Improve Output with Automated Data Transformations

Automated data transformations empower manufacturers to gain critical insights into their data that improve production quality and output.

By Armon Petrossian, CEO and co-founder of Coalesce

Robust, cutting-edge analytics are a must-have for manufacturing companies. When the goal is to yield as much output as possible—in the most efficient manner possible—it’s critical to have the ability to derive insights from data in a way that’s scalable.

Manufacturing organizations arguably generate the highest volume and the most complex data of any industry. According to one study, the average factory generates 1TB of production data each day. Whether it’s a semiconductor or a car part being manufactured, there are countless avenues where data about the product is being collected: on the shop floor, the assembly line, via IoT sensors or other manufacturing equipment, ERP and MES systems, and more.

Over time, the volume, velocity, and variety of data being created only compounds. So, for companies that have been around for decades, the question becomes: How do we take ‘X’ years worth of data and quickly prepare it in a way where we can derive insights from it in a scalable fashion?

It’s no simple task: Today’s manufacturers need the ability to do data transformations at a scale that would typically break the foundation of tools from the past. Up until now, tools that performed data transformations were too far on either side of the continuum; GUI-based and lacking flexibility, or code-first and lacking efficiency.

Enter a column-aware architecture for data transformations. In simple terms, this refers to the ability to leverage column-level metadata for easily applying difficult transformations within a data set. For example, when building a type two dimension, one can easily identify and track changes from specific columns such as address, name, phone number or any other column in the table. This is especially pertinent to the manufacturing industry because column-level lineage tends to be a significant problem for organizations trying to be data-driven, and is compounded by how large the scale of the project is.

Column-aware architectures provide the best of both worlds: Manufacturing companies benefit from the efficiency of a tool that’s GUI-based, but they also have the flexibility of a code-based tool to tackle edge cases and other nuances within the data warehouse. This is essential because—as is often the case with complex data sets like those found in manufacturing—there are many edge cases that can’t be shipped out of the box for any product. Manufacturers need the flexibility to customize at scale, but shouldn’t be burdened with writing everything by code.

Perhaps most importantly, these transformations can all be supported at scale with added visibility into things like column-level lineage. A column-aware architecture gives manufacturers the insight required to understand how their large data sets interact with each other at a granular level.

What do the benefits look like?

With a column-aware architecture, manufacturers have the power to make timely decisions based on their data to create a more streamlined process, ultimately resulting in a better product. By adopting this type of architecture for data transformations, manufacturing companies can boost production and quality, cut costs, and detect and remediate issues promptly.

Take semiconductor production, for example. If a company is manufacturing sheets of semiconductors that are only a few nanometers in size, a seemingly small physical loss in product can actually result in a significant financial loss. So, if one of the company’s sensors becomes too hot and damages 10% of a sheet of semiconductors, this might translate into millions of dollars lost. Automated data transformations can help prepare data to then subsequently sound the alarm on issues like this before they happen, identify what caused them, and clue companies in to any notable patterns in the data. Having these insights readily available can have a profound impact on a manufacturer’s top line.

As technology continues to advance, manufacturing companies will be generating more data than ever before. It will be vital for manufacturers to ensure they’re deriving the insights they need so that their factories run smoothly. Data transformation tools with a column-aware architecture will be an essential part of making this happen at the massive scale manufacturing demands.

Armon Petrossian

As Co-Founder and CEO, Armon created Coalesce, the only data transformation tool built for scale. Prior, Armon was part of the founding team at WhereScape, a leading provider of data automation software. At WhereScape, Armon served as national sales manager for almost a decade.


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