The success of data-driven initiatives for enterprise organizations depends largely on the quality of data available for analysis.
This axiom can be summarized simply as garbage in, garbage out: low-quality data that is inaccurate, inconsistent, or incomplete often results in low-validity data analytics that can lead to poor business decision-making. To avoid the pitfalls of low-quality data, organizations are adopting new data cleansing strategies to elevate and ensure the quality of enterprise data used for analytical purposes.
In this week’s blog post, we’re taking a closer look at the data cleansing strategies that organizations are using to support their data analytics initiatives. We’ll provide a definition and overview of data cleansing, explain why it’s important, highlight common data cleansing use cases, and outline six strategies that your organization can implement to ensure the quality and integrity of your data.
What is Data Cleansing?
Data cleansing is the process of identifying and correcting issues that impact the overall quality of a data set across five dimensions of data quality:
- Accuracy - Ensuring that the recorded data values are as close as possible to the “true” values.
- Completeness - Ensuring that all required data is present in the data set.
- Consistency - Ensuring that data values are consistent within the same data set and/or between data sets.
- Uniformity - Ensuring that data is specified to a uniform standard, including things like units of measure and significant figures.
- Validity - Ensuring that data conforms to predefined business rules.
When organizations capture and store data in its raw format, the data may not be of sufficient quality for immediate use in analytics applications. Through the development of a data cleansing strategy, organizations govern how data should be cleansed, transformed, and prepared before it can be used to support analytics and business intelligence initiatives.
At the tactical level, data engineers can choose from a variety of techniques for data cleansing. These techniques include things like removing irrelevant values from data sets, removing duplicate or incomplete entries and values, correcting typographical errors standardizing naming conventions and capitalization across data fields, filtering outliers and anomalous data, removing observations or log entries with missing values, appending missing numeric or categorical data, merging datasets from multiple sources into one, or categorizing data based on desired criteria.
At the operational level, data cleansing may be performed manually, by a human data engineer/technician, or automatically with software assistance. Data cleansing is sometimes referred to as data cleaning, data scrubbing, or data hygiene.
Why is Data Cleansing Important?
The purpose of data cleansing is to ensure the quality of data that feeds into data analytics applications and business intelligence initiatives.
Organizations who can implement an effective data cleansing strategy should expect to achieve the following three benefits.
More Accurate Insights
Starting with strategically cleansed and normalized data means that organizations can produce more accurate insights through their data analytics and business intelligence programs. Higher quality insights give business leaders the confidence to make smart decisions that can help mitigate risk, drive revenue growth, or enhance business results.
Employees in modern organizations are increasingly reliant on the quality of enterprise data to support daily productivity in their job roles. When the available data is of poor quality (incomplete, inaccurate, nonuniform, etc.), analysts may spend 40% or more of their working time manually validating their data.
An effective data cleansing strategy means that employees across all areas of the business can spend less time on tedious data correction tasks and more time on value-generating activities.
Increased Business Efficiency
When organizations make decisions based on low-quality data, they end up with misconceptions and erroneous assumptions about their performance. This can lead to poor business decisions, misallocation of resources, and failure to identify issues that negatively impact the customer experience. All of these inefficiencies have a real economic cost: a Gartner survey estimated that poor data quality costs organizations an average of $12.8 million per year.
With an effective data cleansing strategy, organizations can start to eliminate the waste and inefficiencies that result from poor data quality.
3 Data Cleansing Use Cases
Up until now, we’ve discussed data cleansing strategy in general terms and treated all enterprise data as one and the same.
But the truth about data cleansing is that your strategy will always vary depending on the type of data you are working with and its intended purpose(s) within your organization. To illustrate this point, we’ve highlighted three of the most common use cases for data cleansing in enterprise organizations and how the associated strategies might be different.
B2B Data Cleansing
Compared to business-to-consumer (B2C) sales, business-to-business (B2B) sales are usually characterized by a higher price point, a longer sales cycle with more customer touchpoints, and multiple customer stakeholders. To manage this complexity, organizations who sell B2B use a customer relationship management (CRM) software tool to collect, store, and organize structured data about prospective customers.
Data from the CRM is used by sales teams to manage relationships with prospective customers at every step in the marketing/sales funnel. As a result, sales agents depend on the accuracy and completeness of CRM data to be productive in their roles. When CRM data is incomplete, inaccurate, or duplicated, agents waste time manually searching for phone numbers and email addresses instead of generating high-quality conversations with prospects.
An effective data cleansing strategy for our B2B example might include tactics like:
- Removing duplicate CRM entries.
- Removing incorrect or outdated contact information.
- Standardizing data between marketing and sales teams.
- Appending missing contact information from other sources.
Log Data Cleansing
Applications, network devices, and endpoints all generate log data that can be analyzed to support IT functions like network security and application performance monitoring. Log data is machine-generated and written into log files, usually as unstructured or semi-structured text data. Before this data can be effectively analyzed, it must be captured, stored, parsed into a machine-readable format and cleaned to ensure high data quality.
An effective data cleansing strategy for our log data example might include tactics like:
- Identifying and parsing log data automatically (using a data platform like ChaosSearch).
- Removing duplicate logs to save storage space.
- Removing or selectively retaining logs with a specific status code.
- Standardizing the format of log data from multiple sources.
Transactional Data Cleansing
Organizations generate transactional data whenever they make a purchase or complete the sale of a product or service. Transactional data includes information about the customer (personal data, payment card information, etc.), the product or service being sold (name, price, SKU number, etc.), as well as transaction metadata (sale ID, timestamp, etc.).
Business analysts and accountants rely on high-quality transactional data to develop insights that help the organization better understand the behavior of its customers, identify high-performing products and services, and measure its financial results.
An effective data cleansing strategy for our transactional data example might include tactics like:
- Removing credit card information to comply with the PCI DSS standard.
- Anonymizing data to protect consumer privacy.
- Converting coded data fields into a human-readable format.
- Standardizing transactional data formats across multiple revenue channels.
6 Data Cleansing Strategies to Improve Your Data Quality
1. Build a Business Case for Strategic Data Cleansing
Poor data quality is already costing organizations millions of dollars every year, but many still haven’t discovered the connection between data quality improvement and enhanced business results.
Building the business case for data cleansing within your organization requires a clear understanding of your strategic business goals and how those goals might be supported by enhanced data quality. You’ll also need to identify KPIs that can be used to measure the performance of data cleansing initiatives, and estimate the financial impact of improving the quality of your data.
2. Develop a Data Quality Plan
Once you have successfully advanced a business case for strategic data cleansing, it’s time to create a data quality plan. A data quality plan is a project plan for improving the quality of your data.
Your data quality plan should identify which types of data will be targeted and the biggest quality issues present in those data sets. It should identify which data cleansing tactics and techniques will be applied and which software tools will be used to support the process. Your plan should also establish roles and responsibilities, along with a clear definition of success for your data cleansing initiative.
3. Standardize and Validate Data as You Capture It
Standardizing data as it is captured is one of the easiest ways to enhance the consistency and uniformity of data collected by your organization. This means applying data entry standards, such as requiring specific data fields to be completed in a valid format before the data is submitted to your organization or added to a database.
You can also improve data quality by validating it at the point of entry. Information like phone numbers, emails, and credit card numbers can be validated by software or authenticated by the user in real-time to reduce the number of false entries and preserve the integrity and usability of data sets.
4. Choosing the Right Data Cleansing Techniques
Which data cleansing techniques and tactics will you apply to your data?
Ultimately, that depends on what types of data you generate and how the data will be used to support the business goals and objectives identified in your business case and data quality plan. Regardless of the specific application, you’ll usually want to do things like:
- Remove irrelevant data
- Remove duplicate entries
- Standardize data across multiple sources
- Remove outliers and anomalous data
- Remove or append incomplete data entries
To move beyond the basics, you’ll need to think more deeply about each targeted data set and how its quality can be improved with data cleansing. You’ll need to ask questions like:
- What data fields are most important for this data’s intended purpose?
- Are required data fields often missing? How should we address that (by appending data, removing the entry, sourcing it from elsewhere, etc.)?
- How should data in each field be formatted?
- How should similar data from multiple sources be standardized or normalized?
5. Cleanse Data Directly in Cloud Storage
Organizations that store data in the cloud can use software solutions like ChaosSearch to clean, prepare, and transform data directly in cloud storage buckets.
While traditional databases use a schema-on-write approach that makes it complex and time-consuming to clean and process data, ChaosSearch has innovated a schema-on-read approach that gives organizations the ability to apply customized data cleansing strategies to data directly in cloud storage with no need for data movement or reindexing.
With the ability to strategically cleanse data in cloud storage buckets, organizations can save time and money in the data cleansing process while accelerating time-to-insights and maximizing the value of their data.
6. Automate the Data Cleansing Process
Software technologies that automate the data cleansing process help organizations accelerate the development of insights and reduce the cost of maintaining high-quality data sets.
Data cleansing automation can be executed using Regular Expression (RegEx) functions, scripts that check for patterns in strings of text and execute predefined operations on them. Regex expressions can be used to clean and transform data in a variety of different ways, ensuring its quality and preparing it for use in business analytics applications.
Implementing Your Data Cleansing Strategy
The ability to make decisions based on high-quality data is a competitive advantage for modern organizations that leads to increased revenue, greater efficiency, and better responsiveness to customer needs. We also know that organizations are losing millions of dollars every year because of issues related to low-quality data.
If you’re concerned about data quality in your organization, it’s time to start building the business case for data cleansing, documenting a data quality plan, and investing in technologies that automate the data cleaning process and accelerate time-to-insights for your data.