Picture this Thanksgiving table: sparkling crystal goblets, fine china, linen napkins, and the meal you’ve spent hours preparing for your friends and family. But imagine your horror if your guests tuck into the sumptuous-looking spread only to find that the turkey is bone-dry, the potatoes soggy, and the green beans’ expiration date is in the rearview mirror.
This is just what it’s like if you’ve worked to construct a successful business model, replete with high-caliber personnel and a stellar concept, only to serve up subpar data. Nearly impossible to make a world-class product with rotten ingredients, isn’t it?
If your organization’s data system is churning out low quality data, that’s exactly what’s happening. Bad data quality can lead to significant financial losses and operational issues, undermining your business efforts.
Data Is Your Fastest Growing Asset
Data has emerged as the most important and fastest-growing asset businesses can acquire in today’s business environment. The numbers are staggering. It’s predicted that more data will be created in the next three years than during the previous thirty. In the next five years, we’ll see triple the amount of data created globally than in the last five.
Low-quality data can significantly impact a business, including inaccurate analysis, poor decision-making, reduced customer satisfaction, and increased operational costs. The problem is widespread: one in three business leaders say they don’t trust their own data. Even information technology professionals are suffering from a lack of faith. Over 90% of IT department leaders report that they need to improve their data quality.
The “5 I’s” of Poor Data Quality
How do we define it, exactly? Low quality data meets some or all of the criteria of the five I’s:
- Inaccuracy: The data contains errors, such as incorrect values or typos, e.g., incorrect customer names, physical addresses, or phone numbers
- Incompleteness: Critical information is missing from data records. This could mean blank fields in a database or lack of necessary data points, such as missing customer emails or details about a transaction. Incomplete customer data hinders your data quality metrics.
- Inconsistency: The data isn’t standardized or consistent across different systems or datasets. An example might be a name spelled differently in separate records, duplicates of the same information, or conflicting information about an account status.
- Irrelevance: The data is not relevant to the business’s specific needs or goals. This might include collecting data that doesn’t align with the business’s objectives or failing to update data when the company’s focus shifts.
- Integrity (or lack thereof): The data has been altered or manipulated in ways that compromise its integrity. This could be due to unauthorized access or poor security measures.
What Causes Data Quality Issues?
There are several common reasons that data quality issues arise:
People: Often, the culprit for poor data quality is human error. It often happens when data entry processes are not standardized or when employees manually enter values into spreadsheets, both of which raise the likelihood of errors. Duplicate data is a specific issue caused by poor data entry and lack of standardization, leading to inconsistencies and confusion. These errors are frequently overlooked once they are introduced, and errors that remain undetected tend to have compounding negative consequences. Missing data, duplicate records, and inaccurate data are common with manual input.
Decay: Data decay is another common cause for data quality issues. Information like customer data can change over time, rendering it outdated and useless. Incorrect or incomplete data received from third parties, suppliers, or partners can also hamper attempts at producing high data quality. This outdated information can compromise your data integrity.
Mergers: Restructuring a company or merging with another commercial entity can undermine data accuracy efforts. When data is combined from different sources during a merger or acquisition, the overall integrity of the resulting data can be compromised.
Lack of structure and governance: Without a structured approach to data management, including policies and procedures, data quality can suffer. Companies that do not invest in data quality tools or dedicated staff may struggle to ensure data quality. Having clear best practices and policies for data management is crucial for promoting seamless sharing of insights, informed decision-making, and reliable data.
The Good, The Bad, and The Costly
If you think the prevalence of low quality data is unnerving, brace yourself for the high price tag that comes with it (it’s not pretty). Some of these costs can be measured directly, but the insidious tolls are built in and harder to observe.
Maintaining good quality data can significantly reduce these costs and lead to improved business outcomes.
Inefficient Operations
Poor quality data leads directly to reduced efficiency. It negatively impacts the readiness of your data consumption and decision-making, as employees spend extra time correcting errors, reconciling data discrepancies, and verifying data accuracy. This results in increased labor costs and lost productivity.
On average, employees whose companies use data spend a whopping 19% of their working hours searching for information. Maintaining high quality data is essential to avoid these inefficiencies and improve overall operational performance.
Lost Revenue
Bad data and a broken compass have one thing in common: you will miss the destination every time. Poor data quality poses significant challenges, especially for sales and marketing teams, as it can lead to confusion and frustration when multiple representatives inadvertently reach out to the same customer.
Data metrics riddled with inaccuracies will result in missed sales opportunities, incorrect pricing, and misinformed marketing strategies. This results in potential revenue losses that can cost your organization both actualized dollars and opportunities.
Take the airline industry, which loses millions of dollars of net revenue every year due to what they call “mistake fares.” Mistake fares are posted fares that are lower by orders of magnitude than they should be–all because of data entry errors. Airline industry’s losses are merely a drop in the bucket compared to the $3 trillion every year of US dollars lost due to data quality problems.
You Can’t Sell When You Can’t See
One of good data’s superpowers is its ability to help decision makers get a glimpse into the future of the marketplace. Keen insights into short and long-term retail trajectories allow you to fine-tune your marketing campaigns, narrow the scope and concentration of your budget, and identify opportunities to cross-sell or upsell to existing customers. Inferior data, on the other hand, will keep those opportunities for growth just out of your purview.
Missed Opportunity Costs
Missed opportunity costs are a critical area affected by data quality issues. Data-driven organizations using inaccurate or inconsistent data run the risk of misinformed business intelligence. An organization using poor quality data is likely missing out on identifying trends or patterns that could lead to new revenue streams or operational efficiencies. The price of forgoing reliable data per business, per year, is $15 million on average.
Misdirected Efforts and Lack of Business Intelligence
Another way data quality can affect business operations is through marketing and sales departments. Marketing and sales departments that rely on acquiring one-time lists for customer outreach open themselves up to vulnerabilities. These lists cost money, and are often rife with errors, which could mean misallocating resources and more lost profits on the wrong customers while missing the right ones.
Customer Dissatisfaction
Frustrated consumers are yet another by-product of data accuracy problems. Data quality impacts a customer’s retail journey if they receive content that does not align with their needs. Customers expect a personalized experience, and failing to deliver on that experience causes dissatisfaction and can devastate customer conversion rates.
Consider the case of Hyperwolf, a cannabis product delivery service based in Los Angeles, California. The owners built an innovative service that promised lightening-fast delivery in 2 days or less. Prior to working with Data-Sleek, Hyperwolf provided customers with suggestions based on aggregate user preferences and past order history. However, scaling their company put tremendous pressure on their logistics data management.
The company’s data management infrastructure did not take into account inventory or locally popular products, resulting in overselling already out of stock items. It also failed to adequately respond to unexpected demand surges in certain locations that cost it revenues and potential market share.
Behind-the-Curtains Cost
It’s injurious enough to see revenue gutted by mismanaged, poor quality data. A data quality problem can wreak havoc in insidious ways by leaving you open to costly privacy breaches, reputation damage, and inexpediency in decision making. Taking proactive measures to improve data quality is essential to prevent these hidden costs and keep your business performance in tip-top shape.
Even the best intentions can sometimes add to the hefty price tag of bad quality data. Companies that invest in a data management system have the right idea, but without due diligence, the strategy falls flat and detracts from value.
Data Governance
Don’t overlook the significance of data quality dimensions. Neglecting to validate the accuracy of the data compounds the problem of sales and marketing chasing the wrong customers. This can snowball into finance missing forecasts and employees spending 60% of their time cleaning and organizing data.
To top it all off, 56% of data scientists view data preparation as the least enjoyable part of their work. Obviously, this is not a scalable path to achieving success. System outages lead to disruptions and require higher maintenance costs. Poor business processes and bad data are to blame for much of it.
Regulatory Infractions
Getting caught in the crosshairs of regulatory bureaus is an incontestable way to let growth opportunities slip through your fingers. Inaccurate, unreliable data doesn’t just mess with your day-to-day operations—it can also lead to regulatory infractions that hold your business back from growing. Keeping your data squeaky-clean means staying on the right side of industry-specific rules and avoiding the migraine of a data audit.
Poor Data Quality
Poor quality data can jeopardize compliance with regulatory requirements. This is where wishful thinking can get dicey, fast. The avoidance approach, or believing that a non-compliance issue is unlikely to occur, is riskier than many business leaders think. Here are the nasty truths about bad data:
- The cost of non-compliance has risen more than 45% in the last 10 years
- A single non-compliance event costs $4 million in revenue on average
- General Data Protection Records (GDPR) fines start at $11 million or 2% of a company’s annual revenue for corporate abuses and breaches to private user information
Don’t leave expensive, time-consuming litigation and disruption in your business up to chance. Implementing a reliable information management program to improve overall data quality is a fail-safe way to keep you protected and avoid data pitfalls.
Long-Term Brand Damage
Data errors can undermine customer trust and damage a company’s reputation. This can have long-term effects on a business’s market position and customer base.
Remember those “mistake fares” we talked about earlier? These types of data entry errors do more than cost airlines millions of dollars. The cause can be as innocuous and currency conversion errors or incorrect calibration on a pricing algorithm, but they are a nightmare for airlines. The airlines can either honor advertised prices and lose money, or retract it and cancel the customer’s reservation. The ensuing PR nightmare is not a situation any executive wants to find themself in.
When operational costs rise, those costs are passed on to your clients, further damaging trust. Repairing a damaged reputation and earning back the trust of customers and stakeholders is complex and expensive, but fortunately, avoidable. Secure your business data strategy with data professionals to avoid the headaches and retain your revenue.
Leading the Way Forward
Being proactive about your data is the best way to avoid missed opportunities and achieve business outcomes aligned with your goals. Ridding your database of the problems can free up your business to scale without limit. Maintaining clean, reliable data will allow you to personalize your communications with customers, give clear and accurate insight to your stakeholders, and avoid the dreaded regulatory microscope. Avoiding gutting fines while building trust and scaling upward? It’s no pipedream. Good quality data is within your reach.
What you can do
Assemble an audit team and perform a self-audit on your business’s data. Determining the metrics you want the team to use to assess data quality, such as accuracy, completeness, and consistency. This is a great first step toward taking control of your company’s decision making and data ownership, and improving overall data quality.
How we can help
If poor quality data is a challenge you’re ready to tackle, Data-Sleek is ready to help. With the flexible data architecture, data scientists, and management tools to monitor data across all of your assets, we can help your business lighten the load caused by poor data quality.
Treating your data as critical infrastructure is key to keeping your data from causing your company harm and holding it back from its growth potential. The bad data problem isn’t going away any time soon, but Data-Sleek has the instruments to get you where you need to go. Give us a call and schedule your complimentary consultation today.
