Common data pain points for Marketers and how to solve them

At the TMF&A conference recently there were many marketers from a variety of diverse company backgrounds with similar tales of data problems. As one company director explained to me:

“How can my company and I take advantage of all that is possible with data analytics if our data capture is not even allowing me to carry out basic CRM functionality?”

Unstructured data sources

Industries that have a range of data capture points have the added issue of unstructured data entry. If you are gathering in business cards via promotions or at conferences via your sales teams then it is likely that data quality will suffer. In this instance then who is responsible for the quality of data capture and entry? Often it is left to temporary data entry teams to process the data sources for entry and they don’t really have the right level of incentive to ensure that the data entry is accurate and correctly reflects the marketing source for each entry. This kind of non-digital data entry is easy to collect but it is time consuming to process into data storage systems.

Large organisations without data quality teams

It wasn’t just the smaller or medium size companies that mentioned data quality. Marketers from larger companies talked to me about the complex issues for their data quality. What happens when companies merge with different data sources and data storage? Different sales team cultures mean that there can be a conflict between sales and marketing about who is responsible for ensuring data quality.

There can also be the organizational issues in larger companies where silos of data are stored in isolation and geographic spread of teams of people mean that it is difficult to get agreement on company-wide data quality protocols. Larger companies are also more exposed to data security issues.

Why you should invest in data quality

“By 2017, 33 percent of Fortune 100 organizations will experience an information crisis, due to their inability to effectively value, govern and trust their enterprise information.” Gartner

Data IQ reports that 92% of companies suspect that their customer data is inaccurate. Data quality can impact across all areas of your business:

  • Basic inability to track customer or client information flow
  • Problems with engaging with customer
  • Data security and data protection compliance issues

Automation of data quality

Some of the data quality issues can indeed be automated using data profiling tools and for large volumes of data this is vital. By automating your data profiling your system administrator will be immediately alerted to quality issues or anomalies as they enter the system. The data source can then be investigated to see if it is a single issue or whether there is a wider problem with a particular data entry source.

Data profiling will also help companies to report on their existing data by highlighting quality issues; missing or blank data, nonsense or non-existent post-codes and telephone numbers for example. Data profiling sources can also help marketers to rank external data suppliers in terms of the return on investment. You can track particular lists for data quality and then rank them to inform managers which data suppliers are the most cost effective.

Data Migration

The move away from legacy IT infrastructure to more Cloud-based solutions means that there should me an even tighter focus on data quality. Migration projects that over run due to bad data quality will be expensive and could impact on continuity of service to internal and external customers.


Once you have eliminated poor quality data from your data systems it becomes more worthwhile seeking to enhance that data. So you could, for example, use external data sources to augment the information that you have using socio-economic profiles against postcodes. For further enhancement you can use specialist data list suppliers for in depth segmentation on social media usage, attitude, buying and transactional behavior to research the best ways to engage with your different sets of customers.


Once your organisation fully accepts the importance of data quality in terms of revenue, customer engagement and return on investment measures, you can then seek to standarise your data protocols.  Creating a data quality team is a useful way to embed good practice throughout the business and it helps companies to feel that somebody has overall responsibility for data.

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