How to mitigate and avoid common data discovery challenges

How to mitigate and avoid common data discovery challenges

Accessing your business data is the first step towards making more informed decisions that drive growth. However, a number of common data discovery challenges can affect the quality and quantity of information you gather, restricting the accuracy and reliability of the insights you can generate. Thankfully, with the right approach to data governance, these challenges can be overcome.

What is data discovery?

Data discovery refers to the collection of data from various sources and is the precursor to data preparation and analysis. It allows you to rapidly assess your current data landscape and its potential to support insight generation that informs critical business decisions. A key component of the process is identifying the systems where data is kept and who is responsible for them.

“It’s about ensuring you have the best possible source for the information that you’re looking for,” explains Steve Bedford, one of Optivia’s principal consultants.

“That means consulting with the custodians of the data – the subject matter experts – to ensure you have the correct information, correct platforms and correct version of the data to answer your question.”

Data discovery affects everyone in an organisation who uses data to make decisions. Every member can also play a role in ensuring the integrity of data within the organisation so that it can be relied on to provide accurate insights.

We’ve outlined the most common data discovery challenges that businesses face and how to solve them below.

Common data discovery challenges and how to solve them

1. Ownership

Each dataset or data source should be owned by someone within the business. Unfortunately, it’s often challenging to determine who this is. Without data ownership, it can be difficult to ascertain whether you have the correct or most robust data or data source for a particular purpose.

“The way we usually solve this challenge is to cast a wider net. So we ask other teams and build relationships with other areas of the business to try to discover who owns the system and can give us access to it,” explains Steve.

2. Access and location of data

Accessing data, whether it be via a direct connection to the live source or a warehouse, can also be problematic if no one knows where it is or how to access it. This can lead to significant roadblocks in projects if not overcome.

“Again, solving this is about finding contacts within the company’s internal data and analytics team to learn how to gain access to the data and what the best source is,” says Steve.

“In instances where it’s completely inaccessible though, we raise it as a risk and ask the team we’re working with to escalate the problem within their organisation. In most cases, we do get some response back and there’s generally an avenue to find what you’re looking for – it just sometimes takes a bit of pushing.”

3. Understanding the data

Finding people who can actually understand the data is the most difficult data discovery challenge. If no one has a deep understanding of the information already, it requires someone to spend significant time reverse engineering or pulling the data apart to understand what each field means and how it’s used in the information system.

Thankfully, with the increase in data literacy and data maturity within organisations, this challenge is becoming less common.

“These days, there are generally subject matter experts or at least people with an okay understanding of the information who can help. But there’s still that rare instance where it’s a matter of digging through ourselves as part of the discovery process,” says Steve.

What causes data discovery challenges?

One of the biggest barriers to data discovery is data immaturity, according to Steve.

“If you’ve got numerous information systems with very little structure or control over who uses them and how the data is entered or managed, then this will lead to messy data and big data quality problems,” he says.

In organisations with low data maturity, there is typically no data governance strategy in place to set the standard of how to enter, process and control information across a business.

“A data governance strategy is a key indicator of some degree of data maturity. If a business doesn’t have any sort of framework like this to manage their information, then we know we’re probably in for a challenge.”

How to minimise data discovery challenges

Short answer – implement a data governance strategy. A properly formed strategy should include not just technology but solid, well-communicated processes that will ensure the reliability of your business data.

“Implementing data quality policies and processes will set you on the right path to controlling your data. That way, when you have external resources come in, they can hit the ground running,” says Steve.

Data tools and practices should take into account the full lifecycle of your data and be reevaluated based on the volume and accuracy of your information over time.

Investing in data literacy training and education for teams will also improve the quality and value of data in your businesses, as well as benefit your risk and compliance operations by increasing the control of data through the proper application of your data governance strategy.

Want to know how to level up your organisation’s decision-making capabilities by tapping into the power of high-quality data? Download our free eBook to find out how your organisation can improve data quality and data governance to gain more value from their business intelligence.