Poor Quality Data Can Affect Your Business 

  • John Doe
  • January 08, 2022

You’ve probably heard time and again that data is king. But not all data is king — or rather, not all data is good. Poor quality data can harm your business more than it can help it — especially when you’re making important decisions for your company based on that information. 

Small businesses and corporations alike treat data as a significant investment and an essential strategic tool. It’s no surprise that big data analytics spending is poised to reach $103 billion by 2023. At the same time, 56% of CEOs are concerned about data quality, per a KPMG report.

Businesses need to gather data — specifically, quality, reliable data that allows them to make the right decisions. 

But how can you distinguish poor quality data from good quality data, especially in the context of web scraping? Here’s everything you need to know about gathering quality data.

What Is Poor Data Quality?

what is poor data quality

Poor quality or bad data is simply inaccurate or incomplete data. That’s just a generalized definition, as the quality ultimately depends on the type of data set and its purpose. 

Regardless of the size, niche, or industry, most businesses collect data one way or another. For instance, eCommerce businesses collect user data from their websites and applications for various purposes. eCommerce businesses also collect data from competitor websites for comparison purposes. 

This data can have tens of thousands or even millions of records to be analyzed. However, if there are gaps in the data or information is incorrect, analysis results may be contaminated. 

There can be many different causes behind poor quality data. Some poor data quality examples to consider include:

  • Data format protocols: Inconsistencies in how the data is collected and formatted can result in the inaccuracy of the overall data set. 
  • Integration of databases: Poor integration of different databases can result in data duplication, which results in poor quality. 
  • Data migration: Compatibility issues, duplicate data, and data loss often result from poor data migration and integration. 
  • Old data (data decay): Using old data is unreliable and considered poor quality. 

Poor quality data examples

The quality of data is highly subjective and depends on the individual data set, and to an extent, the business as well. If a company uses information irrelevant to its mission, it would naturally qualify as poor data — no matter how accurate or complete it is. 

That said, here are some examples of poor quality data:

  • Missing fields or records (name, address, or other contact information missing) 
  • Wrong field of data (prices in the product name field)
  • Duplicate entries (the same record entered twice or more)
  • Spelling errors (typos in spelling-sensitive records)
  • Non-normalized entries (multiple similar values)

Quality in the Context of Data Scraping

what are the business costs or risks of poor data quality?

While data is collected in many ways, one of the most commonly used methods is web scraping. Web scraping is the process of scraping websites using web crawler robots and collecting data. The use of web scraping ranges from eCommerce for price comparison to search engine optimization (SEO) to collect keywords. 

Now, a data scraping robot’s job is to collect data based on the search and collection rules you have set. It’s probably very good at its job as well. But is that data high quality? That’s another question.

How to gather quality data when web scraping

The hard part of scraping isn’t to avoid getting blocked — you have proxies and various strategies to help you around those. The difficulty is in ensuring the data is reliable and useful. For that, you need to avoid the common pitfalls of collecting and sorting data. Using tools like proxies for web scraping the right way can help you with this (more on that later). 

More importantly, the data quality must be consistent throughout the different stages. When collecting data, you need to ensure the information is of high quality right off the bat. Similarly, the data quality needs to be measured or analyzed once again when the data is used. 

In other words, quality is essential for the entire lifecycle of data. 

In the context of web scraping, it means that the parameters of data scraping should ensure that data meets the requirements of the business for which it’s intended. Then, there’s the question of time limitations, as a lot of data scraped from websites is time-sensitive. For instance, the price of a product, the rate of a hotel room, or the value of a stock can change multiple times a day. Data would be high quality in this context if the information is scraped and updated regularly, multiple times a day. 

Can Artificial Intelligence Help? 

how does artificial intelligence help

There’s a growing use of artificial intelligence (AI) in data collection and analytics — and for a good reason. Advanced algorithms can help improve and maintain the integrity of the data you collect, but to bet everything on a single technology is not the smartest decision. 

Ensuring the best data quality is not just about using the latest high-tech tools to gather and sift through information. As mentioned before, this begins as early as the planning phase, when you’re determining what data to collect and from where. 

Challenges of gathering quality data and AI

It gets even more complicated: even AI relies on data, so poor quality data can also negatively impact whatever AI technology you’re using. AI, particularly machine learning, uses data to learn patterns. If it’s getting inaccurate, biased, or mislabeled data, it won’t be able to do its job adequately, or worse, learn things incorrectly. 

AI can help you with data scraping, sorting, and analyzing, but only to a certain extent. Some simpler things can do just as well, granted they are planned effectively. 

In simpler words, first, work on the basics to ensure you understand what data quality means in your business context and how it can be achieved. Then you can invest in AI solutions to use the data you’ve gathered. 

Risks and Costs of Poor Quality Data 

 

how poor data quality can lead to bad decisions

You won’t truly appreciate data quality until you learn the risks and consequences that poor quality data brings. It’s not simply that the data won’t be useful — it may result in poor decisions and inefficiencies at your end. 

What are the business costs or risks of poor data quality?

Bad decisions

For many businesses, data is vital for higher-level decision-making. CEOs that rely on a data-driven approach can also lead the organization to adopt a data-driven culture. But that can only happen if their decisions based on data are working. 

Executives and CEOs typically receive reports based on the collected data. They take appropriate actions for the business based on that data. If the data is not good, you cannot expect the decision-makers to make good decisions.

Even though the leaders are listening to what the data says and taking the best approach to deal with issues, they will still not see progress — as a result of poor quality data, they haven’t even realized the true problems their company faces. 

According to Gartner, poor data quality costs organizations $12.8 million every year. 

Missed opportunities

Part of using data for your business is to aid in the lookout for opportunities. In any competitive industry, a missed opportunity may not simply mean the loss of growth or expansion — it may also make it difficult to survive. 

In comparison, a competitor with accurate and complete data and the right strategy can capitalize on untapped opportunities. 

Trust issues

Poor-quality data may damage your reputation, depending on your industry and business niche. 

For instance, there are investor relations to maintain and government regulations to comply with within the finance industry. Believe it or not, poor data quality may result in non-compliance, and as a consequence, your business may see fines and lawsuits. 

Even for a business operating in another industry, poor decisions based on bad data can lead to mistrust from their consumers. As we all know, it’s difficult to climb stairs but easier to come down — reputation damage may take years to fix and can even cause irreconcilable damage. 

Process inefficiencies

Standardized processes that depend on data can see high inefficiencies resulting from inaccurate, incomplete, or duplicate data. 

For instance, a retail business using data to order its inventory in advance can order less or more of the inventory than they need, all thanks to poor data quality. 

Good quality data is essential for businesses that follow standardized data-driven processes. It can make or break the business because those inefficiencies will ultimately have a domino effect.  

Revenue losses

Poor quality data often causes a loss of revenue. This results from all the other consequences of poor data we have already discussed above. 

Bad decisions, missed opportunities, and inefficiencies in business processes ultimately result in revenue loss, leading to an overall loss. That’s the last thing you want!

If your data comes from an audience that isn’t relevant to your business, all your marketing and sales efforts will go in vain, and you won’t have nearly as many conversions as you would have had you collected data from the right audience.

How to Ensure Good Quality of Data 

ensure good quality data

Now that you fully understand the impact of poor quality data on your business, it’s time to discuss what steps you can take to ensure your organization doesn’t fall into that spiral.

With a few strategic moves and the right tools, you can rest assured you’re getting high-quality data for your business, ensuring its success.

Focus on accuracy, completeness, and consistency

You need to ensure you’re collecting the right data from the right users in the right place. That’s how you know that you’re getting meaningful data for your business. This calls for a detailed plan based on analysis as to what data matters the most. 

Secondly, you need to ensure that the scope of the data is clear and that the data is complete. Make sure to employ processes that filter out incomplete data, so it doesn’t end up in your data analysis phase. 

The guidelines you have created for data should be consistently followed for all types of data sets you’re collecting. For multi-business organizations with interconnected services, this is all the more important. Inconsistency in one data set can cause a ripple effect and render other good-quality data sets useless. 

These practices will ultimately depend on your business model and infrastructure, so you have to approach them accordingly. 

Data sharing

Although data has become an indispensable asset, it’s worth noting that sharing it can result in growth in many cases. Gartner calls it a business necessity and predicts that data sharing will help organizations outperform their competitors in 2023.

Collaborating with other businesses to obtain authentic, complete data can help companies grow. However, you must ensure you comply with government regulations and industry practices when sharing data, both at the giving and receiving ends.  

Timeliness

It goes without saying that the data should be current. You cannot make good decisions based on data from two years ago. 

In our fast-paced digital world, technology is developing and evolving fast, and consumer behavior is changing without warning signs. Such a dynamic climate leaves businesses that rely on old data extremely vulnerable to inaccurate estimations and projections. 

As we discussed earlier, time-constrained data needs to be current. Therefore, businesses need to ensure that they get data in real-time for such time-constrained applications. 

This does not mean that historical data is completely useless. It may still be useful for analyzing patterns and predicting future trends for some organizations. However, for most of your data-driven decision-making, use the latest data — and be sure to update this information regularly. 

Web scraping with proxies the right way

How can those businesses that use web scrapers to collect data ensure quality? Using proxy servers is one way to help gather quality data quickly — but that’s not all. 

The primary purpose of proxies for web scraping is to avoid getting IP bans and make geo-target requests. Using proxies alone isn’t enough, however. You have to use them efficiently and get the best out of them to ensure you’re gathering accurate data and staying competitive.

Consider a retail enterprise based in Asia with a global consumer base and regional websites. To stay competitive, they use web scraper bots to gather data from a competitor based in North America. They use a proxy server and set the location somewhere in Europe. Even though they target the right competitor and get data, because they set the location in Europe, the scraped data is from the website’s international version and has different pricing, currency, taxes, and products. 

This is obviously problematic, and the resultant data isn’t accurate. The retailer cannot make a decision for its website for the North American market based on the data collected. 

Establish data quality standards

One way to ensure data quality becomes a priority or, rather, a foundational principle throughout the organization is to create standards that all must follow. 

The use and reliance on data may vary for different business units, but having a common standard will push each unit to ensure quality checks. It may benefit the company to create a policy for data quality and set specific benchmarks. 

This will be an ongoing process as you learn what works for your business and what standards are achievable. 

Best Proxies to Avoid Poor Quality Data

If you’re looking for a web scraping tool and proxies to go with it to collect valuable data, Blazing SEO has you covered. 

Scraping Robot can handle all your web scraping needs and integrate seamlessly with your proxy pool — regardless of the type of proxy. It can ensure that you collect accurate and complete data that meets your business demands and helps you make those important decisions. 

Similarly, you can take advantage of the different types of proxies provided by Blazing SEO. The residential proxies are the most reliable for widespread data scraping. Based in every major region of the world, these IPs allow you to strategically collect the right data from the right location. Not just that, residential proxies can easily avoid IP bans and rate limits. 

Data center proxies are a more viable solution for those looking to build a bigger proxy pool. These proxies are more affordable and not as static as residential proxies — but they may be more susceptible to bans. However, with 9 ASNs and 27 countries, these proxy servers cover the whole world — and you can easily swap one proxy for another if you run into blocks and bans. You can select a data center in your market to ensure high-quality data scraping. 

Final Thoughts

Because it will not give you an accurate picture of what’s going on, poor quality data can make your investments in data collection and analysis go to waste. Simply collecting data is not enough — you have to separate the usable data from the unusable. 

Whether you get your data through web scraping or not, high-quality data is essential for any data-driven business. Adopting practices that ensure quality is the only way to get the best out of data.

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