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DQ Blog Data Quality Common CRM Data Problems

Common CRM Data Problems

Mary Doyle June 12th, 2014 Data Quality, Master Data Management
common crmproblems

Common CRM Data Problems

When setting up a CRM, businesses make the mistake of thinking their data is evergreen. But data that is fit for purpose today will not stay that way forever

When was the last time you checked your data for C.R.A.P?

Is it Corrupt, or is it Correct?

Is it Rubbish, or has it been Re-purposed?

Is it Abortive, or is it Appropriate?

Is it Poor, or is it Perfect?

Data quality research reveals some startling statistics about, well, C.R.A.P. In the average business contact database, up to one third of the data could be of poor quality. That means a relatively small database with 50,000 customer records could contain 15,000 that are flawed in some way.

But it’s important not to be downhearted by the challenge.

Many of these flaws are caused by extremely common problems. If your business is suffering from data decay, it’s in the majority; most area. Once you realise the problem, take ownership and look positively at the situation, you have a golden opportunity for improvement.

How Data Problems Occur

When left to its own devices, a CRM system quickly develops flaws. The foundation of data you use to build information about your customers begins to develop hairline cracks. As the cracks grow, the foundations start to give way. Your CRM can no longer be trusted.

While problems with data sound relatively minor, they can make a CRM massively inefficient and partially ineffective. The problem is, you won’t know where the problems lie until you tackle the issue head on.

So what do we mean by data quality problems in a CRM? The kinds of things you’re probably used to working around or living with:

  • Duplicated records; customers who are recorded under several names or addresses, for example
  • Inaccuracies; data that may once have been true but is no longer true, or has never been true at all
  • Incomplete records; data that has been corrupted, mistyped, abandoned or orphaned
  • Invalid information; records that your CRM simply cannot work with, and records that crash the software or cause problems elsewhere

These flaws are introduced in a variety of ways, and the three most common problems will ring true with most people who work with a CRM on a daily basis. The figures below are based on actual research; if you’re not familiar with data quality challenges, they may paint a surprising picture.

Cause 1. 76 per cent of records are damaged by poor data entry

Data entry is the process by which most records are entered into a CRM. Someone, somewhere typed in that information. It may be someone in your office, someone in another department or even someone in another country. Often, there are hundreds of people with the access rights to create new records manually

Poor data quality could be caused by human error, and sometimes that’s unavoidable. We all mis-type and mis-spell. But sometimes, the problem is more complex. Even the best typist can be outsmarted if they do not have the correct systems and tools to support correct data entry.

Training issues can also cause data entry concerns, since many people aren’t told how to format data in a consistent way. This also becomes a problem with data entry clerks are located in different countries. Take the date, for example. To record a customer’s date of birth, or the date of their last invoice, we need to know which format the date is and apply that format consistently across all records. But someone in the USA will write the date differently to someone in the UK.

Cause 2. 53 per cent of records are damaged by a change to systems

Most of us have worked through change. Restructures, office moves, new systems – these are all improvements that can have a ripple effect on our productivity.

A system change can wreak havoc with data, since existing information can be thrown off course and existing conventions rendered ineffective. Even the way a system limits the length of data fields can introduce problems with otherwise healthy records.

3. 48 per cent contain flaws introduced by data migration or conversion from one format to another

Sometimes, data needs to be converted from one format to another to make it compatible with different systems and software. Upgrades, changes and system replacements are a major cause of data quality challenges.

After a system change, data may need to be encoded in different ways, or presented in different formats; businesses need to use data in linked systems that import and export data from their CRM. All of this can lead to the introduction of errors.

Additionally, abbreviations may have a standard, understood meaning in one system that is drastically different in another.

Finding a Resolution

In order to fix your data, you must make data quality a corporate imperative. Those at the top of the organisation must understand the phenomenal waste that poor quality data creates. And they must see how inevitable it is that data in a CRM goes bad.

Even more importantly, they must see this in financial terms. There must be a demonstrable cost.

Once the business and the data are aligned, the business can move towards a culture where everyone gets it right first time. Indeed, it’s in everyone’s best interests to do so. An investment in data quality software is an essential part of any strategy.

The best motivator for data quality improvement is always cost savings. Businesses tend to adopt things that improve profits, and a data quality regime certainly will.

In addition, high CRM data quality helps to:

  • Improve staff morale
  • Improve customer relationships
  • Give the business a competitive advantage thanks to its leaner, more agile outlook
  • Facilitate good quality decision making, fuelled by accurate reporting
  • Allow staff to trust the data they are using
  • Prevent the development of costly workarounds
  • Make training more efficient

For all businesses, these are desirable outcomes to a data quality initiative. In fact, your business CRM data could become useless without one.

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