Remove Data Collection Remove Data Quality Remove Enterprise
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When is data too clean to be useful for enterprise AI?

CIO Business Intelligence

Once the province of the data warehouse team, data management has increasingly become a C-suite priority, with data quality seen as key for both customer experience and business performance. But along with siloed data and compliance concerns , poor data quality is holding back enterprise AI projects.

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AI adoption in the enterprise 2020

O'Reilly on Data

The update sheds light on what AI adoption looks like in the enterprise— hint: deployments are shifting from prototype to production—the popularity of specific techniques and tools, the challenges experienced by adopters, and so on. By contrast, AI adopters are about one-third more likely to cite problems with missing or inconsistent data.

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The quest for high-quality data

O'Reilly on Data

A recent O’Reilly survey found that those with mature AI practices (as measured by how long they’ve had models in production) cited “Lack of data or data quality issues” as the main bottleneck holding back further adoption of AI technologies. The problem is even more magnified in the case of structured enterprise data.

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SAP Datasphere Powers Business at the Speed of Data

Rocket-Powered Data Science

We live in a data-rich, insights-rich, and content-rich world. Data collections are the ones and zeroes that encode the actionable insights (patterns, trends, relationships) that we seek to extract from our data through machine learning and data science. Plus, AI can also help find key insights encoded in data.

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7 enterprise data strategy trends

CIO Business Intelligence

Every enterprise needs a data strategy that clearly defines the technologies, processes, people, and rules needed to safely and securely manage its information assets and practices. Guan believes that having the ability to harness data is non-negotiable in today’s business environment.

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Deep automation in machine learning

O'Reilly on Data

We won’t be writing code to optimize scheduling in a manufacturing plant; we’ll be training ML algorithms to find optimum performance based on historical data. If humans are no longer needed to write enterprise applications, what do we do? Given the rate at which data is created, data collection has to be automated.

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Why Data Driven Decision Making is Your Path To Business Success

datapine

3) Gather data now. Gathering the right data is as crucial as asking the right questions. For smaller businesses or start-ups, data collection should begin on day one. Once it is identified, check if you already have this data collected internally, or if you need to set up a way to collect it or acquire it externally.