Remove Data Collection Remove Data Quality Remove Metadata
article thumbnail

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. As you would guess, maintaining context relies on metadata.

article thumbnail

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.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

AI adoption in the enterprise 2020

O'Reilly on Data

By contrast, AI adopters are about one-third more likely to cite problems with missing or inconsistent data. The logic in this case partakes of garbage-in, garbage out : data scientists and ML engineers need quality data to train their models. This is consistent with the results of our data quality survey.

article thumbnail

Deep automation in machine learning

O'Reilly on Data

Data management isn’t limited to issues like provenance and lineage; one of the most important things you can do with data is collect it. Given the rate at which data is created, data collection has to be automated. How do you do that without dropping data? Toward a sustainable ML practice.

article thumbnail

What is data governance? Best practices for managing data assets

CIO Business Intelligence

The Business Application Research Center (BARC) warns that data governance is a highly complex, ongoing program, not a “big bang initiative,” and it runs the risk of participants losing trust and interest over time. The program must introduce and support standardization of enterprise data.

article thumbnail

Constructing A Digital Transformation Strategy: Putting the Data in Digital Transformation

erwin

Once you’ve determined what part(s) of your business you’ll be innovating — the next step in a digital transformation strategy is using data to get there. Constructing A Digital Transformation Strategy: Data Enablement. Many organizations prioritize data collection as part of their digital transformation strategy.

article thumbnail

What you need to know about product management for AI

O'Reilly on Data

You might have millions of short videos , with user ratings and limited metadata about the creators or content. Job postings have a much shorter relevant lifetime than movies, so content-based features and metadata about the company, skills, and education requirements will be more important in this case.