Workforce Intelligence is a Journey! Have you ever climbed, or hiked, up a mountain? The experts all tell you – walk with your eyes on the ground in front of you, not looking up. The reason is, firstly, if your eyes are facing upwards you won’t see the dangers on the ground, and secondly, you will become disillusioned and want to give up on the journey. You look up only occasionally to make sure you are still on the right track and to address any unexpected problems that could arise on the journey.
The Workforce Intelligence Journey is no different. In this article we describe the different elements that constitute Workforce Intelligence. This is to help you to map your journey and ensure that you are on the right path. It’s a kind of “Route Map” for you to understand and decide what you want to do when on the journey.
Workforce Intelligence is the result of using a combination of tools, metrics, data and statistical analysis to gain greater insight into the organisation’s workforce, Talent Management processes, and business performance information to enable management to plan and build an organisation capable of delivering organisational strategy over the longer term.
Workforce Intelligence is a sub-set of Business Intelligence – a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making.
So the “intelligence” part of both disciplines is the end result of processes that enable improved decision-making that leads to improved organisational performance.
Both Workforce Intelligence and Business Intelligence have a number of sub-processes that are essential to ensuring that good, clean data is used for these business-critical activities. These are:
- Metrics – Any type of measurement used to gauge some quantifiable component of an organisation’s performance, such as return on investment (ROI), employee and customer churn rates, revenues, Profit per FTE, and so on. Metrics can be within a single area of business, such as Employee Turnover Rate for HR, or can come from two or more areas of business, such as Turnover per FTE that uses information from both financial and HR information databases. Systematic approaches, such as the balanced scorecard methodology, can be employed to transform an organization’s mission statement and business strategy into specific and quantifiable goals, and to monitor the organization’s performance in terms of achieving those goals. So Metrics is a collection of standards of measurement by which efficiency, performance, progress, or quality of a plan, process, or product can be assessed.
- Analytics is the discovery and communication of meaningful patterns in data. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance. Organisations commonly apply analytics to business data, to describe, predict, and improve business performance. Analytics builds quantitative processes for a business to arrive at optimal decisions and to perform business knowledge discovery. Analytics has a number of “applied” subsets including:
- Data Analytics is the science of examining raw data with the purpose of drawing conclusions about that information. Data Analytics focuses on inference, the process of deriving a conclusion based solely on what is already known by the researcher. Data analytics is used in many industries to allow companies and organization to make better business decisions and in the sciences to verify or disprove existing models or theories.
- Workforce Analyticsis a combination of software, data, and a methodology that applies statistical models to workforce-related and other relevant data, allowing organisational leaders to optimize their Return on Investment in Human Capital.
The sub-processes, some or all of which are generally included in Analytics, including Workforce Analytics, are:
- Data Mining is sorting through huge data sets using sophisticated software to identify undiscovered patterns and establish hidden relationships. Data Mining is used to produce data for Analytics.
- Process Mining is a technique that allows for the analysis of business processes is to extract knowledge from event logs recorded by an information system. Process Mining aims at understanding business processes by providing techniques and tools for discovering process, control, data, organizational, and social structures from event logs. Process Mining techniques are often used when no formal description of the process can be obtained by other approaches, or when the quality of existing documentation is questionable. Moreover, these event logs can also be used to compare event logs with some prior model to see whether the observed reality conforms to some prescriptive or descriptive model.
- Statistical Analysis is the study of the collection, organization, analysis, interpretation, and presentation of data using a variety of statistical methods such as Frequency Distributions, Regression, Multivariate Analysis, Linear Programming. It deals with all aspects of data, including the planning of data collection in terms of the design of surveys and experiments. Statistical Analysis provides ways to objectively report on how unusual an event is, based on historical data.
- Machine-Learning is the abilityof a machine, generally a computer, to improve its own performance through the use of software that employs artificial intelligence techniques to mimic the ways by which humans seem to learn, such as repetition and experience.
- Predictive analytics encompasses a variety of techniques from statistics, modelling, machine learning, and data mining that analyze current and historical facts to make predictions about future events. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions.
- Predictive modelling is the process by which a model is created or chosen to try to best predict the probability of an outcome. In many cases the model is chosen on the basis of detection theory to try to guess the probability of an outcome given a set amount of input data, for example given an email, determining how likely that it is spam.
- Business Process Modelling, often called process modelling, is the analytical representation or illustration of an organization’s business processes. Business Process Modelling is the activity of representing processes of an enterprise, so that the current process may be analyzed and improved. Business Process Modelling is widely viewed as a critical component in successful business process management (BPM) and is used to map out an organization’s current (or “as-is”) processes to create a baseline for process improvements and to design future (or “to-be”) processes with those improvements incorporated.
- Complex Event Processing is a method of tracking and analyzing streams of data about things that happen (events), and deriving a conclusion from them. Complex event processing, is event processing that combines data from multiple sources to understand events or patterns that suggest more complicated conditions. The goal of Complex Event Processing is to identify meaningful events (such as Opportunities or Threats) and respond to them as quickly as possible.
- Prescriptive Analytics automatically synthesizes big data, mathematical sciences, business rules, and machine learning to make predictions and then suggests decision options to take advantage of the predictions. Prescriptive Analytics computationally determines a set of high-value alternative actions or decisions given a complex set of objectives, requirements, and constraints, with the goal of improving business performance.
HR Metrics can help enterprise leaders to develop and improve recruiting methods, make general and specific hiring decisions, and keep the best workers with the company. There is, of course, value in this. But the true organizational value lies in completing the journey to Workforce Intelligence that can help executive management to:
- Predict the probability of an individual employee’s success.
- Identify the need for new departments and positions.
- Determine which departments or positions can be reassigned or eliminated.
- Identify and quantify physical risks to employees in specific positions.
- Identify and quantify factors that influence employee job satisfaction.
- Analyze and predict current and future technological needs.
- Optimize the enterprise’s organizational structure.
- Assign and delegate responsibility for tasks and goals.
- Help the enterprise to identify, encourage, and cultivate its future leaders.
- Quantify the value of the workforce to the value of the organisation.
- Quantify the Return on Investment for different Talent Management decisions.
- Predict the Business outcomes of Workforce strategy and decisions.
Workforce Intelligence is clearly a Journey, and one that has huge proven results for organisations that have embarked on this journey.
The Workforce Intelligence Journey may seem daunting at first, however, like eating the proverbial elephant, if you approach it one step at a time, taking into consideration the next step of the journey, you are surprised at how quickly you are progressing towards producing information that helps to drive organisational effectiveness into the future.
And, of course, like any other journey, the road needs to be mapped, and you need to understand what is needed at different stages of the journey. TalentAlign, with its partnership with the Human Capital Management Institute (HCMI), has the knowledge to help you map your journey, and the tools to help get you there more quickly and cost-effectively.
Contact us to learn more about how TalentAlign can help you on your Workforce Intelligence Journey.
See also this report from IBM on the topic.