Trustworthy Data; How Much Can You Trust Your Data?

Negative Effects of Data

 

One might easily presume that there could be no harm to having a large amount of data, after all, all data is useful, right? Unfortunately, this is not the case. Bad data is a real threat to companies everywhere around the globe. It would not be an understatement to say that bad decision can be made due to bad data, which in turn, can cause a lot of problems to the business and the damage is not only limited to time or money but can cause an impact on critical business decisions. Some of the problems created by poor quality data include damage to the brand’s reputation, inaccurate customer profiling, and impediment in lead generation or even worse, it could lead to violations of data protection laws. As per a research conducted, not having proper data causes business a loss of approximately 30% in revenue.

Though there is no instant solution to remove bad data, through initiating the proper process of data cleansing and data management, you can ensure that your customer database is accurate and up to data as well as it contains only information that is needed, not anything else.

What Is Considered As “Bad Data”

 

Besides people providing wrong information in order to avoid getting marketing material, bad data can quite easily be created, especially if you have a very large database, containing millions of records and/or is gathered from several channels. However, to help you understand bad data, below are three of the major categories that it may fall under.

Irrelevant

 

Organization tends to collect a vast amount of data throughout the years, this, however, does not mean all the data they have collected is useful data for their business. Firstly, the organization is up keeping the high cost of storing this data; furthermore, bear even more cost to process it. This, in plain words, is a waste of valuable resources that could have been utilized someplace else. Not to forget storing data that is the company is unaware of drastically increases the risk of non-compliance. Sadly, many companies are currently wasting a whole lot of money redundant data that is doing no good to them.

Duplicated

 

Another factor that adds to the unnecessary cost of data storage is storing multiple duplicated data. This also serves as the worst nightmare of marketers as it also adds to the inaccuracy of data. One of the main causes of the duplication of data is the storage of data in multiple repositories. Moreover, duplicate data is also accumulated when new data is gathered or purchased. The origin of duplicate data is mostly due to human error, whether it may be entered incorrectly by a customer or by another point of data entry. Even the smallest variation in the spelling of a name, address or any other contact detail that is recorded can lead the creation of duplicated data.

Obsolete

 

This is one of the unavoidable forms of bad data, simply because it is due to natural or uncontrollable causes. It is estimated the every year marketing databases decay 25% to 40% simply because of the reason that people change their names, move to a different address or switch jobs.

These reasons for bad data create the need for efficient and regular data cleansing as well as data management.

Data Cleansing, the Solution to the Problem

 

As stated earlier data cleansing or data management is the solution to the problems that arise due to bad data. Data cleansing generally includes the methodology of detecting and correcting the corrupt, invaluable or inaccurate data within a database, thus increasing the productivity, accuracy, and usefulness of the stored data. Generally, the process of data cleansing encompasses the below-mentioned stages.

Analysis

 

This stage of data cleansing involves identifying and removing of errors and inconsistencies with the use of detailed reports. Mostly this will include the screening of data related to address verification, inadequate words, user suppression, mortality or bankruptcy.

Removing Duplication

 

Though this requires one of the most difficult compression techniques and are hard to get right, this moves the data to what exactly every business is aiming for, which is, having one record per customer. To create a consolidated record, it is essential to match and merge records derived from existing records from different systems. While at merging stage, it is best to get a hold of records that are most recently accessed and contain fields that are more complete or have external reference numbers, in order to create data of higher quality.

Standardization

 

This step is the key to maintaining a good and efficient database. Eliminating the “garbage in, garbage out” policy, this step focuses on creating a standard and consistent format for data entry, in order to prevent duplication of records. Common practices of standardization include widening of acronyms, standardizing dates and removing punctuation. As long as an organization thoroughly follows this step, it can benefit from a significant increase in productivity.

Data Enhancement

 

To further enhance your data through the data cleansing process, it is best to add extra information along with your records once they have been cleansed. The best way to enhance your data is to tag along features with your records like property characteristics, demographic characteristics, financial characteristic, and behavioral data. These will, in turn, add more capabilities for your marketers to gain valuable and precise insight while utilizing customer records.

Priority of Data Cleansing

 

Many organizations today are heavily investing their time and money in gathering data from various sources, but not putting in as much investment in maintaining the database could result in their efforts going in vain. Similarly, having a good marketing team but not having proper tools could mean losing ROI on the marketing campaign, as inaccurate data can lead to inefficiency of the campaign. Though it may seem like a continuous and ongoing process of data management but you can only rely on your data as long as it is trustworthy.