We are already similar to the three V’s of big data: volume, velocity and variety. And yet, the cost and effort invested in dealing with poor data quality makes us consider the fourth aspect of Big Data – veracity.
In the era of Big Data, with the huge volume of generated data, the fast velocity of incoming data, and the large variety of heterogeneous data, the quality of data often is rather far from perfect. Even if your company’s Big Data solution characteristics meet the 3 Vs, your company, too, may have a “treasure trove” of useless and potentially harmful data that must be dealt with. According to the 2013 case study published in “Advancing Federal Sector Healthcare,” poor data quality costs an organization between 20 to 40% due to extraneous work and customer complaints. Techrepublic.com estimates that poor data quality costs US companies $600 billion per year. Another recent study shows that in most data warehousing projects, data cleaning accounts for 30–80% of the development time and budget for improving the quality of the data rather than building the system.
At present, Big Data faces the following challenges:
- The diversity of data sources results in countless data types and complex data structures which increases the difficulty of data integration.
- Vast data volumes make it is difficult to assess data quality within a reasonable amount of time.
- Data changes very fast and the lifetime of data is very short, which necessitates higher requirements for processing technology. (Li Cai, Yangyong Zhu)
Being proactive during the data gathering process would help address Big Data issues and sidestep the need to run continuous cleanup services on poor data.
High quality data
Data quality standards are achieved by having data that is accurate, consistent, timely, and comprehensive. All data needs to be time-stamped and entered into the database without missing or incorrect information. With high quality Big Data, there would be no need for manual searches due to high user accessibility. In addition, the standardization of data would enable exchanges across different departments or industry sectors.
Importance of data quality
Search engines are one of the most effective channels to connect prospective clients with businesses. If poor data is getting in the way of users not finding a business in search indices, your company’s bottomline suffers. Poor data quality drives up the overhead costs across all areas of business operations including marketing where sales materials sent to those who are listed incorrectly within your database waste company funds.
Big Data and marketing
Quality data is crucial to your sales and marketing departments. Quality data opens the door to better leads and helps you strategize future campaigns. Big Data allows for an improvement in responsiveness and in gaining deeper customer insights. Marketers are no longer working blindly, but using Big Data to determine the best way to go about customer acquisition.
Address verification software is an essential part of your toolkit to clean up Big Data. With address verification and geosearch tools, you’re guaranteeing the address information entered into a database is valid and complete. Even if your customers only supply minimum details, the real-time address verification system fills in the blanks for you. Verification systems build search strings to locate an address and then grade it to determine the best match. The system also taps into verified address databases to check whether the particular address actually exists.
An address verification system is most effective when it operates in real time. Estimates state that each month, approximately 2 percent of all data goes out of date. Correcting names, emails, and addresses with verification programs will eliminate poor data permanently from your databases.
Paragon, our address verification service, enables you to build an effective contact data management strategy. The real-time address verification solution maintains the integrity of your address database at the point of capture, whereas our batch address verification component cleans up large volumes of addresses at once.
Given the exponential growth of data, ensuring Big Data quality and transforming it into an effective aid for business decision making are becoming major issues for companies today. Poor data quality leads to low data utilization, lack of efficiency, higher costs, customer dissatisfaction and occasionally might even lead to erroneous decisions.
Exastax covers all the aspects of Big Data management solutions. For additional services contact us at email@example.com.