Data quality can be measured by six dimensions which

COMPLETENESS (the proportion of stored data against the potential of “100%” complete).

Completeness easily tells us how much we know about a customer, how identifiable is a location, or how well a product is defined. We all know when a field has a value and when it does not.

Moreover, not having a telephone or mobile number means you cannot call the customer. Not defining product attributes means your customer does not understand enough about what they are trying to buy.

Solution: Appending missing data or simply identifying incomplete records is the first step in having complete data.

UNIQUENESS (nothing will be recorded more than once based on how that thing is identified).

Uniqueness tells you what makes a data entity one of its kind when it is not maintained, we get duplicates. People, products, suppliers are all entities that you expect to be unique.

Data that is not unique can waste time and money. Duplicate data delivers multiple letters to the same customer creating a negative impact. It hides the true view of inventory held on a product, wreaking havoc on your purchasing strategy.

Solution: When you have duplicates in your CRM, you can either use a de-duping tool or hire de-duplication services to clean your records.

VALIDITY (Data is valid if it conforms to the syntax such as format, type, range of its definition).

Data is valid when it conforms to format, type and range that have been set up as a part of its definition.

The problem with invalid data is that the impact can vary from simply not adhering to a set “look and feel” to violating a core principle that drives a business process.

Solution: Mark invalid, bogus records and use third party data validation services to make sure all your data is valid.

TIMELINESS (The degree to which data represents reality from the required point in time, or customers have the data they need at the right time).

This dimension is all about the likelihood of data being affected by time and the degree to which data represents reality at a particular point in time. Classic examples include change of address when a person moves, change in surname after marriage, the age of a person, expired passport etc.

However, Change in time may not affect all data. Some data may age differently to others, some age naturally and others on a particular trigger.  Similarly, the impact can vary depending on the use of data.

Solution: Make sure you have date created and last activity date in your CRM, that way you can automatically add leads and contacts to nurturing.

CONSISTENCY (The absence of difference, when comparing two or more representations of a thing against a definition).

Consistency tells you information is being captured as expected and nothing is out of the norm. We expect that the calculated age is derived from the date of birth and we know that the net price is a combination of gross price, taxes and discounts.

Inconsistent data stands out, once you know what questions to ask. When defining consistency rules, you need to know about the relationships between data.

The Solution: Having drop downs in forms and normalizing/standardizing data prior to it being uploaded to your CRM is the first step to fixing inconsistency. For Historical data, you would need to normalize and update your CRM.

ACCURACY (The degree to which data correctly describes the “real world” object or event being described).

This is probably one of the toughest dimensions to measure and is always subjective depending on the context in which data is used. Just because we correct the address and postcode using reference data does not mean that it is the right one for the customer. We often need repeated and manual checks to increase our confidence in accuracy, often bolstered by the rest of the dimensions.

Accuracy requires a thorough knowledge of your data entity, and what makes it accurate. It may require comparing the electronic information against the real world. For example, identify checks in person when vetting security or scanning a product barcode to tally up the electronic and real world item. This dimension was the most debated one, probably due to the fact it is quite subjective.

However, inaccurate data supports the old adage, garbage in – garbage out. Decision made on inaccurate data can set your business backwards. Incorrectly billing a customer for their neighbor’s bill not only incurs a loss of business but also negative press, and worse still, a fine from the regulator.

The Solution: While you cannot use third party data to validate all data, you can instill data quality in your sales team to validate data when it is needed. For example, when your sales rep is on the phone they can confirm the date and time.

For those businesses that have data quality issues which are stopping them from marketing and selling, there are data cleaning companies such as StrategicDB that can help solve some of data issues mentioned above.

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