People Analytics Design Company
hero_pattern.jpg

Defining

“If you define the problem correctly, you almost have the solution.” - Steve Jobs

 

People Analytics Definitions


people analytics

People analytics is the systematic application of science and statistics to people strategy to achieve probability-derived business advantages.

Four-S people analytics framework

One way of defining people analytics is to say that people analytics is what lives at the intersection of people strategy, science, statistics. and systems — what I call the Four S framework. If you take any of the S’s away, you might get something like people analytics, maybe even a precursor of people analytics, but it is not people analytics. That is not to say it is not important, just different.

Below are some ingredients to each of the Four S elements that represent an important concept or activity to learn if you hope to be successful at people analytics.

strategy

Strategy encompasses the plans and policies intended to help a company gain a sustainable edge over its competitors as efficiently as possible. It represents the art and science of developing and using the object and actions within control of an organization in a deliberate manner in order to increase the probabilities of victory and to lessen the chances of defeat.

Key Concepts: Business Model, Segments / Target Segments, Differentiation, Job Families & Job Levels, Key Jobs and Key Talent, Performance Management and Compensation Philosophy / Compensation Strategy.

science

Science involves the systematic study of the structure and behavior of the physical world through the organization of facts and theories and continual refinement of those facts and theories through observation and experiment.

Key Concepts: Scientific Method, Inductive Analysis, Deductive Analysis, Research Design, Survey Design and Experiment Design.

statistics

Statistics is the branch of mathematics that deals with the collection, organization, analysis, and interpretation of numerical data.

Key Concepts: Chi-Square, T-Test, Correlation, Multiple Regression, Factor Analysis, Machine Learning

Systems

A system is computer software designed to perform a group of coordinated functions, tasks, or activities for the benefit of the user. A system is made up of components that work together. A system takes an input, changes it according to the system's use, and then produces an outcome. Inputs and outputs are strung together between systems to accomplish broader objectives.

Key Concepts: Operational Systems, Data Collection Systems, Data Management Systems, Data Analysis Systems, Data Delivery Systems, Data Warehouse, Operational Data Stores & Analytical Data Stores, Data Visualization, Data Dashboards, APIs & Code (SQL, Python, Node.js).


dual Inductive & deductive Analysis development tracks:

Most of the time when people talk about analytics in the workplace, they are talking about taking an existing dataset and mining some meaning from it. This type of analysis rooted in inductive reasoning.  There are two methods of developing insight: inductive and deductive. The scientific method, for the most part, works in exactly the opposite direction of what I just described, it uses deductive reasoning.

Inductive analytics project takes existing data from systems into a predefined workflow to produce a known output, which goes on a dashboard. A deductive analytics project starts with theory, and works towards data collection to test that theory, to eventually produce an intended insight.

If you know you have all of the data that is relevant to answer a specific business question, you just don’t know what of the data you have will answer that question best, then your job is inductive. If your question is new and/or you don’t know at the outset what data is most useful to answer the question, then you your job is deductive. It is our contention that most of people analytics still requires deductive work - only a handful of problems can be reliably answered using an inductive method.

I believe both have a purpose, but to avoid major misses, and ultimately waste, deductive analysis should come before inductive analysis. Some of unique measurement frameworks I have mentioned - for example CAMS - were developed through deductive reasoning and intended to come before or assist other inductive analysis.


triple-a measurement framework

The Triple A Measurement Framework provides the fundamental measurements and analysis for the three big people-related problems each company needs to solve if they hope to grow as a business: attracting talent, activating talent, and controlling the rate of talent exit (attrition).


Below is an overview of each component of the Triple-A framework:

attraction

Attraction represents a set of metrics and analyses intended to measure the attractive force of the company to acquire the quality of talent it wants. In other words, it poses the question "How are you doing on getting talent into the company?"

Key Measures: Headcount, Average Headcount, Hires, Hire Rate, Headcount Growth, Headcount Plan, Headcount Plan Achievement Percent, Candidates, Applications, Interviews, Offers, Offer Accepts, Recruiting Stage Pass Percent, Segment Yield Percent, Average Hires Per Recruiter, Average Phone Screens Per Hire, Average Interviews Per Hire, Average Time to Fill, Average Time to Start, Brand Index.

activation

Activation represents a set of metrics and analyses intended to measure the proportion of people and teams who have all the basic requirements to produce at a high-performance level. In other words, it poses the question "How are we doing at creating the conditions that make for productive employees?"

Key Measures: Human Capital ROI (HCROI), Expected Employee Lifetime Value (ELV), CAMS Index, Activated Percent, Net Activated Value (NAV), Culture, Climate.

CAMS Activation Index: The theory of activation proposes that, taken down to its essence, four conditions must exist for an employee or a team to consistently produce at or above performance expectations. The employee or team must: a) be capable of performing the actions required (Capability), b) be aligned on what a good result looks like (Alignment), c) be motivated to perform the actions (Motivation), and d) have all the tools and support that are required for successful performance of those actions (Support). If any of the four essential conditions (Capability, Alignment, Motivation or Support) is missing, it’s difficult, if not impossible, for the employee or team to perform reliably.

Capability: In its most basic sense, an individual who is capable has the knowledge, skills, ability, and other characteristics necessary to perform the job. Capabilities are what people bring to the company — personal qualities such as technical knowledge, learning agility, social skills / emotional quotient (EQ), and grit, for example.

Alignment: Employees who are aligned know what they’re expected to accomplish, under what conditions, and how they’re performing in relation to those expectations. The company can increase alignment by way of goal setting, performance appraisal, and regular executive, manager, and employee communication.

Motivation: Motivation is the general desire or willingness of someone to do something. Motivation reflects the interaction of personal preferences with the job, working environment, company culture, leadership, managers, peers, rewards, and incentives, which result in motivation or demotivation to perform the tasks at hand.

Support: This category covers not only the particular technical tools used to perform work but also any other support that’s necessary, such as access to documentation, access to manager and teammates to help solve problems, resources designed to produce skills and knowledge in the individual, technical support, and camaraderie.

attrition

Attrition represents a set of metrics and analyses intended to measure the degree of control the company has over the quality of the talent it is able to retain versus the quality of talent it allows or encourages to exit. In other words, it poses the question "How are you doing keeping your highest performers, while letting others go on to the next stop in their career?"

Key Measures: Exits, Exit Rate, Voluntary Exit Rate, Involuntary Exit Rate, Regretted Exit Rate, Retention Rate, Commitment Index.


Five-jobs Systems framework

Most technology companies design and market their products around features. Muddling through all the technology options and how they fit together can be like sifting through a cold alphabet soup looking for the one little cube of chicken. If you want to simplify all of this, for yourself and for others, it is better to drop discussion of the features and simply ask the question, what is the most important job I need completed and if I hire this product, how does it do that job better than the next one? This important decision should be treated no differently than a job interview, however the tool is the candidate. Like people, you are looking for job performance related characteristics. Also, just like in people, in technology you should also be looking for team players.

While the specific technology choices will vary from one company to the next, there is a commonality in terms of the jobs that can be done by systems to support a complete people analytics workflow. In my experience the five key jobs are: 1) operating, representing the places where the primary people data relating to company operations are gathered and initially processed; 2) collecting, representing the functions where structured data is captured for purposes of analysis; 3) managing, representing the functions where the data are controlled, moved around, processed and warehoused; 4) analyzing; representing those functions where information and knowledge are teased from data; and 5) delivering; representing those functions where the information and knowledge go out to other people for use.

In our experience, any given application can typically only do one really well at a time, maybe two but certainly not all five. Therefore, state of the art people analytics working environment will consist of multiple applications selected to perform a job (or two) and integrated with other systems selected to perform other jobs and outputting into an analytical data store. I always suggest integrating modern cloud software-as-a-service systems combining those systems built in APIs and a little JavaScript on a node.js framework. More details below.


Below is high-level overview of the five jobs of technology systems in support of people operations and people analytics. We implement, integrate and automate these systems.

people operations systems

Systems that facilitate the processing of transactions for purposes of day-to-day administration.

data collection systems

Systems that facilitate the process of structured data collection for purposes of analysis.

data management systems

Systems that facilitate the process of controlling, moving, changing and storing data.

data analysis systems

Systems that facilitate process of discovering insight and validating or invalidating hypothesis using data and statistics.

data delivery systems

Systems that facilitate the process of cognitive absorption and use of data by placing it in a visual context in reach of users.


HR Dashboards: Role-centered and lean:

Commonly, a data dashboard is understood to be the output of a system that displays together two or more metrics visually so that the user can monitor some measurable features of a business, department, or process.

A popular data visualization expert, Stephen Few, defines a dashboard more specifically as “a visual display of the most important information needed to achieve one or more objectives; consolidated and arranged on a single screen so the information can be monitored at a glance.”

What I mean by ROLE-centered: A dashboard can be many different things and different people may have different stylistic preference, but it is important it is not just a collection of data with an unclear purpose and design. Instead, I believe dashboards should be designed for the needs of its user, which are varied. Therefore, I design dashboards to organize information by ROLE, not by data category or system source - which are technically arbitrary distinctions that result in difficulty for users to find meaning. These arbitrary divisions also, in fact, leave out important data that could be used to provide meaning with statistics.

What I mean by lean: Multiple data sources and data categories is a trivial little problem for technologists to resolve. People not using dashboards because they don’t convey valuable insights to them in their day-to-day job is a much bigger and more difficult problem to solve - but if you don’t have that problem solved it means every IT step on the journey to produce the dashboard was totally wasted. This is why we work on the meaning problem first, then work backwards to the data and systems problems.