Precisely

OCTOBER 19, 2023

That’s what makes spatial analytics so important. Let’s explore more on what spatial analytics is, why it matters, and what you need to get started and deliver the best results? What Is Spatial Analytics? Spatial analytics is the process of conducting an analysis of data with a geographic or spatial component.

Analytics 64

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Dataversity

APRIL 1, 2021

In the data-driven world we live in today, the field of analytics has become increasingly important to remain competitive in business. In fact, a study by McKinsey Global Institute shows that data-driven organizations are 23 times more likely to outperform competitors in customer acquisition and nine times […].

Analytics 69

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AWS Machine Learning Blog

DECEMBER 3, 2024

Amazon Q Business is a generative AI-powered assistant that can answer questions, provide summaries, generate content, and securely complete tasks based on data and information in your enterprise systems.

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Alation

MARCH 21, 2023

For example, airlines have historically applied analytics to revenue management, while successful hospitality leaders make data-driven decisions around property allocation and workforce management. What is big data in the travel and tourism industry? Why is data analytics important for travel organizations?

Analytics 52

Analytics Analytics Data Silos Big Data 52

Alation

OCTOBER 12, 2021

Enterprise data analytics enables businesses to answer questions like these. Having a data analytics strategy is a key to delivering answers to these questions and enabling data to drive the success of your business. What is Enterprise Data Analytics? Business strategy.

Analytics 52

Analytics Analytics Data Governance DataOps 52

phData

NOVEMBER 9, 2023

The primary objective of this idea is to democratize data and make it transparent by breaking down data silos that cause friction when solving business problems. What Components Make up the Snowflake Data Cloud? What is a Data Lake?

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Pickl AI

DECEMBER 10, 2024

Summary : Data Analytics trends like generative AI, edge computing, and Explainable AI redefine insights and decision-making. Businesses harness these innovations for real-time analytics, operational efficiency, and data democratisation, ensuring competitiveness in 2025. from 2023 to 2030. from 2023 to 2030.

Analytics 52

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IBM Journey to AI blog

MAY 9, 2023

IBM today announced it is launching IBM watsonx.data , a data store built on an open lakehouse architecture, to help enterprises easily unify and govern their structured and unstructured data, wherever it resides, for high-performance AI and analytics. What is watsonx.data?

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Dataconomy

APRIL 27, 2023

OLTP systems require both regular full backups and constant incremental backups to ensure that data can be quickly restored in the event of a problem. OLTP vs OLAP OLTP and online analytical processing ( OLAP ) are two distinct online data processing systems, although they share similar acronyms.

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Database Data Scientist Data Mining Data Mining 159

Tableau

SEPTEMBER 14, 2021

In hindsight, business leaders experienced disruptions to operations like customer service, sales, finance, the supply chain, and more. And data-driven decision making, defined as taking action and influencing innovation with data and analytics, played a critical role in business response to the pandemic.

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Tableau

SEPTEMBER 14, 2021

In hindsight, business leaders experienced disruptions to operations like customer service, sales, finance, the supply chain, and more. And data-driven decision making, defined as taking action and influencing innovation with data and analytics, played a critical role in business response to the pandemic.

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Tableau Analytics Analytics Data Silos 98

Smart Data Collective

JUNE 3, 2019

Big Data’s most effective strategies identify business requirements first, and then leverage existing infrastructure, data sources and analytical solutions to support the business opportunity. Customer-focused analysis dominates Big Data initiatives. Here are the main approaches.

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AWS Machine Learning Blog

DECEMBER 29, 2022

You can quickly launch the familiar RStudio IDE and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML) and analytics solutions in R at scale. Users can also interact with data with ODBC, JDBC, or the Amazon Redshift Data API.

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IBM Journey to AI blog

MARCH 14, 2024

Businesses face significant hurdles when preparing data for artificial intelligence (AI) applications. The existence of data silos and duplication, alongside apprehensions regarding data quality, presents a multifaceted environment for organizations to manage.

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IBM Journey to AI blog

JULY 3, 2024

This allows for easier integration with your existing technology investments while eliminating data silos and accelerating data-driven transformation. The following four components help build an open and trusted data foundation.

AI 45

AI AI Data Quality Database 45

phData

FEBRUARY 13, 2024

SAP is the most widely used ERP provider for enterprise companies worldwide, creating vast amounts of data on various business units and functions. However, using that data for predictive models and analytics while the data resides in SAP can be complex, time-consuming, and costly.

Analytics 59

Analytics Analytics Data Scientist Data Models 59

Pickl AI

NOVEMBER 15, 2023

Importance of Data Lakes Data Lakes play a pivotal role in modern data analytics, providing a platform for Data Scientists and analysts to extract valuable insights from diverse data sources. With all data in one place, businesses can break down data silos and gain holistic insights.

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IBM Journey to AI blog

FEBRUARY 2, 2023

In today’s world, data warehouses are a critical component of any organization’s technology ecosystem. They provide the backbone for a range of use cases such as business intelligence (BI) reporting, dashboarding, and machine-learning (ML)-based predictive analytics, that enable faster decision making and insights.

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IBM Journey to AI blog

JANUARY 5, 2023

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases. Creating a data architecture roadmap.

Data Quality 52

Data Quality Data Lakes Data Warehouse Big Data 52

IBM Journey to AI blog

JANUARY 18, 2023

Data platform architecture has an interesting history. Towards the turn of millennium, enterprises started to realize that the reporting and business intelligence workload required a new solution rather than the transactional applications. A read-optimized platform that can integrate data from multiple applications emerged.

Data Lakes 52

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phData

JULY 14, 2023

Adtech is a rapidly growing industry that is transforming the way businesses interact with their customers. It includes the use of data, analytics, and automation to deliver ads to the right people at the right time. Snowflake provides a secure and cost-effective platform for data storage, analytics, and machine learning.

Analytics 52

Analytics Analytics Machine Learning Machine Learning 52

Alation

APRIL 12, 2022

Early CDOs largely sought to ensure compliance with regulations around financial data, taking a defensive posture to guard company and customer information. Two decades on, the role has expanded to include responsibility for analytics, and even data monetization. Promoting Self-Service Analytics. Reducing Data Silos.

Data Quality 52

Data Quality Data Governance Data Silos Data Analyst 52

Alation

JUNE 28, 2022

Establish business glossaries: Define business terms and create standard relationships for data governance. Collaborate more effectively: Break down data silos for better understanding of data assets across all business units. Detect unused data that can be archived/removed.

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Alation

APRIL 7, 2021

Insights, like “popularity”, gleaned from the BAE, power recommendations that help you easily find and understand data. Alation also surfaces guidelines and policies to ensure accurate, well-governed analytics. Active Data Governance. A cloud-based data catalog supports unified data governance.

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Pickl AI

DECEMBER 4, 2023

What is Data Intelligence with an example? So, what is Data Intelligence with an example? For example, an e-commerce company uses Data Intelligence to analyze customer behavior on their website. Implementing interoperable data platforms. Implementing integrated data management systems.

Data Analysis 52

Data Analysis Data Analysis Artificial Intelligence Artificial Intelligence 52

Dataversity

NOVEMBER 10, 2021

Data is generated and collected at each one of these – and numerous other – touchpoints. The post 4 Key Steps to Using Customer Data More Effectively appeared first on DATAVERSITY. Customers now interact with brands in a variety of ways. But many companies do not know […].

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ODSC - Open Data Science

SEPTEMBER 29, 2023

In today’s digital world, data is king. Organizations that can capture, store, format, and analyze data and apply the business intelligence gained through that analysis to their products or services can enjoy significant competitive advantages. But, the amount of data companies must manage is growing at a staggering rate.

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IBM Journey to AI blog

SEPTEMBER 5, 2024

Regularly reviewing the framework and adjusting it based on feedback, new regulations or changes in business strategy fosters a culture that values data as a strategic asset, supporting effective business intelligence and data use across the organization.

Data Governance 75

Data Governance Data Quality DataOps Artificial Intelligence 75

phData

NOVEMBER 13, 2023

Source: The next big step forward for analytics engineering Picture the hustle it takes to keep that many models in line—ensuring they’re reliable, the dependencies make sense, and the data is solid. In mid-2023, many companies were wrangling with more than 5,000 dbt models.

Database 52

Database Business Intelligence Business Intelligence Data Silos 52

The MLOps Blog

MAY 17, 2023

These pipelines assist data scientists in saving time and effort by ensuring that the data is clean, properly formatted, and ready for use in machine learning tasks. Moreover, ETL pipelines play a crucial role in breaking down data silos and establishing a single source of truth.

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Flipboard

DECEMBER 4, 2024

The use of separate data warehouses and lakes has created data silos, leading to problems such as lack of interoperability, duplicate governance efforts, complex architectures, and slower time to value. You can use Amazon SageMaker Lakehouse to achieve unified access to data in both data warehouses and data lakes.

Data Lakes 137

Data Lakes Data Warehouse AWS Database 137

Alation

JULY 12, 2022

Utilize A Data Catalog To Classify and Label Data. All the applications and workloads you move to the cloud use data. If you have on-premises data silos, then you want to make sure that your data migration doesn’t lead to a budget overrun. How Alation Supports On-Premises to Cloud Migration.

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Dataconomy

MARCH 18, 2025

Continuous intelligence is the real-time analysis and processing of data streams to enable automated decision-making and insights. It integrates artificial intelligence, machine learning, and analytics to provide dynamic responses, often used in fraud detection, IoT monitoring, and operational optimization.

Machine Learning 91

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Pickl AI

APRIL 8, 2025

Types of Dimensions in Data Warehouse include conformed, role-playing, slowly changing, junk, and degenerate dimensions. Each type serves a specific purpose in organizing and analysing data for effective business intelligence, ensuring consistency, historical accuracy, and simplified queries.

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