Data quality: definition and best practices in B2B marketing

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data quality

The role of data quality in marketing is sufficiently discussed here and there and known so that we do not need to recall it. “How to better leverage data to improve marketing performance and make better decisions?”: this is undoubtedly one of the main questions that companies are asking themselves today, in B2B as in B2C for that matter.

data quality

What is Data Quality? [Definition]

Data Quality refers to the ability of a company to maintain the sustainability of its data over time, and therefore their usability in the context of marketing and sales operations. Therefore designates a way of managing data so that this remains viable over time. It is well known that time naturally erodes/degrades the quality because customer data is a living matter: people change email addresses, just addresses, phone numbers but also preferences, purchasing behavior, etc. A database that is not maintained ends up storing data that is no longer correct, which no longer corresponds to any reality.

But Data Quality management encompasses a wider field and is based on taking into account six dimensions or criteria:

The quality of the data available is the basis of the effectiveness of marketing and sales activities. It is often said that data is the fuel of customer relations, commercial dialogue, and marketing efficiency. But this is only true on the condition that the data are of high quality, in the multiple sense that we have just listed.

Why is inaccurate data dangerous data?

In the data analysis business, maintaining data quality is an essential issue. All organizations that do big data know the risks and dangers of bad data. The concept of Data Quality is not yet another buzzword, it is something really important, which describes a highly legitimate concern. However, Data Quality is rarely one of the top priorities for companies.

However, data as such is not sufficient on its own. What allows a company to improve its performance is not data as such, but quality data. Let’s put it a little bluntly: if you are not ready to maintain your data, you might as well not collect any data at all… We could even invent a slogan: data can be maintained or it is worthless. An infographic shared by Halo Business Intelligenceshowed that almost 40% of the data held by companies was inaccurate… Even more worrying, more than 90% of companies admit that the customer data they have is inaccurate… In the study of Business Intelligence, 66% of companies respondents admit the possibility that incorrect data could negatively impact their business. They even estimate the likely cost to be $ 8,200,000 on average… Yes, we are dealing with big fish, but still.

Why and how does having poor data affect business performance so much? The dangers associated with poor quality data are twofold. On the one hand, they entail financial costs, on the other hand, they damage the reputation of the company.

As the study cited above shows, accurate data first has a financial impact/cost. Without Data Quality, you have to make decisions and implement strategies based on inaccurate facts. Let us take an example that is both simple and banal. Say you want to create your next marketing campaign. To do this, you use the data available to target the right people. But if your data is wrong, you’re going to target the wrong people with the wrong offers. You will be based on supposed behaviors that are not those of your contacts. Your marketing campaign is in danger of flopping. It will have cost you money, with no consistent return on investment. Apart from that, having inaccurate data generates significant operational costs. If you have imprecise or inaccurate data about your customers, you will have to spend time trying to find the right information. However, in business, time is money …

Setting up Data Quality management costs money, supposes an investment by the company. Initially, you will feel like you are losing more than you are winning. But always tell yourself, if in doubt, that the cost associated with poor data quality will always be greater than any strategy. Repairing errors, linked to poor data quality, is always in the end more costly than limiting the risk of error … In this regard, let us point out a study, quite academic certainly, but fascinating from 2011, which shows proof to support that the costs associated with incorrect data are always greater than the costs associated with maintaining.

Finally, as we said above, having poor quality can also have a very damaging impact on a company’s brand image. Because, quite simply, the poor quality of the data necessarily impacts the quality of your communications, the quality of the dialogue that you maintain with your customers. And then, put it vulgarly, she does not care to show your customers, through your messages, that you have completely false information about them. This inevitably affects your credibility. This is true in B2C, it is even more so in B2B.

The benefits of Data Quality management

Not being concerned with Data Quality can have serious consequences on the business of the company. But beyond this aspect, setting up Data Quality management has several “positive” benefits. First of all, it allows an organization to reduce costs in different departments. The Business Intelligence study showed that organizations that managed Data Quality were successful in reducing:

  • 10% to 20% business expenses
  • 40% to 50% IT costs
  • 40% operational costs

Again, most of these cost reductions are the result of more relevant data use. Planning management, decisions, and actions are most effective when they are driven by correct data. But management also contributes directly or indirectly to improving economic performance (turnover, income). Because having quality data makes it possible to avoid big strategic errors, improve the brand image, and guarantee customer loyalty. Using accurate data improves the performance of your marketing campaigns and sales strategy.

Above all, having Data Quality management improves risk management. The chances of error are greatly reduced if you base your decisions on correct information. It helps limit the risk of error in many activities, from customer service to product development and even accounting! Having quality data makes life easier for employees and makes them more efficient. They are no longer obliged to double-check the information each time to ensure its validity.

The key components of effective Data Quality management

How to put your data in order? How to set up effective management of Data Quality? There are different possible approaches. As a first step, your business needs to clearly articulate its data usage needs and goals.

There are three key components to consider in Data Quality management:

  • Data governance
  • Data Quality Assurance (QA)
  • Data Quality control

# 1 Data governance

All companies should have a data governance team to monitor the quality of the data, it’s updating, the procedures in place to maintain the quality. In recent years, it has become common to find a Chief Data Officer (CDO) responsible for keeping management informed of data-related problems encountered by the company.

A data management team should focus on the strategic goals of the business. She must constantly ask herself the question of knowing what are the key objectives of the company and how to achieve them. The answers to this question help define the data the business needs to grow. It also helps prioritize Data Quality goals.

Once you have defined the business objectives in terms of data, you need to get the database in order. Several programs can be deployed for this purpose. It is also possible to outsource the cleaning of your databases, by calling on a consulting firm with expertise in customer data. The most important thing is to focus on the data that makes sense for your organization and the pursuit of its goals and to remove unnecessary or irrelevant data. Organizations often struggle to delete data, but effective management means focusing exclusively on critical data.

Be aware that Data Quality management does not essentially consist in implementing your action plan using methods that are all more sophisticated than the others. You must also and above all be able to make trade-offs between the cost of implementing programs and the cost in financial terms and reputation associated with the use of poor quality data. Management, to be efficient, does not necessarily have to be expensive.

There is one final aspect of data governance that should not be overlooked. As we know, it is most of the time human errors that are the source of incorrect data. It is therefore also essential to make employees aware of the importance of Data Quality. Everyone in the organization needs to get involved and understand why it is important. This outreach work can be much more effective in removing bad data than implementing sophisticated new software.

Read more: 10 major trends in the world of B2B CRM

# 2 Data Quality

Assurance Data Quality Assurance refers to all the daily processes and techniques that make it possible to identify inaccurate, inconsistent, incomplete data and therefore ultimately guarantee the maintenance of overtime.

Implementing a Data Quality Assurance approach makes it possible to ensure, on a day-to-day basis, that the data used by the company has a high level of quality and makes it possible to achieve the objectives identified by the team in charge of data governance.

#3 Data Quality control

Finally, you must also implement control protocols. Quality control takes over from Data Quality Assurance. Controlling consists of ensuring that the data is accurate and that the databases do not contain unnecessary data. This involves controlling both the quality and the use made of it by the company’s employees.

Assurance makes it possible to measure the level of inconsistency, incompleteness, and precision of the data available. The data control process consists of deciding whether the data is useful, relevant, deserves to be exploited. Let’s take an example. The person in charge of Data Quality Assurance will be the one who identifies the inconsistent data. The person responsible for the control will be the one who decides to delete this data so that it is not used by the company. Its role is to prevent the organization from using incorrect data.


Data is an essential material for B2B companies. But data is only valid if it is correct, complete, consistent, precise, up to date. On the contrary, poor quality can harm the business. Hence the interest in implementing Data Quality management within the organization. But instead of just implementing costly and time-consuming programs, organizations must first take care to identify the use cases for their data. It is also the art and the way of identifying relevant data and removing all the others.


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Posted by Rebecca
Rebecca is an Independent content writer for breldigital, She writes content on any given topic. She loves to write a case study article or reviews on a brand, Be it any topic, she nails it
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