download-white-paper: Overcoming Data Quality Challenges to Extract Value from Industrial Data

According to Gartner, Fortune 1000 enterprises will lose more money in operational inefficiency due to data quality issues than they will spend on data warehouse and CRM initiatives on a whole. Poor data quality costs the average company more than $8 million annually.

Whether you are in the aviation, oil and gas, manufacturing, or utility space, there are a number of industry-agnostic data quality issues that restrict the usefulness and efficacy of the insights gleaned from data. However, there’s simply no time to wait for perfect data. You need to accelerate your data quality improvement program, as part of your broader data management strategy, in order to drive operational insights and efficiencies today.

This paper will explain how a data management platform for the Industrial Internet of Thing (IIoT) can provide an operational lens into your data quality challenges so you can filter your data and extract faster time to value for your data-driven operations.

Overcoming data quality challenges to extract value from your smart grid
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1fb4cc1.jpgAbout the Author: Andrew Miller is an outcome focused sales engineer with over five years in network consulting, and a decade in in the information technology industry. Andrew brings experience in international project and program management and technical team leadership and management. At Bit Stew, Andrew drives eastern US and European direct technical sales support activities for the company. Andrew also drives technical activity within the partner ecosystem development team and is responsible for partner recruitment, onboarding, and sales support.