Sisense

MARCH 27, 2019

To provide real-time data, these platforms use smart data storage solutions such as Redshift data warehouses , visualizations, and ad hoc analytics tools. This allows dashboards to show both real-time and historic data in a holistic way. Who Uses Real-Time BI?

Business Intelligence 74

Business Intelligence Data Warehouse Predictive Modeling Visualization 74

AWS Big Data

AUGUST 21, 2024

The current scaling approach of Amazon Redshift Serverless increases your compute capacity based on the query queue time and scales down when the queuing reduces on the data warehouse. This post also includes example SQLs, which you can run on your own Redshift Serverless data warehouse to experience the benefits of this feature.

Optimization 89

Optimization Data Lake Data Warehouse Cost-Benefit 89

AWS Big Data

OCTOBER 23, 2024

Amazon Redshift is a fully managed cloud data warehouse that’s used by tens of thousands of customers for price-performance, scale, and advanced data analytics. It also was a producer for downstream Redshift data warehouses. This blog post is co-written with Pinar Yasar from Getir.

Data Warehouse 97

Data Warehouse Cost-Benefit Data Lake Data-driven 97

Smart Data Collective

SEPTEMBER 26, 2021

There’s not much value in holding on to raw data without putting it to good use, yet as the cost of storage continues to decrease, organizations find it useful to collect raw data for additional processing. The raw data can be fed into a database or data warehouse. If it’s not done right away, then later.

Metadata 130

Metadata Visualization Unstructured Data Data mining 130

AWS Big Data

OCTOBER 9, 2024

Today, customers are embarking on data modernization programs by migrating on-premises data warehouses and data lakes to the AWS Cloud to take advantage of the scale and advanced analytical capabilities of the cloud. This helps prevent bad data from entering your data lakes and data warehouses.

Data Quality 96

Data Quality Data Lake Data Warehouse Metrics 96

Domino Data Lab

JANUARY 11, 2021

Data Science works best with a high degree of data granularity when the data offers the closest possible representation of what happened during actual events – as in financial transactions, medical consultations or marketing campaign results. Writing data from Domino into Snowflake.

Data Science 103

Data Science Recreation/Entertainment Data Warehouse Publishing 103

IBM Big Data Hub

APRIL 28, 2022

Problem : Traditionally, developing a solid backorder forecast model that takes every factor into consideration would take anywhere from weeks to months as sales data, inventory or lead-time data and supplier data would all reside in disparate data warehouses.

Data Warehouse 77

Data Warehouse Metadata Forecasting Predictive Modeling 77

Cloudera

MAY 23, 2024

In modern enterprises, the exponential growth of data means organizational knowledge is distributed across multiple formats, ranging from structured data stores such as data warehouses to multi-format data stores like data lakes.

Management 80

Management Metrics Data Processing Machine Learning 80

Cloudera

APRIL 13, 2021

But while the company is united by purpose, there was a time when its teams were kept apart by a data platform that lacked the scalability and flexibility needed for collaboration and efficiency. Disparate data silos made real-time streaming analytics, data science, and predictive modeling nearly impossible.

Enterprise 78

Enterprise Cost-Benefit Data Warehouse Data Architecture 78

CIO Business Intelligence

MAY 10, 2022

Data management consultancy, BitBang, says CDPs offer five key benefits : As a central hub for all your customer data, they help you build unified customer profiles. They eliminate data silos, and, unlike a traditional data warehouse, CDPs don’t require technical expertise to set up or maintain. Treasure Data CDP.

Advertising 122

Advertising Interactive Marketing Structured Data 122

AWS Big Data

MARCH 7, 2023

Data lakes are more focused around storing and maintaining all the data in an organization in one place. And unlike data warehouses, which are primarily analytical stores, a data hub is a combination of all types of repositories—analytical, transactional, operational, reference, and data I/O services, along with governance processes.

Analytics 79

Analytics Data Warehouse Data Lake Metadata 79

BizAcuity

JULY 13, 2021

In many cases, source data is captured in various databases and the need for data consolidation arises and typically it takes around 6-9 months to complete, and with a high budget in terms of provisioning for servers, either in cloud or on-premise, licenses for data warehouse platform, reporting system, ETL tools, etc.

Optimization 52

Optimization Marketing Analytics Data Warehouse 52

Birst BI

OCTOBER 30, 2018

The credit scores generated by the predictive model are then used to approve or deny credit cards or loans to customers. A well-designed credit scoring algorithm will properly predict both the low- and high-risk customers. Add the predictive logic to the data model. Accounts in use.

Predictive Analytics 73

Predictive Analytics Descriptive Analytics Analytics Metrics 73

Cloudera

DECEMBER 9, 2020

As the center of excellence for data analytics, the EDO leads Globe Telecom’s data revolution. It changes the way the company makes decisions through data insights and helps to increase the value Globe Telecom is able to deliver to its customers.

Predictive Modeling 72

Predictive Modeling Data Warehouse Enterprise Data Strategy 72

Cloudera

MAY 12, 2021

If you are working in an organization that is driving business innovation by unlocking value from data in multiple environments — in the private cloud or across hybrid and multiple public clouds — we encourage you to consider entering this category. SECURITY AND GOVERNANCE LEADERSHIP.

Digital Transformation 70

Digital Transformation Machine Learning Optimization Data Lake 70

IBM Big Data Hub

OCTOBER 16, 2023

Foundation models can use language, vision and more to affect the real world. GPT-3, OpenAI’s language prediction model that can process and generate human-like text, is an example of a foundation model. They are used in everything from robotics to tools that reason and interact with humans.

Risk 70

Risk Modeling Management Metadata 70

IBM Big Data Hub

SEPTEMBER 19, 2023

This iterative process is known as the data science lifecycle, which usually follows seven phases: Identifying an opportunity or problem Data mining (extracting relevant data from large datasets) Data cleaning (removing duplicates, correcting errors, etc.) Watsonx comprises of three powerful components: the watsonx.ai

Data Science 76

Data Science Data Analytics Prescriptive Analytics Analytics 76

Smarten

JUNE 5, 2019

CEO Patel, says, “As the Smarten product evolves, it is truly exciting to see the ways in which business users, data scientists, IT staff and business managers have embraced and adopted advanced analytics as part of the day-to-day and strategic business decision process.”

Analytics 40

Analytics Data Analytics Predictive Modeling Data Warehouse 40

Cloudera

FEBRUARY 16, 2022

This allows data scientists, engineers and data management teams to have the right level of access to effectively perform their role. This might require making batch and individual predictions. CML supports model prediction in either batch mode or via a RESTful API for individual model predictions.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

Smarten

SEPTEMBER 9, 2024

Review Technology and Business Processes Look at your current technology and all the places your data resides (data warehouses, the cloud (private or public), best-of-breed software, legacy software, ERP, CRM, HR, SCM, and other focused solutions that support a particular division, team or department.

KPI 52

KPI Consulting Business Intelligence Predictive Modeling 52

Smarten

MAY 30, 2023

It is also supported by advanced analytics components including natural language processing (NLP) search analytics, and assisted predictive modeling to enable the Citizen Data Scientist culture. Flexible Deployment via public or private cloud, or enterprise on-premises hardware.

Slice and Dice 52

Slice and Dice Business Intelligence Cost-Benefit Dashboards 52

CDW Research Hub

JULY 17, 2019

For example, Sirius recently helped an organization improve their employee experience simply by analyzing badge scans and sensor data from lights and parking garages.

Predictive Modeling 44

Predictive Modeling Machine Learning Sales Modeling 44

AWS Big Data

NOVEMBER 14, 2023

However, in many organizations, data is typically spread across a number of different systems such as software as a service (SaaS) applications, operational databases, and data warehouses. Such data silos make it difficult to get unified views of the data in an organization and act in real time to derive the most value.

IoT 59

IoT Data-driven Data Lake Data Strategy 59

Smarten

NOVEMBER 26, 2023

TechTarget defines business intelligence this way: ‘Business intelligence (BI) is a technology-driven process for analyzing data and delivering actionable information that helps executives, managers and workers make informed business decisions.’ The primary difference between traditional and modern BI lies in flexibility and accessibility.

Marketing 40

Marketing Business Intelligence Key Performance Indicator Prescriptive Analytics 40

AWS Big Data

JULY 28, 2023

Amazon Redshift is a petabyte-scale, enterprise-grade cloud data warehouse service delivering the best price-performance. Today, tens of thousands of customers run business-critical workloads on Amazon Redshift to cost-effectively and quickly analyze their data using standard SQL and existing business intelligence (BI) tools.

Data Lake 73

Data Lake Data Governance Data Warehouse Modeling 73

IBM Big Data Hub

JULY 6, 2023

Data from various sources, collected in different forms, require data entry and compilation. That can be made easier today with virtual data warehouses that have a centralized platform where data from different sources can be stored. One challenge in applying data science is to identify pertinent business issues.

Machine Learning 54

Machine Learning Data Science Statistics Deep Learning 54

Alation

APRIL 14, 2022

Similar to a data warehouse schema, this prep tool automates the development of the recipe to match. Organizations launched initiatives to be “ data-driven ” (though we at Hired Brains Research prefer the term “data-aware”). Automatic sampling to test transformation. Scheduling. Target Matching.

Metadata 59

Metadata Data Governance Data-driven Modeling 59

Alation

APRIL 25, 2023

As firms mature their transformation efforts, applying Artificial Intelligence (AI), machine learning (ML) and Natural Language Processing (NLP) to the data is key to putting it into action quickly and effecitvely. Using bad data, or the incorrect data can generate devastating results. between 2022 and 2029.

Data Governance 52

Data Governance Marketing Machine Learning Sales 52

IBM Big Data Hub

OCTOBER 16, 2023

Banks and other financial institutions train ML models to recognize suspicious online transactions and other atypical transactions that require further investigation. Banks and other lenders use ML classification algorithms and predictive models to determine who they will offer loans to. Many stock market transactions use ML.

Machine Learning 48

Machine Learning Marketing Forecasting Modeling 48

Smarten

DECEMBER 3, 2023

Perhaps you want to shift from a data warehouse to cloud storage. If you choose the right self service data analytics tools with the right self service analytics capabilities, you can avoid a cumbersome, long training schedule.

Analytics 40

Analytics Data Warehouse Metrics Interactive 40

Alation

APRIL 25, 2023

As firms mature their transformation efforts, applying Artificial Intelligence (AI), machine learning (ML) and Natural Language Processing (NLP) to the data is key to putting it into action quickly and effecitvely. Using bad data, or the incorrect data can generate devastating results. between 2022 and 2029.

Data Governance 40

Data Governance Marketing Machine Learning Sales 40

Smarten

SEPTEMBER 9, 2024

Preparing for a Citizen Data Scientist Initiative Once you have made the decision to begin a Citizen Data Scientist initiative, you must plan carefully to be sure you can accomplish your goals. Be sure the solution you choose has all the features you need and will be easy for your users to learn and adopt.

Consulting 52

Consulting Cost-Benefit Data Science Business Intelligence 52

Cloudera

DECEMBER 20, 2017

And, we often use some form of pattern detection in our daily lives to help us solve problems or predict our next-best course of action. Here’s a puzzle for you: 3, 1, 2, 0, 1, -1, ? 240, 48, 12, 4, 2, ? 2, 5, 11, 17, 23, 31, ? Find the pattern? . We humans are pretty good at pattern detection.

Machine Learning 40

Machine Learning Reporting Statistics Modeling 40

O'Reilly on Data

SEPTEMBER 11, 2019

But the database—or, more precisely, the data model —is no longer the sole or, arguably, the primary focus of data engineering. If anything, this focus has shifted to the ML or predictive model. Increasingly, the term “data engineering” is synonymous with the practice of creating data pipelines, usually by hand.

IoT 23

IoT Big Data Data Warehouse Uncertainty 23

Jet Global

MAY 9, 2024

The key components of a data pipeline are typically: Data Sources : The origin of the data, such as a relational database , data warehouse, data lake , file, API, or other data store. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

Jet Global

SEPTEMBER 1, 2023

Data-Driven Decision Making: Embedded predictive analytics empowers the development team to make informed decisions based on data insights. By integrating predictive models directly into the application, developers can provide real-time recommendations, forecasts, or insights to end-users.

Predictive Analytics 52

Predictive Analytics Risk Analytics Software 52

Jet Global

AUGUST 13, 2024

Demand Forecasting: Machine learning analyzes sales data to predict future demand, leading to better inventory management and resource allocation. Risk Management: AI-powered anomaly detection and predictive modeling identify potential supply chain disruptions, allowing for proactive risk management.

Manufacturing 52

Manufacturing Reporting Optimization Sales 52