Data Cleansing: The Impact of Data Quality on Business Performance
Data is at the core of every business. Companies rely on data to gain insights for business development and decision-making. This means that data cleansing plays a key role in the performance of your business initiatives.
High quality data leads to better analytics and business intelligence, encouraging smarter decisions, thus increasing overall success. Data management is therefore crucial; a clean and well-maintained database will help to ensure you’re always working with high quality data.
If your company is working with low quality data, this would affect all departments from finance to compliance, marketing, sales, product development, customer relations and more.
Research shows that 94 per cent of businesses suspect that their customer and prospect data is inaccurate and, on average, 25 per cent of these are critical errors.
These are concerning statistics as the longer inaccurate data remains in a database, its associated costs and harms exponentially increase.
The “1-10-100 rule” illustrates that it costs $1 to verify an entry as it is entered, $10 to cleanse it, and $100 if nothing is done.
The Importance of Data Cleansing
Clean data is high quality, accurate and complete; it empowers you to make important decisions. It generates useful insights and keeps your business communications and services relevant and valuable.
Working with clean data enables businesses to:
– Increase profits and operational efficiency
– Better understand customer credit risk and the entities they work with
– Perform better credit risk management and reduce commercial credit risk
– Improve product and service offerings
– Refine marketing and sales methods; increase conversion rates
– Maintain long-term customer relationships and increase customer satisfaction
– Discover new opportunities within existing or brand-new markets
Unfortunately, when working with high volumes of data from different sources stored in various locations, it is easy for this data to become “dirty”.
Harmful Effects of Dirty Data
Dirty data refers to information that is incorrect, outdated, invalid, irrelevant, incomplete, inconsistent, or wrongly formatted.
This could be caused by a variety of reasons including user entry errors, corruption in transmission or storage, security flaws and hardware failure.
Here are some ways dirty data could harm an organisation:
- Reduced productivity (for instance, wasting time and resources to process incorrect data and to confirm its credibility)
- Creation of invalid and useless reports which could harm decision-making (40 per cent of business objectives fail due to inaccurate data)
- Internal and external communication breakdown due to incorrect data
Finance & Accounts
- Incorrect customer records could lead to invoicing errors
- Missing information about customer credit risk could lead to poor assessments of creditworthiness
- Incomplete understanding of the types of entities you’re dealing with could lead to costly decisions
Marketing, Sales & Customer Relations
The success of any customer relationship management system (CRM) is predicated on clean customer data. When such data is dirty, consequences include:
- Poor lead generation, conversion rates and revenue (organisations with clean data are estimated to have a 25 per cent higher conversion rate between the inquiry and marketing qualified lead stages)
- Ineffective marketing campaigns and lower customer acquisition rates
- Poorly executed marketing strategies due to inaccurate data analytics, audience segments and retargeting
- Harm to brand image and reputation
- Poor customer experience
Cleanse Your Data with a Credit Portfolio Health Check
Information stored in databases gets outdated over time as individuals change jobs or positions, and companies merge or shut down. Data cleansing involves detecting all the dirty data within a database and removing or updating that information. It corrects and consolidates data to ensure your system performs at peak effectiveness.
A Health Check ensures quality data through the following steps:
- Data validation
- Data standardisation
- Eliminating duplicate data
- Data matching
- Data enhancement
- Data reconstruction
Data validation ensures your database is authenticated and contains correct and useful information.
It detects and removes errors, certifies that data processes are uncorrupted, and confirms that your data is fit, accurate and consistent for various system inputs.
Data validation rules can be designed and deployed in various contexts. Examples include:
- Allowed characters – checks that a field only contains specific characters; e.g. numeric fields may only allow the digits 0–9 and phone numbers must contain 10 digits.
- Consistency – checks that data correspond to their respective fields
- Spelling and grammar – checks for spelling and grammatical errors
Data standardisation is the process of taking data from different sources, formats and naming conventions to transform them into a cohesive data set with a common format, enabling effective collaboration and large-scale analytics.
A standardised list ensures accuracy and consistency. This is crucial in business decision-making; for instance, key marketing activities like lead scoring and nurture messaging rely on accurate and consistent data about job title, industry, state and country.
Eliminating Duplicate Data
It is important to remove duplicate files as they could cause potential harm. For instance, duplicates could skew data resulting in inaccurate records and misleading results. Also, sensitive data from a private database could be replicated in a public folder, resulting in the information being subject to misuse.
Data matching is the process of cross-checking the information in your database with external data sources. This involves comparing information from various validated sources to identify, match and merge records that correspond to the same entities. The ATO, for instance, uses this method to identify people and businesses who fail to comply with their tax obligations, detect fraud against the Commonwealth, and recover debt.
This is the practice of making data more complete by adding supplementary information. For example, adding all related phone numbers to each address, or enhancing a list of loan applicants with their credit scores.
Data reconstruction is the overall process of transforming dirty and disorganised data into clean and accessible information to solve business challenges.
It involves the following steps:
- Outlining goals and identifying relevant data sources
- Extracting and combining data
- Standardising data
- Understanding and validating data
- Analysing data
- Providing insight and solutions
In an ideal world, everyone would be consistently proactive to ensure that all data is correct, clean, complete, formatted and verified before it enters their CRM. Yet this still wouldn’t be enough to achieve clean data!
Regular data cleansing would still be necessary as data that is manually entered is prone to human error. Also, data will naturally become outdated and require updates. Furthermore, it could also get corrupted in transmission. Not to mention, you might also be working with limited data sets which could benefit from enhancement.
It is important for businesses to establish data management processes across all departments. Data cleansing is a part of a wider data management strategy which would often include database management, data security, data storage, data sharing and other practices to ensure data is well maintained.
Get a Credit Portfolio Health Check Now
As part of our credit risk management services, CreditorWatch conducts a Credit Portfolio Health Check to provide a comprehensive review of your database. This extensive data cleanse empowers businesses to easily validate information, identify risky customers (or suppliers), reduce commercial credit risk and perform due diligence on a database.
Utilising large data-sets available through our credit reporting platform, a Credit Portfolio Health Check cleanses your database by performing the key tasks of data validation, data standardisation, eliminating duplicate data, data matching and data reconstruction.
We also have a robust data enhancement procedure in which we append vital information from ASIC, ABR, AFSA, Australian courts, mercantile agents and CreditorWatch to each record. There is even an option to include a visually appealing, in-depth analysis on the enriched database.
Wondering whether a Credit Portfolio Health Check is right for your business? Check out this case study on CreditorWatch customer SecurePay to see how they benefitted from a data cleanse.
Get in touch below for a free consultation with one of our data cleansing experts.