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Data-Driven Companies Leverage OCR for Optimal Data Quality

Smart Data Collective

Optical character recognition is able to convert any text present on a medium into computer-readable textual data. To continue your document analysis, the second step extracts all the data present on the blue card. How does OCR work? However, at this stage, they are not structured.

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Why Data Quality and Data Governance are Important to Achieve Compliance

Analysts Corner

In this article, we present a brief overview of compliance and regulations, discuss the cost of non-compliance and some related statistics, and the role data quality and data governance play in achieving compliance. The average cost of a data breach among organizations surveyed reached $4.24

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6 Big Data Mistakes You Must Avoid At All Costs

Smart Data Collective

To help you identify and resolve these mistakes, we’ve put together this guide on the various big data mistakes that marketers tend to make. Big Data Mistakes You Must Avoid. Here are some common big data mistakes you must avoid to ensure that your campaigns aren’t affected. Ignoring Data Quality.

Big Data 302
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IKEA’s Data Transformation: Lessons from a Global Giant

Timo Elliott

In the digital era, data is the backbone of innovation and transformation. At IKEA, the global home furnishings leader, data is more than an operational necessity—it’s a strategic asset. The Strategy: A Greenfield Approach IKEA adopted a greenfield strategy with SAP, rethinking its processes, technology, and data from the ground up.

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How Data Cleansing Can Make or Break Your Business Analytics

Smart Data Collective

This market is growing as more businesses discover the benefits of investing in big data to grow their businesses. One of the biggest issues pertains to data quality. Even the most sophisticated big data tools can’t make up for this problem. Data cleansing and its purpose.

Big Data 301
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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

Data Pine

1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data.

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How To Maintain Accurate Data Through Conversational Analysis?

Smart Data Collective

There is no question that big data is very important for many businesses. Unfortunately, big data is only as useful as it is accurate. Data quality issues can cause serious problems in your big data strategy. It relies on data to drive its AI algorithms. What social media influencers connect with customers?

Big Data 307