Data Cleansing leads to effective data-driven decisions

Data cleansing or scrubbing is a form of data management. Although over the years, businesses accumulate a lot of personal information; ultimately, information becomes outdated. For instance, more than ten years one may change address or name, and then change the address again.

Data cleansingt is a process in which you go through all of the data within a database. And you require removing or updating information that is incomplete, improperly formatted, duplicated, or irrelevant. It typically involves cleaning up data compiled in one area. While data cleansing involves deleting information, it is focused more on updating, correcting, and consolidating data to make sure your system is effective as possible.

The data cleansing process is generally done at once; however, it can take a while if the information has been piling up for years. That's the reason why it's essential to perform data cleansing regularly.

Importance of Data Cleansing

Data cleansing is equally essential for both businesses and individuals.

Data Cleansing for Individuals

You, as an individual, can collect a lot of personal information on your computer in a few moments. Information such as credit card details, banking information, tax information, birthdays, and mortgage information can be stored on various files on your computer. For instance, if you have a digital copy of your T4, you can store a lot of information on just a few pages.

Data cleansing is crucial for individuals because ultimately, all this information can become overwhelming. Sometimes it becomes difficult to find the most recent paperwork, and you may have to wade through dozens of old files before you find the one you are looking for. As a result, disorganisation can lead to stress and even lost documents.

Data cleansing ensure storing only the recent files and essential documents so that you can find them with ease when you need. It also ensures limiting significant amounts of personal information on your computer to minimise security threats.

Data Cleansing for Businesses

Businesses traditionally hold on to a lot of personal information, such as business info, employee info, and even customer or client information. Unlike individuals, businesses must secure many different people and organisations' personal information and keep safe and organised.

Having accurate employee information is the most important so that you can know your audience better and contact customers if required. Having the newest and most accurate information will help your business get the most out of the marketing efforts.

Cleaning data is also vital because it enhances your data quality. In doing so, it increases overall productivity. Cleaning data removes all outdated or incorrect information and leaves you with the highest quality information. This means your team don't have to go through countless outdated documents absorbing most of their work hours.

Bad Data Leads to Bad Decisions

Cleaning data indicates omitting only bad or insignificant data. While cleaning data, if insufficient data is somehow skipped to be removed, it might lead to bad decisions. So, now you might wonder what bad data is.

Bad data is an inaccurate set of information. It could be a set of missing data, wrong information, inappropriate data, non-conforming data, duplicate data, or even insufficient entries, such as misspells, typos, variations in spellings, format etc.

The most common reason for the data being bad or insufficient is strategic players generate them. It is common in a two-sided marketplace, where on the side you have the buyers and sellers on the other. Every action by the buyers, such as deciding whether to purchase something instantly or wait until an upcoming sale manipulates data. Sellers take data generated from the buying side to optimise operational decisions such as pricing. But how do the buyers react when they are aware of this? And does their behaviour influence the seller to lower prices?

It often occurs in online advertising markets where sellers run a vast number of auctions for ad views. The targeted buyers are advertisers who purchase millions of ad views in a given day, leading to frequent interactions with sellers. The buyers are already aware that their bids are used to set future prices to act strategically to lower their bids. By following this, the data appears to set a lower valuation on the ad views, leading to reduced prices.

The key is the pricing algorithm. Instead of using bids to set prices directly, MIT Sloan School of Management designed an algorithm that uses censored bids. In the case, as mentioned earlier, a binary signal could be used to indicate if the buyer wins in the prior auction. Buyers need to change the binary signal to influence future prices. As for manipulation decreases, sellers can access cleaner data which leads to better data-driven decision-making.

Sellers can design an algorithm to generate clean or good data. The model cited for advertising can also be useful for other marketplaces, including financial markets, and stock markets in which buyers' behaviour can manipulate the price. This algorithm should likewise enhance robust pricing in those circumstances.

Conclusion

Although you shouldn't entirely trust data, you can use an algorithm after cleaning the data to censor them to ramp up its reliability. It's a temporary solution, but it is an excellent option to start to clean up the data and make more effective data-driven decisions.