Whether you want to understand market conditions, customer behavior or campaign efficiency, you need data. Data can be a catalyst to streamline business processes, align campaigns with customer needs and create exceptional customer experiences.
But, if the data you’re looking at is inaccurate or out of date, decisions based on it could have the opposite effect. Bad data can wreak havoc on decision-making, mar your reputation and have expensive repercussions. Take Samsung for example. A data error cost the company $105 billion!
Hence the need to prioritize fighting against bad data. The good news – there are tools that make it easy to maintain a clean database. Let’s find out more.
What is Bad Data?
Recognizing bad data is the first step to keeping it out of your database. Broadly speaking, bad data refers to data that does not meet the expected quality standards. Some of the common reasons why data may be categorized as bad data are:
- The values are incorrect
- It misses out on key information
- It contains non-standard attributes
- It is identical to data existing in the database
- It is outdated
- It is similar to existing records but has conflicting attributes
The quality of your database impacts everything from daily operations to how you set long-term goals. Bad data may result in launching a product at the wrong time and place, delay deliveries, cause compliance issues and so on. According to a study by Gartner depending on poor data like this can cost organizations an average of $12.9 million annually.
Dealing with Bad Data
Bad data can sneak into your database in many ways – a typographic error, integration errors, the use of outdated systems, a lack of due diligence when importing data and so on. Sometimes, data that met quality standards at the time of entering the database may decay with time to become outdated. Hence, fighting bad data should be seen as an ongoing process rather than a one-time exercise.
Here are 5 steps to keep bad data at bay.
1. Define data quality standards
Establishing standards is the first step to improving your overall data quality. These are usually focused on the key data quality dimensions. For example, a customer must have a first and a last name to ensure completeness. You can establish similar standards for the number of digits in a phone number to maintain consistency.
Data quality standards may vary according to requirements. For example, social media campaign metrics may need to be refreshed every hour to be considered updated while email campaign results may be updated daily.
2. Profile incoming data
Given that a significant amount of data will be collected through external sources, it is important to profile all incoming data before it is added to the database.
Data profiling refers to examining the values, formats and relationships between data fields to determine its quality and legitimacy. Data profiling can immediately identify records with unknown or null values or those with unusually high or low values and thus keep them out of your database.
It can also help identify data quality issues that need to be fixed at the source to keep similar issues from arising in the future. For example, adding a field for area codes can help you get complete addresses.
3. Automate data verification and validation
All data must be regularly verified. This involves comparing the data against reliable third party sources to ensure it matches. For example, customer addresses may be checked against postal records. This can be automated with data verification tools. In addition to comparing data and identifying missing or inaccurate details, these tools can also enrich the data according to the predefined standards. For example, it may change outdated street names to reflect the current names.
This should be applied not only to incoming data but also data already existing in the database. Regular validation is the easiest way to fight data decay.
4. Enforce data integration
The more data systems you manage, the harder it is to maintain data quality. Not only is it harder to access, but data stored on multiple databases also increases the risk of duplicity. That said, it may not be possible for all datasets to live in the same system. After all, according to the latest estimates, we generate about 402.74 million terabytes of data every day.
Hence, you need to enforce foreign key constraints, processes and applications that maintain the referential integrity of data across systems. For example, customer details and order details may be linked through a customer ID.
5. Eliminate duplicate data
Duplicate data or data redundancy is one of the biggest quality concerns for any organization. While it may seem inconsequential, having multiple records for the same data can skew analysis and thereby impact the reliability of data-driven decisions.
Verifying data and standardizing formats are critical to fighting data duplication. For example, standardizing mobile phone numbers to include only 10 digits can keep you from having 2 records for the same person with 01234567891 and 1234567891 as their phone number.
Records may also be duplicated accidentally with time as they are used for different purposes. Establishing a strict data governance program with clearly defined data owners and regular auditing can help minimize this.
Choosing a Tool To Make Data Quality Management Easier
Given the volume of data being dealt with, manually checking records and verifying them is next to impossible. This is where it becomes imperative to choose the right tools. Automated data quality solutions can help you maintain a high-quality database in the most cost-efficient way. Look for a data quality solution with the ability to:
- Profile incoming data
- Automatically cleanse and standardize data
- Verify customer details such as names, phone numbers, addresses, etc.
- Enrich customer data with demographic and other information
- Match and consolidate data to eliminate duplicates and maintain a golden record for each customer
Above all, ensure that it has a user-friendly interface, can be easily integrated with your other systems and applications and is built for growth.