The Cost of Bad Data
In today’s data-driven context, organizations are racing to unlock value from their data. But there’s a catch: garbage in, garbage out (GI-GO). If the data fueling decisions is flawed, the outcomes will be too. Poor-quality data doesn’t just create inefficiencies—it erodes trust, stifles innovation, and hits the bottom line.
Bad data is expensive. According to Gartner, poor data quality costs organizations an average of $12.9 million annually. For individual businesses, the impacts ripple across every function:
- Decision-Making: Flawed data leads to misguided strategies. Imagine a sales team targeting the wrong demographics because customer records are outdated.
- Compliance: Regulatory fines escalate when data inaccuracies lead to reporting errors (e.g., GDPR or CCPA violations).
- Customer Satisfaction: Duplicate records or incorrect contact details frustrate customers. Imagine running a loyalty program with duplicate profiles, causing a 30% spike in marketing costs and customers receiving repetitive promotion offers.
- Market Efficiency: Supply chain delays, inventory mismanagement, and missed sales opportunities stem from unreliable data.
- Competitive Advantage: Companies with trusted data outpace rivals by acting on insights faster.
The bottom line – maintaining high standards for data quality and trust is crucial for building a reliable and successful business. Without trust in data, businesses operate in the dark—and the consequences are real.
Bad data isn’t just a nuisance—it’s a threat to survival. By decentralizing data quality and fostering a culture of trust, businesses can transform data into a driver of innovation and growth. With platforms like Qlik Talend Cloud, organizations don’t just manage data; they leverage it to stay ahead in a competitive landscape.
In context of data trust, QQinfo has created a toolkit, methodologies, and best practices that minimize the risks of using erroneous analytics in decision-making.
No matter how fast, innovative, interactive, or efficient a BI system is, the base-point in the construction of a set of analytics must be the CORRECTNESS OF THE INFORMATION PROVIDED.
This correctness is determined by 2 essential factors:
- The data used should be accurate, complete, and fresh enough;
- All data processing (intermediate and final) does not alter the quality of the source information.
QQtrust™ is a set of methods and solutions that QQinfo applies to the analytics and solutions provided to maximize confidence in the correctness of the information delivered as a decision support system and to minimize associated risks.
The technology has multiple benefits, but it also comes with a flip side of the coin, which requires a specific approach to minimize its risks. QQtrust™ is a conclusive example of this.
Click here for details about QQtrust™.
Article sourced from here: https://community.qlik.com/ and supplemented by QQinfo.
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