“Who has the data has the power!” ~ Tim O’Reilly
Today, as we welcome the information age, we can find data everywhere. Data can be a valuable asset to businesses, providing valuable insights and enabling more effective decision-making, increased efficiency, better risk management, and competitive advantage.
With that, if inaccurate or full of errors, the same data can cause more harm than good. That data is like garbage; if you don’t know how to use that data. Therefore, better know how to maintain a well-executed data architecture before collecting the data.
Now that we understand the importance of data keep reading this blog to learn about data cleaning, the significance of data cleaning, and how you can clean insufficient data.
Data cleanup during a CRM implementation involves organizing and improving the quality of the data stored in the CRM system. It may include tasks such as:
Data cleanup aims to make the data in the CRM system accurate, consistent, and complete so that it may be used efficiently to support company operations and decision-making.
As stated by marketing and sales professionals, about 30% of the data goes bad every year. Inadequate data collection has a negative domino effect on your marketing campaigns and sales, and it can even harm the reputation of your business.
Any information that requires updating, accuracy, duplication, improper formatting, or missing information is considered insufficient data. Your CRM database could contain inaccurate data in several different ways.
Human error: This is the primary cause of inaccuracy. Data entry mistakes, typos, and other human errors can cause data to be inaccurate or inconsistent.
Systemic issues: Technical problems, software glitches, and system failures can cause data to become corrupted or lost.
Incomplete data: When data is not collected or recorded entirely, it can result in missing or incomplete information.
Outdated information: As said by Simms, “Data gets abandoned and forgotten. Make sure it doesn’t.” Over time, data can become obsolete and no longer reflect current conditions or circumstances.
Duplicate entries: Duplicate data is caused by manual data entry, system migrations, or other causes, leading to inaccuracies and inconsistencies in the data.
Poor data quality: Data quality can result from a lack of data governance policies, data quality checks, or a lack of resources dedicated to data management.
Data silos: When data is not adequately integrated or consolidated, it can result in multiple copies of the same data, leading to inconsistencies and inaccuracies.
Data cleaning is essential in a CRM implementation because it directly impacts the quality and accuracy of the data stored in the system and, ultimately, the effectiveness of the CRM itself. Here are some key reasons why data cleaning is essential:
Data cleaning ensures that the data stored in the CRM system is accurate and up-to-date by removing duplicates, correcting errors, and filling in the missing information. This is important for making informed business decisions and maintaining the system’s integrity.
Data cleaning can standardize data formats and ensure that data is consistently entered and stored uniformly. This makes searching, sorting, and analyzing data more accessible and reduces the risk of errors or inconsistencies.
A clean and organized database can improve the efficiency of data-driven processes and reduce the time and effort required to maintain and update the CRM system. This helps ensure that the system runs smoothly and that business operations are not disrupted.
With accurate and consistent data, business users can trust the information in the CRM system and make informed decisions based on that data. This can lead to better outcomes and improved overall performance.
When data is clean and organized, it can be easier for users to find the information they need, increasing user adoption and satisfaction with the CRM system. This is important for achieving a successful CRM implementation.
Data cleaning can be a time-consuming process, especially for large data sets. Still, ensuring the accuracy, consistency, and quality of the data stored in a database is crucial. Here are some steps to clean data:
Bad Data can affect every corner of your business. Any company using a CRM wants to set aside time yearly for data cleaning tasks.
Fixing the problem of data decay is really painless. Many companies set a standard procedure for data cleaning in CRM, which can be time-consuming. The challenge is building a habit you can keep because data cleaning cannot be a one-time thing.
Remember that data cleaning is an ongoing process. Finding smart solutions and process automation can save hundreds of hours. With a set process and the right tool, a company can build enriched CRM Data.
In conclusion, data cleaning is a critical step in a CRM implementation that helps ensure the quality and accuracy of data stored in the system, supports better decision-making, and enhances overall user adoption and satisfaction.