Two Key Benefits of HR Analytics

In my last article, I wrote about the definition of HR Analytics and the skills needed to be successful in this field. In this article, I want to discuss two key benefits of HR analytics to the HR function in an organization and to the business: Evidence Based Decisions and Reducing Human Bias.

HR professionals want to be strategic partners with business leaders, not simply a cost center designed to maintain policies and procedures. While these policies are important, analytics provides HR with a means to demonstrably improve the efficiency of a company’s people resources. It does this in several ways.

Evidence Based Management Decisions

Through its dependence upon data and facts, HR Analytics delivers evidence, and evidence trumps intuition. To support these benefits, I’ll ask two questions:

Is your interview process optimized to find the best candidate for a position?

If you have ever participated in an interview process from the hiring perspective, you may be aware that at many companies, interviewing candidates can be an informal, non-standardized process. At worst, interviewees are simply asked by HR “What did you think?” More sophisticated HR methodologies define a standardized process for who the candidate meets and what questions are asked. At each stage, feedback is collected and quantified, typically in the form of ratings. Are these ratings predictive of future performance in the job role to be filled? HR Analytics can tell you the factors that are predictive of high performers in certain job roles (or tell you that you don’t know and that you should either change your process or collect different data points).

Does internal employee training improve company performance?

Most HR professionals would say ”Yes, employee training is a good thing and we need to do it.“ Many top companies spend precious resources to train their sales staff or send aspiring leaders to leadership training. Does this training have a material impact on performance? On the company’s bottom line? HR Analytics aspires to quantify that benefit. To do this, we may need to pull together data from several systems, such as on-the-job performance data, financial data and data collected during the training process. We should define the performance metrics that are most important in that job role. We must also consider a baseline of performance (i.e., comparable employees who were not able to take the training). By taking a more scientific approach, we can quantify the benefit and produce evidence of impact. We may also demonstrate that certain training is ineffective.

Reducing Human Bias

If you have read Michael Lewis’s book The Undoing Project, then you know about the work done by psychologists in the last 50 years to explain how bias interrupts the human mind’s ability to perceive information. Literally, our personal bias leads us to see things that simply are not there. We all have expectations, and these expectations are based upon hard won human experience—most of which has served us very well in life. But in the case of making HR judgments, or indeed any judgement requiring us to process large amounts of information, bias is quite detrimental.

In the questions/examples provided above, we see the opportunity for human bias to creep into common HR processes and potentially undermine them. First, let’s examine the interviewing process. As people, we may have expectations about how a qualified candidate dresses, how they speak, and which personality traits are most prominent in a good candidate. These are likely informed by our own experience, and colleagues who may have made a deep impression on us. Just as likely, information contradicting the same bias is dismissed. This means that our human minds are not able to process large amounts of information in a uniform and objective manner. When applied correctly, HR analytics can do this much better.  For example, an HR analytics team would consider data collected during the evaluation phase and performance data for successful applicants; in other words, before and after hire. Hopefully, many applicants become very successful at your firm, but you also know that many do not. We can apply a label certain to each candidate profile, recognizing that the candidate either was or was not successful.  We can then train our analytics algorithms to learn what a successful employee will look like, mathematically, at hire time and reduce our human bias. Bear in mind that bias can still creep into the process, if interviewers fail to recognize the need for standardization and quantification.

Similarly, as it relates to evaluating training against performance, we see an opportunity for bias to lead to conclusions that are false, or at least for which there is no evidence. Business leaders can (and should) demand this evidence from HR, so that they know that capital is being deployed correctly in support of the firm’s financial well-being. To be clear, it can be very difficult to prove causation between training and financial ratios (i.e., that training causes an increase in Net Income). However, HR should be able to provide evidence demonstrating correlation between employees who perform well on the job (be that metric in sales figures or on-time delivery) and those who attend certain training activities. When HR provides evidence of this correlation, it becomes a strategic partner with business leaders, helping them see and understand the patterns in human behavior.

See Differently, Know the Facts

Analytics offers HR professionals an opportunity to approach decision making differently. Measurements and quantification of candidate and employee characteristics and performance can provide evidence of correlation between the policies HR is supporting and the outcomes the business seeks to drive. By thinking differently about HR, we can reduce our propensity to see things that are not there, replacing that vision with a clear eyed, scientific, data-driven approach.

Want to learn more about the world of HR Analytics? We are speaking at this year’s CBIA Human Resources Conference on the topic. We hope to see you there!

Berry_Brian-240About Brian: Brian Berry leads the Microsoft Business Intelligence and Data Analytics practice at BlumShapiro. He has over 15 years of experience with information technology (IT), software design and consulting. Brian specializes in identifying business intelligence (BI) and data management solutions for upper mid-market manufacturing, distribution and retail firms in New England. He focuses on technologies which drive value in analytics: data integration, self-service BI, cloud computing and predictive analytics

5 Critical Skillsets for HR Analytics

Increasingly, companies are applying analytics and data science procedures to new areas of their business. Human Resources (HR) management, with its central role in managing the People in a business, is one such area. HR Analytics is a fact-based approach to managing people. A fact-based approach helps organizations validate their assumptions about how best to manage their people. This makes good business sense: on average, companies spend 70% of their budget on personnel expenses.

Using data and statistical methods, HR may look to examine people-oriented questions, such as:

  • Can we better understand employee absenteeism rates at a labor-intensive business, such as retail, food service or industrial manufacturing? Can we predict it?
  • Do our compensation realities reflect fair and balanced job classification policies? Asked differently, which factors are most predictive of compensation: ones we want to reward (i.e. education level, on-the-job performance) or ones we need to ignore (i.e. gender, age or race)?
  • What is our real employee churn rate? Can we identify employees headed out the door and take preventive steps?
  • Are our service response times keeping pace with spikes in customer demand?

These questions, and many more, can be answered with datasets, data science and statistics.  But how?  Analytics involves skill sets that go beyond those considered “traditional.” Knowledge of recruitment, hiring, firing and compensation are key to understanding HR processes. However, HR professionals often struggle to answer these questions in a data-driven manner, because they lack the diverse skills required to perform advanced analytics. These skills include statistical and data analytical techniques, data aggregation, and mathematical modelling. Finding the right data can be another challenge. Data analytics requires data, and that data is likely to reside in several different systems. IT professionals play a critical role. Finally, communication to the business is a key skill. HR Analytics projects may produce analysis and models that contradict conventional wisdom.  Action on these insights requires the team to communicate the what, why and how’s of Data Science.

To be successful, HR Analytics projects require five distinct skillsets to be successful in creating value for an organization.

  • Without Business input, HR Analytics projects may answer questions with no value added to the organization.
  • Without Marketing input, insights from HR Analytics will fail to be adopted by the business.
  • Without HR input, the team will struggle to recognize relevant data and interpret the outcomes.
  • Without Data Analytics input, analysis will be “stuck in first gear” – producing basic descriptive statistics (i.e. Averages and Totals), but never advancing to diagnostic (i.e. root cause) or predictive (i.e. Machine Learning) models.
  • Without IT input, the team struggles to acquire relevant data in a usable format.

HR leaders must engage all the required perspectives and skillsets to be successful with analytics. Business, marketing, HR and IT are common perspectives found in most organizations. But Data Analytics professionals, able to cleanse data, identify candidate predictive models and evaluate model output, are typically lacking.  We encourage HR professionals, interested in learning more about The Power of Data, to reach out to our Data Analytics Advisory Services team. Our goal is to help you understand the data science process, identify business opportunities, and potentially offer analytics services that fill in the missing pieces for your puzzle.

Want to learn more about the world of HR Analytics?  We are speaking at this year’s CBIA Human Resources Conference on the topic. We hope to see you there!

Berry_Brian-240About Brian: Brian Berry leads the Microsoft Business Intelligence and Data Analytics practice at BlumShapiro. He has over 15 years of experience with information technology (IT), software design and consulting. Brian specializes in identifying business intelligence (BI) and data management solutions for upper mid-market manufacturing, distribution and retail firms in New England. He focuses on technologies which drive value in analytics: data integration, self-service BI, cloud computing and predictive analytics

6 Steps For Creating Golden Records

If you are an organization seeking to improve the quality of the data in your business systems, begin by automating the creation of Golden Records. What is a Golden Record? A Golden Record is the most accurate, complete and comprehensive representation of a master data asset (i.e. Customer, Product, Vendor). Golden Records are created by pulling together incomplete data about some “thing” from the systems in which they were entered. The System of Entry for a customer record may be a Customer Relationship Management (CRM) or Enterprise Resource Planning (ERP) system. Having multiple systems of entry for customer data can lead to poor quality of customer master data – even giving your employees bad information to work off of.

But why not simply integrate the CRM and ERP systems, so that each system has the same information about each customer? In theory, this is a perfect solution; in practice, it can be difficult to achieve. Consider these problems:

  1. What if there are duplicate records in the CRM? Should two records be entered into each ERP? Or the reverse: what if one CRM customer should generate two customer in the ERP (each with different pricing terms, for example)?
  2. What if one or more ERP systems require data to create a record, but that data is not typically (or ever) collected in the CRM? Should the integration process fail, what will be the remediation process?
  3. What if one of your ERP systems cannot accommodate the data entered in CRM or other systems? For example, what if one of your ERP systems cannot support international postal codes? Are you prepared to customize or upgrade that system?

There are many more compatibility issues that can occur. The more Systems of Entry you must integrate, the more likely you are to have many obstacles standing between you and full integration. If your business process assumptions change over time, the automated nature of systems integration itself can become a source of data corruption, as mistakes in one system are automatically mirrored in others.

Golden Record Management, by contrast, offers a significantly less risky approach. Golden Records are created in the Master Data Management (MDM) system, not in the business systems. This means that corrections and enhancements to the master data can be made without impacting your current operations.

6 Steps For Creating Golden Records

At a high level, the process of creating Golden Records looks like this:

  1. Create a model for your master data in the master data management system. This model should include all the key attributes MDM can pull from Systems of Entry that could be useful to creating a Golden Record.
  2. Load data into the model from the variety of SOE’s available. These can be business systems, spreadsheets, or external data sources. Maintain the identity of each record, so that you know where the data came from and how the SOE identifies it (for example, the System ID for the record).
  3. Standardize the attributes that will be used to create clusters of records. For Customers and Vendors, location and address information should be standardized.
  4. If possible, verify attributes that will be used to create clusters of records.
  5. Create clusters of records, by Matching key attributes, to create groups of master data records. The cluster identifier will be the Golden Record identifier. You can also think of this in terms of a hierarchy. The Golden Record is the Parent and the source records are the Children.
  6. Populate the Golden Record, created in MDM, with attributes from the records in its cluster (the source data). This final step, called Survivorship, requires a deeper understanding of how the source data was entered than the previous five steps. We want to create a Golden Record that contains all the best data. Therefore, we need to make some judgements about which of the SOE’s is also the best System of Record for a given attribute (or set of attributes).

Great! We’ve consolidated our master data, entered from a variety of systems, into one system which also contains a reference to a parent record, called the Golden Record. This Golden Record is our best representation of the “thing” we need to understand better.

But wait! The systems of entry, the systems your business USES to operate, have not been updated. Can you still take advantage of these Golden Records?

The answer is “yes” – you can take advantage of the Golden Records in two ways:

  1. As the basis for reporting, because each Golden Record is also a “roll-up” of real system records that are referenced by orders, returns, commissions, etc. Golden Records provide a foundation for consistent Enterprise Reporting.
  2. As the basis for data quality improvements in each system of entry, assuming these systems can import a batch of data and update existing records that match a system ID.

These benefits of Golden Records are gained without the high risk and high costs that come with systems integration. Further, if you have modeled your master data correctly, it is possible to automate the data quality benefits of Golden Records Management, by updating these systems in real-time. See how BlumShapiro can help with your master data needs and golden record creation.

Berry_Brian-240About Brian: Brian Berry leads the Microsoft Business Intelligence and Data Analytics practice at BlumShapiro. He has over 15 years of experience with information technology (IT), software design and consulting. Brian specializes in identifying business intelligence (BI) and data management solutions for upper mid-market manufacturing, distribution and retail firms in New England. He focuses on technologies which drive value in analytics: data integration, self-service BI, cloud computing and predictive analytics

The Value of Golden Records

Running multiple ERP systems simultaneously can be quite painful for any mid-size organization. Since each ERP maintains their own chart of accounts, financial consolidation and reporting can become all-consuming for the finance teams. When each ERP has its own Customer Master, sales team visibility into strategic accounts is limited, while smaller accounts receive terms that can become big problems for AR. These separate ERP systems lead to issues for other departments—marketing wants a single comprehensive product master; supply chain managers want a single comprehensive vendor master.

Obviously, there is hyperbole involved in my description. However, these are some of the many reasons executive management would like all business units working from a single ERP, with integrated financial reporting, consistent business processes for the whole company and lowered costs of operations.

So, you initiated a multi-year ERP implementation / migration / consolidation project.

At the outset, each ERP specialist is skeptical of the consolidation strategy. “Our ERP is tailored to our business unit” is a common argument for keeping each ERP running. When asked, “How’s the quality of the data?” the same ERP specialists may complain that the data quality is poor. Unfortunately, data problems don’t get better by maintaining the status quo.

Severe master data quality problems present an obstacle to an efficient ERP transition. Let’s think about the customer: if you were to bring all customer master records into a new system wholesale, you’d have many duplicated accounts. You’d have diverse naming convention issues. You’d have some accounts that refer to distribution centers, some to end users, some to drop ship locations. You’d have a wide variety of payment terms.

Get your ERP ambitions moving again, and focus on data quality in a way that enables the final goal—centralized and integrated business processes. Here’s how:

  1. Build Golden Records for Customer. A Golden Record is a representation of your master data, which is the fullest, cleanest and most accurate information available. They are created from consolidating master data from multiple Systems of Record (ERP’s and other systems), standardizing that data, verifying the accuracy where possible, and then building clusters of similar records. This process of matching facilitates the creation of Golden Records, which contain the best information from all the master data in the cluster.
  2. Do the same for Product
  3. Do the same for Vendor

Are you sensing a pattern? Provided your systems of record have a reasonable amount of data characterizing each row of data, similarity clusters can be built. Inaccurate, non-standard data makes the process a little harder, but feasible. Accounting Master Data (i.e., GL Accounts) further benefit from a Uniform Chart of Accounts, to which all other systems may be mapped.

Golden Records Management is a non-intrusive, low-risk tool for accelerating the ERP migration process. Building Golden Records is repeatable for many types of master data and provides a means for preparing the best possible data for import into any new system. In Part 2, I’ll talk about how Golden Records and Master Data Management deliver a perpetual framework for Data Quality, extending the lifetime of legacy systems.

Want to learn more about the impact of master data on your organization? Join us on December 6 in Hartford, CT for our half-day workshop Discovering the Value in Your Data. Hear from data governance experts from BlumShapiro Consulting and Profisee as they address key topics for business, finance and technology leaders on data and master data management.

Berry_Brian-240About Brian: Brian Berry leads the Microsoft Business Intelligence and Data Analytics practice at BlumShapiro. He has over 15 years of experience with information technology (IT), software design and consulting. Brian specializes in identifying business intelligence (BI) and data management solutions for upper mid-market manufacturing, distribution and retail firms in New England. He focuses on technologies which drive value in analytics: data integration, self-service BI, cloud computing and predictive analytics