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Do you want to weed out bad data? It can be one of the most practical ways for companies to improve their bottom line. As a striking example, a 2009 Gartner survey of 140 companies revealed that missed opportunities and inefficiencies due to “bad data” cost each business more than $8 million per year—4 percent of respondents estimated annual losses of $100 million while 25 percent annually reported the cost of bad data of $20 million or more.

Do you want to know whether your company has bad or insufficient data? This article provides an overview of bad data categories of bad data and elaborates on what commonly causes the bad data problem and what you can do about it for minimizing bad data. It also highlights the efficiency of data cleansing services regarding bad data.

What Exactly Does the Term “Bad Data” Mean?

The term “bad data” may initially seem vague, so we advise businesses to avoid using it, but they are often unaware of its exact meaning. Poor data and wrong information are the same in the context of a company. Although accurate data may be potentially flawed, this flaw does not necessarily mean the data is untrue.

Insufficient data includes, among other things, incomplete information unsuitable for the purpose for which it is to be used, duplicated, or incorrectly aggregated. Using incorrect information can affect a company’s success and, in some cases, create disastrous results.

Businesses consistently remind us that the methods they employ to manage and acquire data can be just as important as the products or services they offer to the public. So, weed out bad data is fundamental to a company’s success.

Categories of Bad Data: A Must-Know for Every Industry to Weed Out Bad Data

Before weed out bad data, you have to know their categories. Bad data can take many forms, including duplicate files, damaged files, incorrect data fields, and relational data sets with disconnected data sets. Bad data comes in many forms – for example, unconnected data sets for relational data, corrupted files, incorrect data fields, and duplicate files. While there are many variations of insufficient data, there are five primary categories that you have to know to prevent bad data:

  • Non-compliant Data: Data does not follow your company’s naming conventions.
  • Missing Data: Empty data fields where data should be.
  • Irrelevant Data: You entered data in an incorrect field.
  • Inaccurate Data: Incorrect data (including data that needs to be updated correctly).
  • Duplicate Data: A contact appears in more than one database record.

The Effect of Bad Data: A Must Know to Weed Out Bad Data

Using insufficient data for analytics, AI, and other apps can have disastrous consequences for any organization, and they must pay for bad data. The worst-case scenario is making bad business decisions with that data – investments, product changes, or hiring moves. Ignoring and not removing insufficient data leads to misleading insights and misleading choices. It’s like blindly following a GPS without confirming its precision or understanding its end goal. You could potentially drive yourself into the ocean for minimizing bad data.

It has a broad chilling effect on a company. When insufficient data leads to skewed or incorrect insights, employees more broadly lose confidence in data and systems. As a result, they stop relying on data to make decisions entirely and instead move towards making decisions based on gut feelings. As a result, it is necessary to weed out bad data.

At a bare minimum, you should throw out insufficient data as often as you use it to make decisions. Ideally, though, it should be during data reception. Removing inadequate data as it enters the system is the only way to avoid reliably contaminating a clean data source. While some may take all data and clean it later, having a clean source from the start is preferable to maintaining data integrity.

Evidence Bad Data is Bad for Every Company

Data problems cost businesses money, time, and customers. Recovering and correcting mistakes is a huge hassle and reflects poorly on your business as bad customer service. Here are five statistics to demonstrate just how much harm poor data can do and what you can accomplish about it-

It Costs the US Economy More Than $3 Trillion a Year

A 2016 Harvard Business Review report demonstrated that insufficient data costs the US economy $3 trillion annually. That trend continues today. 2022 sees Gartner discussing insufficient data responsible for $12.9 million in losses for every business annually.

Business Expenses Can Range from 10-25% of a Business’s Revenue

If you need help applying the first statistic to your business, wrap your head around it. Monte Carlo believes that data professionals spend 40% of their time assessing or testing data quality. They consistently report that poor data quality affects 26% of company revenues. Remember that if your data is correct, you have more money to return to the business and increase your income. So, it is mandatory to weed out bad data from your company.

A Single Defect Can Cost More Than $100 to Fix

You may know that. There is something named the 1/10/100 regulation. Simply put, it costs about $1 to verify an entered record, about $10 to correct later, or $100 if something needs to be done because you can repeatedly feel the mistake’s impact.

Let’s put this in a real-world scenario:

It affects later if an error is overlooked (or someone needs to be more active and correct a mistake). At some moment, a coworker will search the CRM for the business and find records that he suspects are incorrect. First, he would check his notes, then call someone else to verify and possibly change the record. Discovery would destroy confidence in the data and waste the ten minutes he spent correcting the data entry.

Furthermore, if someone in sales contacts a lead to discuss a potential sale with incorrect information, the situation quickly deteriorates. Result? Missed an opportunity to partner with a great company due to bad timing or loss of confidence or credibility. The expense to the business could eventually be in the millions of dollars in lost revenue alone – if trust is lost, it adds up! So, weed out bad data is necessary.

About 15-45% of Operating Costs are Wasted on Categories of Bad Data

Data experts such as Thomas Redman, Jack Olson, and Larry English agree that nearly 15-45% of all organizations’ operating costs are well-spent due to data quality issues. So, for this reason, every company has to pay for the cost of bad data. They need to prevent bad data.

Statistics illustrate it. The average company involved in direct mail spends approximately $180,000 annually on direct mail not reaching its intended audience. This number does not include other mistakes, such as mailing to the wrong demographics or existing customers. IT departments are also affected, with nearly 50% of their IT budgets going toward “data scrap and rework” related to poor data quality (Larry English).

Poor Data May Result in Little or No Standardization

So, this is speaking from experience, statistically more anecdotal, but we repeatedly see that this is the root cause of the worst data. We know it wastes time and money due to small things like data duplicates and some features you must include. The good news is that by implementing the correct data integration technology. You can weed out bad data and immediately reduce the number of errors, especially those resulting from duplicate data entry across your company’s multiple applications and systems.

However, there are two caveats:

An integration product can’t just fix your insufficient data on its own – it’s essential to take the time to follow proper data validation rules and check for duplicates before moving data between systems using these technologies. Without it, you can create duplicate data much faster than when you are doing manual data entry.

Second, just any integration product won’t do. There are popular but simplified integration products that cannot apply logic for valid or duplicate data. These products can perform essential one-to-one integration, such as transferring data from one app to another, regardless of the quality of the data or whether that data, say, a consumer document, already exists in the target app.

The High Cost of Bad Data

Whatever insufficient data is, it will likely cause severe damage to your company through inefficiencies, reputational damage, missed opportunities, reduced employee morale, poor customer service, inaccurate forecasts, and poor business decisions. As mentioned in the first paragraph of this article, one study put annual costs at $8 million to $100 million. So, every business should concentrate to weed out bad data.

In a more recent study by Experian Data Quality, 88 percent of companies experienced a direct impact on the bottom line – on average, each company lost 12 percent of revenue due to insufficient data. Revenue losses were usually due to excessive staff time, wasted resources, and unnecessary marketing expenses.

Based on the 1-10-100 value principle, the cost of solving problems can increase exponentially over time. For example, if $1 is the initial cost to prevent bad data from affecting your CRM system, then $10 is the cost of correcting an existing data problem, and $100 is the cost of fixing a bad data problem after it fails with a customer. or company.

Adopting a Two-Pronged Strategy to Weed Out Bad Data

Effectively dealing with insufficient data requires two-pronged operations for data entry accuracy:

  • Data Prevention: Controlling and minimizing bad data “before” your system.
  • Data Remediation: Monitoring and cleaning data “after” it enters the system intending to maintain the prescribed quality standards.

To weed out bad data, start with user training that addresses issues such as checking for duplicate entries and completing all data fields before data entry. To modify existing data, define a data quality process and then monitor the databases based on those standards.

A few words of caution – cleaning data can be a tedious process. To avoid overburdening your staff, consider practical options such as outsourcing to a data cleansing specialist.

How to Find a Data Cleansing Expert?

In the era of “big data” and increasingly data-sensitive processes, the potential damage caused by insufficient data is virtually limitless. While no organization is immune to inadequate data, you can identify and correct bad data problems.

In the first paragraph, we already discussed the categories of bad data. Data cleaning or cleansing is an effective technique that can dramatically improve your bottom line by transforming all categories of bad or fuzzy data into consistent data sets, and it successfully weed out bad data.

Data validation at the “point of entry” is equally important – think of it a data passport check before data is allowed into your system. Outsourcing data cleaning services can help weed out bad data in your two-front operation. Data cleaning and validation are two of their primary daily duties on a 24/6 basis.

Palash RoyData Advisor
Data Advisor at AskDataEntry – India’s leading data entry and processing services provider for businesses and individuals. He is a seasoned data professional who is an expert in big data processing and enrichment.

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