Data Maintenance and Quality

What is data maintenance?

There are six main data maintenance strategies that your business should implement to improve your CRM data:

Data quality

Data quality can make or break your business, which is why it’s top of the priority list for data maintenance. Data quality is determined by whether your data fits the following six factors:

• Accuracy
• Completeness
• Reliability
• Relevance
• Timeliness
• Validity

Depending on how your data ranks for each of these factors will determine whether your data serve your organization’s needs. If the data is not meeting professional standards in one or more of these categories, data maintenance will work to improve your data quality.

Data cleaning

It can be easy to lump data maintenance and data cleaning into the same boat, but the two refer to different ideas. While data maintenance is a consistently run strategy that aims to improve your data, data cleaning is a process that is identifying and correcting or removing errors from your data.

Data cleaning should be performed regularly as part of your data maintenance strategy. Both are equally important and intrinsically tied together as part of regular data maintenance upkeep and improvement.

Data deduplication

Data deduplication is a process that removes extra or excessive copies of data in your data sets. The copies are removed so that only the singular piece of data remains in the master data.

This effective technique works to improve efficiency by reducing the chances of your team using redundant data, which can hurt any and all business processes. It can increase data insight accuracy and improve your customer engagement and experience. It also allows for more data storage space and helps to improve your brand’s overall operations.

Data operations

Data operations work throughout the data lifecycle to improve your data quality, as well as to ensure your data can be accessed and utilized securely by your teams.

Data purging

The data purge process removes the clutter of data that is taking up space in your system, which increases space and access to quality data. Factors that data purging considers are the age of the data, the type of data that your system recognizes, regulatory compliance and more.

Data monitoring and KPIs

With defined KPIs, your data monitoring can ensure that you have clean data that will help grow and improve profitability for your business.

How does data maintenance impact sales and marketing?

Marketing relies on high-quality data to create accurate and effective strategies, from segmenting contacts to building targeted campaigns. If your data is substandard, it can spell out failure for your marketing strategies before they’ve even been implemented. Meanwhile, your sales teams use data to properly connect with customers and present your company’s products to the market. If they’re working with poor data, your company won’t be able to stay ahead of the competition.

Below are three ways that data maintenance can impact sales and marketing:

CRM segmentation

If you have poor data quality due to inconsistent data maintenance, your CRM segmentation won’t get the right messaging to its intended targets — ultimately dooming your campaign efforts.

Marketing personalization

Consistent data lets you know how customers feel and what they want from your business, which helps you cater your messaging to create quality engagement.

Without data maintenance, your teams will be working off of assumptions rather than trustworthy data, which will harm your business in the long run.

Customer experience

If your data is filled with unnecessary details or redundancies, it will negatively affect your brand’s reputation. For example, if you have duplicate data that hasn’t been removed due to inconsistent data maintenance, you may accidentally target the same customer more than once. Annoyed, they’ll either form a worse opinion of your company or opt-out of future messaging.

To prevent customer experience issues, your business needs a reliable data maintenance plan to ensure high-quality data.

What are the challenges of data maintenance?

If data maintenance is critical to business strategy, then why do some companies find it difficult to sustain?

A major challenge is a lack of time and resources.

Companies are working with large amounts of data, which can become overwhelming and difficult to quality-check — especially if your team doesn’t have the right data tools. Manual data checks are also extremely difficult in terms of time and expertise required.

Investing in efficient data tools that can guarantee regular data maintenance is the best solution to these challenges. Read more about the tools that data managers need here.

What are the key benefits of data maintenance?

Your organization’s data is an invaluable resource. Proper data maintenance includes the following benefits:

• Increased productivity
• Cost-efficiency
• Improved marketing and sales efforts
• Positive customer feedback
• Accurate business strategy
• Reliable data security
• Effective analytics
• Regulatory compliance

Who should own data maintenance in a company?

The reality is that maintaining high-quality data requires leadership buy-in and a cross-functional operations team. Although it seems like your CRM department should have full ownership of your company’s data maintenance, the fact is that everybody in your organization has a role to play in data maintenance. This includes:

• Sales representatives
• Marketing managers
• Senior managers
• Any employee who utilizes company data

This doesn’t mean they are in the system fixing data, that’s the admin’s job, but rather when they notice a data issue they report it to the correct channel that can take action and fix the discovered problem.

As data fuels more day-to-day practices within your organization, it makes the most sense that every business should have a cross-functional data operations team, with admins with different background experience (e.g. sales or finance) that will help the team understand functional business needs and the expectations of the data maintenance process.