Insights

Spot a Data Quality Disaster Before It Destroys Your Business

SMEs know that surviving in business is tough. Around 56% of businesses don’t last beyond their first 5 years and between 25-50% of all start-ups fail in their first year (depending on the figures you look at). This high rate of failure is bad news for entrepreneurs and micro businesses that are so dependent on cash flow to survive.

The RSA conducted a survey to find out why so many new companies run out of cash and the results are quite revealing. Here are four key things SMEs blamed for their downfall:

  • The tax system (44%)
  • Lack of bank lending (38%)
  • Cost of running a business (36%)
  • Cash flow problems (35%)

Clearly, businesses fail for a variety of reasons, but cost is a recurring theme. You owe it to your employees and investors to keep unnecessary expenses to a minimum to avoid making the same mistakes.

The Cost of Poor Quality Data

So…how much does poor data cost? It’s difficult to put an exact figure on the problem, but we can use a simple formula to understand its scale.

The 1-10-100 Rule is a handy way to calculate the cost of error prevention vs data cure:

  • Verifying a record costs £1
  • Correcting a record costs £10
  • Working with an uncorrected record costs £100

These extra costs appear when data is not fit for purpose, because poor data generates waste.

Inevitably, money is wasted by every business on the planet and in a sense, a business that wastes cash is not unusual. But its fate may depend on the course of action you take when evaluating data quality. Every business needs to act, no matter how small or large.   Failure rates prove the consequences of unchecked cost.

Spotting the Cause of the Problem

Data quality problems show themselves in different ways. Here are some typical warning signs  (Not all of these examples will be relevant to your business, but they should give you some idea of what to look for):

  • Dissatisfied Staff

Employees that enjoyed their job before seem to have lost all enthusiasm. They feel unproductive, restrained and feel their IT systems are difficult to use. Chances are the data in the system is not supporting them and they’re forced to use their own record-keeping methods to ensure a high quality of data.

  • Invalid Entries

If people complain that they can’t save records or that entries are marred by strange characters, it’s a sign that automatic imports are bringing data quality problems into your database. A classic example is the import of customer records to a CRM; often if the fields are not valid, any edited records will not be saved.

A simple error, such as an incorrect date field, could spell validation problems that wreak havoc in future. Once these errors appear, they can be fiendishly difficult to weed out.

  • Impatient Customers

When calling a company for support, it’s infuriating to be passed from pillar to post. Yet many businesses fail to ensure continuity of support.

Every time a customer speaks to a member of staff, the contact should be recorded so it can be referred back to later and the data should be meaningful and valid. If someone gets cut off and has to start their explanation from scratch, it’s easy to see why they become frustrated. That’s your problem, not theirs.

  • Returned Emails

Mailchimp is an email marketing platform that handles billions of marketing emails every month. It looked at deliverability rates for all campaigns in August 2013, split into different sectors.

Hard bounce (failure) rates reached up to 1.7% for email marketing in construction sector, 1.56% in manufacturing, and 1.19% for emails sent from creative agencies.

Extrapolated over the course of a year, we can clearly see that email lists need to be cleansed and de-duplicated to remain useful.

  • Duplicated Contacts

In CRMs and contact databases, duplicated contact records are akin to a virus. Once several copies of a customer have been put into a database, workers are immediately thrown into a state of confusion. Nobody knows which one is valid.

People use clumsy workarounds, adding words like ‘COPY’ to a customer’s name to mark the duplicates from the master record. And the customer is irritated by multiple catalogues, multiple marketing letters and multiple sales calls.

Duplicated data sounds relatively harmless, but it could be the number one cause of waste in your database. These additional records spread like wildfire and it’s important to understand the cause of their existence.

  • Separate Silos

The Single Customer View is the utopian state for your data, where every customer has one record and that record is kept clean and tidy. This allows every member of staff to work from the same data.

The opposite is the use of separate silos – an extremely undesirable state of affairs. Once staff begin to use breakaway systems, they introduce changes that cannot be verified and they create a version of the data which may be miles from the truth.

Splitting data this way also has serious security implications, since self-governed data silos are rarely compliant. If your business has multiple ways to store the same data, it’s a sure sign of wasted spend.

What Next?

Once you’ve spotted a potential data quality problem, it’s time to act. Data goes bad extremely quickly; 2% per month is a conservative estimate of the rate of data decay. The longer you leave it, the more those existing flaws will be compounded.

Your action plan should involve a complete ownership of the problem, from the boardroom to front line staff. It may require the appointment of a CIO, or even better, a CDO. It will certainly involve the adoption of data quality software, which will provide fast results as well as on-going monitoring.

The success of B2B marketing campaigns is highly dependent on data quality. Staff retention is at stake when systems don’t serve people as they should. But the biggest risk of all is a drop in profits, which could spell the end for your enterprise.

Don’t let bad data destroy your business: notice now and put it right today, before it’s too late to take action.

Article written by Martin Doyle
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