CIO Business Intelligence

AUGUST 9, 2022

Data engineers are often responsible for building algorithms for accessing raw data, but to do this, they need to understand a company’s or client’s objectives, as aligning data strategies with business goals is important, especially when large and complex datasets and databases are involved. Data engineer vs. data architect.

Analytics 131

Analytics Data Science Statistics Unstructured Data 131

Sisense

OCTOBER 6, 2020

Let’s consider the differences between the two, and why they’re both important to the success of data-driven organizations. Digging into quantitative data. This is quantitative data. It’s “hard,” structured data that answers questions such as “how many?” The challenge comes when the data becomes huge and fast-changing.

Statistics 98

Statistics Unstructured Data Data-driven Visualization 98

Smarten

MAY 17, 2017

If you are considering a Business Intelligence solution, you ought to give some consideration to the concept of Smart Data Visualization and review your prospective solution to determine its capabilities in that regard. How do users perceive and use data? How do formats and reporting affect a user’s understanding of data?

Visualization 40

Visualization Business Intelligence Unstructured Data Interactive 40

IBM Big Data Hub

JULY 6, 2023

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. appeared first on IBM Blog.

Machine Learning 60

Machine Learning Data Science Statistics Deep Learning 60

FineReport

JULY 28, 2021

Business Intelligence describes the process of using modern data warehouse technology, data analysis and processing technology, data mining, and data display technology for visualizing, analyzing data, and delivering insightful information. Typical tools for data science: SAS, Python, R.

Business Intelligence 98

Business Intelligence Data Science Data Analytics Analytics 98

BizAcuity

OCTOBER 21, 2019

Over the decade’s Hospitality Industry wings expand to the new horizon due to the widespread usage of mobiles which allows customers to plan the vacation & visualize the ambiance at their fingertips. Text analytics helps to draw the insights from the unstructured data. .

Analytics 73

Analytics Unstructured Data Data mining Structured Data 73

FineReport

MARCH 7, 2024

However, due to regulatory controls on sensitive data like phone numbers and technical challenges in cross-platform integration of Internet and mobile reporting data, our current matching rates are relatively low, reaching around 20% in ideal scenarios, excluding telecom data. We assess revenue streams.

Dashboards 52

Dashboards Reporting Advertising Statistics 52

Alation

JUNE 2, 2022

A data catalog is a central hub for XAI and understanding data and related models. While “operational exhaust” arrived primarily as structured data, today’s corpus of data can include so-called unstructured data. How Data Lineage Is a Use Case in ML. Other Technologies.

Modeling 52

Modeling Data Governance Statistics Unstructured Data 52

CIO Business Intelligence

JUNE 28, 2023

The R&D laboratories produced large volumes of unstructured data, which were stored in various formats, making it difficult to access and trace. Working with non-typical data presents us with a reality where encountering challenges is part of our daily operations.”

Digital Transformation 105

Digital Transformation Cost-Benefit Informatics Data mining 105

BizAcuity

OCTOBER 21, 2019

Over the decade’s Hospitality Industry wings expand to the new horizon due to the widespread usage of mobiles which allows customers to plan the vacation & visualize the ambiance at their fingertips. Text analytics helps to draw the insights from the unstructured data.

Analytics 40

Analytics Unstructured Data Data mining Structured Data 40

bridgei2i

OCTOBER 7, 2020

Underwriting essentially means evaluating the risk proposition of an insurance cover and determining how much an insurer should charge for the same by studying historical data and statistical models. But the sheer volume of data might cloud manual judgments and here is where automation and digitization could help.

Insurance 52

Insurance Sales Strategy Digital Transformation 52

Sisense

JUNE 30, 2020

In a world increasingly dominated by data, users of all kinds are gathering, managing, visualizing, and analyzing data in a wide variety of ways. One of the downsides of the role that data now plays in the modern business world is that users can be overloaded with jargon and tech-speak, which can be overwhelming.

Visualization 93

Visualization Analytics Dashboards Data-driven 93

Sisense

JUNE 26, 2020

The human brain processes visual data better than any other kind of data, which is good because about 90% of the information our brains process is visual. Visual processing and responses both occur more quickly compared to other stimuli. This where the power of visualizations is apparent.

Visualization 97

Visualization Unstructured Data Measurement Data-driven 97

DataRobot

NOVEMBER 9, 2021

Signal classification models are typically built using time series principles; traditionally used features that include central, windowed, lag, and lead statistics can do the job but sometimes there might be scenarios where we want to eke out more performance out of the data. See DataRobot in Action.

Visualization 52

Visualization Unstructured Data Statistics Metrics 52

Domino Data Lab

JANUARY 21, 2021

Producing insights from raw data is a time-consuming process. Predictive modeling efforts rely on dataset profiles , whether consisting of summary statistics or descriptive charts. The Importance of Exploratory Analytics in the Data Science Lifecycle. Exploratory analysis is a critical component of the data science lifecycle.

Statistics 106

Statistics Unstructured Data Data Science Predictive Modeling 106

datapine

NOVEMBER 19, 2019

Exciting and futuristic, the concept of computer vision is based on computing devices or programs gaining the ability to extract detailed information from visual images. Visual analytics: Around three million images are uploaded to social media every single day. Artificial Intelligence (AI).

Technology 279

Technology Internet of Things IT IoT 279

Cloudera

NOVEMBER 25, 2020

Sample and treatment history data is mostly structured, using analytics engines that use well-known, standard SQL. Interview notes, patient information, and treatment history is a mixed set of semi-structured and unstructured data, often only accessed using proprietary, or less known, techniques and languages.

Data Warehouse 58

Data Warehouse Unstructured Data Analytics Visualization 58

Sisense

AUGUST 21, 2020

One result is that systems become much more intuitive: Users can take advantage of the “Simply Ask” feature to check “what are my sales next two months” and receive chatbot messages with projected visualizations and suggestions for further exploration routes. My take: The world is wider than the traditional BI tabular data.

Analytics 112

Analytics Machine Learning Dashboards Visualization 112

Cloudera

JULY 26, 2021

A common pitfall in the development of data platforms is that they are built around the boundaries of point solutions and are constrained by the technological limitations (e.g., a technology choice such as Spark Streaming is overly focused on throughput at the expense of latency) or data formats (e.g.,

Strategy 117

Strategy Data Science Marketing Unstructured Data 117

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. Graph analytics has revolutionized business intelligence.

Business Intelligence 212

Business Intelligence Prescriptive Analytics Analytics Predictive Analytics 212

Jet Global

MAY 9, 2024

Real-Time Analytics Pipelines : These pipelines process and analyze data in real-time or near-real-time to support decision-making in applications such as fraud detection, monitoring IoT devices, and providing personalized recommendations. For example, migrating customer data from an on-premises database to a cloud-based CRM system.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52