Build vs. Buy: The Right Path to Data Quality

Examining the pros and cons of building and buying a data quality solution.

Build vs. Buy: The Right Path to Data Quality

Build vs. Buy: The Right Path to Data Quality 560 420 APERIO

In recent years, there has been a massive increase in the amount of industrial data generated globally. According to a report by IDC, the global volume of global data is expected to reach 175 zettabytes by 2025, with most of this data coming from industrial sources. This explosion in data can be attributed to several factors, including advances in sensor technology, and the digitization of industrial processes.

From working with some of the world’s leading industrial companies to resolve their data quality issues, we are seeing that this unprecedented growth in industrial data presents both challenges and opportunities, as companies look for ways to adapt and effectively manage and analyze this data in order to gain insights and remain competitive in their respective sectors.

One of the key decisions that companies have to make is whether to build custom data quality application into their data stacks to or to purchase off-the shelf solution. This decision can have a significant impact on the success of a company’s data strategy. In this article, we will examine the pros and cons of each path.

What is a data stack?

A data stack refers to the combination of technologies and tools used to collect, store, manage, and analyze data. It typically includes a data source, data warehousing, data processing, data visualization, and data governance. A well-designed data stack enables companies to process and analyze large amounts of data in real-time and make informed decisions.

Building a custom data quality solution

Building any custom data quality solution from scratch offers some benefits, including a high level of control over the design and architecture, the ability to tailor the solution to meet specific business needs, and the ability to achieve total integration with other systems and tools. This level of customization can provide some advantages, as it allows companies to process and analyze data in a way that is optimized for their specific business.

However, over time this high level of customisation may actually become a liability.  Building an effective application for a large-scale industrial data-stack takes months, if not years. In that time, the operational environment can undergo significant changes where new assets, data sources or configurations are introduced and the original scope of the application becomes redundant.

There is also the cost to consider: Building a custom solution is a time-consuming and resource-intensive process by its nature. It requires hiring expensive, long-term resources such as data scientists, engineers, devops specialists and others, as well as investing in hardware and software infrastructure. Additionally, ongoing bug-fixes, performance optimisations, security patching and other forms of maintenance in a custom-built solution also inevitably become a drain on resources.

Below is an illustration of a typical development roadmap for an in-house data-quality solution and its associated costs (based on our experience, and communications with our customers, prospects and partners).

Typical data quality solution roadmap

Being aware of all of these factors, APERIO has sought to replicate the benefits of a custom solution by carefully considering how to stay ‘data-stack agnostic’ and preserving the effectiveness of our data quality engines in the vast majority of scenarios and configurations. We have also developed an accelerated R&D programme that can quickly pivot and cater to new customer requirements, especially if we see that a particular feature can be a benefit to that particular sector as a whole.

Buying an off-the-shelf data quality solution

Buying a ready-made addition to your stack offers several benefits, including lower upfront costs, faster time to market, and reduced maintenance and support requirements. Additionally, if the right choice is made, an off-the-shelf solution can come with a wide range of pre-built integrations that reduce the need for developing custom integrations and the risk of incompatibility with other components in the data stack. For instance, APERIO can connect with most historians, data lakes and other industrial data sources, and APERIO’s onboarding and deployment process is designed to be seamless and fast. Companies can start processing and analyzing data almost immediately, without the time and resources required to build a custom solution.

When considering all of these factors, the total cost of ownership of a purchased solution may be significantly lower. However, if the solution lacks a high level of integration with your current systems or the deployment process is cumbersome this could become a false economy in the long run.

Choosing between the two paths is not a ‘zero sum game’.

The decision to build or buy a data quality solution is an important one that requires careful consideration. However, companies don’t necessarily have to choose one approach over the other. They can take a hybrid approach, where they build certain components of their data stack and buy others. This allows companies to take advantage of the benefits of both approaches and create a data stack that is tailored to their specific needs. Ultimately, the key is to prioritize the factors that are most important for your business and make a decision that aligns with your long-term data strategy.

To to learn more about APERIO’s category-leading data quality solution, book a demo with one of our team or download our data quality E-book:

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