Many organizations are plagued with poor data quality while using outdated, inconsistent, and flawed data from multiple data sources, as simple as having five different names for a same customer. This eats into the precious time of business users and analysts who work on contradictory reports, incorrect business plans and finally end up making wrong decisions.
Wrong decisions come with their own costs.
According to Gartner research, "the average financial impact of poor data quality on organizations is $9.7 million per year." In another research covering companies across the globe Gartner estimates that poor-quality data is costing them on an average $14.2 million annually. Ovum Research reports that poor quality data is costing businesses at least 30% of their revenues.
To understand more on how poor data quality can affect an organization, let's look at the situation at a large global telecommunication company with a broad service portfolio for millions of customers. The company manages huge data sets of customer information in a combined legacy CRM, billing and analytics solution, which also offers a single view of customer information across operations.
Now, if the company's sales personnel or data analysts were to query these multiple systems, with quality issues like different names for the same customer, to create a single report, they would be most likely spend lot of time and also produce error prone information as datasets may not match appropriately. And given the size of the large organizations, you can easily multiply such erroneous reports by thousands. The extent of loss, due to incorrect decision making, arising out of these faulty reports could be unfathomable.
Here's where ConverSight.ai, an Artificial Intelligence (AI) powered conversational analytics platform comes in handy.
New age AI-powered business intelligence and analytics solutions can leverage machine learning algorithms to reconcile data from various systems and propose suggestions to handle data discrepancies.
Organizations have tried to address quality problems at the data entry stage and integration stage, however with the growth of information systems and 3rd party data, it’s not possible to fix all the issues. New-age analytics systems should start ‘handling’ instead of attempting to ‘fix’ it.
How cool will it be for a system to understand any form for the customer name, abbreviated or partial names and match with the customer data and get the intended results?
Self Service BI is increasingly moving towards insights generated through conversational analytics. Hence, it's even more important for solutions to share correct real-time information by parsing through tons of data from disparate data sets.
An AI-powered conversational analytics solution like ConverSight.ai can handle data integrity issues at the earliest point of data processing, rapidly transforming these vast volumes of data into trusted business information. These solutions use advanced algorithms which let the user use their own language, infographics and map it into the correction to deliver accurate real-time reporting to support error-free decision making.
They also extend the data quality and report anomalies. Anomaly detection algorithms flag "bad" data, identifying suspicious anomalies, that can adversely affect data quality. By tracking and evaluating data, anomaly detection gives valuable insights into data quality while data is processed.
Learn more about ConverSight.ai and how it can help your organization by logging on to www.thickstat.com