Kevin Rahan Kevin Rahan
Sep 14, 2022 10:34:24 AM

Many businesses do not realize how much their poor data is currently costing them. According to Gartner, organizations believed that their bad data was causing an average of $15 million in losses per year. And 60% of those surveyed didn't know the exact costs because they lacked metrics to measure the impact.

Over the last 17+ years, at STAEDEAN, we've assisted over 2,000 customers overcome their data challenges. If you're unsure about the impact of bad data on your business, it's advisable to look for the signs outlined in our blog. This will help you assess whether bad data is affecting your business, and then you can decide whether it's time to consider implementing a data governance strategy to improve data-related processes across your organization.

7 Signs that indicate you have bad data

1. Data credibility

At any time during a company meeting, if you or other members of your management team find yourselves debating the credibility of the data, that's the first sign of poor data quality.

When one department within your organization maintains data that slightly differs from another department's records, a significant amount of time is wasted in identifying the correct data. Not to mention the frustration generated by this lengthy process.

If your management teams are spending time discussing data validity, it's a clear indication that you have a data problem on your hands.

2. Data Silos

The reason companies spend so much time debating the source of data or the reports derived from it is due to the existence of data silos. By 'data silos,' we mean disparate, independent business application systems that are critical to the operations of the business but share common data.

The sharing of common data and the inability to control that data is what causes data errors in the first place. When your application landscape and data across business systems are not interconnected, you end up with islands of data that must either be manually transferred or, in some cases, get left out. Either way, without synchronized data across systems, you cannot share recommendations in a timely manner, potentially missing out on opportunities. This can also lead to data duplication and human errors in manual data processing.


3. Resources spending too much time on data

If your organization lacks any data governance practices, your team will waste time gathering data from various systems and likely need to reach out to different teams to source and verify that data. However, if you had software for data synchronization, data entry, and master data management, your data would automatically flow between systems, with data entry access granted only to responsible data owners. Data quality rules could ensure consistency, accuracy, and validation.

By automating data processes throughout the organization with a data governance strategy and the right software to support your teams, you can free up your resources' time spent on cleaning, re-entering, and rectifying mistakes. This will also translate to greater efficiency in all your business processes. 

4. Cannot make the right business decisions

If your data is not clean, it impairs managers' ability to make accurate business decisions based on that data. This can lead to several negative outcomes for the business, such as overordering inventory or failing to order enough.

When your data is not clean, the risk to the business increases during cyclical downturns, such as recessions or economic shocks. If you have poor data during these times, you may be unable to make the right decisions or could make choices that harm the company. Ask yourself this question: 'Can you trust the data to make an instant decision today?' If your answer is 'No,' then you recognize that there's a data problem, and it's not clean enough.

If your team members are spending excessive time reevaluating spreadsheets and addressing issues, it's likely that they don't have access to clean data for analysis. To harness actionable insights from clean data, which can ultimately increase revenue, the right software is essential.

To derive valuable business insights that can identify patterns, guide confident goal setting, estimate consumer consumption trends, and justify return on investment, you require suitable software support. If you are using a system that is challenging to assess or cannot handle large data volumes, it's important to step back and identify what isn't working for you.

5. You can't hire enough specialists to deal with bad data

If you can't control the ingestion of new data, no matter how many data specialists you employ, they will have an endless task of cleaning poorly ingested data within operational systems. For example, you could hire 1000 people to clean data, but if you continue to input incorrect data, the job remains never-ending. You cannot hire sufficient data specialists to clean data once it's already in the system.

Remember, there is no efficient way to hire people solely for data cleaning. Instead, focus on preventing errors at the source. If you can control and govern data, you can prevent poor data from entering critical systems like Dynamics 365 ERP. While data cleaning is valuable, it's an inefficient use of your data specialists' time. They could be dedicating that time to strategic initiatives with a positive impact on the company.

If you've hired data specialists without investing in complementary software systems to support your data governance strategy and expect miracles, but you haven't achieved your goals, you may find that these data managers spend most of their time sourcing, cleaning, and preparing data instead of deriving valuable business insights and identifying patterns that can aid in solving customer problems. While investing in data specialists is beneficial for your organization, it would be more advantageous for both your organization and the specialists themselves to dedicate their time to the tasks they were hired for, rather than working on data cleanup and assembly.

You would prefer to have your key resources focused on the growth and future of the company, rather than correcting data errors from the past, wouldn't you?

6. To err is human

Manual data processing is prone to human errors, and without automated software in place to detect these errors, the likelihood of them going unnoticed increases. This can result in different information for the same product or customer across various systems, or even missing data.

Furthermore, if you use software for functions like inventory management that rounds up data, you introduce another source of conflicting data that doesn't align with information from other systems.

As a result, when it's time to report back to your department heads, you may find teams sharing inconsistent reports, making it challenging to make informed business decisions. A reliable solution for addressing this challenge is to establish data processes that ensure consistent and accurate data across the organization, utilizing software like MDM Studio.

7. Paying the price for bad data

If you have clean data that helps you derive valuable business insights, it becomes easier to identify which products are being used and where opportunities for new product launches may lie. However, if your data is not synchronized, and you lack a holistic view across systems, it can lead to lost revenue. For instance, if order data is not communicated to your logistics team promptly, it could result in revenue loss, an unhappy customer experience, and loss of goodwill.

Here's a real-life example: When Target, an American retail corporation, expanded its operations in Canada, they ordered an excessive amount of products due to over-optimistic forecasting. Consequently, Target Canada ended up with an excessive inventory in stock. Data entry errors, such as product dimensions being entered in the wrong unit of measurement (e.g., inches instead of centimeters), led to situations where products didn't fit into shipping containers. Incorrect and missing information regarding pricing resulted in wrong tariff codes or the absence of tariff codes. Within just two years of opening, Target Canada had to close all 133 stores, resulting in an inventory management disaster that cost Target $5.4 billion.

Most organizations, regardless of size, have made inventory management mistakes at some point. Ordering too little leads to missed opportunities, while ordering too much can result in revenue loss. If you are experiencing any of these issues, poor data is often the root cause.

What is the road ahead for your organization?

According to a report by Forrester, 'less than 0.5% of all data is ever analyzed and used.' Furthermore, their report states that Fortune 1000 companies that increase data availability by just 10% could potentially boost their annual revenue by an estimated $65 million.

If you recognize any of the signs of poor data mentioned in this blog, and your data is not regularly checked for key aspects such as accuracy, consistency, validity, completeness, timeliness, and integrity, feel free to schedule a call with our experts to discuss your specific needs.

Over the past few years, we have strengthened our data governance portfolio with solutions such as MDM Studio (master data management), Data Quality Studio (data quality management), and Data Entry Workflow (data entry management). Collaborating closely with our customers, we have gained a deep understanding of the challenges they face due to poor data, such as operational delays, data duplication, and erroneous business decisions. We have successfully assisted them in digitally transforming their data governance processes through the implementation of our solutions.

If your organization lacks processes to validate data, how can you rely on that data for reporting or deriving business insights? Implementing web services for data validation can save your team valuable time, especially when manually checking data authenticity is required. Our Data Quality Studio software provides this capability.

Additionally, software like Data Entry Workflow enables you to establish approval processes and identify errors during data entry, reducing the time spent on coordination and approvals. This automation is particularly beneficial when manually scanning files for errors becomes impractical.

If you wish to improve the current state of your data, start by analyzing your current challenges and then consider implementing a data governance strategy for your organization. To assist you on your journey toward becoming a more data-driven organization, you can download our beginner's guide to master data management. This resource will help you gain a better understanding of the crucial aspects of data governance. Click the link below to access the resource and learn how to streamline your data processes.

Kevin Rahan Kevin Rahan
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