Data Cleansing Tips
Oct
18

 How to get rid of dirty data?  Data Cleansing Tips

Data cleansing is one of the most important techniques in data management. People sometimes make mistakes on data entry forms intentionally for all sorts of reasons. Some even tend to omit or enter invalid information just to avoid marketing calls. There’s just no way to prevent errors from getting into your database.

The best way to keep your data error-free is to utilize efficient data cleansing tools.

The best way to keep your data error-free is to have regular data cleansing

Data cleansing is part of a rich data organization framework. Since you need to clean your data regularly, creating an easily repeatable data cleansing process will make cleansing more cost-efficient at the end of the day.

As we tackled in our previous article 3 Effortless ways to Validate Phone Numbers; validating data helps your business stay efficient and makes you more productive, focused and results driven while helping you save money, time, energy, and reputation.

In this article, we’ll talk about:

  • What Is Dirty Data?

  • How does dirty data affect your business?

  • Getting Rid of Dirty Data

  • Data Washing Machine (for a squeaky clean data)

  • Benefits of Having Quality Data

What Is Dirty Data?

So, what exactly is Dirty data? also known as ‘bad data, rogue data’ — is usually defined as inaccurate, inconsistent and incomplete due to the errors found within the dataset. This can be costly in the long run. It can eventually lead to lower productivity, unnecessary spending, and unreliable decision-making.

Dirty data contains errors caused by human error and can take multiple forms:

  • Incorrect – The value entered does not comply with the field’s valid values. For example, the value entered for month is likely to be a number from 1 to 12. This value can be enforced with lookup tables or edit checks.
  • Inaccurate – The value entered is not accurate. Sometimes, the system can evaluate the data value for accuracy based on context. For most systems, accuracy validation requires a manual process.
  • In violation of business rules – The value is not valid or allowed, based on the business rules (e.g., an effective date must always come before an expiration date.)
  • Inconsistent – The value in one field is inconsistent with the value in a field that should have the same data. Particularly common with customer data, one source of data inconsistencies is manual or unchecked data redundancy.
  • Incomplete – The data has missing values. No data value is stored in a field. For example, the street address is missing in a customer record.
  • Duplicate – The data appears more than once in a system of record. Common causes include repeat submissions, improper data joining or blending, and user-error.

 

How does dirty data affect your business?

 Studies show bad data can impact a business’s annual revenue by up to $9.7 million.

A yearly rate of 40 percent. Twenty percent of that data is outright dirty. According to data scientists, it takes $1 to verify records, $10 to clean it, and $100 if you do nothing. 

77% of companies believe they lose revenue, 12% on average as a result of inaccurate and incomplete contact data.Not only does poor data impact your financial resources, it also negatively impacts your efficiency, productivity, and credibility. … Additionally, errors and bad data can easily leave a bad impression on both customers and clients. Inaccuracy lowers credibility, increases risk, and failure of compliance management.

According to Forbes, these are some of the negative impacts of poor-quality data:

  • Undermining confidence: According to research, 84% of CEOs are concerned about the quality of the data they’re basing decisions on, according to KPMG’s “2016 Global CEO Outlook.” When there’s a lack of trust in data quality, confidence in the results it provides is quickly eroded. That can cause obstacles to gaining executive buy-in, dampening enthusiasm for further investment in data and quality improvement initiatives.
  • Missed opportunities: If your competitors are gaining more insights from data than you are, they will have insights you don’t. Companies should treat data as an asset and manage it to maintain quality in order to derive insights that can lead to competitive advantage
  • Lost revenue: Poor data can lead to lost revenue in many ways — communications that fail to convert to sales because the underlying customer data is incorrectOne example is where property locations are estimated, instead of precisely specified. In most cases, that might not matter, but where the difference is a property — or a whole neighborhood — located inside or outside of a flood zone, revenue losses could be significant.

  • Reputational damage: Reputational costs range from small, everyday damage that organizations may never be aware of, to large public relations disasters. As an example, recall Apple’s widely panned Maps rollout in 2012. At the time, it quickly became clear that much of the underlying data was inaccurate or missing, resulting in a product that TechCrunch later called “barely usable.”

Efforts to improve customer experience may also be undermined by bad data resulting in an incorrect spelling of a customer’s name, or obliviously sending communications to a deceased customer.  Data cleaning

 

Getting Rid of Dirty Data

 Bad data is outdated and therefore incorrect. Most businesses instruct their marketing workers to “fix it when you find it.” But this doesn’t fix the problem at all.

 Data should be cleansed at least every quarter. But, since this process is quite time-consuming, many businesses choose to depend on an external vendor to help identify and remove bad data, especially before rolling out a new marketing campaign.

Searchbug tools allow users to clean, update and verify and remove bad data records at once. Obviously saves you time, money and effort- letting you focus on your core business.

 

Data Washing Machine (for a squeaky clean data) 🧼ₒ৹๐

It’s always in your best interest to wash dirty clothes before wearing them. In the same case, data hygiene is also important because it improves your data quality which results in overall productivity. Clean data leaves you the highest quality of information.

We understand that all data isn’t dirty, but with proper cleaning; just like that shirt or blouse that could probably be worn again,  it would be much nicer fresh out of the dryer; and your clean data will be much more useful.

 Table 1. How does a Data Washing Machine works? 🧼ₒ৹๐

STEPS

INSTRUCTION

SORT

Just like your cloths, begin by sorting your duplicate and/or incomplete data

FIX

Replace any duplicate or incomplete data by either repairing or eliminating it

REPLACE

Inspect and repair the structural integrity of the data by making sure data sets follow the same structure

SCRUB

Check the accuracy of data by running it through a data scrubber such as Searchbug’s Data Append tools

DOWNLOAD

Retrieve your newly cleansed data and put it to work for you

Benefits of Having Squeaky Clean and Quality Data

Truly, big data is the future of business and there is no denying the importance of data cleaning in that process.

Here are some of the benefits of having squeaky clean data!

  • Time and cost-efficient – Dirty data leads to business strategies based on false assumptions. Data cleaning saves your company from potentially wasting both time and money, developing an ineffective strategy.

  • Better business results – We touched on this before, but it’s important enough that it’s worth repeating. Better data = better decisions. Data cleaning is the key to a properly functioning data analytics solution. Whenever these two things occur, you can expect;

  • Increased productivity less time wasted– Effective data cleansing leads to consistent and highly functional databases. No errors means faster, more effective workflows, which directly impacts productivity.

  • Improved decision-making – There is a direct correlation between clean, quality data and reliable business insights: the cleaner the former, the more abundant the latter.

  • Maintained business reputation – Bad business decisions cost more than money. If you make a decision based on inaccurate data, it makes you look bad and unprofessional. But when your insights are useful, people will notice, and your reputation will grow.

  • Faster sales cycle – Marketing decisions depend on data. Giving your marketing department the best quality data possible means better and more leads for your sales team to convert. The same concept applies to B2C relationships too!

 

Let Searchbug do the work!

While it might seem like “too much data” can never be a bad thing, more often than not, a good portion of the data simply isn’t usable. This means that your team is spending excess time digging through the bad so they can get to the good. Data hoarding and outdated data go hand in hand. So you will find that these two types of dirty data can be solved simultaneously. Of course with the use of a great tool.

Life’s too short, and there will always be dirty data. So enjoy life and let Searchbug the Data Cleaner do the work.