O'Reilly on Data
MARCH 18, 2020
The new survey, which ran for a few weeks in December 2019, generated an enthusiastic 1,388 responses. This year, about 15% of respondent organizations are not doing anything with AI, down ~20% from our 2019 survey. It seems as if the experimental AI projects of 2019 have borne fruit. But what kind? Bottlenecks to AI adoption.
datapine
NOVEMBER 27, 2019
Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards. The rise of self-service analytics democratized the data product chain. 2019 was a particularly major year for the business intelligence industry. 1) Data Quality Management (DQM).
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IBM Big Data Hub
JANUARY 25, 2024
trillion, up from USD 864 billion in 2019 to 2020. Consider these questions: Do you have a platform that combines statistical analyses, prescriptive analytics and optimization algorithms? Generative AI has the potential to deliver powerful support in key data areas: Master data cleansing to reduce duplications and flag outliers.
Smart Data Collective
SEPTEMBER 25, 2019
If you trust the data, it’s easier to use confidently to make business decisions. Statistics show that poor data quality is a primary reason why 40% of all business initiatives fail to achieve their targeted benefits. Ponder the statistics and points of focus here as you plan how to proceed.
Cloudera
SEPTEMBER 15, 2022
What is a data mesh? How does it compare to data fabric? Mark: Yes, another concept gaining traction with data leaders is the data mesh, which was introduced by Zhamak Dehghani in 2019 as an approach to address the challenges when deploying data programs.
Jet Global
JULY 11, 2019
One of those areas is called predictive analytics, where companies extract information from existing data to determine buying patterns and forecast future trends. By using a combination of data, statistical algorithms, and machine learning techniques, predictive analytics identifies the likelihood of future outcomes based on the past.
datapine
DECEMBER 4, 2019
Applied to business, it is used to analyze current and historical data in order to better understand customers, products, and partners and to identify potential risks and opportunities for a company. The accuracy of the predictions depends on the data used to create the model. Mobile Analytics. Graph Analytics.
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