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Many organizations are plagued with poor data quality. Most use outdated, inconsistent, and flawed data from multiple data sources. In essence, this can be as simple as having five different names for a same customer. These inaccuracies eat into the precious time of business users and analysts who work on contradictory reports. Inevitably, incorrect business plans will ultimately lead to 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.

How Poor Data Quality Can Affect An Organization

To understand more on how poor data quality can affect an organization, let’s look at an example. First, let’s evaluate a global telecommunication company with a broad portfolio for millions of customers. The company manages huge data sets of customer information. This is done with a combined legacy CRM, billing, and analytics solution. The solution also offers a single view of customer information across operations.

Now, what happens if the company’s sales personnel or data analysts were to query these multiple systems? By asking quality issues like different names for the same customer in order to create a single report, they probably spend lot of time and produce inaccurate information as datasets may not match. Given the size of the organization, errors are multiplied by the thousands. In summary, the extent of loss due to incorrect decision making in the faulty reports could be unfathomable.

Here’s Where AI Tools Can Improve Business Operations

New age AI tools provide business intelligence and analytics solutions that leverage machine learning algorithms to reconcile data from various systems. The result is insightful suggestions on how to handle data discrepancies.

Organizations have tried to address quality problems at the data entry stage and integration stage. However, it’s not possible to fix all the issues with the growth of information systems and 3rd party data. 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. Whether it was abbreviated or partial names and match with the customer data and to get results? Today, self service BI is moving towards insights generated from conversational analytics. Hence, it’s critical for solutions to share correct real-time information by sorting through tons of data from disparate data sets.

Track Data, Report Anomalies, and Rapidly Evaluate Business Insights

AI tools with conversational analytics solutions like ConverSight.ai can handle data integrity issues at the earliest point of data processing. Moverover, they are rapidly transforming these vast volumes of data into trusted business information. The solutions use advanced algorithms that let the user use their own language. They deliver accurate real-time reporting to support error-free decision making by using infographics and data visualization.

Ai tools also extend the data quality and report anomalies. Anomaly detection algorithms flag “bad” data, 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.conversight.ai

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