In the digital economy, data functions as the lifeblood of businesses: fueling strategic decisions, driving customer engagement, and shaping the competitive landscape.
Just as the circulatory system nourishes the body with a continuous supply of blood, business systems rely on continuous availability and flow of high-quality data. When tainted blood courses through the human body, the results can be harmful or even fatal. Similarly, bad data coursing through an organization’s systems can have grave consequences.
In the first installment of this two-part analysis, I compared and contrasted the importance of data to business with blood to the human body. In this installment, I’ll explore the impact of bad data on business and how it can be just as devastating as bad blood running through the human body.
Business Impact of Bad Data
When the human circulatory system encounters “bad blood” — be it toxins, infections, or disorders — the repercussions can be far-reaching. Such bad blood can compromise the immune system, deteriorate organ function, and disrupt the body’s equilibrium. If untreated, it can lead to serious health complications or, in the worst cases, death. Similarly, bad data — data that is inaccurate, outdated, incomplete, non-standardized, inconsistent, or inappropriately accessed — can have severe implications for a business.
Here are five concrete ways that bad data negatively impacts a business:
1. Poor Decision-Making: Decisions fueled by inaccurate or inconsistent data can steer an organization off course, leading to financial losses, missed opportunities, and a compromised competitive position. When a CEO requests sales reports as part of board-meeting prep, those reports must be not only accurate but consistent. If the report coming from sales on the number of qualified accounts in the pipeline differs from the report coming from marketing, which data gets reported to the board? Believe it or not, this is not an uncommon issue, and the fallout isn’t pretty.
2. Damaged Customer Relationships: Outdated or inaccurate customer data can result in misguided communications, inappropriate product recommendations, and poor customer service. This can lead to erosion of customer trust and damage to an organization’s reputation. Customers today are demanding a higher level of personalized service and expect that the company they are doing business with, or contemplating doing business with, knows and understands their needs. The information needed to satisfy this requirement cannot reside solely in the head of an account rep or customer service manager, it needs to be memorialized in the data. If the data is wrong, you run the risk of lost revenue or a decreased pipeline.
3. Operational Inefficiencies and Increased Costs: Bad data can create operational bottlenecks leading to misallocation of resources, increased operational costs, and decreased productivity. Efforts to rectify errors resulting from bad data can drain manpower and time, lowering overall efficiency. Building organization-wide standards is the first step in solving many of these problems.
4. Compliance and Legal Risks: In an era of stringent data protection regulations, poor data governance can lead to non-compliance, resulting in hefty penalties, legal issues, and reputational damage. Marketers have an obligation to respect the privacy and preferences of their audience. Having confidence in the geo-location of an email recipient or ensuring there aren’t duplicates in the system could mean the difference between a well-received email and a General Data Protection Regulation (GDPR) complaint or violation. If you want to know how those types of compliance and privacy violations can impact a business, just ask Meta.
5. Misguided Innovation Investments: Companies often rely on data to identify trends and innovate. Bad data can lead to misdirected innovation efforts, resulting in failed products or services, wasted resources, and reduced competitive strength.
Which companies are the most important vendors in data? Check out the Acceleration Economy Data Modernization Top 10 Shortlist.
5 Steps to Data Quality
Just as the body needs regular detoxification, businesses need systematic data quality management to purge their systems of bad data. This process has five required measures:
1. Data Cleansing: While a good nutritionist may recommend detox to provide a biological “reset,” good data engineers build data cleansing pipelines to identify, correct, or simply remove corrupt, inaccurate, or irrelevant data from an organization’s database.
2. Data Validation: Just as medical tests validate the presence of toxins or infections, data validation verifies that the data meets established quality standards and is correct and useful. For marketing teams, data validation is crucial to verifying incoming records from content marketing efforts and/or inquiry forms. Just recently, Imperva released a study showing an increase in bot traffic of 5.2% year-over-year, putting the amount of bot traffic at a record 47% of all internet traffic. Along with contributing to a solid security posture, validation can be an effective way to keep the garbage out of your database.
3. Data Standardization and Deduplication: This step parallels the body’s regulation of blood components. It involves maintaining consistency in data formats and removing redundant data, which helps ensure accuracy and reduce storage costs. Standardization is critical in enabling clean reporting across departments, and it can be a critical component for segmentation and building personalized journeys for buyers.
4. Data Enrichment: In a way very similar to how nutrients enrich blood, data enrichment involves augmenting existing information with additional, relevant data to provide a more complete picture.
5. Data Security: Much like maintaining blood purity, safeguarding data integrity and privacy is critical. This includes setting up secure firewalls, encryption, and access controls.
In today’s data-driven economy, organizations need to acknowledge the destructive potential of bad data and take proactive steps toward effective data quality management. After all, data is not just the lifeblood of a business; it is also the key to its vitality and longevity. Proactively combating bad data is imperative to survival and growth.
