20 July 2022

Common Data Pitfalls (and how to avoid them)

Data scientist

Making the right decisions for your business is important for your continued success. As much as you might like to rely on gut instinct or intuition, the reality is that you need cold hard facts to inform your choices and, whether that’s launching a new product or service, opening a new location or something else entirely, limit the risks involved in the steps you take next.

We live in a data-driven world and it’s impossible for businesses to make well-informed decisions without accurate data. Yet, despite companies gathering more data than ever before, reports suggest that between 60% and 73% of all data within an enterprise goes unused for analytics, and it’s as high as 86% when it comes to people or HR data.

There are many reasons why this can happen, ranging from issues with data extraction and organisation, to where it’s stored, how it’s managed and how the people in your business use it to make decisions about both the short-term operations and longer-term future of your business.

While it’s impossible to talk about all the potential pitfalls of data for businesses, we can share the most common, including how to circumnavigate them and ensure that you get the most from the information you have at your fingertips.

Around 95% of companies say that their inability to understand and manage unstructured data is holding them back.

Data extraction?

Data extraction is one of the first challenges faced by businesses who collect reams of information during their day-to-day operations but aren’t sure how to organise it to get the greatest value. It’s also a task that can be expensive and time-consuming. In fact, in a survey by CrowdFlower, data scientists estimate that preparing the data for analysis accounts for 80% of the work on an analytics project.

Even if your data is already structured, such as that produced from point of sale technology or from loyalty programme information, you’ll still need to ensure that it’s organised so that any associated data sets can easily be compared and contrasted – something that’s known as being ‘transcodeable’. If data doesn’t share a basic common structure, it can’t be analysed accurately, affecting its quality and its value to your business.

If you don’t have the in-house expertise to extract and structure your data, outsourcing to a specialist may be the best option. They can work with you to establish your goals, discover what data you need to access, implement solutions for extracting that data and then through processes such as ETL or ELT, ensure your data is ready to be analysed and used as an asset.  

Data governance

Data has the potential to be a huge asset to your business, but in order for it to deliver value it needs to be properly managed. To do this, organisations should implement a comprehensive data governance framework. However, it’s an area that many organisations neglect or underestimate, creating one of the most common data pitfalls.

Data governance is all about effectively managing your data and your data processes so that the information you obtain can be relied upon as a valuable asset that meets policies and standards and keeps your business compliant. In short, data governance helps to ensure that the data your business collects is usable, accessible and protected.

There are several critical components to consider when establishing an effective data governance framework, we take a look at some of these below:


Any successful data governance strategy relies on accountability. The best way to do this is to establish a data governance council with representatives from all departments, who are responsible for developing data processes and policies that will be executed and followed across the organisation.


All data governance should be as transparent as possible from the outset. Transparency allows anyone to see exactly how sensitive data gets handled by your business, what you use it for and why you use it in the way that you do.

Quality standards

For data to be useful and accurately inform decision making, it needs to be reliable and of high quality. Governance should create a shared set of data quality standards that all data should adhere to, ensuring that your business knows that it is making decisions based on trustworthy information.


Data security is paramount for any business, but effective governance should ensure that safeguards are in place to provide sufficient data protection. This includes classifying data according to sensitivity and storage policies.

Failure to have robust data security in place can make you vulnerable to breaches, putting your information and that of your suppliers,  customers and partners at risk.  With this comes both liability – since data owners are fully liable for data security, and the potential to incur huge fines. In the UK, the maximum fine for infringement of GDPR is £17.5 million or 4% of your annual global turnover whichever is greater.

Regulatory compliance

One of the key drivers behind a data governance framework is to address your compliance requirements and ensure that your organisation is operating within them. In doing so, you can ensure that any data is managed, organised and used according to the specific rules and regulations you may need to follow.

Siloed data

Another way to ensure you get the most out of your data is by avoiding siloed data. These occur in many organisations when a collection of stored data is held by one group or department but isn’t shared or accessible to others. Data silos tend to form quite naturally as organisations grow, often due to issues with technology that prevents information passing easily between departments, or businesses growing too large for data to pass efficiently.

The main problems with data silos are that they:

  • Give an incomplete view of the business
  • Create a less collaborative environment and make it harder for workers to share a common vision
  • Can affect the quality of the customer journey with your business
  • Slow the operational capabilities of your organisation
  • Waste resources on unnecessary storage
  • Threaten the overall accuracy of your data
  • Waste time and resource by duplicating output that’s already available elsewhere


While overcoming siloed data may involve a slower cultural shift, system integration can be a great first step in accelerating change. By enabling data to be stored in one key application that is accessible to everyone, but where varying levels of access can be implemented where required. By collating and storing data in one place, it ensures that any updates are system-wide, and all of your employees are on the same page at all times.

People power

Although most businesses have employees dedicated to general IT requirements, effective data collection, analysis and governance require a special level of skill that you may not automatically have in-house. As we know, human error is a key issue for data quality and security.

While errors are nearly always unintentional, investing in training and development can help to minimize the risk of personnel causing damage to your data that could have short or even long-term implications for your business. Specialised training  ensures that your teams have the knowledge and skills needed to successfully manage your data and data reporting.

 Data accuracy

Poor data quality is another common pitfall that business owners face. Unfortunately, data inaccuracies are common, and can make it hard to get the true value out of the data you’ve collected. Research from Experian Data Quality shows inaccurate data impacts the bottom line of 88% of businesses, with the average company losing 12% of its revenue as a direct result.  

The first step to tackling inaccurate data is to identify the ways in which poor quality data could arise. Some of the most common include:

  • Poor data entry, especially when information is input manually by staff.
  • Errors when transferring manual data onto a digitized system.
  • A lack of standardised practice for the collection, formatting or accessing of your data, for example, dates being input in multiple formats.
  • Insufficient or incomplete data.
  • Duplicate data, for example, having multiple tracking codes on the same page of a spreadsheet or database.
  • Outdated and inefficient systems.

So how do you ensure good quality data? Automating tasks to remove human intervention and the risk of errors is a great start. When supported by solid data governance, robust infrastructure that is regularly updated, and training – you can ensure you have a continual flow of reliable and accurate data to steer strategic business decisions.


With so many touchpoints, today’s organisations are constantly generating fresh data about their customers and business operations. And as the masses build up, it’s easy to lose your way or even know where to start. Defining/establishing your goals and ensuring you have the tools, processes and procedures in place to effectively manage your data will ensure you can avoid the common mistakes and maximise your data’s potential as a valuable business asset.  

At MHR, we have a large team of experts with decades of experience in BI and data analytics who can offer a bespoke, full data management and analytics consultancy service and support you achieving your goals. This enables our customers to reap the benefits of becoming truly data-driven businesses. Ready to start getting more from your data? Download our data and analytics consultancy brochure or give our data and analytics consultancy team a call today.

Ross Bruce, Head of Analytics Professional Services

Ross Bruce

Ross' career started in ERP implementation then led into data & analytics utilising SAP & Microsoft technologies. Ross has over 15 years' experience working with major organisations, optimising data and analytics to provide transformative business insights.

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