Data Cleansing 101: Introduction

Small businesses are inundated with data. From transaction and customer information to financial records and operational reports, SMBs are constantly creating, collecting and storing data.

Used effectively, this data makes it possible for small businesses to discover market trends, improve the customer experience and drive increased revenue. The challenge? Data isn’t inherently valuable. Accurate, relevant and timely data — also known as “clean” data — is useful. Incomplete, inconsistent and invalid data — or “dirty” data — is not.

Data cleansing can help companies better manage data volumes to deliver operational value. Here’s how it works.

What Is Data Cleansing?

Data cleansing is the process of removing or modifying detrimental data with a given data set.

Consider a set of digital financial records. If these records contain multiple copies of the same data, such as invoices, companies could find themselves double- or triple-paying these invoices if the duplicates aren’t detected. Conducting data analysis before taking action makes it possible to pinpoint potential duplicates or discover data that isn’t accurate.

Dirty Data Dangers

Given that most of the data collected by companies serve some useful purpose, it can be tempting to ignore the risks of dirty data — how could a few duplicate or inaccurate records hurt business bottom lines?

In practice, dirty data come with cost, performance and operational concerns. From a cost standpoint, dirty data can lead to losses of $100 per bad record. Performance-wise, dirty data slows down operations as users sort through duplicate information, and when it comes to operations, fragmented or inaccurate information can lead to bad decision-making.

Consider an investment strategy based on poor-quality data that doesn’t represent current market forces. In the best-case scenario, SMBs discover their mistake and lose time correcting their course. In the worst case, investments based on bad data cause significant revenue loss.

Keeping it Clean: Steps for Success

With SMBs now collecting data on every transaction and interaction, cleansing can seem like an overwhelming task. Here are four steps to help drive success.

• Set Your Goals

Before diving into data cleansing, companies need to set goals and expectations. These include a timeline to detect and remediate dirty data, what key performance indicators (KPIs) will be used to measure the efficacy of cleansing efforts and which teams will be responsible for data cleansing.

• Find Your Data

Next is finding your data. This means evaluating and auditing all data to find errors, discover missing data, pinpoint incorrect formatting and locate duplicate records. Given that many of these datasets are stored across both local and cloud-based servers, it’s worth leveraging automated solutions where possible. This helps reduce the time and effort required to find dirty data and gives companies a head start on the cleansing process.

• Standardize Cleaning Processes

Once dirty data has been identified, SMBs need to create and apply standardized cleansing processes. This means creating a framework that describes both the techniques and toolsets used to clean data and confirm its accuracy. Standardization is a critical part of data cleansing operations — if different teams use different methods to clean data, the resulting disparity can create an entirely new set of dirty data.

• Fill in the Gaps

With dirty data identified and removed, SMBs can turn their attention to filling in the gaps. For example, if specific aspects of customer data are missing, it’s worth going back to customers and asking for additional input or using adjacent data sources to complete these records.

Cleanliness Is Next to SMB Success

Data drives business operations. Dirty data, however, is a constant companion of its clean counterpart. As a result, regular data cleansing and data analytics software are critical for companies to ensure they’re using accurate, relevant, and complete data sets. For more information on the problems with dirty data and data cleansing, please see the accompanying resource.

Author bio: Julie Sciullo is CEO of Association Analytics and founder of Acumen. Under Sciullo’s leadership, Acumen is the No. 1 provider of data and analytics software to the association community. In her role, and through the introduction of Acumen, she has set out to change the way associations do business by giving them the ability to use data to make decisions with confidence and leverage insights that drive engagement and profitability.