Big data is a part of every major modern industry. How well do you know yours? Can you even tell the difference between good data vs. bad data?
Organizations frequently deal with problems like incomplete, inaccurate, or unsecured data. Managing undiscovered or hidden data adds to this. Gartner claims that poor data quality costs organizations nearly $12.9 million every year.
But thanks to IoT technologies, vehicles are becoming more intelligent and interconnected, generating enormous volumes of it. This data helps revolutionize various facets of the industry. By tapping into this information, automakers can improve vehicle reliability and performance through predictive maintenance to lower the risk of equipment failures.
Beyond that, analyzing big data can make operations safer by catching potential risks before they happen and adjusting features in response, which helps prevent accidents and injuries. The ongoing analysis also pushes for fuel efficiency, smarter navigation systems, and more advanced driver assistance technologies, all contributing to real-time updates and personalized driving experiences.
Knowing consumer preferences through data and refining the supply chain helps automakers build brand loyalty and develop connected cars, reshaping the future of driving experiences and the automotive world.
The Price of Poor Data
- Flawed Insights: Bad data can create erroneous interpretations and decisions and compromise the validity of strategic business choices.
- Higher Costs: Processing and rectifying poor quality data is resource-intensive, causing financial losses over time.
- Operational Inefficiency: Bad data creates bottlenecks in business processes, disrupting workflows and impeding productivity.
- Migration Challenges: Bad data can cause compatibility issues and data loss when moving data between systems, hampering seamless transitions.
- Customer Experience Problems: Erroneous data affects personalization and creates disjointed and poor customer experiences.
What is Bad Data? 4 Examples
1. Inaccuracy
This occurs when your dealership tracks customer preferences but lacks updated information about their buying behaviors. For example, if a customer’s interest in eco-friendly cars isn’t accurately captured, you might miss key sales opportunities, decrease customer satisfaction, and lose revenue.
In 2022, Unity Technologies ingested bad data into its Audience Pinpoint tool, which created large inaccuracies in its predictive algorithms, resulting in a $110 million revenue loss. CEO John Riccitello explains the extent of the data hit in this transcript of financial earnings.
2. Incomplete Data
What if you were analyzing sales trends to stock high-demand vehicles, but some sales records were missing? With incomplete data, you might end up investing in the wrong model years or brands, which lowers inventory turnover and raises inventory holding costs.
3. Inconsistent Data
Inconsistent data may include varying descriptions of similar car features across different databases. This inconsistency can create confusing inventory reports and affect communication and promotions. Can you imagine something like this occurring with a data-led car like the BMW Neue Klasse?
“For BMW, this means the next generation of cars will create and process about 3x the volume of vehicle data compared to current models. In BMW’s “Neue Klasse” models, “driver-assist features, like adaptive cruise control, parking assist, and partially automated highway driving, create tons of data from engineering teams to process and analyze for future updates and improvements.” – The Verge
4. Biased Data
If your data collection methods inadvertently favor one type of data over another (e.g., focusing solely on luxury car buyers and ignoring budget-conscious customers), your marketing strategies may get skewed and miss a broad segment of potential buyers.
Tips to Avoid Bad Data Quality
1. Set Clear Objectives
Before analyzing data, dealer partners need to define clear objectives. Knowing the problems you aim to solve helps tailor data use. With solid goals, determining essential resources becomes easier, boosting the value of data.
For example, if customer satisfaction is the target, prioritize collecting feedback centered on that goal. Clear objectives ensure that chosen data collection methods and tools capture relevant data, making the information practical and giving valuable insights for strategic decisions and growth.
2. Do Rigorous Data Cleaning
Raw data can actually turn into good data through data cleaning and processing, but it takes time (worth it). Do this to remove inaccuracies, duplicates, and inconsistencies. Utilize data cleaning tools and establish standardized procedures to maintain data integrity across all platforms. To get a full view of your data, you can use an advanced AI-powered dealership inventory management platform like Lotlinx to track real-time inventory data and consumer behavior.
3. Change Your Data Collection Methods
In order to have healthy data, you’ll need complete and unbiased data collection. Use an updated platform and systematic surveys that capture critical insights about customer preferences and purchasing patterns.
4. Conduct Regular Data Audits
Schedule periodic audits to assess data accuracy, completeness, and reliability. Identifying and addressing errors ASAP helps preserve the integrity of your data-driven strategies.
5. Utilize Reliable Vendors
When outsourcing data collection or analysis, partner with vendors with a strong reputation for accuracy and reliability. Read reviews to choose vendors that meet your dealership’s data needs.
Get Data Insights With Lotlinx
In many cases, you need expert intervention to maintain good data quality and address underlying issues.
When we at Lotlinx collaborate with our dealer partners, we ensure that we actively listen to truly understand all unique goals and challenges. We are committed to uncovering the root causes of any issues and letting our data-driven AI do the work to address your dealership goals. Our approach integrates the most advanced technology on the market with deep industry expertise to provide a toolkit that empowers.
Contact Lotlinx for comprehensive analysis, insights, and support.