Data engineering plays a crucial role in enabling the collection, management, and utilization of data for workforce management (WFM) platforms powered by artificial intelligence (AI). In this article, we will explore the importance of data engineering in AI-Powered Workforce Management Software, highlighting its role in automating the data management processes necessary. By understanding the basics of data engineering, non-technical individuals can gain insights into why it is essential for optimising WFM solutions and harnessing the full potential of AI in this context.
What is Data Engineering vs Data Science?
Before delving into the importance of data engineering in WFM and AI, it’s essential to differentiate between data engineering and data science. While data science involves using data to create AI or machine learning models, data engineering focuses on building systems that facilitate data collection, storage, and preparation for subsequent analysis and data science tasks.
In the context of WFM and AI, data engineering ensures that the necessary data required to train AI models is readily available in the right format through an automated process. On the other hand, data science leverages this prepared data to develop AI models that can be used for various purposes in workforce management. Data engineering then extracts the output generated by these AI models and loads it where it is needed, forming a vital link in the AI-powered WFM process.
What is Platform-Wide AI vs Bespoke Customer AI?
When considering the importance of data engineering in AI-Powered Workforce Management Software, it’s crucial to differentiate between platform-wide AI solutions and bespoke customer AI solutions. Platform-wide AI refers to AI models that are trained using data collected across an entire software platform. In contrast, bespoke customer AI solutions utilize data specific to individual customers for training AI models customized to their specific needs.
To illustrate this concept, let’s consider the example of platform-wide AI found in voice assistant technologies like Alexa or Siri. These voice assistants are trained on millions of voices, accessing a single dataset that represents the collective voice data of all customers. In other words, they are not specifically trained on an individual’s voice or designed to recognize unique accents and vocabulary. To achieve a bespoke AI solution, the voice data from an individual would need to be stored separately and used to train an AI model that recognizes their voice. This would require managing millions of data sets and individual AI models.
Similarly, when we explore this in the context of WFM, a bespoke AI solution can be created for each department or venue within a larger organization with hundreds or thousands of employees. This allows for the development of AI models that focus on specific data sets related to a particular department or venue. For instance, in the retail industry, every store has unique characteristics, making it ideal to have a dedicated AI model per store to forecast sales and demand accurately.
The Significance of Data Engineering in AI-Powered WFM
Now that we understand the distinction between data engineering and data science and the difference between platform-wide AI and bespoke customer AI let’s explore why data engineering in AI-Powered Workforce Management software is essential.
To meet the specific requirements of organizations and maximize return on investment, WFM platforms often require bespoke AI models for forecasting and auto-scheduling. This level of customization can only be achieved through data engineering and the implementation of a data management pipeline.
A comprehensive workforce management system typically incorporates inputs from multiple AI models, including:
Forecasting AI
Forecasting AI focuses on creating AI models per store or department that generate accurate demand forecasts based on factors such as sales or footfall. In some cases, multiple forecasts may be built per store to ensure a precise demand forecast. These forecasts are usually generated on a per-store, per-hour basis for an entire week, providing the foundation for the most accurate employee scheduling and assisting in building the optimal rotas.
Demand AI
The demand AI leverages the forecasts generated by the forecasting AI models to determine the required staffing levels for every role in each store or department on an hourly basis. For businesses with multiple roles, such as different positions in a restaurant, a bespoke AI model is needed for each role, tailored to the specific demands of the venue. Factors such as kitchen layout and service delivery distance can significantly impact the demand requirements, even within the same chain.
Auto-scheduling AI
The auto-scheduling AI is the most complex employee scheduling software that builds optimal employee schedules (rota) based on various criteria. This AI model can often work across multiple departments within a company, as organizations typically have standardized rules that apply across the entire organisation. The scheduling AI takes into account factors such as employee availability, skills, preferences, labour laws, and business requirements to create efficient and effective schedules.
To achieve seamless integration and optimal results, the data generated by the forecasting AI needs to be passed on to the demand AI, which, in turn, provides inputs for the auto-scheduling AI. This data flow requires advanced data engineering techniques to manage and automate the movement of data along the pipeline.
What is the Role of Data Engineering in WFM Software Tasks?
Understanding the tasks performed by data engineering in the context of WFM software can provide further insights into its importance. Let’s take a closer look at the example of Forecasting AI to explore the data engineering process involved.
The first task in the data engineering process is to build a dataset suitable for training the forecasting AI model. This involves gathering historical transaction data spanning a couple of years leading up to the time of the forecast. Additionally, third-party data such as weather conditions and sporting events may be incorporated into the dataset. The data is then cleaned, formatted, and separated on a per-venue basis.
The next step is to perform “labelling,” which involves running calculations and applying specific criteria to facilitate supervised machine learning. Labels are generated to help the AI model identify patterns within the data. For example, in a retail setting, labels might include the percentage of pre-prepared items versus live-prepared items. Depending on the complexity of the dataset, hundreds of labels may be required.
Once the datasets are suitably labelled, they are split into training and test data sets. Multiple models are then trained using the training data, and an algorithm is employed to evaluate and select the best-performing model based on its performance against the test data.
The chosen model is then applied to real-time data to generate accurate forecasts. In the case of WFM, multiple forecasts per store are typically produced and shared with the relevant demand AI models. These forecasts may also be utilised by business intelligence tools to provide valuable insights into workforce management and operational planning.
Throughout the entire process, data engineering techniques are employed to ensure the smooth running and automation of the tasks involved.
Conclusion
In conclusion, data engineering plays a critical role in enabling the effective use of AI in workforce management solutions. By automating data management processes, data engineering ensures that the necessary data is readily available in the right format for training AI models. This allows for the creation of bespoke AI solutions tailored to the specific needs of organizations, leading to optimised labour demand forecasting, demand planning, and auto-scheduling.
The importance of data engineering in WFM and AI becomes evident when considering the complex data flows required to synchronise multiple AI models. By implementing advanced data engineering techniques, organisations can ensure the seamless integration of these AI models, facilitating accurate demand forecasting and efficient employee scheduling.
Overall, data engineering is a vital component in harnessing the power of AI for workforce management, enabling organisations to maximise productivity, optimise labour utilisation, and improve overall operational efficiency.
