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Avoiding the major risk challenging success: Poor data quality

(Image credit: Image Credit: Shutterstock/Sergey Nivens)

Many organisations view poor data quality as an issue that needs to be addressed, yet instead of doing something about it, they often resort to temporary workarounds. Although data problems are typically relegated to the IT department, in reality, the entire organisation relies on strong data to inform all aspects of the business and ensure high performance.

Therefore, it’s critical to understand what departments are prone to risk and how potential pitfalls may impact success. After understanding the risks and issues each department faces, you can take the appropriate steps to protect your entire organisation from future data quality issues. 

Marketing: Campaign ROI

Marketing teams rely heavily on data to develop and predict the results of their campaigns. This means inaccurate data can be a threat to the entire process. When marketers forecast ROI for their campaigns and pipeline, they’re doing so with the whole database in mind. If a company has a contact list with wrong or incomplete information, the actual results will differ strongly from what was previously projected. Not only will this result in lower than expected performance, it will also affect other aspects of the business that rely on this data.

Sales: Forecasting and efficiencies

When it comes to forecasting for sales results, many sales leaders use contact data from lead generation efforts as a baseline for their forecasts. These projections take into consideration aspects such as expected lead count and anticipated lead conversion rate.

The estimates help the sales engine prepare to act on leads as they come in, but like marketing, the sales team has also made their plans and projections based on a database full of contact names, numbers, and addresses. If they anticipate 20 per cent of the list will become a lead, but 10 per cent of the list never received the email, the sales team simply won’t reach their projections or goals.

When accurate contact data isn’t collected for leads – such as during trade shows or when customers enter their information on web forms – it creates inefficiencies in prospect pipelines and follow-ups. Wrong information in data fields can make it impossible to connect with prospects at all, let alone in a timely manner.

Operations: Inventory management

When creating inventory forecasts, operation teams also rely on CRM data and contact counts. If an increase in demand is expected based on a particular contact list or marketing campaign, it may prompt the product team, warehouse staff, or fulfilment department to add inventory in anticipation of those orders. But if the number of accurate contacts doesn’t match the forecast, the team’s numbers will be off, resulting in money wasted getting products ready for sale that just wind up on the shelf.

Overall risk to revenue

These departmental risks pose an even greater one to the entire organisation – a loss in revenue. If CRMs are filled with the wrong contact data, companies will experience lower engagement, performance and sales – ultimately resulting in a loss of revenue on every initiative.

Ensuring the quality of your customer data

There are a few things you can keep in mind when assessing the quality of your database in order to help protect your organisation from the potential risks of bad data:

  • Assess the cause and identify solutions

Unfortunately, recognising and fixing where database problems exist is only half the battle. Companies must also assess the root cause of an error in customer data to prevent it from happening again.

To do so, establish a uniform definition across the entire organisation for what data quality truly means. This can be done by discovering what each stakeholder considers to be high-quality data. Next, identify the components of an ideal contact record, including the information that must be captured to make the record valuable. Here, you can implement tools that keep data standardised and reduce data entry errors. In addition, data governance is also key to ensuring data quality is achieved, respected, and maintained.

  • Address shortcomings that may exist

Without data quality processes in place to prevent errors in data entry and to routinely clean the database, bad data is likely in the mix. Ideally, a successful data management strategy can prevent your team from doing an audit on the information that has been collected, but if processes weren’t in place from the start, you may have some work cut out for yourself. First, start by looking to reduce duplicate entries and then work on data field standardisation to improve the results pulled from database searches. Finally, check to make sure contact information is void of natural decay caused by changes in a company’s main point of contact or office address, for instance.

  • Automate data quality processes

Once databases are scrubbed and manual processes are in place, the final step is often implementing automation tools. Automation technology can help improve accuracy, identify missing information and standardise the way in which information is stored.

Data quality management is an initiative shared across entire organisations. To ensure high performance and efficiency, it’s important to build and continuously improve a strong business foundation with quality CRM data.

Tom Sather, senior director of marketing, Validity