How is Bad Data Crippling Your Data Analytics?
As data touch-points increase and businesses get access to more data, it is critical to design and implement good data governance mechanisms to maintain data quality.
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There are tons of data businesses can collect about their customers – you can get to know basic demographics, earning profiles, shopping behavior, tastes, and preferences, etc. In theory, this means that companies can make data-driven decisions and assure the success of their ventures.
A recent survey showed that only 54% of all marketing decisions were influenced by marketing analytics – the reason, poor quality data that makes analytics unreliable. As data touchpoints increase and businesses get access to more and more data, it becomes critical to design and implement data governance mechanisms else this data will be nothing but a white elephant.
What Is Bad Data?
Before you can address data quality issues within the organization, you must understand what bad data is. According to a Harvard Business Review study, only 3% of the data held by organizations meets data quality standards. Around 47% of data has at least one error that could impact work. Some of the points defining bad data are:
- Inaccurate – misspelled detail or wrong numbers
- Static- not have been updated since it was entered and hence not reflective of the current status
- Inconsistent – not follow preconceived patterns
- Incomplete - fields left blank
- Not unique - the presence of duplicate records
- Noncompliant – not meeting regulatory standards
- Unsecure – vulnerable to theft or pollution due to lack of monitoring
The GIGO rule holds no matter how much technology evolves. Garbage In will always result in Garbage Out. Thus, the effect of bad data is only compounded when it is used for business analytics.
Challenges Facing Data And Data Analytics
In today’s data-driven environment, data is the foundation for business outcomes. When data meets quality standards and is in context, it provides information. When that information is actionable, it becomes knowledge, and applying this knowledge can drive a positive business outcome. But, when the data itself is not reliable, it results in a risky business outcome. There are two main challenges to be faced here.
- Bad Quality Data
You cannot overemphasize the need for clean, good-quality data in analytics. Decisions taken on data analytics based on bad data could lead to the wrong diagnosis, missed opportunities, operational inefficiencies, poor risk assessment, etc. that hinders the customer experience and causes financial losses. It could also affect the organization’s reputation. Did you know that poor data quality could be responsible for an average of $15 million in losses each year?
The oldest example of how bad data could affect the outcome of a decision - Christopher Columbus discovering the Americas instead of India because he used Arabic miles instead of Roman miles in his calculations. More recent examples- Kodak choosing to not use digital technology as they thought it wouldn’t satisfy customers and Blockbuster not buying Netflix when they had the opportunity.
- Disconnected Sub-Systems
The way data is stored also contributes to the problem. Using legacy backend systems could cause a lack of continuity and connection between the backend, middleware and front-end systems. The creation of duplicate records is the most common fallout. For example, a company may have separate records for a single customer in the sales department and accounts department. This could leave you with an incomplete customer profile and affect the reliability of decisions taken based on this data.
Ways To Maintain Data Quality In Data Analytics
The only way to avoid such disasters in your story is by improving the quality of data being entered into your database and regularly evaluating data to make sure it meets your data quality standards. You also need a single data platform that integrates the backend, middle and frontend systems for maximum efficiency. Here’s what you could do.
1. Check All Incoming Data For Completeness And Validity
The cost of validating and verifying data when it is being entered is much lower than the cost of correcting data in an existing database. Check all data as it is being entered to make sure it is correct and complete. Using an autocomplete tool could help.
For example, customers may miss out on entering the pin code in their address but using an address autocomplete function makes sure all addresses entered are complete. Similarly, verifying identities, addresses, and phone numbers at the time of data entry makes sure all data records are accurate and valid.
2. Maintain Consistent Formatting
Imagine the chaos if you entered prices in dollars in the sales records but used the INR format for the accounts records! Consistency in formatting is critical to ensuring your data is usable and relatable. It’s not just currency, you need consistency for address fields, date formats, product SKUs, etc. In terms of the product SKUs and categorization, you also need to keep the formats simple.
3. Create A ‘Golden’ Record
The presence of duplicate records in an organization can lead to many problems. Duplicate records could skew analytics and affect the reliability of the decisions taken. For example, let’s say a company with 100 customers had 60 orders from city A and 40 from city B. But, all the records from city A were duplicates, which means, there were only 35 customers in the city.
This would mean that the company actually had a better presence in city B than city A. Organizations need to structure their data such that a single ‘Golden’ record is created for each customer. This data must be accessible to all departments so that they can refer to it when needed but do not need to create another set of records.
You may have the best product but bad data can destroy your business value. In light of the rising importance of data quality in data analysis, analytics teams are set to grow over the next few years. Taking small steps towards implementing good data governance plans like regular data verification, consistent formatting, and removing duplicates can have a big impact on improving data quality and making data-based decisions more reliable. It’s all about having data you can trust.
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