How to avoid bad data

Often we tend to underestimate how bad data affects the business, or we even have data of poor quality or poorly integrated into the business and we are not aware of the problem. In this post we will tell you what bad data is, how it affects the business, and how to create best practices to avoid bad data. Let’s take a look at the value proposition we offer at Scraping Pros.

Today, data quality, reliability, and integration are essential to almost everything, from business analysis to training AI models.

In previous posts, we highlighted the importance of data cleansing for making business decisions and creating an optimal data-driven strategy (see post). At the same time, we discussed the power of data integration to capture data from multiple sites and transform it into a cohesive workflow (see post).

In short, what is bad data and why is it important to pay attention to it in our business processes? Bad data refers to incomplete, inaccurate, inconsistent, irrelevant, or duplicate data that creeps into your data infrastructure for a variety of reasons. Bad data manifests itself in a variety of ways, each of which presents unique challenges to data usability and integrity.

Types of bad data

1. Incomplete data: Incomplete data is a data set that is missing one or more of the attributes, fields, or entries necessary for accurate analysis. This missing information renders the entire data set unreliable and sometimes unusable. Common causes of incomplete data include intentional omission of certain data, unrecorded transactions, partial data collection, data entry errors, or unseen technical problems during data transfer, etc. Examples include a customer survey with missing contact records, which makes it impossible to follow up with respondents later, or a hospital database with missing patient medical records, which is critical for medical history.
2. Duplicate data: Duplicate data occurs when the same data entry, or a few nearly identical data entries, are recorded multiple times in the database. This data redundancy leads to misleading analyses and incorrect conclusions, and sometimes complicates merge operations and system failures. In summary, statistics derived from a data set with duplicate data become unreliable and inefficient for decision making. A clear example of this problem would be a customer relationship management (CRM) database with multiple records for the same customer, which can distort the information derived after analysis, such as the number of different customers or sales per customer. Similarly, an inventory management system that tracks the same product under different SKU numbers makes inventory estimates completely inaccurate.
3. Inaccurate data: Having incorrect and erroneous information within one or more entries of the record is identified as inaccurate data. A simple error in a code or number due to typographical error or inadvertent oversight can be serious enough to cause serious complications and losses, especially if the data is used for decision making in a high-risk area. And the existence of inaccurate data itself reduces the reliability and trustworthiness of the entire data set. For example, a shipping company’s database that contains incorrect shipping addresses for deliveries could end up sending packages to the wrong places, causing huge losses and delays for both the company and the customer. Situations where a human resource management system contains incorrect information about employee salaries can lead to payroll discrepancies and potential legal issues.
4. Inconsistent data: Inconsistent data occurs when different people, teams, or areas of the organization use different units or formats for the same type of data within the organization. It is a common source of confusion and inefficiency when working with data. It disrupts the consistency and continuous flow of data, resulting in incorrect data processing. For example, inconsistent date formats across multiple data inputs (MM/DD/YYYY vs. DD/MM/YYYY) in a banking system can cause conflicts and problems during data aggregation and analysis. Two stores in the same retail chain entering inventory data in different units of measure (number of cases versus number of items) can cause confusion during replenishment and distribution.
5. Obsolete data: Obsolete data is records that are no longer current, relevant, or applicable. Obsolete data is especially common in fast-changing fields where change is rapid and constant. Data from a decade, a year, or even a month ago may no longer be useful or even misleading, depending on the context. For example, in a healthcare facility, a patient may develop new allergies over time. A hospital that prescribes medication to a patient with completely outdated allergy information may be putting the patient’s health at risk. In addition, non-compliant, irrelevant, unstructured, and biased data are also bad data types that can compromise the data quality in your data ecosystem. Understanding each of these different types of bad data is critical to understanding their root causes, the threats they pose to your organization, and developing strategies to mitigate their impact.

Causes of Bad Data

In this topic, it is important to identify the main reasons why bad data is generated.

Among these causes, we can mention:

• Human error in data entry: Inadequate training, lack of attention to detail, misunderstandings about the data entry process, and mostly unintentional errors such as typos can ultimately lead to unreliable data sets and huge complications during analysis.
• Poor data entry standards and practices: A strong set of standards is key to building strong, well-structured practices. For example, if you allow free text entry for a field such as country or phone, a user may enter different names for the same country, resulting in an inefficiently wide range of responses for the same value. These inconsistencies and confusion result from a lack of standards.
• Migration issues: Incorrect data is not always the result of manual entry. It can also occur as a result of migrating data from one database to another. This problem causes misalignment of records and fields, data loss, and even data corruption that can require hours of review and repair.
• Data degradation: Any small change, from customer preferences to a shift in market trends, can update company data. If the database is not constantly updated to adapt to these changes, it will become outdated, causing it to deteriorate or become obsolete. As mentioned earlier, outdated data has no real use in decision making and analysis, and contributes to misleading information when used.
• Merge data from multiple sources: Inefficiently combining data from multiple sources or poor data integration can result in inaccurate and inconsistent data (when different data sources being combined are formatted in different standards, formats, and quality levels).

How bad data affects decisions

If you are an executive processing data sets that contain bad data, you are undoubtedly putting your final analysis at risk. In fact, bad data can have devastating and long-lasting effects. For example:

• Poor data quality can harm your business by increasing the risk of making poor decisions and investments based on misleading information.
• Inaccurate data results in significant financial costs and wasted resources that can take significant time and money to recover.
• The accumulation of inaccurate data can even lead to business failure by increasing the need for rework, creating missed opportunities, and negatively impacting overall productivity.
• Business reliability and trustworthiness decline, significantly impacting customer satisfaction and retention. Inaccurate and incomplete business data leads to poor customer service and inconsistent communication.

How to avoid bad data and improve business practices

It is important to note that no data set is perfect, and it is very likely that we will have data with errors. However, establishing practices to improve the quality and reliability of data will ensure that our data-driven strategy is well managed and reliable for the organization as a whole, which will help us make better decisions.

Recognizing that this problem exists in our organization is the fundamental step to correcting it. How to achieve it? At Scraping Pros, we have over 15 years of experience in web scraping, and with our world-class technical capabilities and resources, we provide high quality data for our dataset extraction, cleaning and maintenance services.

Here we suggest concrete practices to avoid bad data:

• Use reliable data extraction and integration tools or services: At Scraping Pros, we have the knowledge and experience to integrate a dynamic working platform into your organization that includes new personalized web scraping tools and services.
• Perform periodic cleaning and fine-tuning of the extracted data: As a personalized solution, at Scraping Pros we perform periodic data cleaning that includes monitoring the correction and maintenance of the work platforms to avoid errors and improve the quality of the data.
• Implement solid data governance and infrastructure: At Scraping Pros we can advise you on the most appropriate policies, protocols, standards and regulations for processing your business data, including its security, compliance and legality.
• Perform data audits: This is the key to finding inconsistencies and outdated data before complications arise, a task on which Scraping Pros can advise you so that you can check the validation of the data and the uniformity of formats or rules so that the standards are robust, your data is integrated and does not contain procedural errors.
• Ensure project scalability with no hidden costs: Scraping Pros has the resources and infrastructure to handle large data extraction projects, for both large and medium or small clients, at a low cost.
• Advanced Training: With our agile working methodology, Scraping Pros makes sure that the client is adequately trained and informed throughout the entire process of working with data, beyond the fact that they do not have to worry about the complexity and automation of the technology.

With our personalized service, you get the web data you need in real time, with the quality and accuracy you need, so you can make informed decisions quickly and confidently. We extract the data you need for your business, tailored to each customer and with personalized delivery (including information about competitors and their products, prices and promotions, among other types of data). We also adhere to all ethical and legal standards for web scraping.

In short, Scraping Pros solutions have high quality, support and maintenance. You will not have to worry about the complexity of the solutions, you will have more time and resources to focus on your strategic goals and objectives, without neglecting the technical and operational aspects of achieving them.

Want to learn more? Contact our specialists today.