9 Data Quality Problems and their Solutions

Businesses today depend on data. It's vital in making the right decisions about a company's future direction, ensuring that customers are offered the products or services that are most relevant to them, and developing a deeper understanding of what's going on in the business and the wider market.

Yet all of this can only be achieved if businesses utilize and rely on high-quality data. There's a line of thinking that given the huge amount of information businesses now possess, the sheer volume of information being processed by analytics tools will compensate for the occasional error.

But this isn't necessarily the case, and if your data is of poor quality, you're likely to end up making decisions based on flawed results. As the old saying goes, 'garbage in, garbage out'. Indeed, Gartner estimates that around 40% of enterprise data is either inaccurate, incomplete or unavailable, and this poor data quality costs the average business around $14 million a year.

Therefore, taking the time to fully review and clean up your data before handing it over to your analytics solutions is an essential step.

With this in mind, here are nine of the most common data quality issues you're likely to encounter, as well as what to do about them.

1. Duplicated data

Duplicated data is a common issue every business will have to deal with. This often comes about as the result of siloed processes and multiple systems that record the same information. When these sources are pooled together for processing, having multiple copies of the same records can significantly skew the results or lead to wasted effort.

This could result in customers receiving several identical marketing materials, which can annoy users and lead to wasted time and money. Or, it could be difficult to help a customer if they contact you with a query, but have multiple entries in your system with different details.

To avoid this, data deduplication tools are a must. These use algorithms to hunt through very large data sets and identify duplicate records. In the past, such solutions may have missed cases where there were minor differences, but today's solutions are now smart enough to spot even substantially different entries for the same customer.

2. Inconsistent formats

If you're inputting data that covers the same information but is stored in different formats, many systems may struggle to recognize items as belonging to the same category, and consequently throw up inaccurate results.

For example, dates are a common stumbling block for many systems, as there are many potential ways these could be entered into different systems. It may be especially tricky for tools to distinguish between US and European-style dates - if you have one data source that uses the DD/MM/YY format and another that uses MM/DD/YY, you can get incorrect results.

Other potential difficulties may arise from phone numbers, especially when some have area codes and others don't, while differences in how data is inputted, such as using 'Street' or 'St' when entering addresses, can also result in more duplication issues. Therefore, it's vital you specify exact formats for every piece of data to ensure consistency across every source your organization uses.

3. Incomplete information

Fields that aren't filled in fully, or are left blank altogether, can be a major pain for tools such as CRM software and automated marketing solutions, as well as big data algorithms. For example, entries that lack ZIP codes aren't just a pain when it comes to contacting customers directly - it can also make key analytics processes useless, as the data will lack essential geographical information that can help you spot trends and make decisions.

Ensuring records can't be created unless all essential information is included is a good start, while setting up systems to exclude incomplete entries may be another way to reduce the issues this can cause.

4. Multiple units and languages

As is the case with formatting, sometimes differences in language, script or units of measurement can create difficulties. There are many examples of disastrous errors being made because someone forgot to take into account these issues, such as the multi-million dollar NASA Mars satellite that crashed because its navigation software was programmed in imperial instead of metric units.

Similarly, dealing with data stored in multiple languages can also create difficulties if the analytics tools don't recognize it or know how to translate it. Even special characters such as umlauts and accents can wreak havoc if a system isn't configured for them. Therefore, you have to consider these potential issues if you're dealing with international data sets and program your algorithms accordingly.

5. Data overload

Having too much data can lead to data quality issues, despite the focus on data-driven analytics. With an abundance of data, it's easy to get lost when searching for relevant information for analytical projects. This often results in wasted time for business users, data analysts and data scientists who spend the majority of their time locating and preparing data.

Predictive data quality solutions can provide continuous data quality across multiple sources, without requiring data movement or extraction. It offers fully automatic profiling, outlier detection, schema change detection, and pattern analysis to help make sense of large volumes of data.2

6. Inaccurate data

There's no point in running big data analytics or making contact with customers based on data that's just plain wrong. There could be many reasons for this, from customers giving incorrect information to a human operator making a typo when entering data manually, or inputting details into the wrong field.

These can often be among the hardest data quality issues to spot, especially if the formatting is still acceptable - entering an incorrect, but valid, social security number, for example, might go unnoticed by a database that's only checking the veracity of the input in isolation.

There's no cure for human error, but ensuring you have clear procedures that are followed consistently is a good start. Using automation tools to reduce the amount of manual work when moving data between systems is also hugely useful in reducing the risk of mistakes by tired or bored workers.

7. Ambiguous data

Errors can happen in data lakes and large databases, regardless of how strict you are with supervising them. Data streaming at high speed can exacerbate this problem. Confusing column headings and formatting problems are common, which can result in multiple flaws in reporting and analytics.

Predictive data quality utilizes autogenerated rules to continuously monitor and quickly resolve ambiguity by identifying issues as they occur. This results in the creation of high-quality data pipelines for real-time analytics and trustworthy outcomes.

8. Data imprecision

Dara imprecision is an issue that can arise when data has been processed and stored at a summarized level through an extract, transform, load (ETL) process. Poorly designed data warehouses or summarized datasets can prevent users from accessing the level of detail necessary for analysis. This imprecision in data can be due to errors in coding, misclassification and inaccurate or miscoded inputs. In serious cases, this could lead to skewed analytical results and misinterpretations of underlying issues, introducing bias into processes where unbiased data is paramount.

In order to maximize accuracy when dealing with data, it's essential to ensure that all steps of the ETL process are conducted with care. By ensuring that each step is optimized for quality assurance, errors are minimized and full detail from the original source data can be obtained during analysis. This will ensure more accurate results and allow users to draw more meaningful conclusions from their data.

As technologies evolve, AI-based solutions offer better ways for detailing data by bringing visibility into datasets so teams get a closer insight into granular outcomes at scale. This helps provide faster access to actionable information which supports effective decision-making throughout organizations.

9. Invalid data

Data invalidity is a situation where the data set contains incorrect or incompatible values. This could be due to manual entry mistakes, data manipulation errors, equipment malfunctions, unvalidated input sources or corrupted files. It can arise in any application where wrong or obsolete information is stored and can cause serious problems if left unchecked.

Some typical examples of invalid data are dates that have impossible combinations (e.g. April 31st) or numerical input like inventory levels with negative numbers. These types of errors can often be identified by setting parameters on the field inputs, but other more complex types are harder to spot without further analysis and inspection. Organizations must spend time and resources managing these issues by establishing measures such as regular validations to periodically detect incompatible entries and take proactive action against them before it's too late.