cultural readiness | STRIMgroup

In a 3-episode series I wrote last year about the foundations of Workforce Analytics (german blogpost: Evidenz-basierte Entscheidungsfindung: daten- und systemseitige Grundlagen). In our conferences we have also dealt specifically on these Workforce Analytics foundations

They are decisive for the success of a Workforce Analytics initiative. Soumyasanto Sen has now written an interesting article that I would like to make available to you. He is a Blogger, Speaker and Evangelist in HRTech.

There is a lot of talk about Workforce Analytics these days. However, companies often still struggle to start with analytics. This article is about having the right foundation for analytics.

Before we begin, it is important to understand the different maturity levels of Analytics. These levels do not only provide the opportunity to create a roadmap for the future but also help to understand the strengths, weaknesses, and possibilities for growth in analytics maturity.

The descriptive approach uses operational reporting based on business needs. This approach focuses on data exploration, data accuracy, and metrics analysis. Advanced reporting is also used for benchmarking, manual decision making and to generate dashboards.

There are already some impressive examples of Predictive Analytics in many organizations today. These approaches use statistical analysis, forecasting, correlations, and predictive models to take smarter decisions. To do this, one needs to explore talent data for predictive models and statistical approaches. In addition, one needs ask the right business questions. If one doesn’t start with a business question the results of an analysis will not be actionable and will not add value to the organizations.

The major purpose of analytics is to make business decisions which are supported on the data. Prescriptive approaches takes this to the next level. They optimize this process by providing strategic foresight, and real-time analysis. Prescriptive analytics not only anticipates what will happen and when it will happen but also tell why it will happen.

The cognitive approach helps in decision automation by applying cognitive computing. Using machine learning, natural language processing, and intelligence, cognitive computing attempts to reproduce the behavior of the human brain by combining artificial intelligence and machine-learning algorithms. The cognitive, perspective and predictive approaches slightly overlap.

One of the most important parts of this maturity curve is the foundation. The foundation is the part where most of the time during any analytics project is spend. It forms the basic building blocks for matured and advanced approaches.

For this reason, it is important to understand the foundation before you get started with Workforce Analytics. How should this foundation look like?

Foundation for Workforce Analytics

There are certain aspects and factors which need to be explored before one should start with an analytics projects. Organizations should understand these aspects to get started in a proper way.

Data Preparation: The process of collecting, cleaning, validating and consolidating data into a single repository. This is the most important factor to get started with Analytics. It is also necessary to collect the right and relevant data sources to help the workforce and the business. Having the right data at right time make things easier for the business and for the organization. Another important aspect could be to gather non-HR data. Examples are net profits, cost effectiveness, sales revenue, and other important metrics from the organization. This adds more relevance in the data preparation phase.
Cultural Readiness: Organizations need to specify the need to adopt to analytical disruptions and this must be able to fit into the company culture. Leaders, managers, and key influencers should share a data-driven vision. They should also ensure readiness to drive the initiative throughout the organization. Without this readiness, the business will not understand the real value of analytics and their insights. As a consequence, analytics will not significantly impact the business.
Platform Adoption: Most of the HRIS solutions come up with their own analytics options. However, these are confined to their own functional perspective (and data) only. When these are not relevant for the business insights and decision-making, there is no value in investing in them. There is always the option to build one’s own analytics solution or work with an analytics partner.
Business Insights: It is necessary to know the business challenges and metrics which are critical for the organization. Based on the critical issues, proper data sources need to be defined. Identifying the critical business question from the various business stakeholders is necessary. It is also important to clarify the need of Analytics to gain a better competitive advantage.
Data Integration: Integration is always an important factor for any change. Proper data integration is necessary among all different systems, businesses, and technologies. In addition, data security, privacy, and protection are also becoming critical challenges for organizations. Any analytics project must be compatible with laws, rules, policies, and localizations. IT, HR and Business should therefore work closely together.
Governance: Data quality is the biggest challenge for most organizations, especially when working with people data. Data is the most important aspects of the foundation. It is important to prepare the data to gain valuable business insights before it can be trusted and properly managed. Data governance plays a vital role in all these aspects. Governance is also needed in terms of management, support, and sponsorship of the analytics project.

By gathering, analyzing and exploring all relevant data one can not only answer the critical business questions but also take the necessary actions needed to interpret the data and contexts.

While analyzing data one should keep an eye on the bigger picture. It is important to focus on making the best decisions for the workforce and the business as a whole. In most cases, an Workforce Analytics leader is needed. This is the person who leads the analytics projects, who is involved in all decision-making processes and who focusses on quantifying the impact of talent investments of the business. This person should use Workforce Analytics to improve some of the core processes within the organization.

As a final note: one should know the aspects which are necessary for the foundation for analytics. These may vary between organizations, as leaders, stakeholders, and human resources units are different. In other words: there is some need for brainstorming before preparing for any foundation.

Once an organization is ready with its the foundation for Analytics, the journey has just begun. There are tremendous opportunities for exploration for any organization.