CIO Business Intelligence

JUNE 7, 2022

What is data analytics? Data analytics is a discipline focused on extracting insights from data. It comprises the processes, tools and techniques of data analysis and management, including the collection, organization, and storage of data. What are the four types of data analytics?

Data Analytics 135

Data Analytics Diagnostic Analytics Management Analytics 135

Data Science 101

OCTOBER 31, 2019

This data alone does not make any sense unless it’s identified to be related in some pattern. Data mining is the process of discovering these patterns among the data and is therefore also known as Knowledge Discovery from Data (KDD). Machine learning provides the technical basis for data mining.

Data mining 77

Data mining KDD Data Science Forecasting 77

CIO Business Intelligence

JUNE 14, 2023

Data and big data analytics are the lifeblood of any successful business. Getting the technology right can be challenging but building the right team with the right skills to undertake data initiatives can be even harder — a challenge reflected in the rising demand for big data and analytics skills and certifications.

Big Data 126

Big Data Data Analytics Analytics Predictive Modeling 126

datapine

MAY 14, 2019

By gaining the ability to understand, quantify, and leverage the power of online data analysis to your advantage, you will gain a wealth of invaluable insights that will help your business flourish. The ever-evolving, ever-expanding discipline of data science is relevant to almost every sector or industry imaginable – on a global scale.

Data Science 350

Data Science Machine Learning Data-driven Big Data 350

IBM Big Data Hub

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? This post will dive deeper into the nuances of each field.

Machine Learning 60

Machine Learning Data Science Statistics Deep Learning 60

CIO Business Intelligence

MAY 12, 2022

What are predictive analytics tools? Predictive analytics tools blend artificial intelligence and business reporting. But there are deeper challenges because predictive analytics software can’t magically anticipate moments when the world shifts gears and the future bears little relationship to the past. Highlights. Deployment.

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Predictive Analytics Analytics Statistics Machine Learning 137

CIO Business Intelligence

AUGUST 9, 2022

What is a data engineer? Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. They create data pipelines used by data scientists, data-centric applications, and other data consumers. Data engineer job description.

Analytics 131

Analytics Data Science Statistics Unstructured Data 131

Smart Data Collective

SEPTEMBER 16, 2020

Predictive analytics, sometimes referred to as big data analytics, relies on aspects of data mining as well as algorithms to develop predictive models. These predictive models can be used by enterprise marketers to more effectively develop predictions of future user behaviors based on the sourced historical data.

Predictive Analytics 121

Predictive Analytics Analytics Predictive Modeling Data mining 121

CIO Business Intelligence

NOVEMBER 14, 2022

With organizations increasingly focused on data-driven decision making, decision science (or decision intelligence) is on the rise, and decision scientists may be the key to unlocking the potential of decision science systems. Commonly used models include: Statistical models. Crop planning. Clinical DSS.

Data mining 144

Data mining Data-driven Statistics OLAP 144

Data Science 101

DECEMBER 11, 2019

This weeks guest post comes from KDD (Knowledge Discovery and Data Mining). Every year they host an excellent and influential conference focusing on many areas of data science. Honestly, KDD has been promoting data science way before data science was even cool. 1989 to be exact. ACM SIGKDD.

KDD 81

KDD Knowledge Discovery Data mining Statistics 81

CIO Business Intelligence

OCTOBER 13, 2023

The data architect also “provides a standard common business vocabulary, expresses strategic requirements, outlines high-level integrated designs to meet those requirements, and aligns with enterprise strategy and related business architecture,” according to DAMA International’s Data Management Body of Knowledge.

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Data Architecture Data Warehouse Statistics Visualization 132

IBM Big Data Hub

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects.

Data Science 59

Data Science Machine Learning Cost-Benefit Deep Learning 59

FineReport

MARCH 24, 2024

Rapid technological advancements and extensive networking have propelled the evolution of data analytics, fundamentally reshaping decision-making practices across various sectors. In this landscape, data analysts assume a pivotal role, tasked with interpreting data to drive informed decision-making.

Statistics 52

Statistics Data mining Visualization Reporting 52

Jen Stirrup

SEPTEMBER 30, 2021

Azure ML can become a part of the data ecosystem in an organization, but this requires a mindshift from working with Business Intelligence to more advanced analytics. How can we can adopt a mindshift from Business Intelligence to advanced analytics using Azure ML? AI vs ML vs Data Science vs Business Intelligence.

Business Intelligence 135

Business Intelligence Data mining Machine Learning Testing 135

Rocket-Powered Data Science

JULY 7, 2019

Analytics: The products of Machine Learning and Data Science (such as predictive analytics, health analytics, cyber analytics). Edge Computing (and Edge Analytics): Industry 4.0: Robotics: A branch of AI concerned with creating devices that can move and react to sensory input (data).

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

Smart Data Collective

DECEMBER 19, 2021

Therefore, if you don’t preprocess the data before applying it in the machine learning or AI algorithms, you are most likely to get wrong, delayed, or no results at all. Hence, data preprocessing is essential and required. Python as a Data Processing Technology. Open Source: Python has an OSI-approved open source license.

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Optimization Machine Learning Data mining Statistics 135

datapine

MARCH 31, 2022

Moreover, companies that use BI analytics are five times more likely to make swifter, more informed decisions. Despite these findings, the undeniable value of intelligence for business, and the incredible demand for BI skills, there is a severe shortage of BI-based data professionals – with a shortfall of 1.5

Business Intelligence 239

Business Intelligence Statistics Visualization Data-driven 239

Rocket-Powered Data Science

APRIL 25, 2019

A few years ago, I generated a list of places to receive data science training. Learn the what, why, and how of Data Science and Machine Learning here. Learn the what, why, and how of Data Science and Machine Learning here. That list has become a bit stale. Follow Kirk Borne on Twitter @KirkDBorne.

Data Science 38

Data Science Machine Learning Deep Learning Statistics 38

Smart Data Collective

JUNE 14, 2022

Data scientists need to have a number of different skills. In addition to understanding the logistics of networking and a detailed knowledge of statistics, they must possess solid programming skills. When you are developing big data applications, you need to know how to create code effectively. Improper Use of Comments.

Data mining 92

Data mining Big Data Data Science Statistics 92

Smart Data Collective

OCTOBER 14, 2019

Whether they want a career as an app developer or data analyst, the skillsets below can help them find lucrative careers in a competitive job market. Big Data Skillsets. From artificial intelligence and machine learning to blockchains and data analytics, big data is everywhere. Machine Learning. Apache Spark.

Big Data 75

Big Data Software Data mining Prescriptive Analytics 75

Peter James Thomas

AUGUST 18, 2018

From 2000 to 2015, I had some success [5] with designing and implementing Data Warehouse architectures much like the following: As a lot of my work then was in Insurance or related fields, the Analytical Repositories tended to be Actuarial Databases and / or Exposure Management Databases, developed in collaboration with such teams.

Data Lake 63

Data Lake Data Warehouse Data mining Statistics 63

BizAcuity

MAY 7, 2019

An area of predictive analytics, demand forecasting takes into account the historical data of a business and uses that to harnesses the demand for their goods and services. Today, several methods involving data science, statistical model, trend line, time-phased analysis, data mining and more are used to predict consumer demand.

Forecasting 75

Forecasting IT Sales Statistics 75

Sisense

APRIL 10, 2020

One of the most-asked questions from aspiring data scientists is: “What is the best language for data science? People looking into data science languages are usually confused about which language they should learn first: R or Python. NLP can be used on written text or speech data. R: Analytics powerhouse.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

Analytics Vidhya

JANUARY 25, 2023

The World Happiness Report rates happiness on six indicators: positive emotions, […] The post Analysing World Happiness Report (2020-2022) appeared first on Analytics Vidhya. In line with the latest World Happiness Report, it is evident that being happy has become a worldwide priority.

Reporting 310

Reporting Analytics IT Data mining 310

Rita Sallam

JULY 22, 2016

And here we are now, past the tipping point of a more than 10 to 11 year transition away from IT-centric reporting platforms to modern BI and analytics platforms that make up much of the new buying in the BI and Analytics market. A modern BI platform supports IT-enabled analytic content development.

Analytics 40

Analytics Business Intelligence Metadata Statistics 40

FineReport

DECEMBER 19, 2019

Professional data analysts must have a wealth of business knowledge in order to know from the data what has happened and what is about to happen. In addition, tools for data analysis and data mining are also important. Excel, Python, Power BI, Tableau, FineReport are frequently used by data analysts.

Knowledge Discovery 98

Knowledge Discovery Visualization Experimentation Reporting 98

datapine

AUGUST 29, 2022

The saying “knowledge is power” has never been more relevant, thanks to the widespread commercial use of big data and data analytics. The rate at which data is generated has increased exponentially in recent years. Essential Big Data And Data Analytics Insights. million searches per day and 1.2

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CIO Business Intelligence

DECEMBER 22, 2022

DataOps (data operations) is an agile, process-oriented methodology for developing and delivering analytics. It brings together DevOps teams with data engineers and data scientists to provide the tools, processes, and organizational structures to support the data-focused enterprise. What is DataOps?

Analytics 130

Analytics Machine Learning Software Data Quality 130

Grooper

AUGUST 19, 2019

Choosing the right analytics solution isn't easy. Successfully navigating the 20,000+ analytics and business intelligence solutions on the market requires a special approach. Read on to learn how data literacy, information as a second language, and insight-driven analytics take digital strategy to a new level.

Analytics 54

Analytics Machine Learning Data-driven Data Science 54

CIO Business Intelligence

JANUARY 20, 2023

BI tools access and analyze data sets and present analytical findings in reports, summaries, dashboards, graphs, charts, and maps to provide users with detailed intelligence about the state of the business. Whereas BI studies historical data to guide business decision-making, business analytics is about looking forward.

Business Intelligence 144

Business Intelligence Dashboards Data mining OLAP 144

Edwin Chen

SEPTEMBER 6, 2011

But to summarize a bit what we’ve seen so far, here are some statistics on the role the social graph played in spreading the post: There are 5 clusters of activity after the initial post, sparked both by power users and less-connected folks. 35% of users made their way to my answer because of someone else’s upvote.

Machine Learning 52

Machine Learning Broadcasting Visualization Data mining 52

FineReport

JULY 18, 2022

There exists a variety of data analysis tools for you to choose from. In order to assist you in selecting the one that best fits your company’s needs, let’s examine several best data analytics tools that are popular in 2022. How to Choose Data Analysis Tools. From Microsoft Power BI.

Forecasting 52

Forecasting Dashboards Visualization Statistics 52

Cloudera

NOVEMBER 30, 2017

They hold a series of well-attended industry events that focus advancing the art of modern analytics and big data. They have empowered countless companies to move beyond legacy reporting practices to lean, agile analytics practices. David sets the stage for us on the importance of marrying data with customer intelligence. “It

Strategy 40

Strategy Big Data Data mining Visualization 40

datapine

NOVEMBER 27, 2019

Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards. The rise of self-service analytics democratized the data product chain. Suddenly advanced analytics wasn’t just for the analysts. 1) Data Quality Management (DQM).

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361