Headshot of Prateek Joshi, CEO and Founder of Plutoshift AI Industrial Technology
Prateek Joshi
CEO and Founder

Within large companies, data is stored across many systems. If we specifically look at companies with large operations infrastructure, there are many different types of data they have to work with -- sensors, inventory, maintenance, financials, and more. In order to perform the operational tasks, this data has to be centralized and piped into different workflows. The data needs to be ready for that! This allows the operations teams to use that information and ensure that the business is running smoothly. How do you assess the data readiness? How do you make sure that the frontline teams are well equipped to perform their tasks? From what we've seen, here are 5 items you'd need from data on a daily basis:

1. Ability to access the data
As fundamental as it seems, accessibility has always been a big issue. The data that's stored across many systems is difficult to access. It's inside arcane systems that are not friendly to use. In today's world, anyone in the organization should be able pull the data via a simple API.

2. Centralizing the data
Once we pull the data from different systems, what do we do with it? Operations teams need the data to be centralized so that they can perform their tasks. These tasks usually require data from multiple sources. Centralizing the data and keeping it ready for use is a useful step here.

3. Preprocessing the data in an automated way
To make the data useful after it's centralized, it has to be preprocessed to make sure it's ready for different types of transformations. Operations teams need to prepare the data and pipe it into various data workflows. This is usually done manually using Excel spreadsheets. Automating this step will be very helpful so that operations teams can focus on high-impact items.

4. Piping the preprocessed data into different workflows
To put the preprocessed data to use, we need workflows that can transform raw data into useful information. A data workflow usually consists of 5-7 steps of transformation depending on what we're aiming to achieve. These steps can be done manually using Excel, but it's not a good use of anyone's time because they are repetitive computations. Having a set of prebuilt workflows and automating the work of pushing the data through these workflows is a big time-saver. In addition to that, it will lead to a significant increase in accuracy of their work. Machine Learning is very impactful on this front.

5. System of record for the centralized processed data
Operations teams have to frequently access historical information for many reasons. Having a system of record that can store the centralized processed data is very useful. Operations teams need to reference them for various tasks such as internal reporting, knowledge-based tasks, learning, best practices, and more.

Data readiness is critical and lays the foundation for success.

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