Big data, dirty data

Managing Quality in an Ocean of Information

Managing Quality in an Ocean of Information


Big data is an incredible resource for businesses, gathering vast amounts of information from various sources like social media, e-commerce platforms, mobile devices, and IoT. However, this data can be complex, constantly evolving, and hard to manage, making it challenging to extract meaningful insights. Additionally, since big data comes from so many sources, it's easy for dirty data to sneak in, which can profoundly impact businesses.

Not cleaning your data is like letting trash pile up at your place. Eventually it gets out of control. Investing in effective cleansing techniques is crucial to ensure that data is reliable and clean, giving you the the confidence to make informed decisions.

What is Dirty Data? 

When it comes to big data, "dirty data" refers to information that's inaccurate, incomplete, or irrelevant. This may include duplicated records, missing or wrong information, and outdated data. The consequences of dirty data can be staggering - US businesses are estimated to lose a whopping $3.1 billion every year because of it! Bad data can impact everything from decisions to customer relationships.

Data that is duplicated, incomplete, or inaccurate

Duplicated records can lead to confusion and wasted resources, as companies may end up targeting the same customers multiple times. Incomplete data can result in missed opportunities, such as failing to follow up with a potential lead or not having the necessary information to make informed decisions. Inaccurate data can be even more damaging, as it can cause businesses to make misguided decisions based on faulty information. This can lead to lost revenue, decreased productivity, and ultimately, damage to the company's reputation. 

Identifying Data Sources

Dirty data can originate from a variety of sources within a company. One of the most common sources is human error - data entry mistakes, typos, and incorrect formatting can all contribute to dirty data. Another source is outdated systems and processes that don't adequately capture and update information. Additionally, data integration issues between different systems and departments can result in inconsistencies and inaccuracies. It can also occur due to issues with external data sources, such as inaccurate third-party data or outdated public records. Identifying and understanding the sources of your data is key to improve quality.

The Impact of Dirty Data on Businesses

In the UK, the problem of dirty data carries an equally hefty price tag. According to a report by the Royal Mail, businesses in the UK are losing an estimated £6 billion annually due to incorrect customer data. The same report found that 64% of UK businesses acknowledge that their data is inaccurate in some way. This can lead to a range of issues, from wasting marketing budgets on ineffective campaigns to losing customers due to poor communication. In addition, GDPR regulations have made it even more important for UK businesses to ensure that their data is accurate and up-to-date. Investing in data cleansing offers a remedy, preventing the costly aftermath of dirty data and fostering a competitive edge.

Data Cleansing

Data cleansing, also known as data scrubbing, is the process of identifying and correcting or removing inaccurate, incomplete, or irrelevant data from a database.

Effective data cleaning involves:

  • Identifying inaccurate, duplicate, or irrelevant entries

  • Correcting formatting errors and typos

  • Standardising data from disparate systems

  • Verifying information against reliable sources

  • Removing outdated and risky data

The Value of Data Cleansing

Data cleansing is important for several reasons. For one, it helps businesses improve the quality of their data, making it more reliable and useful for decision-making. It also helps businesses comply with regulations such as GDPR and the CCPA, which require companies to protect sensitive data and ensure its accuracy.

Cleansing can help businesses save time and money by reducing the amount of manual effort required to  manage data. By automating this process, it's easier to streamline operations and free up valuable resources.

The Advantages of Clean Data

Having clean data can provide businesses with several advantages. For one, it can help businesses make more informed decisions based on accurate and accurate information. Clean data can also help businesses uncover trends, patterns, and insights that can inform their strategy and drive growth.

Clean data can improve customer relationships and enhance the customer experience. With accurate and up-to-date information, marketing, sales and support teams can provide personalised and relevant experiences to customers, leading to increased loyalty and retention.

Keep your data ecosystem clean and thriving!