“Data cleansing, data cleaning, datawash or data scrubbing is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table or database. Used mainly in databases, the term refers to identifying incomplete, incorrect, inaccurate, irrelevant, parts of the data and then replacing, modifying, or deleting this dirty data. Maintaining excellent quality data is essential to ensure long-term customers relationships.1
An organization in a data-intensive field like banking, insurance, retailing, telecommunications, or transportation might use a data scrubbing tool to systematically examine data for flaws by using rules, algorithms, and look-up tables. Typically, a database scrubbing tool includes programs that are capable of correcting a number of specific type of mistakes, such as adding missing zip codes or finding duplicate records. Using a data scrubbing tool can save a database administrator a significant amount of time and can be less costly than fixing errors manually.2
Data cleansing is sometimes compared to data purging, where old or useless data will be deleted from a data set. Although data cleansing can involve deleting old, incomplete or duplicated data, data cleansing is different from data purging in that data purging usually focuses on clearing space for new data, whereas data cleansing focuses on maximizing the accuracy of data in a system. A data cleansing method may use parsing or other methods to get rid of syntax errors, typographical errors or fragments of records. Careful analysis of a data set can show how merging multiple sets led to duplication, in which case data cleansing may be used to fix the problem. Many issues involving data cleansing are similar to problems that archivists, database admin staff and others face around processes like data maintenance, targeted data mining and the extract, transform, load (ETL) methodology, where old data is reloaded into a new data set. These issues often regard the syntax and specific use of command to effect related tasks in database and server technologies like SQL or Oracle. Database administration is a highly important role in many businesses and organizations that rely on large data sets and accurate records for commerce or any other initiative.3The Basics of Data Cleansing4
A succinct data cleansing definition can be derived from the phrase data cleansing itself. Simply put, data cleansing consists of the discovery of errors in a data record and the removal or correction of these mistakes. Industry experts recognize that data cleansing is the most important aspect of data quality management. It is impossible to prevent absolutely every error from entering client databases, and the expanding number of ways that customers can interact with companies and organizations means that there will likely never be a system that catches every error at the point of entry every time. This is not to deny the need to be proactive in regard to helping to keep customers, clients, and even company and organizational staff from entering errors. However, it is to recognize the reality of human error, the complexities of our digital age, and the problems that go along with the opportunities afforded by vast quantities of customer and client data. Data cleansing is necessary for all forms of customer data. Back-end address verification, for example, reviews customer mailing addresses and makes sure that they are updated with correct ZIP codes, new addresses when clients move, and so forth. Email verification does the same kind of thing with email addresses, eliminating incorrect email addresses from the systems of companies and organizations, processing email changes, and much more. In the long-run, regular cleansing of data keeps contact information accurate, enabling you to stay in touch with clients and to market to them effectively.How is Data Cleansing Performed?5
Data cleansing techniques are usually performed on data that is at rest rather than data that is being moved. It attempts to find and remove or correct data that detracts from the quality, and thus the usability, of data. The goal of data cleansing is to achieve consistent, complete, accurate, and uniform data. Data cleansing uses statistical analysis tools to read and audit data based on a list of predefined constraints. Data that violates these constraints is put into a workflow for exception data handling. Data cleansing leads to high quality data. When data is of excellent quality, it can be easily processed and analyzed, leading to insights that help the organization make better decisions. High-quality data is essential to business intelligence efforts and other types of data analytics, as well as better overall operational efficiency.
To cleanse a column is to clean up the values within that column, commonly by replacing them.
source: VeeraData Cleaning Process6
The data cleansing process can help target the areas where data is weak and needs more attention. What’s important to remember about the process, is that it’s a continuous circle. So you can start small and make incremental changes, repeating the process several times to continue improving data quality.
Challenges and Problems of Data Cleansing7
- Plan: identify the set of data that is critical for making your marketing efforts the best they can possibly be. When looking at data you should focus on high priority data, and start small. The fields you will want to identify will be unique to your business and what information you are specifically looking for, but it may include: job title, role, email address, phone, industry, revenue, etc. It would be beneficial to create and put into place specific validation rules at this point to standardize and cleanse the existing data as well as automate this process for the future. For example, making sure your postal codes and state codes agree, making sure the addresses are all standardized the same way, etc. Seek out your IT team members in help with setting these up! They are more help than just deleting a virus!
- Analyze to Cleanse: prioritize data your company desires; it’s important to go through the data you already have in order to see what is missing, what can be thrown out, and what, if any, are gaps between them. You will also need to identify a set of resources to handle and manually cleanse exceptions to your rules. The amount of manual intervention is directly correlated to the amount of acceptable levels of data quality you have. Once you build out a list of rules or standards, it’ll be much easier to actually begin cleansing.
- Implement Automation: begin to cleanse; you should begin to standardize and cleanse the flow of new data as it enters the system by creating scripts or workflows. These can be run in real-time or in batch (daily, weekly, monthly) depending on how much data you’re working with. These routines can be applied to new data, or to previously keyed-in data.
- Append Missing Data: this step is important especially for records that cannot be automatically corrected. Examples of this are emails, phone numbers, industry, company size, etc. It’s important to identify the correct way of getting a hold of the missing data, whether it’s from 3rd party append sites, reaching out to the contacts or just via good old-fashioned Google.
- Monitor: set up a periodic review so that you can monitor issues before they become a major problem. You should be monitoring your database on a whole as well as in individual units, the contacts, accounts, etc. You should also be aware of bounce rates, and keep track of bounced emails as well as response rates. It’s important to keep up-to-date with who is working at the company; so if a customer does not reply to any campaign in more than 6 months, it’s a good idea to dig a little deeper and make sure that that person still holds that position, is still at that company, or quite frankly, depending on how well you’ve maintained the database, hasn’t already kicked the bucket. The end of this cycle, or step six if you will, is to bring the whole process full circle. Revisit your plans from the first step and reevaluate. Can your priorities be changed? Do the rules you implemented still fit into your overall business strategy? Pinpointing these necessary changes will equip you to work through the cycle; make changes that benefit your process and conduct periodic reviews to make sure that your data cleansing is running with smoothness and accuracy.
Benefits of Data Cleansing8
- Error correction and loss of information: The most challenging problem within data cleansing remains the correction of values to remove duplicates and invalid entries. In many cases, the available information on such anomalies is limited and insufficient to determine the necessary transformations or corrections, leaving the deletion of such entries as a primary solution. The deletion of data, though, leads to loss of information; this loss can be particularly costly if there is a large amount of deleted data.
- Maintenance of cleansed data: Data cleansing is an expensive and time-consuming process. So after having performed data cleansing and achieving a data collection free of errors, one would want to avoid the re-cleansing of data in its entirety after some values in data collection change. The process should only be repeated on values that have changed; this means that a cleansing lineage would need to be kept, which would require efficient data collection and management techniques.
- Data cleansing in virtually integrated environments: In virtually integrated sources like IBM’s DiscoveryLink, the cleansing of data has to be performed every time the data is accessed, which considerably increases the response time and lowers efficiency.
- Data-cleansing framework: In many cases, it will not be possible to derive a complete data-cleansing graph to guide the process in advance. This makes data cleansing an iterative process involving significant exploration and interaction, which may require a framework in the form of a collection of methods for error detection and elimination in addition to data auditing. This can be integrated with other data-processing stages like integration and maintenance.
- Improves the Efficiency of Customer Acquisition Activities: Business enterprises can significantly boost their customer acquisition efforts by cleaning their data as a more efficient prospects list having accurate data can be created. Throughout the marketing process, business enterprises must ensure that the data is clean, up-to-date and accurate by regularly following data quality routines. Multi-channel customer data can also be managed seamlessly which provides the enterprise with an opportunity to carry out successful marketing campaigns in the future as they would be aware of the methods to effectively reach out to their target audience.
- Improves Decision Making Process: The keystone of effective decision making in a business enterprise is customer data. Precise information and data quality are essential to decision making. Data cleansing can support better analytics as well as all-round business intelligence which can facilitate better decision making and execution. In the end, having accurate data can help business enterprises make better decisions which will contribute to the success of the business in the long run.
- Streamlines Business Practices: Data cleansing along with the right analytics can also help the enterprise to identify an opportunity to launch a new product or service in the market which the consumers might like, or it can highlight various marketing avenues that the enterprises can try. For example, if a marketing campaign is unsuccessful, the business enterprise can look at various other marketing channels that have the best customer response data and implement them.
- Increases Productivity: Having a clean and properly maintained database can help business enterprises to ensure that the employees are making the best use of their work hours. It can also prevent the staff of from contacting customers with outdated information or create invalid vendor files in the system by helping them to work with clean records thereby maximizing the staff’s efficiency and productivity.
See AlsoData MiningData ManagementData WarehouseCustomer Data Management (CDM)Big DataBusiness IntelligenceData AnalysisData AnalyticsPredictive Analytics