How Important is Data Accuracy to Your Organisation?

Share on facebook
Share on google
Share on twitter
Share on linkedin

Data accuracy, or a lack thereof, can have wide-ranging impacts on your organisation’s reputation and operations. For example, inaccurate data in a retail operation can cause costly mistakes over stock or business expansions.

According to Gartner, inaccurate data costs companies an estimated 15% of their revenue. In the US alone, IBM estimates businesses lose $3.1 trillion annually due to poor data quality.

It is therefore very important for organisations to ensure they have accurate data when generating reports and data visualisations. Without accurate data, it is hard to ensure accurate decisions and organisation trust.

Table of Contents

Join the BDI mailing list

Keep up to date with the latest events, webinars, and product launches from BDI.

Sign Up

Data accuracy overview

Data accuracy refers to error-free records that can be used as a reliable source of information. Within the data quality framework, data accuracy is the first component/standard to be met. It is also one of the most crucial.

Jack Olson’s popular book, Data Quality: The Accuracy Dimension describes the two most important characteristics of data accuracy as form and content. Data standardisation is, therefore, key to data accuracy, as it involves establishing a consistent structure for form and content.

An example of where data standardisation informs data accuracy is the difference in date formats around the world. In the US, dates are written in the MM/DD/YYYY format. On the other hand, DD/MM/YYYY is the most common format in European countries and others across the world. This variation has the potential to compromise data accuracy; for example, is 04/01/2021 January 4th or April 1st? This inaccuracy may seem minor, but it can greatly impact the accuracy of a report or planning document when applied in a business scenario.

Inaccurate data impacts an organisation’s BI operations, budgeting, forecasting, and other critical activities. It is therefore very important to ensure data accuracy where possible. Small amounts of accurate and high-quality data are more useful to an organisation than large amounts of inaccurate data.

Business benefits of data accuracy

Using accurate data and implementing good data practices when collecting data to begin with, has tangible benefits for businesses. These include:

  • Increased revenue

    Reliable and clean data supports effective decision-making, thus, in turn, driving sales.

  • Cost savings

    Up-to-date and accurate data can help prevent wasting money on ineffective tactics. For example, sending physical promotional material to non-existent addresses that go undelivered is a waste of resources.

  • Greater customer satisfaction

    Accurate and current data about your customers helps marketing teams deliver the right messages at the right time and on the right platform. This also helps potential customers move to the next step of the customer journey process.

  • Time savings

    Maintaining accurate data records from the start saves valuable time and money in the long run. Teams are saved the effort of cleaning data and cross-referencing information.

  • Improved return on investments

    A greater ROI is provided through reducing the costs of recovering and/or cleaning the data.

Data accuracy also aids organisations in their mission to meet the common primary goals of data usage: insights, analytics, and intelligence. These goals are achieved when the data is complete, timely, accurate, and reliable. In turn, data can inform decisions such as expansion into new markets or launching new services, thereby generating further business benefits.

The causes of data inaccuracy

Barriers to data accuracy prevent good data practices from being established. When organisations focus on the volume of data collected rather than the quality, decisions made are uninformed and can have negative consequences. This creates a negative spiral, with distrust created over data leading to less effort around data maintenance, generating poor data, ad infinitum.

The causes of data inaccuracy can be broken down as follows:

Not regulating data accessibility

Organisations need to track who can access and overwrite the various formats of data they collect. For example, information stored in a CRM can be accessed simultaneously by sales, marketing, account managers, and customer service team members. This creates the possibility for data errors, with multiple people potentially editing files.

Managing who can access editing permissions regulates against this practice and ensures users are aware of the value of the data they handle.

Poor data entry practices

If an organisation does not have data standards and rules established when first collecting data, they will see inconsistent data entered. For example, a sales representative may record a customer’s name in a different format or spelling to another member of a different team, creating confusion. Furthermore, copying data from social media is highly prone to typos and copy/paste mistakes.

Create a house style for data entry for standardised data entry. For example, define whether your organisation enters dates in the DD/MM or MM/DD format.

Not addressing data quality

Teams are often too busy working on their tasks to consider any potentially incorrect information in a dataset. Data quality and accuracy are only usually addressed, therefore, when something goes wrong, and it is too late. An example of this would be a flawed report or an ineffective marketing campaign. Such mistakes can be very costly.

Poor data culture

Often, companies invest in new technologies but do not train enough staff to use them appropriately. The same applies to data awareness training. Concepts such as data quality and accuracy were previously reserved as the domain of IT teams and BI specialists, so other employees have been unaware of good data practices. The rise of self-service BI tools and the importance of data in contemporary business decisions means companies need to foster a wider data culture that encourages good quality collection to make positive data decisions in the future.

Stubborn reliance on outdated methods

Manually preparing data, such as through Excel, SQL, or ETL tools opens the doors for errors. Using these tools means a team would take a very long time to clean and match thousands of rows of data, furthering the issue of data inaccuracy.

This is especially true of companies who spend a lot of money to collect a large amount of data, which then sits in spreadsheets unused.

Tips to improve data accuracy

Whilst it may seem like a quick fix, hiring a data analyst is not the perfect solution to data inaccuracy problems. Instead, follow one of these tips to improve your organisation’s data accuracy.

  • Conduct a data quality audit

    Find out the top five issues affecting your data quality. This could include duplicates, incomplete information, and data stored in multiple silos.

  • Measure the estimated impact of new data

    What is the ROI for the new data you have gathered? For example, if you have generated a thousand new leads in the last month, how many of these contacts are high quality and likely to lead somewhere? Only add high-quality data to your existing databases.

  • Focus only on specific datasets rather than the whole data source

    Trying to perform a blanket cleanup operation on a data source or database is a waste of time and effort. Focus instead on optimising data for immediate tasks, such as preparing for a report or a new promotional campaign.

  • Measure the work required to fix inaccurate data

    how much time is your team spending to verify and fix the data? Assess the cost of manually fixing the information. If your team needs a month to verify, clean, and fix a 1000 row dataset, then a faster, more automated solution should be considered instead. A data match solution, for example, could easily remove duplicates and consolidate multiple datasets from multiple sources.

Conclusion

Maintaining data standardisation to inform data accuracy practices, therefore, has positive business benefits in the long term. Establishing these practices early in the data collection process ultimately reduces time spent on resolving data issues in the future. As a result, good data practice is in your organisation’s best interests. If you recognise any data issues from this article within your organisation, BDI may be able to help.

Our Business Systems Review service can identify areas of your business operations that require updating. Our team monitor any recommended solutions and adjust strategies where necessary for your organisation to reach its goals.

Ready to start?

Our team of data intelligence experts are ready and waiting to work with your organisation

Recent Comments

    Leave a Reply

    Your email address will not be published. Required fields are marked *