Data Quality

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The Big Data Problem That Market Research Must Fix

  • Blog
  • Read Time: 9 min 

Insight into what customers really care about often is hampered by the quality of the information being collected. Big data can support smart market research, but only if researchers embrace psychometric best practices and the basics of understanding what it is they want to measure and how. That means asking the right questions, asking enough questions, understanding how to weigh questions, and taking into consideration how people felt about the brand to begin with.

Want the Best Results From AI? Ask a Human

Companies are adopting artificial intelligence at an accelerated pace — and learning that developing and deploying AI is not like implementing a standard software program. Before diving into AI systems, companies should consider three principles that can greatly improve the chances for a successful outcome. First, they need to recognize that humans and machines are in this together. Second, they need to teach the AI systems with a lot of data. And third, they need to continually test what the systems have learned.

Seizing Opportunity in Data Quality

Bad data is the norm. Every day, businesses send packages to customers, managers decide which candidate to hire, and executives make long-term plans based on data provided by others. When that data is incomplete, poorly defined, or wrong, there are immediate consequences: angry customers, wasted time, and added difficulties in the execution of strategy. Getting in front on data quality is crucial, and presents a terrific opportunity to improve business performance.

The Flood of Data From IoT Is Powering New Opportunities — for Some

IoT promised, and delivered, a data deluge. But is the data any good? Survey results from MIT SMR’s recent internet of things research suggest that it is — but the most value goes to those who got into IoT early and have years of experience under their belt. The message to those considering IoT projects: Don’t wait.

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