What is Human Analytics?


“In its simplest of forms, surveys or questionnaires are forms of human analytics. But the possibilities of solving significant problems and adding strategic value are endless with human analytics.” Marketing litters our vernacular with pleonasms used for describing the process of examining data, and sometimes the more you read the more confusing it becomes.

For this post, I’ll first take a step to clear up the confusion by defining just a few of the basic terms to arrive at unambiguous clarity, and in turn identify which terms are merely synonymous. And then I’ll add one more to your vernacular, on behalf of ACL.

Terminology: Analysis vs. Analytics vs. Business Intelligence, Data vs. Human

Analysis: Analysis is an aged, well-understood term and represents a detailed examination of the elements or structure of something, typically as a basis for discussion or interpretation. In short, analysis is the examination process.

Analytics: Analytics is much newer term with more derivative connotations, but in an audit, risk, compliance and IT context analytics is the examination and processing of raw data through technology with the purpose of drawing conclusions about that information.

Data: Data is any information stored in business systems or application software across an organization. In short, data is any information from systems.

Data Analytics: So…data analytics is the practice of exploring raw data from systems with the purpose of drawing conclusions about that information.

Human Analytics: Human analytics is the examination and processing of any information that requires interpretation by a human in order to draw a conclusion.

Neoplasms: All the other terms like BI, Business Intelligence, Business Analytics, Analytic Software and Analytic Technology are just synonymous to data analytics. Largely the difference between data analytics and business intelligence is fashion and vendor differentiation.

Let’s take a look at real business needs to see how human analytics adds value, and why differentiating it will create more opportunity and value.

The Business Need for Human Analytics

Human analytics is about automating the ability to detect a business condition like risk or opportunity, and trigger a query that requires human interpretation, and where that conclusion may be used to remediate the risk, or further trigger an escalated workflow for further human interpretation. In its simplest of forms, surveys or questionnaires are forms of human analytics. But the possibilities of solving significant problems and adding strategic value are endless with human analytics.

Here are some business risks or conditions with needs for human interpretations and conclusions, of which we’ll explore the first three in detail:

  1. Regulatory Change Management
  2. Healthcare Providers – Sunshine Act Compliance
  3. Purchasing Card Policy Violation, Remediation & Training
  4. Enterprise Risk Assessment Survey
  5. Compliance Audit (e.g., gathering fridge/freezer temperatures from grocery chains or transportation fleets)
  6. SOX Self Assessment Testing from Control Owners

1. Regulatory Change Management

Internal legal, audit, risk or compliance functions have a complex job to determine the business impact from regulatory changes. The easy part is to outsource the monitoring to an external legal party; the difficult part is determining the business impact, because often those assurance functions simply don’t know enough about their business to make the determinations alone.


This is exactly the type of risk that requires human interpretation from a business expert in order for the assurance analyst to determine legal risk or compliance.

Lawyers love Microsoft Excel, and this is often how external legal firms communicate regulatory change to their clients. So wouldn’t it be great if the internal assurance function could trigger, manually or automatically, the regulatory changes along with a query seeking the impact from business experts, and then collate their responses to the query? And then, based on their response, could determine if further action was required, automatically?

One specific example of regulatory change management is the resulting financial services rules from Dodd-Frank legislation, where a complex set of rules exist for international inter-bank lending, requiring the trading practitioners to seek input from their legal teams to determine if a pending trade is compliant, based on the origin of the money.

That’s human analytics solving a complex and highly strategic business need.

2. Healthcare Providers – Sunshine Act Compliance

Another illustration is in the healthcare industry, to ensure healthcare providers comply with the Sunshine Act. The Sunshine Act is intended to remove conflict of interests with physicians and drive healthcare costs down; for example, sales reps from a healthcare vendors (pharmaceutical, medical equipment, medical services) can treat a group of physicians to a dinner with a subject matter expert for education purposes, but they cannot take a physician out golfing or on a vacation to the Bahamas.

So how do T&E administrators or audit, risk or compliance analysts ensure that a group expense submitted by their sales rep is compliant if the attendees of their expense were not included? How can they do this without drowning in manual process and emails?

An automated way is to use human analytics by using data analytics to detect the condition (e.g., where a group field is detected in the expense transaction), and then trigger a questionnaire that requires the transaction owner to supply the names of attendees of the group in order to comply with the Sunshine Act. The human analytic response is attached to the data analytic transaction and the risk is mitigated automatically. Sending a questionnaire or email is not enough if its not able to reconcile the risk and do so in a way that leaves an audit trail.

3. Purchasing Card Violation, Remediation & Training

Similar to the Sunshine Act, any data transaction that exceeds a business threshold is a possible control or policy violation. Using data analytics, you can automate the detection of Purchasing Card (P-Card) policy violations in transactional data, and trigger a human analytic based on the condition, such as “exceeds policy by 100%.” You can then send the transaction owner a questionnaire requesting supporting evidence for the transaction and whether they are aware of the policy violation. Based on their human response, you can create an additional trigger; for example, if their response states they were not aware of the policy, you can trigger a policy-training questionnaire requiring acceptance that the training is completed. All these triggers and analytics and responses are fully automated, they can be stored and collated to the triggering human or transactional data, and are available for reporting.


Data analytics is awesome at analyzing unthinkable volumes of data to illuminate the manifestation of strategic business risk and opportunity. But data analytics cannot replace the need for human interpretation. Do you have business risk and conditions exist that require human intervention? How does your technology handle that need?

(Source: ACL Blog)

Tuesday, July 1, 2014 In: Hot Topics Comments (None)

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