Chapter 2: Why Clean Data?
Data cleansing is the process of spotting and correcting inaccurate data. Organizations rely on data for many things, but few actively address data quality. Whether it’s the integrity of customer addresses or ensuring invoice accuracy. Ensuring effective and reliable use of data can increase the intrinsic value of the brand. Business enterprises must assign importance to data quality.
A data-driven marketing survey conducted by Tetra data found that 40% of marketers do not use data to its full effect. Managing and ensuring that the data is clean can provide significant business value.
Improving data quality can eliminate problems like expensive processing errors, manual troubleshooting, and incorrect invoices. Data quality is also a way of life because important data like customer information is always changing and evolving.
Business enterprises can achieve a wide range of benefits by cleansing data and managing quality which can lead to lowering operational costs and maximizing profits.
Who are the heroes who allow the organization to seize and enjoy all these benefits? I affectionately refer to these poor souls as PWCD’s, or People Who Clean Data.
These brave people, and hopefully you are reading this because you hope to be one of them, are the noblest. They often get little recognition even though they clean up the messes of hundreds, if not thousands of other people every day. They are the noble janitors of the data world. And I salute them.
Business enterprises can significantly boost their customer acquisition and retention efforts by cleansing their data regularly. With the high throughput of the prospecting and lead process, filtering, cleansing, enriching having accurate data is essential to its effectiveness. Throughout the marketing process, enterprises must ensure that the data is clean, up-to-date and accurate by regularly following data quality routines. Clean data can also ensure the highest returns on email or postal campaigns as chances of encountering outdated addresses or missed deliveries are very low. 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.
The cornerstone of effective decision making in a business enterprise is data. According to Sirius Decisions, data in an average B2B organization doubles every 12-18 months and though the data might be clean initially, errors can creep in at any time. In fact, in nearly all businesses where data quality is not managed, data quality decay is constantly at work. Each time new records are added; duplicates may be created. Things happening outside your organization, like customers moving and changing emails and telephone numbers will, over time, degrade data quality.
Yet the majority of enterprises fail to prioritize data quality management, or even acknowledge they have a problem! In fact, many of them don’t even have a record of the last time quality control was performed on their customer’s data. More often than not they merely discard or ignore data they believe to be of poor quality, and make decisions through other means. Here you can see that data quality is a massive barrier toward digital transformation and business intelligence, much less every company’s desire to become more Data-Driven.
Accurate information and quality data are essential to decision making. Clean data 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.
Eradicating duplicate and erroneous data can help business enterprises to streamline business practices and avoid wasteful spending. Data cleansing can also help in determining if particular job descriptions within the enterprise can be changed or if those positions can be integrated somewhere else. If reliable and accurate sales information is available, the performance of a product or a service in the market can be easily assessed.
Data cleansing along with the right analytics can also help the enterprise to identify an opportunity to launch new products or services into the market at the right time. It can highlight various marketing avenues that the enterprises can try. In practically any other business process you can name, decisions are made every day, some large, but many small. It is this systematic pushing of high-quality information down the chain of command, into the hands of individual contributors that helps them improve decisions made at all levels of the organization. Called Operational Intelligence, it is used more commonly for quick lookups and to inform the thousands of decisions that are made every day inside the organization.
Having a clean and properly maintained enterprise dataset can help organizations ensure that the employees are making the best use of their time and resources. It can also prevent the staff of the enterprise from contacting customers with out-of-date information or create invalid vendor files in the system by conveniently helping them to work with clean records thereby maximizing the staff’s efficiency and productivity. High-quality data helps reduce the risk of fraud, ensuring the staff has access to accurate vendor or customer data when payments or refunds are initiated.
Business enterprises that work on improving the consistency and increasing the accuracy of their data can drastically improve their response rates which results in increased revenue. Clean data can help business enterprises to significantly reduce the number of returned mails. If there are any time-sensitive information or promotions that the enterprise wants to convey to their customers directly, accurate information can help in reaching the customers conveniently and quickly.
Duplicate data is another aspect which can be effectively eradicated by data cleansing. According to Sirius Decisions, the financial impact of duplicate data is directly proportional to the time that it remains in the database.
Duplicate data can significantly drain the enterprise’s resources as they will have to spend twice as much on a single customer. For example, if multiple mails are sent to the same customer, they might get annoyed and might completely lose interest in the enterprise’s products and services.
 “People with Crappy Data” is an alternate interpretation coined by some of my clients.