{"id":244007,"date":"2023-03-30T16:24:50","date_gmt":"2023-03-30T16:24:50","guid":{"rendered":"https:\/\/solvedby.ai\/?post_type=blog&p=244007"},"modified":"2023-03-30T16:25:24","modified_gmt":"2023-03-30T16:25:24","slug":"why-is-data-engineering-important-for-wfm-and-ai","status":"publish","type":"blog","link":"https:\/\/solvedby.ai\/blog\/ai-consulting\/why-is-data-engineering-important-for-wfm-and-ai\/","title":{"rendered":"Why is data engineering important for WFM and AI?"},"content":{"rendered":"\n

Suppose you are considering an enterprise-level Workforce Management platform with AI-powered demand forecasting and auto-scheduling. In that case, it is worth having a basic understanding of the role of data engineering in your proposed solution. Why? Because a big part of what your software vendor provides relies on data engineering to automate the data management required for fully AI-powered workforce management software<\/a>. In this blog post, we outline the basics for a non-technical person to understand better why is data engineering important for WFM and AI and what it does.<\/p>\n\n\n\n

What is data engineering<\/a>?<\/h2>\n\n\n\n

Data engineering refers to the building of systems to enable the collection and usage of data. This data usually allows subsequent analysis and data science, which often involves machine learning.<\/p>\n\n\n\n

What is the difference between data engineering and data science?<\/h2>\n\n\n\n

Data engineering is used to make sure all of the data needed to train an AI is in the right place and format through a fully automated process. Whilst data science takes that data and uses it to create an AI or machine learning model. Data engineering then extracts the output and loads it where it is needed. Data engineering becomes more important if you want bespoke AI solutions for your organisation.<\/p>\n\n\n\n

\"Why<\/figure>\n\n\n\n

What is a platform-wide AI compared to a bespoke customer AI?<\/h2>\n\n\n\n

Platform-wide AI solutions train an Ai model using all the data across a given software platform. In comparison, a bespoke solution will use data specific to the customer. <\/p>\n\n\n\n

To help us understand the concept, let us consider an example of a platform-wide AI. Alexa or Siri are both platform-wide AIs trained on millions of voices; they access one data set of every customer’s voice and learn from those voices. They are not trained on your voice or to recognise your accent and vocabulary. To be bespoke, they would have to store the voice data from you separately and train an AI to recognise your voice. Alexa and Siri would need to manage millions of data sets and millions of AIs.\u00a0<\/p>\n\n\n\n

Now let’s consider a bespoke AI in a workforce management solution. Because the customer is a larger company with 100’s or 1,000’s of employees, it is possible to keep all of that company’s data separate and build an AI that only looks at that data. That is a bespoke AI. It could even use all the data from a single department or venue to build a model that forecasts the sales and demand curve for just that department or venue. As all retailers will know, every store is different so the ideal requirement would be a dedicated AI per store.<\/p>\n\n\n\n

So why is data engineering so important for a workforce management system that uses AI?<\/h2>\n\n\n\n

To really meet an organisation’s requirements and deliver the best return on investment possible, workforce management requires a bespoke AI for each department for forecasting and a bespoke AI for the company for auto-scheduling. This can only be achieved with data engineering and a “pipeline” that manages that data.<\/p>\n\n\n\n

To deliver the maximum benefit, a workforce management system needs inputs from at least three separate AIs:<\/p>\n\n\n\n