Leveraging data quality software and platforms will allow brands to eliminate bad data and take effective data-driven business decisions
It is not the quantity but the quality of data that is essential. As a result, the integrity of data is the foundation of successful business experiences. Predictive modelling efforts, intelligent automation programs, or artificial intelligence initiatives, bad data can infiltrate any system, and the C-suite understands the importance of trusted data.
Four years ago, IBM estimated $ 3.1 trillion to be lost to bad data, and an MIT review revealed that over 15% of total business revenue was lost. Today, with industries experiencing a rapid digital adaptation, the chances of ending up with bad data have increased. Experts believe that a meaningful strategy revolving around data integration and data quality can help digital transformation initiatives.
Enterprise Talk connected with Anjan Kundavaram, who was recently appointed as the Chief Product Officer at Precisely. Having closely worked with the consequences of bad data in companies, Anjan states that, “Business leaders are now investing more in data integrity, and they aim to grow their competitive advantage through data-driven intelligence.”
As the pandemic accelerated bad data growth, businesses have been forced to reconsider and re-strategize their business process. Anjan adds, “More data is being added to the system, and a good proportion of that data could have quality problems, especially if organizations aren’t careful about managing it upfront. Bad data costs time and money.”
While cleaning data can be time-consuming, Artificial Intelligence can help speed the process. Many organizations depend on vendors who leverage AI to help identify and clean data. According to Anjan, these tools help brands find new data patterns, improving marketing and customer service rating. However, AI and ML “require accurate, consistent and contextual data in order to achieve the end result,” he informs.
The Global Location Trends report indicates that location-based data is considered to be valuable as it brings context to the existing data. Regardless, only 57 percent of respondents believed that the LBS data was accurate. Though location intelligence solves the problem of incorrect customer addresses and drives rich insights, Anjan reflects that “when an organization is able to better understand boundaries, movement, and the environment surrounding the customer, vendor, store location, or other entities, the organization can drive vastly improved decisions.”
Accelerated by the worldwide pandemic, companies are looking to data-driven business outcomes, and leveraging good data is the only way to go. Anjan opines that data quality software is critical to data integrity, and data technology is rapidly evolving. Additionally, there exist tools in the market that can map company data by leveraging data integrity.
“As business data expands into new platforms and systems, we expect that data quality software will incorporate more AI and scale to larger data sets and streams,” concludes Anjan. In this new era of digitization, industry leaders need to push data integrity to the top of their priority list.