Melissa AU Team | Australia, Data Cleansing | Australia, autocomplete APIs, data cleansing, data hygiene, important, strategy

Netflix uses data to understand customer preferences and create blockbuster series based on them. Coco-Cola leveraged data analytics to personalize advertisements. UBER identifies trends to set dynamic prices and solve the demand-supply gap.

Today, data plays a critical role in determining the success of a business idea. That said, it isn’t just the quantum of data available to a company that matters. For data-driven decisions to be beneficial, the data must be reliable, clean and accurate. This is where data cleaning comes in.

What Is Data Cleaning And Why Is It Important?

Data cleaning is the process of checking all incoming and existing data to ensure that it meets data quality criteria. To be qualified as good quality data, it should be

• Accurate
• Valid
• Complete
• Unique
• Relevant
• Formatted

Data quality is a concern for businesses of all sizes. The data cleaning process looks at all the available data from the point of view of these quality dimensions and corrects and organizes it accordingly. It could involve correcting typographic errors in email addresses, updating details like street names, standardizing date formats, de-duplicating files, etc.

Data cleaning needs to be a continuous process. Doing so ensures that the database stays relevant and maintains a high-quality standard. Further, it increases the efficacy of data-driven decisions and provides valuable insights.

When it comes to the financial side of things, studies have shown that relying on poor-quality data costs organizations up to $15 million annually. Paying attention to data quality can minimize such losses. It’s also important to note that the cost of cleaning data increases over time.

According to the 1-10-100 principle, a data quality issue that would cost $1 to correct at the initial stage would cost $100 after it causes a problem.

Proven Strategies To Data Cleaning

If data cleaning hasn’t been a concern until now, here are 5 steps to get started.

1. Prove The Value Of Data Cleaning For All Departments

Maintaining good quality data isn’t a responsibility only for the IT department. High standards can be achieved only when the IT team and the data users work together. Every individual on the team needs to understand the value of clean data and how it can influence the company’s operations and profitability.

Prove this by putting together a business case for data quality that addresses the priorities of all the key stakeholders. This should highlight the reasons for implementing data cleaning processes and the impact they can have on every department.

For example, the sales team needs to understand how better quality data can improve their conversion rates while the customer service team must be able to see the difference clean data will make to order delivery timelines.

2. Allocate Responsibilities

Once the importance and need for clean data have been understood, roles and responsibilities must be allocated. Every organization needs a data quality team to take ownership of the cleaning process and its outcome. This team must consist of:

• Data owners who are held accountable for maintaining data quality
• Data stewards who are responsible for ensuring that the data management rules and guidelines are followed
• Data consumers who define data quality standards
• Data producers who are responsible for data capturing
• Data analysts who derive actionable insights from data
• Data custodians who maintain data on the company’s IT system

Every organization will have a different approach to allocating these roles according to what fits their needs and company culture.

3. Create A Data Cleaning Strategy

Next, the data quality team must create a cleaning strategy. To do this, they must identify all the possible data quality issues and strategize a solution for each. For example, if there are two customer files by the same name with different spellings, which file should the name spelling be taken from?

Similarly, if an organization realizes that the street address field is most at risk of errors, the organization may decide to use an address autocomplete tool to minimize this risk.  

Some of the other questions that need answering include:  Which field is most important for every data set? How can missing values be added to the record? What type of data is relevant and what is irrelevant?

4. Define Standards

Having clear data formatting standards can greatly reduce the risk of having duplicate records. It also makes the data comparable.

For example, the format of a customer’s name may be standardized such that it includes the first name and last name. So, if a customer has a record by the name ‘John Smith’ and but gives his name as ‘Mr. Smith’ when he calls the customer service team, there’s no risk of a duplicate record being created.

Similarly, by standardizing phone names to be written with the area codes, the sales team and data analysts can get a better understanding of customer demographics.  

5. Choose a Software

Lastly, you need to pick a data cleaning software. This is not a process that can be managed manually. Manually looking into each record takes time, is expensive and still has a risk of error. The good news is that there are many software solutions available that can automate data cleaning.

Some software is cloud-based while others need to be installed locally. You can also find data cleaning tools that specialize in CRM and those that offer a visual data cleaning interface.

When you’re comparing tools, look at the features offered, the availability of API connectors, integration capabilities, the annual cost of use, etc. You also need to consider the ease of using the software and whether the user needs coding experience or not.

Wrapping it up

Data cleaning plays many roles; it minimizes errors, reduces the risk of redundant data, makes data reliable, ensures consistency and completeness and makes it useful for data analysts. Ideally, data should be cleaned at the time it enters the database as well as regularly while it exists in the database.

Automating this process with the right software ensures that high data quality standards are maintained in the most timely, cost-effective manner and gives companies reliable data that can be used to improve their productivity, make smarter decisions and ultimately increase their profit margin.