Introduction

Hello Everyone.Welcome to The Updates World,Today in this article i am going to talk on about on Bad Data.Can all the data collected by organizations today be considered good? Reports say that most of the data collected by companies is bad.

But, what does bad data mean for companies that rely on accurate data for making important business decisions?Data quality refers to how accurate, accessible, complete, consistent, relevant, timely, and valid the data is.

For example, the Global Financial Crisis of 2008 was caused by poor data that overstated the actual value of mortgage backed securities and collat­edial debt instruments.Bad data has become a major concern because of the potential financal consequences that its misuse could entail for organizations.

What is the cost of bad data?

According to several surveys and studies, companies lose millions of dollars because they don’t have good data. For example, in 2021, Garter cited that every organization incurs a loss of $12.9 million due to bad data.

According to a 2016 study by IBM, businesses in the United States lose up to $3.1 trillion annually because of bad data.

Bad data also causes businesses to draw incorrect conclusions that have negative short-term and long term consequences. It leads to poor decisions and business assessments which then impact the overall customer experience.

If bad data impacts the operational efficiency of a company, then it could be costing them money. An example would be if a marketing company sends out ads to the wrong target audience, which defeats their entire purpose; or if an insurance company pays out claims to clients who don’t actually need them.

According to Thomas C. Redman, president of Data Quality Solutions at DataDoc, “Bad data” is costly because managers, data scientists and knowledge professionals incorporate it into their daily work.

Incorporating such erroneous data is time-consuming. Often errors occur when these people tweak the data as per their requirements to meet deadlines without consulting the data creator.

Data scientists spend a significant part of their time cleaning and organizing data, identifying and fixing errors and confirming the sources of data. Quality control work takes up half of knowledge workers’ time and increases to 60 percent for data scientists.

Data strategy

Data Strategy – the tools, processes and rules to manage, analyze and act upon business data helps businesses make informed decisions while ensuring their data remains secure and compliant.

Businesses must adopt a systematic approach for collecting, storing, analyzing and managing data to extract value from it.

Data strategies that align with the purposes of an organization can help address challenges like slow, inefficient business processes; issues related to data privacy, data integrity and data quality; inefficient movements of data between different parts or organizations within the business; lack of clarity about business needs and goals; lack of understanding of critical processes and entities.

Companies like Stitch and Zeotape focus on organization-specific data strategies to extract valuable business insights from their data.

Technology

To deal with bad data effectively, adopting advanced technology for robust data storage is a significant step.

To help companies get started, Jonathan Grandperrin, founder and CEO at Mindee suggests using APIs to create an information base. They help make data easier for people to understand and use, which increases their digital competitiveness. Furthermore, they help organisations build faster and more efficient workflows that run smoothly, reducing errors and improving efficiency.

There are two kinds of API: APIs with pre-defined data models and APIs where the user defines their own data models. For example, if we want to extract information from a text file, then the algorithm is pre-trained with lots of documents containing specific types of information. Users upload relevant documents and select the relevant information they want to extract from them.

Segment, a CDP that helps companies harness first party customer data, has developed Protocols that offer quality data. Protocols help validate data at points of collectioin and automatize enforcement controls. Protocols help in standardizing data collection across an organization.

Until we started standardizing our data, people didn’t realize just how messy it was becoming. “With Protocols”, we can be confident that any data quality issues won’t happen again” – Colin Furlong, BI Analyst, Typeform.

Data mining services

Businesses hire data miners to implement best practices for data hygiene that improve their ability to generate better customer-oriented campaigns and sales, enhance their reputations, generate leads, and increase production rates. These services also help organisations streamlining the incoming data and weeding out data impurities.

To thrive, businesses need data mining services to ensure accurate data. Data mining refers to the process of finding patterns in large amounts of data. For example, BizProspexo provides several on-demand data analysis services such as data appends (i.e., uploads missing or incorrect customer information), data scrubbing (cleans up bad data), CRM cleaning (removes old contacts from the database) and email list building.

Treat data like a product

Data has become an integral part of our lives. It drives everything from business decisions to personal choices. Therefore, productising data must be given priority.

Treating data as a product ensures the preservation of the same level of quality across the board at any cost. Furthermore, it would be helpful for companies to ensure that they monitor and maintain their data like a production-grade systems.

Jonathan Grandperrin recommends other useful ways to solve the problem of bad data, including ensuring data portability, improving the institutional security consciousness, and establishing stronger learning models.

Back to basics

Besides such helpful advice, sometimes the simplest solution for organizations plagued by bad data is to return to the original source of the data, even if there’s a chance that the data came from an incorrect location. Sometimes, even if the source of the data is correct, the nature of that data may not fulfill its intended purpose.

When dealing with sensitive personal data, organisations must ensure that their systems are secure. For example, if you’re focusing on sales figures, then you should also be looking at things besides just sales, like growth.

Businesses can use the data processing techniques at other times to reduce the margin of errors. You can achieve such fine-tuning by checking the data sources for accuracy and reviewing the data with third parties.

Conclusion

Today in this article we are talking about Bad Data.If you have any doubt regarding this post . Please Comment write below.

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