Smart Data Collective
MAY 13, 2022
In addition, several enterprises are using AI-enabled programs to get business analytics insights from volumes of complex data coming from various sources. AI is undoubtedly a gamechanger for business intelligence. As unstructured data comes from different sources and is stored in various locations.
Sisense
APRIL 26, 2020
Different types of information are more suited to being stored in a structured or unstructured format. Read on to explore more about structured vs unstructured data, why the difference between structured and unstructured data matters, and how cloud data warehouses deal with them both. Unstructured data.
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IBM Big Data Hub
SEPTEMBER 19, 2023
Data science is an area of expertise that combines many disciplines such as mathematics, computer science, software engineering and statistics. It focuses on data collection and management of large-scale structured and unstructured data for various academic and business applications.
Cloudera
OCTOBER 28, 2020
To start, they look to traditional financial services data, combining and correlating account activity, borrowing history, core banking, investments, and call center data. Banks are driving initiatives forward in areas of targeted marketing, customer retention and proactive customer care.
Cloudera
FEBRUARY 8, 2021
This revolution is just beginning to accelerate – in fact, according to a recent Applied Market Research study, the global connected car market was valued at $63.03 billion in 2019, and is projected to reach $225.16 billion by 2027, registering a CAGR of 17.1% from 2020 to 2027.
Smart Data Collective
FEBRUARY 2, 2021
Big Data is more than a trend or a buzzword. In 2020, the size of the global Big Data market reached 56 billion, and it’s on track to exceed 103 billion by 2027. Consumers are generating huge amounts of data at a rapid rate, and it is estimated that up to 90% of all data was generated only in the past two years.
datapine
AUGUST 14, 2019
Companies surely need data scientists to help them empower their analytics processes, build a numbers-based strategy that will boost their bottom line, and ensure that enormous amounts of data are translated into actionable insights. But being an inquisitive Sherlock Holmes of data is no easy task.
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