35 Ways Find Out if Youve Got Bad Data and Ways to Improve Validation Process

35 Ways to Find Out if You’ve Got Bad Data

Your data affects all aspects of your business, from marketing to product management. And bad data makes business rough.

The day-to-day decision making and management of your organization suffers if you have incorrect, misleading, or poorly formatted data in your databases,

This means inefficient operation. And it negatively impacts your bottom line.

But, bad data isn’t the source of all your problems, right?

It’s true. So, you must determine whether or not your business has a data problem. Otherwise you might end up chasing a red herring.

Fortunately, bad data has symptoms.

Here’s how bad data shows itself in each aspect of your business.

Real Estate

If you’ve got a data problem, your Agents, telemarketers, and operations staff can suffer from these problems:

  1. Trouble tracking owners for wholesale properties.
  2. Missing owner contact data such as a recent cell phone.
  3. No clue as to who owners are or where to find them.
  4. Buyers Leads lists get stagnant or outdated.
  5. Telemarketers and agents calling the wrong people and waste time.
  6. Little or no standard data management procedures.


If you’ve got a data problem, your marketing operation will suffer from these problems:

  1. Trouble tracking customer preferences and managing customer privacy.
  2. Missing data.
  3. No clue as to how much or what data is missing.
  4. Compliance problems.
  5. No way to perform data matching with internal or external files.
  6. Marketing cannot identify ideal customers.
  7. No standard data management procedures.


Here’s how bad data affects your sales teams:

  1. Data correction occupies a lot of time for sales teams.
  2. Sales representatives must manually enter a lot of data during sales calls, which is increasing call times.
  3. Sales teams must dig through multiple applications to find the data they need.
  4. Incorrect or outdated CRM data.

Business Intelligence

Check for these issues in your business intelligence:

  1. There are a lot of different data sets and spreadsheets in the department.
  2. Data mismatches cause people to distrust the data.
  3. Too many data sources.
  4. No data cleansing tools.
  5. The same information is stored in multiple formats.
  6. Variable definitions are inconsistent.

Human Resources

Look at how your human resources department is running:

  1. Correcting data is a regular part of the work process.
  2. Your human resources department has a high turnover rate.
  3. The hiring process is too long and often hires people that are a poor fit.

Supply Chain

Evaluate the efficiency and effectiveness of your supply chain:

  1. Correcting data is a standard part of your procurement process.
  2. Orders are sent to the wrong suppliers because supplier lists are unmanageable.
  3. Data is not consistent across all systems.
  4. Inability to get spending analytics because there is no centralized material information.
  5. Supplies are purchased, but never used.

Research and Development

How much work is research and development doing:

  1. Product improvement efforts are duplicated or redundant because product data is inaccurate or outdated.
  2. Customers don’t know about product improvements and changes.
  3. Research and development teams have inconsistent product specifications and standards, or no specifications and standards at all.
  4. Development and deployment cycles are excessively long.
  5. Marketing is out of sync with research and development efforts.


Find out what sort of errors and issues finance is having:

  1. Billing information inaccuracies cause payment delays and strain customer service resources.
  2. Customers get billed for cancelled services because CRM and financial systems have inconsistent data.
  3. Account profiles have incorrect payment and contact information.
  4. Customers receive products or services they didn’t pay for. Other customers don’t receive the goods they did pay for.

Information Tech (IT)

IT is responsible for data continuity:

  1. It’s impossible to keep data consistent across all systems.

Obviously, none of these issues alone will sink your ship.

However, each issue chips away at your efficiency. Enough of these problems can add up to a lot of dollars lost.

Of course, the snowballing effect also works in reverse. Improving your data will actually solve quite a few of these problems in one fell swoop.

So, how do you improve your data for big gains on your bottom line?

Verify Data at the Point of Entry

This is a big one because it largely involves your customer data. However, you can verify internal data at the point of entry as well.

People often forget to fill in all form fields or fail to enter complete data. This is the biggest issue with end-user data entry.

There’s nothing malicious about this. But it has widespread effects that can take a lot out of your business.

This is especially true for data that customers enter in form fields. Customers are notoriously bad at entering data. And customer data is one of the most important kinds.

Verifying data at the point of entry is the best way to protect your databases and save a lot of time on the back end.

The most efficient way to do this is to use an API to validate information in real time.

For the form fields that customers typically fill out, you can get a prebuilt API from a data verification company.

Then just plug and play the API on your customer facing form fields.

For internal data validation, you can use the same API in some cases.

For things like human resources, where you’re collecting mostly standard personal information, the same API that you use for customer form fields will work.

However, you may need to do a little bit of development to create a custom API for things like product specifications and other proprietary information validation.

Either way, checking the data as it comes into your databases will save you a lot of time and money in the long run.

The system will stop and prompt the user to correct errors or fill in incomplete fields. The data validation happens in the background and is entirely transparent to the user.

This will keep the bad data bugs out of your system.

Cleanse Your Databases

If you notice symptoms of bad data, you’ve got some bad information running around in your system already.

Validating incoming data will help. But you still need to remove the bad data that’s already made its way in.

In this case, you’ll need to do some data cleansing.

Cleansing the data itself is fairly straight forward. Simply compile and aggregate your data into a usable file—.txt, .csv, and Excel files are best—and hand it off to a data cleansing service for processing.

This can often be done entirely online.

Where things get tricky depends on how you use your data.

In some cases, invalid entries can be simply tossed out. A list of email addresses that you’ve collected using a lead magnet or trip wire? Just get rid of any invalid email addresses.

But say you’re a hospital that’s cleansing patient data. You obviously can’t just delete any patient entry with incorrect information.

In cases like this, you’ll most likely need to separate out the entries with errors. Then you’ll manually contact patients to get correct information.

The process of correcting the data can be arduous. But data cleansing makes it very easy to identify the bad data and create a hit list of entries that need to be corrected. So it still saves a lot of time.

Between validating your data at the point of entry and cleansing your existing lists, you should be in pretty good shape, datawise.

There’s just one last piece of the clean data puzzle.

Setup Data Management Procedures

Data management is all about how you treat the data in your databases.

Poorly organized data can behave a lot like bad data, even if all the information is correct.

This is especially true if you use a lot of bulk data processing or artificial intelligence for big data analytics.

The key is to create standardization for the important aspects of your databases.

Here are the big things to address in your data management policies:

  1. Data formatting. Uniform formatting ensures that both people and computers can find the right data when they need it.
  2. Adding data fields. This goes along with formatting. Always add new variables methodically.
  3. Automate processes. Automating data management processes reduces the margin for error. There’s a chance that errors will be introduced whenever people manually transfer data. So it’s best to let computers do the copy and pasting whenever you can.
  4. Continually cleanse and filter your data. In most businesses, it’s impossible to always handle data in a way that disallows the introduction of errors. So you’ll need a good system for consistently quality controlling your information.
  5. Backup your data. This one goes without saying. Always backup your data. One is the most dangerous number in business and in life.

All this will help keep your data healthy. But keep an eye on things once you’ve got all the bad data bugs worked out.

Bad data gets introduced into your databases as you expand your operations and adopt new software and systems. It’s almost inevitable.

So, periodically audit your organization. Check your various departments. Find out if your data has been significantly compromised.

If you find bad data issues, it may be time to do a little data spring cleaning and refresh your quality control measures.

What’s the most common problem you experience with your data?

Leave a comment and let us know what it is and how you solve it!