Both The Risks And Financial Cost Of Poor Data Quality

How companies gather their information typically gets a bad rep. Poor data collection methods, security breaches, and the invasion of consumer privacy, for example, can all drive customers away.

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But businesses still need data to analyze the customer experience, develop new products, and initiate new marketing strategies though, and your company should strive to collect and maintain good quality data that is:

• Accurate.
• Timely and updated.
• Informative, especially regarding your company’s needs.
• Collected ethically.

Many business owners and executives don’t realize just how high the cost of poor data quality can be, negatively impacting a company’s public image as well as its financials. Failing to address data quality problems is a common issue that many new companies face early on, though. It surfaces when estimating the cost of poor data quality isn’t done in anticipation of any issues that could arise. This lack of foresight means many new companies are forced to face the financial cost of poor data quality when these unanticipated issues have developed into a full-blown crisis. That doesn’t have to be the case, though.

What Is Poor Data Quality?

When a company is collecting information, not knowing the difference between exemplary and poor quality data is a significant problem. One or more of the following factors can result in poor quality data:

• Outdated data sets.
• Inaccurate information.
• Inconsistent collection processes.
• Obsolete company data.
• Unethical data collection methods.
• Duplicated or overlapping data sets.

Data quality problems might derive from various sources within your company, depending on what tools you use to collect information. For instance, if you’re not using an AI tool to collect and compile your information, data entry is the primary source of error.

As another example, if one of your data entry employees is tired or a little overworked, they’re bound to make some mistakes, inadvertently turning 1,000 into 10,000. If your company follows what’s “trending” or works well based on numbers, a mistake like this will result in a false lead.

Other issues can contribute to having poor quality data, too. They include:

• Ineffective data organization.
• Collecting too much information.
• Inadequate data recovery methods.
• Deficient data security.

Human errors happen and so do technological ones. Data scraping software like Scraping Robot can help you avoid those mistakes. Offering AI with machine learning, Scraping Robot’s API produces automatic scrapes, so you don’t have to rely on your employees to do all this work manually.

Does Context Cause Data Quality Problems?

The context or reason you’re collecting data can cause data quality problems for your business. For instance, some systems aren’t made with data quality in mind. One example is scraping for cost data. Cost-related information varies based on location, and many programs can’t maximize the quality of your pricing information.

Furthermore, other quality measures can also vary based on the geolocation of your IP address. For instance, if the IP address of your scraper is in one geolocation, but you’re scraping information from a company in a different country, you might end up having:

• Export-based pricing in your cost-related data.
• Tax issues linked to the country that the company is in.
• Customs information instead of local information.
• Language differences in your data.

You may also be blacklisted for using what looks like an unreliable IP address to local bots. If you need to access information in other regions, you should use a proxy server that can rotate scraping IP addresses and provide your scrapes with the best data.

What Are the Business Costs or Risks of Poor Data Quality?

Poor quality data is harmful to both your company’s reputation and finances. Depending on when you catch the error in your data quality, your company might end up too far into a financial or public relations (PR) woe to recover. Each year the cost of poor data quality for some companies is litigation or bankruptcy.

Public Relations Cost of Bad Data

Poor data collection, scraping, or just the use of insufficient data can be damaging for your company and even your customers. For instance, if you pursue an action plan based on flawed data, you could get into legal trouble. Some examples involve getting sued by your customer, while other forms of legal trouble can stem from:

• Breaches in customer privacy or information.
• Damages caused to your customers’ businesses.
• Hefty fines for getting involved in illegal activity.

Even if you’re able to avoid litigation due to your company’s poor quality data, you may not be out of hot water publicly.

Insufficient data can cause your business to make decisions that ruin your company’s reputation. Watching how your business navigates marketing in the cancel culture era is essential. For instance, if you work with marketing or content creation, you could ruin your companies reputation by:

• Marketing to the wrong audience.
• Miswording communication.
• Embarrassing your company or a group of people your company represents.
• Producing negative customer experiences.

Sometimes, your employees may even question how your company is run, especially if you use inconsistent data. This can cause employee conflict, or you could lose your employees because they feel the company has no clear direction.

Tools to Prevent Public Relations Issues Involving Data

Fortunately, there are methods to avoid all of these issues. For example, if you create content, use a blog scraper to find out what is trending on competing blogs and websites. Collecting data via web scraping is an accurate way to collect data relevant to your business consistently. Plus, it’s secure and super simple:

1. Open up Scraping Robot API.
2. Copy the link from the blog you want to scrape.
3. Paste the link into Scraping Robot’s easy-to-use search bar component and click “run.”
4. Download your results into a spreadsheet or aggregate to plan your upcoming posts.

Financial Cost of Poor Data Quality

Poor quality data can harm a business when you use it to make critical decisions, especially if you’re looking to:

• Create new revenue streams.
• Attempt to drive your company’s sales and profit.
• Attempt to maximize your limited resources.

When you attempt any of these critical business decisions and more with poor quality data, it’s like using the wrong map to get to your destination. You probably aren’t going to get where you want to be.

The main result of using poor-quality data is the cost to your business. You have to pay for the data and invest in your business decisions. When estimating the cost of poor data quality for your business, some researchers have put it above $9 million.

When estimating the cost of poor data quality, the average financial impact on U.S. companies annually is around $3.1 trillion. If you have a small business, basing its future on poor-quality data could hurt it more than you think.

Does the Data Quality Assurance Process Ever End?

The short answer is no. The quality assurance process for your company is endless because the data collection process should be consistent. Data is not timeless. It becomes outdated pretty quickly.

The timing of your data set updates depends on the type of business your company conducts.

You should always follow some scraping best practices, regardless of your company’s industry. By sticking with the best practices, you will have more straightforward data collection and quality assurance processes. Remember:

• Don’t perform scrapes less than 10 seconds apart.
• Perform fewer scrapes during high traffic times.
• Follow each company’s robots.txt file, which is the rules for scraping its site.
• Mix up your scraping patterns to appear more human.
• Don’t violate copyrights.
• Use a proxy server or scraping company to give you a different IP address.

While scraping with an API tool leads to the best quality data sets, a data analyst should still check it at all collection stages.

Scraping Robot Scrapes for the Most Accurate Data

Scraping Robot uses AI technology with machine learning to scrape for the highest quality data for your business. There are other benefits to using a scraping API for your company, including:

1. Real-time Data. Scraping Robot offers real-time data scraping providing the most timely data for your business needs. Scrapes can be set up every 10 seconds.
2. Accurate Data. There is no human error when using Scraping Robot because the API compiles the data into a CVS to download into your aggregate, spreadsheet, or data management program.
3. Consistent Data. You don’t have to worry about lacking data sets because you can have Scraping Robot perform data scrapes at timed intervals. Over time you will have seasonal trend data collected.
4. Integrity. Scraping Robot handles the scraping from its server, so you don’t have to worry about being detected by anti-scraping bots that will ban you from social media or browser platforms.
5. IP Address Proxy. Scraping Robot has an IP address proxy for the best-geolocated scraping services.
6. Complete Data Sets. With Scraping Robot, you can get data sets tailored to your company’s needs.

Complete datasets from web scraping can include:

• Information about visitors from your site or competitor sites.
• Page browsing information.
• Real-time price monitoring.
• Purchase details from e-commerce sites.
• Referral traffic to various sites, including yours.
• Metadata information.
• Social media information.

Scraping Robot can help you scrape for the most accurate and helpful data for your business.

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