Being data-driven is an ambition for most companies today, however, data quality is an underlying challenge that hinders companies from following through with this ambition. To be data-driven, companies need data cleaning solutions to ensure raw, dirty and bad data does not affect their transformation plans.
Data quality refers to the health of your company’s data. Do you have data plagued with problems like:
- Inaccurate information
- Invalid and incomplete information
- Typos, character errors, punctuation issues
- Duplicate data that affects data quality
- Incorrect formatting and messy data (upper/lower case, inconsistencies etc)
If you said “YES” to all these, you’ve got a data quality crisis.
And this is why you need to implement Data Cleaning.
In this detailed guide, we’ll cover:
- What is Data Cleaning
- How Does Data Cleaning Help Businesses
- Characteristics of High-Quality Data
- Available Solutions & Best Practices
Let’s get started!
What is Data Cleaning?
Data cleaning – also known as data scrubbing, data cleansing is a process that makes data usable. It “cleans” duplicate data and also helps with data transformation. Broadly referred to as data cleaning, the process involves:
- Deduplicating data and removing redundancies
- Fixing incomplete or invalid data
- Formatting and standardizing data
- Transforming messy data into usable data
With effective and regular data cleaning, your data sources will be prepared for its intended use – free of damaging errors and messy mistakes.
How Does Data Cleaning Help Businesses?
Data cleaning is not just an IT problem. Across the organization, departments collect data from a range of connected applications and activity logs. Each of these departments need data for analysis, statistical report creation and making strategic business decisions.
Here’s how data cleaning can help different departments of your organization:
Data Compliance: In an age when governments across the world are regulating data collection, organizations need to make sure that they are following data regulations and are data compliant. For example, an e-commerce retailer could be facing penalties from the government if they don’t meet data privacy regulations. To meet these regulations, the business must process their data within the GDPR framework by ensuring that customer data is up-to-date, clean and accurate records are kept. Data inconsistencies in records could affect GDPR compliance goals.
Unifying Disparate Data Sources: An organization may have multiple data sources collecting and storing different kinds of information on an entity. There is always a high possibility for these data sources to store duplicate data. For example, if marketing and customer service use different CRMs or systems to record the contact details of an entity, it means the company has to deal with duplicated data entered in different formats and styles.
Customer Service: A customer service department failing to address customer issues due to wrong, incomplete or invalid address data. An email sent to the wrong ID. An email using the wrong spelling or name of the customer. These are all examples of how bad data can hamper customer service. Clean data will ensure that you have the right and updated contact information to deliver optimal services.
Operational Efficiency: Clean data helps companies create processes and we all know clearly defined processes help with operational efficiency. Take for example, our client Zurich Insurance, being able to improve their operational efficiency and increase their ROI when they were able to identify the errors in their data and cleaned their data of duplicates, typos and messy errors.
Marketing: No other department in an organization is tasked with the burden of maintaining high-quality data than the marketing department. Whether it’s email campaigns, social media campaigns, advertising or any other activity, consumer data is at the forefront. Wrong data can result in disastrous consequences. It’s not uncommon to see companies sending a campaign mail to the wrong audience set.
Sales: As much as customer data is important for marketing, it’s also important for sales. In fact, sales data is the most important data that gives an organization details on ROI, revenue and profitability. Data cleaning enterprise tools are usually deployed in sales departments to deduplicate sales records. If neglected, duplicated sales records may give skewed ROI reports and affect the overall organization.
These are just some very basic examples of the consequences of bad data. The day-to-day struggles companies have with bad data are deeply ingrained in company processes and take considerable effort from managers and executives to solve.
If an organization makes data cleaning as a priority, they will be able to avoid all these problems and avail the benefits of high-quality, clean data.
What Makes High-Quality or Clean Data?
While it’s important to clean data, how do we know what makes data high-quality? There are a few “standards” that are widely used in the industry to measure the quality of data. The whole purpose of data cleaning is to achieve these standards which can be defined as any data that is:
Valid: There are certain rules applied on data sources. For example, one of them being, that all addresses must consist of ZIP Codes or all phone numbers must be written with accompanying country + city codes. Data fields that do not meet these validity rules are considered invalid. For example, addresses without complete ZIP codes are considered invalid. Validity rules are defined by business rules or constraints for example:
- Important columns such as Last Name, Email Addresses must not be empty
- Data input must follow defined formats
- A field or fields must be unique in a dataset
A big part of data cleaning is ensuring invalid data is highlighted and rectified before data is used any further.
Accurate: Typos, spelling mistakes, character mistakes etc affect the quality of accuracy. A name written as Matt instead of Matthew or Cath instead of Catherine is not considered accurate data.
Complete: This is defined by how much a data set has been accurately populated as opposed to being left blank. For example, are all phone number fields complete? Are all unique identifier fields complete?
Consistent: Data consistency is important for accurate data analysis. A good example of consistency would again be phone numbers – some country codes are written with +, some with 00. Data consistency means ensuring that only one method is used for all data records.
Timely: How often is your data updated or cleaned? Most companies simply neglect their data once they’ve collected it or used it for its intended purposes. Most only clean data for a report or analysis and leave that data in the backburner while new data keeps piling up. Old data becomes a bottleneck and even create duplicates if it’s not regularly sorted or updated along with new data.
When implementing a data cleaning framework, it is a good idea to use these standards as your data quality measurement benchmarks.
How Can Companies Achieve Data Quality?
For most companies, bad data is not a problem until a failed initiative, a flawed report or a massive marketing blunder gives a rude wakeup call. At that point, hype takes over and ad hoc data scrubbing tools are preferred over long-term solutions. Don’t let this happen to your data cleaning enterprise.
After having worked with 4,500 enterprises from across the globe, here’s what we suggest you can do to keep your data clean:
- Create a data quality management plan: Before you get the buy-in of executives, before you invest in a tool, make a plan. It’s important to understand the problem with your data and identify the root cause behind it. Your data quality plan should include identifying new roles, new software solutions and any new standard that needs to be implemented.
- Search for the right data cleaning tools: There are dozens of data cleaning tools in the market, but very few of them are affordable and give a holistic solution. Ideally, you’d want a tool that allows you to match, dedupe, clean and merge data. Data Ladder’s flagship data quality tool is a powerful data matching and data cleaning tool that has been used by organizations such as HP, Deloitte, Zurich Insurance and thousands of others to not only clean, but also dedupe and merge data.
Fix the source of data errors: Raw data is inherently bad data which is why it’s necessary for you to fix errors at the source, that is your database. It could be a human input error, a machine error, a data collection method error – the possibilities are endless. Fix data at the source to ensure that it does not cause you stress down the line. This is also where you should be making use of a data quality tool that can fix data errors in real-time preventing flawed data from entering the system.
Additionally, here are questions you can ask your team about the data in your organization when creating the plan.
- How clean is the data?
- What are the most common problems plaguing the data?
- What are some of the most challenging problems teams are facing when trying to use data?
- What systems or checks are in place to manage the problem of data quality?
- What kind of cleansing or data maintenance process is being followed?
- Can this data be trusted enough to give reliable information?
- Does the data perform the task that it was intended to?
- How can data quality standards be implemented and maintained across the organization?
- Is data affecting any of your core processes?
- How can the organization achieve a single source of truth?
If your answers to the above questions indicate a serious flaw in your data, you will need to clean data in order to become operationally more efficient.
Best Practices
The age-old adage, ‘prevention is better than cure,’ applies to the world of data too. As companies step into the world of big data and data lakes, it’s necessary to ensure that you have the right parameters in place to prevent raw data from hampering your business operations.
Here are some recommended best practices:
- Focus on Data Input: Noticed how sometimes you fill a web form that specifically asks for a work email and not a random Gmail account? That’s an example of front-end data input control. While it doesn’t ensure 100% accuracy (a lot of people write fake emails), it will still help you considerably when you’re sorting relevant data from irrelevant ones. Implement such front-end, customer-facing controls to minimize the collection of bad data.
- Always Clean Data Before Generating a Report: You may be tempted to pull out a report from a database in a quick attempt to satisfy your boss, but don’t do that. Either keep your data regularly updated or clean it up before you use data for a campaign, a report or an analysis. You don’t want to end up re-doing an extensive report work just because you missed tackling duplicates in your data.
- Deploy Real-Time Data Cleaning Tools: Prevent bad data from entering your database by deploying data cleaning tools that catch errors during the data ingestion phase.
- Try to Centralize Data Sources: Most data problems occur because of disparate data sources. So many applications used by so many departments, each dumping its data into the database. Try syncing your data sources such as using one CRM for sales + marketing + billing. This will not only help you keep data clean but will also give you access to a single source of truth.
Clean data is mandatory to the success of your organization in this digital and data-driven age. If you truly want to be data-driven, you have to make sure you have data that is good enough to be used for intelligence. Bad, dirty, messy data will take you down.
Using a Self-Service Data Cleansing Tool
Now that you know you have bad data, avoid a knee-jerk reaction to it. Don’t immediately drag your IT resources or hire expensive developers to start building an in-house software. It takes years to build a data cleaning software – one that works efficiently and meets the criteria for quality data.
In-House vs Best In Class Data Matching Solutions
In-house data matching solutions are restricted by available talent, time limitations, costs, experience and many other factors. Discover how in-house solutions fare against commercial solutions
In-house solutions can cost you up to $250K+ per year! Here’s a quick guide to how an automated data cleansing tool can get the job done at ten times lower the price.
Data cleaning, despite being an important task is an incredibly mundane one. Your experts will be wasting hours of their productive time in the creation of algorithms that will either be a hit or a miss. Trials, tests, inaccurate results, and the ballooning talent management costs will become additional problems you’ll have to deal with. This is why it’s better to use an automated data cleansing tool that can do the job without the involvement of any additional talent.
- A powerful data cleaning tool can help you to:
- Automate cleaning schedules for all your data sources
- Clean your data from typos, mistakes, errors, casing and character issues and much more
- Match your data lists and remove duplicates
- Integrate various data sources for real-time data cleaning
- Standardize data and ensure consistency across the data source
- Validate address data and contact data
If you’d like to know how data scrubbing tools like DataMatch Enterprise can clean your data and make it usable for its intended purposes, get in touch!
How best in class fuzzy matching solutions work: Combining established and proprietary algorithms
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