The cost of poor data quality on business operations

As a business owner, data specialist, or business intelligence (BI) analyst, you’re likely aware of the critical importance of accurate data in making well-informed strategic decisions. There’s a reason why data practitioners spend the majority of their time preparing the data for analysis.

This article explores how bad data affects company results and offers tactics to increase data quality for smarter decision-making.

What is the business cost of poor data quality?

Every year, poor data quality costs companies an average of $12.9 million. Aside from the immediate impact on income, low quality data complicates data ecosystems and contributes to poor decision-making in the long run. 

In a world where data is the most central asset of a company, used both for operational and strategic purposes, we constantly face the risk of harming our business when relying on poor-quality data for decision making or operating.

Data should always reflect reality. If it fails to do so, it may lead to inaccurate decision-making. This often results in inefficient business operations and, in the worst-case scenario, even puts the company at risk.

That’s why data quality is of paramount importance to any organization with data-driven processes.

But what exactly are the risks businesses with poor data quality face? Let’s dive into them to understand the value of high data quality for practically every aspect of business operations.

Lost revenue

Poor data quality can result in inaccurate sales projections, lost sales opportunities, and client attrition, which can cause a firm to suffer large revenue losses.

Increased operational costs

Low-quality data might result in more labor-intensive manual labor, ineffective procedures, and greater operating expenses. These might include the cost of handling problems with data quality, manual data input, and fixing data inaccuracies.

Loss in productivity

Bad data accommodation is costly and time-consuming. The required data may contain numerous inaccuracies, and many people just make the necessary repairs on their own to finish the work at hand in the face of a pressing deadline.

Compliance risks

Poor data quality may result in penalties and regulatory breaches. This may involve breaking rules about data security, privacy, or other industry-specific legislation. Here is a detailed study of maintaining compliance with your cyber security obligations.

Reputation risk and damage

Inadequate data quality can harm a company’s reputation and financial losses. Organizations will continue to face inefficiencies, unnecessary expenses, compliance concerns, and customer satisfaction difficulties due to their (often incorrect) assumptions about the health of their data.

Ineffective decision-making

The main issue is that firms’ business-critical data fragments as they grow. Because it is dispersed among applications, including on-premise apps, there is no overall picture. Business-critical data becomes inconsistent as a result of all this change, and it is impossible to determine which application contains the most recent information.

Reduced customer trust and loyalty

Inaccurate customer data might cause your consumers to get frustrated overall, have their marketing initiatives misdirected, and have delayed answers to their questions. In the end, this erodes loyalty and trust.

The impact of poor data quality on business

How bad data impacts business operations

Poor-quality data incidents can present a significant risk to companies. Data teams usually deal with several types of data incidents. 

Some no longer get noticed and become assimilated into our daily routine. Others are the kind of incidents that are small enough, so they don’t get attributed to an actual failure. Their effect on data is often categorized as random bias. 

And then there are other data incidents, ones that are so significant that they end up causing the loss of millions of dollars – or may even put human life at risk.

Here are two examples of operational issues caused by poor data quality:

1. NASA’s Mars Climate Orbiter

Back in 1998, NASA lost $125 million on its Mars Climate Orbiter because of the difference in measuring units. Lockheed Martin helped NASA build, develop, and operate the spacecraft. Its engineering team used English units of measurement for a critical spacecraft function while NASA’s team used the more common metric system.

The unit mismatch prohibited navigation data from being sent between the Mars Climate Orbiter spacecraft team at Lockheed Martin in Denver and the flight team at NASA’s Jet Propulsion Laboratory, causing the loss of the Climate Orbiter.

2. Amsterdam’s tax office blunder

Amsterdam’s tax office once gave out €188 million to around 10,000 households in yearly government rent subsidies instead of €2 million. The reason? Amsterdam’s government software calculated payments in cents instead of euros.

What’s interesting is that no one at Amsterdam’s tax office seemed to have noticed the mismatch. On top of that, the city spent some €300,000 more on trying to understand and resolve the matter.

These two examples show why data consistency is so important. Companies that collect data from multiple systems or collaborate with third-party providers need a strong data quality framework that helps make the data uniform, accurate, and consistent across multiple databases, systems, and applications within a company. 

How bad data impacts the business strategy

How does data quality impact the strategic aspects of running a business? How can stakeholders be sure that the data is reliable for decision making?

In the case of everyday data-driven decisions, making the wrong decision due to bad data quality may go unnoticed. Especially if it happens only once in a while.

But the drama begins when errors become frequent and hide under the unknown unknowns. We might understand the source of the error very quickly in some cases. In others, it may remain an indefinite mystery. 

This means that the company faces issues such as loss of time, resources, and opportunities – all of which translate into money. Making mistakes is costly – and so is analyzing their root causes. But a wrong strategic turn based on poor-quality data isn’t just a loss of resources. In some cases, it might be a fateful turning point for the business.

Each data set is vulnerable to data quality issues, especially when referring to big data generated at high velocity and streamed into our data lake. Poor data quality may lead to errors that bring about poor customer interactions, erroneous analytics, and inappropriate decisions, all of which impair corporate success.

What causes poor data quality?

Here are the primary causes behind poor-quality data:   

• Data integration issues – Conversion problems can occur when data is collected from many databases that aren’t integrated with the organization’s database. Converting one data format to another frequently leads to errors. Conversion challenges can get much more complex when data from an older legacy system is converted for storage in a NoSQL system.
• Data decay – The decline of data quality often occurs in the marketing and sales departments (this is also called data deterioration or data degradation).
• Poor data migration – This issue occurs when data is transferred from a legacy system to a new database or the cloud. Moving data to a new system involves various risks: some of the data values may be missing or data may be corrupted.
• Data duplication – Duplicate data might lead to issues when used for statistical purposes.

The good news is that data quality is measurable, and you can improve it using solid data quality management best practices, including data quality monitoring. This helps build trust in data across all consumers, as well as reduce the uncertainty of the data quality. Provide teams data they can rely on when making decisions.

Types of poor data quality

6 dimensions of data quality

Data quality can be measured against specific data quality dimensions. Depending on the unique nature of your business operations, strategy, and industry, these may take different forms. 

Once you establish these dimensions, you’re ready to line up the data quality metrics that will apply to your case.

Here’s a short overview of data quality dimensions, together with examples:

1. Completeness

Data is considered “complete” when it includes all of the required information. Imagine that you ask a consumer to submit their name. You may make the middle name optional, but the data is complete as long as you have their first and last names. You should assess whether all of the required information is available and whether any components are missing. 

2. Accuracy

This is an important aspect of data quality because it demonstrates how well the information represents the event or item portrayed. For example, if a consumer is 32 years old but the system believes they are 34, the information is wrong. 

What can you do to increase your accuracy? Consider if the data truly reflects the situation. Is there any erroneous information that needs to be corrected? 

3. Consistency

The same data may be stored in several places across different teams. If the information matches, it’s considered “consistent.” Consistency is an important data quality factor, especially in situations where there are many data sources.

For example, if your human resources information systems show that an employee has left the company but your payroll system shows that they are still getting a paycheck, your data is inconsistent.

To address inconsistency problems, inspect your data sets to ensure that they are the same in every instance. Is there any proof that the data contradicts itself?

4. Validity

Data validity is a dimension that indicates how well data matches business requirements or follows a certain format. Birth dates are a popular example, as many systems require you to enter your birth date in a specific manner. If you don’t do it, the data will be invalid. 

To meet this data quality dimension, you must guarantee that all of your data follows a specific format or set of business standards. 

5. Uniqueness

“Unique” data is data that only appears once in a database. We are all aware that data duplication occurs frequently. For example, it is likely that “Daniel A. Lawson” and “Dan A. Lawson” are the same person, but in your database, they will be classified as separate entries.

Meeting this data quality factor requires ensuring that your data is not duplicated.

6. Timeliness

Is your data easily available when it is needed? This data quality dimension is referred to as “timeliness.” Assume you want financial data every quarter; if the data is provided when it is expected, it may be deemed timely.

The timeliness component of data quality refers to specific user expectations. If your data isn’t available when you need it, it doesn’t fit that criteria.

How to minimize the cost of poor data quality?

Proactively resolving problems in your data and systems, preventing new errors from occurring, and altering your organization’s data-related culture and mindset are the only ways to mitigate the potential harm that inaccurate data may cause.

Invest in Data Quality Management

You must invest in data quality management if you want to avoid the above-mentioned traps and cut expenses. Depending on how serious your issues are, you might be able to locate a data quality software solution that works for you, or you could need to hire professionals to do the task.

Data quality software may audit a variety of operations, including address validation, deduplication, profiling, match and merge, and more.

Avoid Low-Quality Data

Not only should data inaccuracies be corrected and quality improved, but methods for preventing poor data quality in the first place should also be explored. While data quality tools can detect new problems as they are added to your system, it is preferable to avoid errors entirely.

Consider reviewing the current problems and determining whether there are any guidelines or rules in the system you can establish if your firm hasn’t established any regarding data entry procedures or if your rules have gotten out of date as your business has expanded.

Rethink Data Quality

Many businesses find that addressing systemic mistakes is not the only way to improve the quality of their data. It’s about transforming your company’s data culture. You’ll see a trickle-down effect of improved data quality as soon as your stakeholders and leadership team agree.

How lakeFS can help prevent poor data quality

lakeFS is a data version control solution that lets teams bring tried-and-true software development best practices to data by utilizing Git-like processes.

Many data operations activities are more efficient when data is managed in the same way as code:

Data versioning and branching

From a lineage perspective, the version history is highly evident when there are several versions of the material. Engineers may quickly link clients to recently released data and keep track of changes made to their repositories or datasets.

Working in isolation

Any updates or adjustments made to the current data pipelines must be tested to ensure that the data improves and that no new problems are introduced. Data engineers must be able to create and test these modifications independently before incorporating them into production data.

Rollback

If something goes wrong and you expose users to production data, you can easily revert to a previous version with a single atomic move.

Time travel

Consider a scenario where a decrease in performance or an increase in infrastructure expenses results from a fault with the quality of the data. You may open a branch of the lake from the point where the changes were put into production if you have versioning. To begin identifying the issue, you may use the information to replicate the problem and all of the environment’s characteristics.

Hooks

With version control systems, you may program events to cause certain actions to happen. A webhook, for instance, can determine if a newly uploaded file belongs to one of the permitted data types.

Using data version control technology eliminates the issues that plague large data engineering teams that collaborate on the same dataset. Additionally, troubleshooting is much quicker when a problem does develop.

Wrap up

Clean, consistent, conformed, current, and comprehensive. The five Cs of data apply to all forms of data, large or small. Your data processing should include checks for all of these dimensions.

Check out this guide to data quality monitoring to get an overview of the processes and tools that help to improve data across entire organizations.