The quality of data is the most crucial element of any business intelligence strategy. Developing systems to collect and distribute information is one thing, but if that data becomes corrupted, then it becomes useless. No matter what niche business is in or what kind of data they collect, data cleansing is going to be a requirement. Why is data cleansing important in business?
Data cleansing is the process of cleaning up data. Think of it as spring cleaning your home. Over time, clutter will start to build up, and those hard to reach spots become dusty. It’s not easily seen with the naked eye, but that dust can still lead to minor symptoms like allergies.
The same can be applied to data. Small clusters become incorrect, obsolete, or even corrupted. Even if it can’t be seen easily, minor symptoms will start affecting the daily operation of your business. Without cleansing, those issues will become more severe.
Manual cleansing of data is quite time consuming and can be overwhelming. That is why big companies outsource data cleansing. This post will discuss seven reasons why data cleansing is essential in business.
1: It improves the ROI of email campaigns
Sometimes a business will have data that is outdated, but they are still using it for email campaigns. This can lead to several unexpected expenses.
- Data cleansing ensures that the right people are opting into your email list. One mistake that we see is businesses emailing the wrong people because they somehow got into their mailing list. This is seen as spam.
- People also mark email from senders they do not recognize as spam, so it’s essential that you are targeting the right people. Nothing screams unprofessional like a business targeting the wrong people with their email campaign.
2: Data cleansing reduces overall costs
Having duplicate data clutters up the work environment, eventually leading to inefficient processes. Businesses need to streamline their operations as much as possible. Lower overall costs lead to higher profits. Data cleansing will also help managers decide on positions within their department. Job descriptions should be updated regularly but the problem is that clutter masks this requirement.
Businesses that combine the proper analytics and cleansing tools are going to be in a better position to see new opportunities. For instance, maybe there is a demand for a different product that they could provide but that data is being masked by outdated, irrelevant statistics.
3: Ensure that a business is still targeting the right customers
When data starts to become coarse, it causes companies to target the wrong market. Customer habits change at such a fast pace now that data can quickly become outdated. Data cleansing will clean up this older data in favor of new, updated information about your target market.
We create systems that automatically implement, sort, and parse customer data in a way so that the newer information is prioritized. This offsets the problem a bit, but the underlying issue remains. Eventually, the sheer volume will become strenuous on the system, so it needs to be cleaned up.
4: Improve process efficiency and productivity
Cluttered databases lead to a decrease in productivity. Computers take longer to pull information. Client menus become filled with past clients, forcing the office administrator to go through a larger list to put in an order. Or worse, managers place order with older suppliers who are no longer contracted with the business. All of these things can easily happen when data starts to get cluttered.
Businesses decide to outsource data cleansing services when things get so far out of hand that it begins to cause significant delays. Don’t wait. Develop a plan today.
5: Remove duplicate data to clean up processes
Duplicate records compromise the quality of data. Usually, these mistakes occur during the collection process. If humans are entering this data, then duplicate records are going to be a much larger problem. Duplicate data leads to poor decisions because the management has only incorrect statistics.
Removing duplicate data requires a specific type of cleansing, so sometimes it’s best to outsource data cleansing to remove them. Experts are better equipped to remove these records. Removing duplicates cleans up overall processes within the company and will improve productivity.
6: Avoid wasting your data investment
Data intelligence is a significant investment, so it must be protected. If you do not protect it, then all that money you spent will be wasted. Without a proper data cleansing plan, your reports will start to become inaccurate and eventually become unusable. There will come a point when a database becomes so corrupted that cleansing it becomes more expensive than starting from scratch.
Don’t waste your investment. Develop a proper data cleansing plan so that you are not being deprived of an asset. Businesses that do not incorporate a data analytics strategy are doomed to failure. Data cleansing plays an important role in this overall strategy.
7: Make Better Business Decisions
Top businesses are finding innovative ways to use data in just about every aspect of a business. One of the biggest advantages is that having access to information allows companies to make better decisions. As a result, they gain a competitive advantage over competitors who do not follow suit.
Clean data boosts a business’s ability to make decisions because management can depend on reports to be accurate. If data has been corrupted or is oversaturated with irrelevant data, then those same reports are not going to be as accurate. Data cleansing cleans up the clutter and will provide businesses with the data needed to make better, more informed decisions.
This is why data cleansing is important!
Why is data cleansing important in business? It all comes down to having clean data. Think of it as your desk. If it’s cluttered, then you’re going to be less productive until the time you get it all cleaned up. If you don’t, then eventually it will overwhelm you to the point where you can’t even work. Data is the same way. It will clutter your database to the point where the information you are pulling cannot be trusted.
