Being data-driven is the dream of many organizations today, however, data quality is still a big challenge for companies, which causes significant losses in the long run. To be data-driven, organizations need data cleaning solutions to get rid of unwanted, messy and incorrect information that does not impact negatively on a company’s ambitions.
Data quality actually indicates the health of useful 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 some or all, you already have some serious underlying problems to deal with.
And this is why you need to implement Data Cleaning.
In this detailed guide, we’ll cover:
- Basics about Data Cleaning.
- How Does Data Cleaning Help Businesses?
- Characteristics of High-Quality Data.
- How To Apply Available Solutions & Best Practices.
Let’s get started!
What’s the purpose of data cleaning?
Data cleaning – also known as “data scrubbing” or, “data cleansing”, is a process that makes data reliable and safely usable across any organization. Data cleaning involves the following processes such as deduplicating data, removing redundancies, fixing incomplete or invalid data, formatting and standardizing data, and transforming messed up 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 Assist Business Growth?
Data cleaning isn’t just an IT issue. In every reputable organization, offices gather information from a range of connected systems and activity logs. Each of these divisions need information for examination, statistical report creation and settling on vital business decisions.
Here are the means by which information cleaning can help various branches of your company:
Data Compliance: During a time when governments over the world are managing information regulation, companies need to ensure that they are following information guidelines and are data compliant. For instance, an online business retailer could be penalized from the administration in the event that they don’t meet information security guidelines. To meet these guidelines, the business must process their information as per GDPR structure by guaranteeing that client information is up-to-date, perfect and precise records are kept. Inconsistent data cleaning in records could influence GDPR goals and objectives.
Unifying Disparate Data Sources: A company may have numerous information sources gathered and stored on different systems. There is a high likelihood for these information sources to store copied information. For instance, if advertising and client care utilize distinctive CRMs or frameworks to record the contact info of an entity, it may create duplicated records of the entity and the organization will end up making flawed analysis and reports based on duplicated information.
Customer Service: A customer care service refuses to entertain client requests due to irrelevant, incorrect address data. An email sent to an inappropriate ID. An email utilizing an incorrect spelling or name of the client. These are for the most part instances of how awful information can sabotage client support. Clean information will guarantee that you have the relevant and correct contact data to provide optimal services.
Operational Efficiency: Clean information assists organizations with stabilizing processes. Take, for instance, one of our clients, having the option to improve their operational proficiency and increment their ROI when they had the option to distinguish the mistakes in their information and cleaned their information of copies, typos, mismatched information, and messy errors.
Marketing: No other department in a company is entrusted with the burden of keeping up top-notch information than the marketing department. Regardless of whether it is email campaigns, social media campaigns, promoting or any other activity, customer information is at the forefront. Wrong information can bring about devastating results. It’s normal to see organizations sending a campaign mail to the wrong audience set.
Sales: As much as client information is significant for promoting, it’s additionally significant for sales. The truth is, sales information is the most significant information that gives a company details on ROI, income, and profits. Data cleaning tools are usually part of sales department systems which deduplicates redundant sales records. Whenever disregarded, copied sales records may give skewed ROI reports and influence the flow of business.
These are only some fundamental instances of the outcomes of flawed information. The everyday battles organizations have with awful information is deeply involved in organization procedures and require extensive efforts from supervisors and officials to eliminate the menace.
Strategy To Maintain Data Quality
- While it’s critical to clean information, how would we realize what makes your data reliable? There are a couple of “norms” that are generally used in the business to measure the nature of data. The entire motivation behind information cleaning is to accomplish these norms which can be characterized as any information that may be:
Validity: There are strict principles applied to data sources. For instance, one of them being that it’s mandatory to include ZIP codes, phone numbers, and city codes in all the addresses. Data that doesn’t meet the said criteria are viewed as invalid. For instance, addresses without complete ZIP codes are viewed as invalid. Validity rules are characterized by business rules or limitations, for example, a website form makes it mandatory for the user to enter a certain field or every field in the form (such as Email, Last Name, First Name, and etc.).
A big part of data cleaning is ensuring invalid data is highlighted and rectified before data is used any further.
Precise Data: Human errors such as typos, spelling mistakes, and character mistakes affect the credibility of your data. A name written as Matt instead of Matthew or Cath instead of Catherine is not considered accurate data.
Completeness: The state of completeness indicates that the dataset possesses all the essential information to reach the level of completeness. For instance, having all the complete and verified information about the user such as First Name, Last Name, and other contact details.
Consistency: Data consistency is vital for accurate data analysis. A good example of consistency would again be phone numbers – emails that are verified, and phone numbers with correct dialing codes. Data consistency means ensuring that only one method is used for all data records.
Punctuality: How regularly is your data sorted or cleaned? Most organizations typically neglect their data once they’ve gathered it or utilized it to the desired extent. Most just clean information for a report or analysis and leave that data flowing into the systems while new information keeps accumulating. Old data turns into a bottleneck and even makes copies if it’s not normally arranged or sorted alongside new data.
When implementing a data cleaning framework, it is a good idea to use these standards as your data quality measurement benchmarks.
How to Achieve Data Quality Effectively?
For most organizations, bad data isn’t an issue until an unsuccessful activity, a flawed report or a huge marketing blooper gives your business a setback. By then, the hype forces organizations to rush towards short-term solutions instead of long-term solutions. Try not to let this happen to your organization.
Here’s what you can do to keep your data clean:
- Set up a data quality management plan: Before you get the buy-in of executives, before you get your hands over a tool, make a plan. It’s important to explore the core of the problems with your data and draft a solution accordingly. Your data quality management 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: Dozens of solutions exist in the market, but not all of them are efficient yet affordable. Ideally, you’d want a tool or a dedupe software 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: A human data blunder, a machine mistake, error in the methods used to retrieve data are just some of the examples of errors at the source level. Fix information at the source to guarantee that it doesn’t cause you to worry down the line. This is likewise where you ought to utilize a data quality solution that can fix data errors progressively keeping flawed information from entering the system.
Additionally, if you feel like your data is not clean, certain problems are plaguing the data, or the data you are using is harming the flow of your business, you will need to clean your data in order to become operationally more efficient. Adapting to right practices and solutions should boost business growth in the long run.