, 77% of today’s executives now view human capital analytics as a key priority within their organizations.1 However, while data analytics can be senior leadership’s greatest asset to making well-informed decisions, it can also be its own worst enemy if not leveraged e�ectively.

By identifying the critical elements driving human capital data analytics, organizations can take protective measures to mitigate the risk of making ill-informed, incorrect, and even costly business mistakes.

To comprehend the sequence of processes that lead organizations towards making e�ective data-driven decisions, it is important to recognize the decision chain. Seen in Figure 1, each component of the chain is dependent on one another, and a break during any stage of the process can negatively impact the data, analytics, and critical workforce decisions being made in the end.

By identifying the potential issues that can exist within each stage of the decision chain and corresponding best practices, organizations can take proactive steps to ensure that their human capital analytics are truthful, informative, and drive desired results.

Did you know?

77%Of today’s executives now

view Human Capital analytics as a key priority within

their organizations?

Figure 1: Data Analytics Decision Chain

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Stage 1 – Data Input

Although often viewed as a laborious and time-consuming task, the data input stage of the decision chain is arguably the most important as it is the foundation for each of the proceeding stages. According to a recent study conducted by Deloitte, it was determined that only 8% of organizations have useable human capital data.2 There are many factors that drive whether human capital data can be utilized to make informed decisions. For example, organizations can run into data integrity issues stemming from data input when the following inhibitors exist:

• Multiple Databases Meant for the Same Purpose• Lack of Standard Operating Procedures• Lack of Data Check/Audit Processes• No Accountable Individuals for Data Quality

Multiple Databases Meant for the Same Purpose

When there are multiple databases managing the same information, it is easy for records and entries to be transposed or captured incorrectly. Employees within an organization may revert to using one database over another or may track information manually on a personal spreadsheet because it is easier or deemed more reliable than the other sources of data. When this occurs, it can create data silos, where the same information is maintained and tracked di�erently across departments within an organization.3 This can lead to inconsistencies in the stored information, create distrust in the data’s validity, and always raise the question of “from what source did this data come from?” Maintaining and operating from a “single source of truth” (SSOT) database will ensure that employees are leveraging, interpreting, and most importantly maintaining human capital information consistently from the same location to feed reporting and analytics.

Did you know?

8%Only 8% of organizations

actually have usable Human Capital data

Figure 2: Single Source of Truth (SSOT)

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Lack of Standard Operating Procedures (SOP)

In order to guide the data input process, there must be training materials and SOPs in place. These guidelines are meant to ensure consistency in data entry, regardless of the individual who may be entering the information into the database at the time. Without these guidelines, employees are left to interpret how entries should be made themselves which can result in weakened data validity and inconsistencies.

However, while SOPs have proven to be an e�ective way to ensure data consistency, recent studies have shown that although they may exist within organizations, few actually use them for routine tasks.4 Reasons for the lack of use include the SOPs being outdated, di�cult to understand, or di�cult to find the relevant procedure.5 As a best practice, ensure that SOPs are regularly maintained and kept up to date, especially as data entry processes change throughout a database’s lifecycle. Also, ensure that employees responsible for data entry are aware of the SOPs that exist so that materials can be quickly referenced and followed.

Lack of Data Check/Audit Processes

Lacking an audit framework can result in unnoticed entry errors which can compound over time if not corrected immediately. According to a recent study, human error rates in data entry can be as high as 10%.6 Performing simple common-sense checks after a series of entries have been made are a quick and easy way to maintain data quality and mitigate the risk of human error. As a best practice, analysts can create an audit trail by running a query out of the database to view items recently added or edited. The extract should be cross-checked with standard operating procedures and other guidelines to ensure that the entries made are logical and fall within protocol.

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No Accountable Individuals for Data Quality

According to Boston Consulting Group’s Six Pillar “Prevent Solution” to enhance data integrity, owners/stakeholders must be held responsible for field integrity and accountable for process adherence.7 By creating accountability, organizations can hold employees responsible for data quality standards and incentivize or discipline as appropriate. However, while accountability may be thought to exist across many organizations, lapses can occur when organizational structures and lines of accountability are not well-defined creating confusion of roles and responsibilities.8 Therefore, it is important for organizational leaders to be clear with their sta� and identify exactly who is responsible for which elements of data integrity.

Organizations must take steps to mitigate these potential issues and avoid disrupting the proceeding events of the decision chain. If the data is incorrect from the start, incorrect decisions will be at risk of being made at the end.

Stage 2 – Data Extraction

When it comes to the decision chain, the data extraction stage is what drives e�ciency and standardization for reporting/analysis. As data is entered and stored in a Human Capital Information System (HCIS), queries can be pulled directly from the database to identify human capital activity such as listings of on-boards, hires, separations, promotions, among other data elements in the form of a single “file extract”. In its simplest sense, this stage needs to set the rules and parameters for the file extracts being pulled from an HCIS through query standardization.

Lack of Query StandardizationThe most critical element driving success during this stage of the decision chain is query standardization which ensures that all file extracts carry the same set of rules and parameters. Lacking query standardization can result in decreased e�ciency and accuracy of analytics, and greatly impact the risk of making improper data-driven decisions.

Figure 3: Clean Human Capital Data Components

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As a best practice, extracted queries from a human capital information system need to have a standard set of fields regardless of the information being extracted. These fields may include: Employee ID, Employee Name, Position/Title, Job Code/Position Number, Department/Business Unit, Date of Hire, and Retirement Date. Once standardized fields are established, they should be present in all database extracts regardless of pulling a report on hires, separations, on-boards, promotions, compensation, etc. Having query extracts standardized allows for:

REPORTLINKAGE

Multiple extracts can be consistently linked and cross-referenced through uniquely identifying field values, leading to more comprehensive and valuable reporting metrics. When standardized fields are not in place, e�ciently connecting information across human capital data extracts can become manual, time consuming, and result in errors.

AUTOMATIONReporting databases can be built o� query extracts (tables) from a Human Capital Information System. When extracts are standardized, reporting databases can be built o� these tables where queries, formulas, macros, and other processes are simply refreshed within the reporting database to deliver updated results. When tables do not have standardized fields and values, e�cient reporting/analysis will not be e�ective in the next stage and likely lead to errors and ine�ciencies.

LOGIC

Having a standardized set of fields across all human capital reporting extracts helps to create unique identifiers that can strengthen the accuracy and e�ciency of analytics. Unique identifiers can dictate employee transactions and internal movement, and guide where employees are grouped within an organizational chart. For example, a manufacturing firm may segment its employee population into two broad buckets: Manufacturing and Headquarters. And then further into Finance, IT, HR within Headquarters, and Machine Operations, Safety, and Distribution within Manufacturing. By referencing standardized fields within a dataset, analysts can develop formulaic logic that dictates where exactly each person in the organization should reside. Since fields are standardized across all file extracts, focused human capital analytics and metrics such as attrition, retention, hiring rates, and sta�ng counts can be calculated by organizational group or department and yield more insightful results.

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Stage 3 – Reporting & Analysis

During the reporting and analysis stage of the data driven decision making process, human capital metrics, assessments, and forecasts are identified and used to paint a picture of the trends that exist within an organization. However, there are certain tendencies that can lead to unnecessary work and invaluable analytics which can negatively impact decision-making leaders and human capital analysts such as:• Measuring the Wrong Things• Applying Broad Analytics to the Entire Organization• Information Overload

Measuring the Wrong ThingsOftentimes, human capital analysts may choose to analyze certain metrics that may be “easy to access” rather than those that are “important to understand”. By only reporting on and analyzing metrics that are easy to obtain, organizations will run the risk of assessing meaningless data, thus creating wasted e�orts by the individual(s) preparing the analytics and those interpreting the information for decisions. The key is to identify what the critical metrics are for the specific analysis taking place. According to the O�ce of Personnel Management, a critical element in understanding how to e�ectively use human capital analytics is to understand business objectives and processes.9 By collaborating with organizational leaders to identify business objectives, reporting and analysis can revolve around specific and meaningful focus areas to help drive e�ective end-decisions.

Applying Broad Analytics to the Entire Organization Typical human capital metrics include attrition, retention, hiring rates, among several others. To gain a sense of the trends that exist within an entire bureau or agency, these metrics can be calculated to assess the organization as a whole. However, in order to drive e�ective analytics, these metrics must be drilled down even further into specific parts of an organization to identify issues, pain-points, and trends.

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According to the Human Capital Institute, organizations are encouraged to look for productive ways to segment human capital data so that metrics such as turnover (for example) can be examined within departments and talent levels.10 In identifying the attrition that exists within each organization of an agency, more e�ective analytics can be assessed as opposed to solely interpreting the corporate-wide summary data.

Information OverloadTo prevent analytics from being overcomplicated and unfocused, reporting and analysis should be kept short, sweet and to the point. Understand the question in need of being answered, and do not tangle the story. It is important that the message being conveyed is clear and can easily be interpreted. This way, data-driven decisions can be made based o� data that is logical and comprehendible, thus mitigating the risk of acting on misunderstood or confusing analytics. As a best practice from OPM, analysts should meet with leadership to understand the questions that are in need of being answered and determine what resources will be needed in order to e�ectively convey the answer.11

So how do all of these factors rely on stage 2 of the decision chain? Within the Data Extraction stage, the ability to slice sta� into organizational groups or departments with the use of formulaic logic enables more focused reporting and analysis. With the ability to take file extracts that can quickly and e�ciently be narrowed to specific components of an organization, more meaningful analytics can be assessed as opposed to interpreting summary metrics that apply to the organization at large. Also, with the standardization of file extracts, more options for analytics and metrics become available that may help organizational leaders identify the questions that are in need of being answered as opposed to presenting information that’s restricted to what can easily be extracted from a database.

Figure 4: Information Overload Model

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Stage 4 – Making Decisions

The final stage of the decision chain comes when it is time to make decisions based o� reporting and analysis. At this stage of the process, it is critical that the following inhibitors are avoided amongst senior leadership to prevent organizations from making ill-advised human capital decisions:• Decisions Made in a Vacuum• Data Illiteracy• Cognitive Bias

Decisions Made in a VacuumWhen business decisions are made in a vacuum, the risk of acting without realizing the impacts of each area of the organization can be overlooked and create significant problems. Therefore, it is critical to invite the thoughts, ideas, and interpretations of others within leadership to weigh in on the analysis being evaluated. Leaders should be integrated from various parts of the organization for their knowledge and expertise to ensure a collaborative and cohesive decision is made in the end. As a best practice, many firms have formed executive committees comprised of diverse leadership populations to evaluate reporting and analysis and drive strategy.

Data IlliteracyNot fully knowing the elements of data within an analysis can be detrimental to organizational leaders tasked with making decisions. According to Fortune 500 company “Dropbox”, increasing data literacy needs to be a top-down and bottom-up e�ort.12 David Gainsboro, Head of Recruiting Analytics at Dropbox, mentions that leadership needs to be “fully educated” on data in order to validate and challenge gut instincts.13 To avoid data illiteracy at the leadership level, analytics teams must take time to educate and clearly explain metrics and illustrations. By understanding where the data comes from and what it represents, organizational leaders can better comprehend the story that the data is telling.

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Cognitive BiasBy involving others in the decision-making process, the thoughts, ideas, and theories from organizational leaders are invited to weigh in on the analysis. As more become involved, the lesser chance there is that cognitive biases will impact data-driven decisions. According to the Board of Innovation, cognitive biases “rely on our past experiences and ways of applying prior knowledge, particularly in decision making”.14 The more previous success organizational leaders have had in applying cognitive biases to making decisions, the harder it becomes for them to imagine alternatives.15 As they are expressed, it is critical that cognitive biases are either supported or refuted through factual data. While years of experience and human capital knowledge are critical to decision making, the world of business is ever-changing and theories that were once true in the past may no longer be valid in present day. Therefore, when assessing analytics to make a decision, it is important to not just focus on what leaders are saying, but what the data is saying as well.

How can the final stage of the decision chain be impacted by stage 3, Reporting and Analysis? As stage 3 is focused on delivering the right analytics to senior leaders, delivering the wrong or too much data can lead to information overload. When there is simply too much to analyze, it becomes very di�cult to make focused and sound decisions as there are too many puzzle pieces being assessed at once. Analytics must be focused, specific, and have purpose. If the analytics being presented at the decision making stage are not meaningful or worth monitoring, it will lead to wasted e�orts and time on behalf of the analysts and decision makers.

Data is what leads to insights. And insights are what lead organizations towards making critical and e�ective decisions surrounding human capital strategy. In order to have a process that leverages data to drive e�ective decision making, organizations must consider the critical elements of the decision chain and take proactive steps to ensure that decisions are being based o� data that is correct, clear, and comprehendible. An organization’s most valuable asset are its people, but it is how people are leveraged and utilized that will determine the success of any business. Data analytics can fuel e�ective human capital decision making and drive organizational e�ectiveness if the stages of the decision chain are followed.

Figure 6: Decision Strength Trajectory

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Citations1 Bersin, Josh, et al. “Global Human Capital Trends 2016.” Deloitte United States, 29 Feb. 2016, www2.deloitte.com/insights/us/en/focus/human-capital-trends/2016.html.

2 Collins, Laurence, et al. “People Analytics: Recalculating the Route.” Deloitte United States, 28 Feb. 2017, www2.deloitte.com/insights/us/en/focus/human-capital-trends/2017/people-analytics-in-hr.html.

3 “What Is a Data Silo?” Search Cloud Applications, July 2015, searchcloudapplications.techtarget.com/definition/data-silo.

4 “The Value of Standard Operating Procedures.” Mosaic Projects, 22 Oct. 2012, https://mosaicprojects.com.au/WhitePapers/WP1086_Standard_Operating_Procedures.pdf

5 “The Value of Standard Operating Procedures.” Mosaic Projects, 22 Oct. 2012, https://mosaicprojects.com.au/WhitePapers/WP1086_Standard_Operating_Procedures.pdf

6 Eichhorn, Gadi. “Why Exactly Is Data Auditing Important?” Realise Data Systems, 14 Oct. 2014, www.realisedatasystems.com/why-exactly-is-data-auditing-important/.

7 Gilliand, Guy, et al. “Creating Value Through Data Integrity”. Boston Consulting Group, Aug. 2011, https://www.bcg.com/documents/file83320.pdf.

8 Gilliand, Guy, et al. “Creating Value Through Data Integrity”. Boston Consulting Group, Aug. 2011, https://www.bcg.com/documents/file83320.pdf.

9 “How to Identify and Use Human Capital Analytics.” Human Capital Analytics, 2015, https://www.opm.gov/policy-data-oversight/human-capital-management/reference-materials/tools/how-to-identify-and-use-human-capital-analytics.pdf

10 Forman, David C. “Practical People Analytics Across the Talent Lifecycle.” www.hci.org

11 Forman, David C. “Practical People Analytics Across the Talent Lifecycle.” www.hci.org

12 Herve, Anne Claire “Does a Data Driven Culture Need to be Implemented From the Top Down?”. Innovation Enterprise Channels, https://channels.theinnovationenterprise.com/articles/does-a-data-driven-culture-need-to-be-implemented-from-the-top-down

13 Herve, Anne Claire “Does a Data Driven Culture Need to be Implemented From the Top Down?”. Innovation Enterprise Channels, https://channels.theinnovationenterprise.com/articles/does-a-data-driven-culture-need-to-be-implemented-from-the-top-down

14 Pinder, Mike “16 Cognitive Biases That Can Kill Your Decision Making.” Board of Innovation, https://www.boardofinnovation.com/blog/16-cognitive-biases-that-kill-innovative-thinking/

15 Pinder, Mike “16 Cognitive Biases That Can Kill Your Decision Making.” Board of Innovation, https://www.boardofinnovation.com/blog/16-cognitive-biases-that-kill-innovative-thinking/

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