Analysts Corner

MARCH 7, 2023

In this article, we will explore what machine learning and data science are, and how they are used in the context of business analytics. Machine learning is a subset of artificial intelligence that enables computers to learn from data without being explicitly programmed. What is machine learning?

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Astera

MAY 30, 2024

What Is Data Analytics? Data analytics is the science of analyzing raw data to draw conclusions about it. The process involves examining extensive data sets to uncover hidden patterns, correlations, and other insights. Data Mining : Sifting through data to find relevant information.

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Astera

OCTOBER 7, 2022

The Rise of Unstructured Data Analytics. Until recently, enterprises solely relied on structured data to make business decisions — as conventional software couldn’t ingest, process, and extract the information from unstructured text mainly due to… the lack of structure. Why Is Unstructured Data Analytics Important?

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Astera

MAY 31, 2024

Data Science vs. Data Analytics Organizations increasingly use data to gain a competitive edge. Two key disciplines have emerged at the forefront of this approach: data science vs data analytics. In contrast, data science enables you to create data-driven algorithms to forecast future outcomes.

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The BAWorld

JANUARY 25, 2021

Machine Learning is an application of artificial intelligence that gives the system the ability to learn and improve from experience without being explicitly programmed automatically. It primarily focuses on developing models that use algorithms to learn and detect patterns, trends, and associations from existing data.

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Tableau

APRIL 18, 2022

A data fabric is an emerging data management design that allows companies to seamlessly access, integrate, model, analyze, and provision data. Monitor data sources according to policies you customize to help users know if fresh, quality data is ready for use. Data modeling. Data preparation.

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Tableau

APRIL 18, 2022

A data fabric is an emerging data management design that allows companies to seamlessly access, integrate, model, analyze, and provision data. Monitor data sources according to policies you customize to help users know if fresh, quality data is ready for use. Data modeling. Data preparation.

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Marutitech

MARCH 10, 2022

With the expanding pace of digital changes in business, most analysts are increasingly asking, “What more can we do with data to assist business decisions?” ” Thankfully, there is predictive analytics. Adopting data analytics solutions is a significant milestone in the development and success of any business.

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Whizlabs

APRIL 19, 2021

Currently she works at Microsoft and concentrates mainly on cloud computing, edge computing, distributed systems and architecture, and a little bit of machine learning and artificial intelligence. Michelle has more than 20 years of experience in the field of research in statistics, data analytics, consulting and market research.

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Sisense

JUNE 12, 2019

In addition, providing a world-class analytics platform requires a deep understanding of how to best leverage AI/ML to support the needs of all users from the novice to the most technical. our annual client conference, I gave a presentation that took a deep dive into artificial intelligence and subgroups including AI, ML, and statistics.

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Marutitech

DECEMBER 17, 2021

You must be wondering what the different predictive models are? What is predictive data modeling? Which predictive analytics algorithms are most helpful for them? This blog will help you answer these questions and understand the predictive analytics models and algorithms in detail.

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Astera

APRIL 28, 2023

To address these challenges, approximately 44% of companies are planning to invest in artificial intelligence (AI) to streamline their data warehousing processes and improve the accuracy of their insights. AI is a powerful tool that goes beyond traditional data analytics.

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Data Pine

MARCH 25, 2022

Well, what if you do care about the difference between business intelligence and data analytics? The most straightforward and useful difference between business intelligence and data analytics boils down to two factors: What direction in time are we facing; the past or the future?

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Sisense

JULY 24, 2019

Last, and still a very painful challenge for most users, is the familiarity with the underlying data and data model. NLQ is gaining traction in the big data analytics tools domain for its quick answers and ease of use. In other words, how the variables are named, and the granularity of their values.

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Actian

JULY 11, 2019

Data science professionals have been working with companies and individual technology providers for many years to determine a scalable and efficient method to aggregate data from diverse data sources. Why operational technology data management may never be standardized.

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Actian

MAY 21, 2019

For many years, companies have been accumulating large amounts of data with an intuitive feeling that it has value and would be put to good use to make more informed business decisions. The refinement process starts with the ingestion and aggregation of data from each of the source systems.

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Data Pine

MARCH 31, 2022

A data scientist has a similar role as the BI analyst, however, they do different things. While analysts focus on historical data to understand current business performance, scientists focus more on data modeling and prescriptive analysis. They can help a company forecast demand, or anticipate fraud.

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The BAWorld

NOVEMBER 22, 2021

Top Data Analytics terms are explained in this article. Learn these to develop competency in Business Analytics. Data Analytics Terms & Fundamentals. Consistency is a data quality dimension and tells us how reliable the data is in data analytics terms. Also, see data visualization.

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The Data Administration Newsletter

MAY 15, 2024

In discussions with data management professionals, conversations often veer toward the technical intricacies of migration to the cloud or algorithm optimization, overshadowing the core business objectives that originally spurred these initiatives.

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Leading Agile

NOVEMBER 22, 2023

It might be interesting to pull the thread around data analytics, maybe pull some threads around the application of artificial intelligence. You’re going to pull your data back into a hybrid sort of data model. It might have implications for security. Absolutely. It’s expensive.

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Leading Agile

NOVEMBER 22, 2023

It might be interesting to pull the thread around data analytics, maybe pull some threads around the application of artificial intelligence. You’re going to pull your data back into a hybrid sort of data model. It might have implications for security. Absolutely. It’s expensive.

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Astera

AUGUST 28, 2024

However, with the abundance of different types of data analysis tools in the market, what was supposed to be a simple task has become a complex undertaking. This article aims to simplify the process of finding the data analytics platform that meets your organization’s specific needs.

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Insight Software

MAY 1, 2023

Third-party data might include industry benchmarks, data feeds (such as weather and social media), and/or anonymized customer data. Four Approaches to Data Analytics The world of data analytics is constantly and quickly changing. The application thus becomes a vital information hub.

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