Companies lose a lot of money because of incorrect data. How do you ensure better quality of your company data, i.e. the data on your business relations such as customers, suppliers and prospects? In 4 steps we explain how your organization can clean up and enrich company data.
$310,000,000,000 was lost in the US in 2016 due to poor data quality. Not surprisingly, companies are making wrong decisions, missing revenue opportunities, wasting time and suffering reputational damage. Do you have clean and enriched data? If so, you can actually discover new revenue opportunities, make faster and better decisions, work more efficiently and prospect more effectively.
Factors affecting data quality
The fact that so many companies have problems with incorrect, incomplete or irrelevant data is because there are numerous factors that affect data quality. Chief among them:
- The data is in different silos;
- Multiple users within an organization work with the data;
- Datasets are being merged through mergers and acquisitions;
- And, by far the biggest contributor to contaminated data: human error in data entry, editing and maintenance.
In other words, it is high time for the best data quality. Our solution for increasing the data quality of your business data consists of 4 steps:
Step 1: Cleaning up the data
Do you find that you are working with contaminated data? Then it's time for the first step: a big cleanup. For organizations of any size, or that process a lot of data due to the nature of their services, it is impractical to manually check and correct all business records. Specialized companies, however, have powerful, automated solutions for this.
We have two main tools to clean up datasets:
- The Dun & Bradstreet database, which contains data on 300 million companies worldwide.
- The D-U-N-S-number, a unique 9-digit identification number that is inextricably linked to one business entity.
Does a company submit a dataset to us? Then the company records get assigned the corresponding D-U-N-S numbers - so that companies that are out of business and companies that are part of the same concern can be mapped. For deviating data, replacement suggestions are made based on the correct data in Dun & Bradstreet database.
The company then receives the cleaned dataset via a spreadsheet or directly into the CRM or ERP system. We also provide an overview of the number of records of companies that are out of business, the percentage of duplicates and anomalous information.
Step 2: Keeping data clean
What is clean should stay clean. In 9 out of 10 cases, data contamination is caused by manual entry errors. So the best quality protection measure is to ensure that data is entered correctly immediately.
Keeping clean can be achieved by connecting CRM or ERP to the Dun & Bradstreet database.. New and existing company data is then immediately checked, corrected and supplemented. The result: you need to enter less data manually and your data is up-to-date, correct and consistent in different applications 24/7. And consistency is one of the most important requirements of data quality.
Step 3: Add external business data
Clean data begs to be enriched. To enrich means to create extra value within existing data. This can be done, for example, by record linkage, statistical matching or adding data elements to a data set.
Altares - Dun & Bradstreet provides verified data elements in the area of business information. For example, you can add industry and geographical codes, group structures, name and address data and D-U-N-S numbers of your customers, suppliers and prospects to your own databases. This creates a complete and verified picture of your business relationships.
Step 4: Use real-time data and insights
As a finishing touch, you can use additional high-quality data and insights about your business relationships in real time with the Dun & Bradstreet database. This is a form of Data-as-a-Service (DaaS), because you have real-time, on-demand, anywhere, anytime, and in the cloud business data and insights.
Amongst which:
- Credit and performance reviews
- Predictive indicators and models
- Court judgments
- Sanction lists
- Information on ultimate stakeholders
- Expensive master data management tools
Veel bedrijven zien het belang van datakwaliteit in en investeren fors in master data management-tools. Enerzijds heel slim, maar anderzijds is het belangrijk om vóór de aanschaf te onderzoeken of dit soort tools wel nodig zijn.
In fact, by following this roadmap, master data management can be managed in a much better way. After all, the link to the Dun & Bradstreet database prevents master data about your business relations from being recorded differently in different systems and applications, as it is automatically corrected and supplemented everywhere.
Good decision demand good data.
Poor data quality can wreak havoc on your business goals. Even if you have an expensive CRM or ERP: poor data quality leads to bad decisions, no matter what. Or, as Dun & Bradstreet's data guru Scott Taylor puts it: "Good decisions made on bad data are just bad decisions you don't know about yet.
Do you have contaminated data? Then the following applies to the decision-making process: garbage in, garbage out. This step-by-step plan ensures gold-quality data. This will allow you to conclude about your decisions: gold in, gold out.
