Jet Global

OCTOBER 22, 2020

The world of business analytics is evolving rapidly. The size and scope of business databases have grown as ERP functionality has evolved, businesses have increased their adoption of CRM and marketing automation, and collaboration networks have become more common. OLAP Cubes vs. Tabular Models.

Modeling 52

Modeling OLAP Reporting Sales 52

Cloudera

OCTOBER 4, 2023

On top of these core critical capabilities, we also need the following: Petabyte and larger scalability — particularly valuable in predictive analytics use cases where high granularity and deep histories are essential to training AI models to greater precision. Kudu has this covered. appeared first on Cloudera Blog.

Data Warehouse 87

Data Warehouse Experimentation Cost-Benefit Dashboards 87

CIO Business Intelligence

MARCH 9, 2022

But the data repository options that have been around for a while tend to fall short in their ability to serve as the foundation for big data analytics powered by AI. Traditional data warehouses, for example, support datasets from multiple sources but require a consistent data structure.

Data Lake 102

Data Lake Unstructured Data Data Warehouse Data Quality 102

IBM Big Data Hub

AUGUST 28, 2023

It’s no wonder then that Macmillan needs sophisticated business intelligence (BI) and data analytics. Additionally, the users’ demand for more and more analytics grew organically over time, to the point where it far exceeded our technology team’s bandwidth to support it on their own.

Publishing 51

Publishing Analytics Business Intelligence Operational Reporting 51

IBM Big Data Hub

SEPTEMBER 19, 2023

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

Data Science 78

Data Science Data Analytics Prescriptive Analytics Analytics 78

datapine

AUGUST 29, 2022

At 156 pages on Kindle, this is a book you could finish in one (long) sitting if you were so inclined, and that you can also use as an inspiration when you work on your business intelligence strategy. 6) Lean Analytics: Use Data to Build a Better Startup Faster, by Alistair Croll and Benjamin Yoskovitz.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Cloudera

NOVEMBER 2, 2020

Users today are asking ever more from their data warehouse. As an example of this, in this post we look at Real Time Data Warehousing (RTDW), which is a category of use cases customers are building on Cloudera and which is becoming more and more common amongst our customers. What is Real Time Data Warehousing?

Data Warehouse 94

Data Warehouse Optimization Dashboards Interactive 94

Sisense

APRIL 26, 2020

Read on to explore more about structured vs unstructured data, why the difference between structured and unstructured data matters, and how cloud data warehouses deal with them both. Structured vs unstructured data. However, both types of data play an important role in data analysis.

Unstructured Data 52

Unstructured Data Data Warehouse Structured Data Data mining 52

Jet Global

MAY 6, 2022

Business intelligence empowers businesses to get the most out of their data by providing tools to analyze information, streamline operations, track performance, and inform decision-making. Their combined utility makes it easy to create and maintain a complete data warehouse solution with very little effort.

Data Warehouse 52

Data Warehouse Visualization Finance Reporting 52

IBM Big Data Hub

AUGUST 4, 2023

By leveraging data services and APIs, a data fabric can also pull together data from legacy systems, data lakes, data warehouses and SQL databases, providing a holistic view into business performance. MLOps creates a process where it’s easier to cull insights from business data.

Data Architecture 60

Data Architecture Data Lake Machine Learning Data Governance 60

Jet Global

SEPTEMBER 19, 2023

Data Access What insights can we derive from our cloud ERP? What are the best practices for analyzing cloud ERP data? How can we respond in real time to the company’s analytic needs? Data Management How do we create a data warehouse or data lake in the cloud using our cloud ERP?

Cost-Benefit 52

Cost-Benefit Data Warehouse Reporting Enterprise 52

Jet Global

JULY 27, 2022

As Business Intelligence (BI) tools, data warehousing solutions, and enterprise data and application landscapes have advanced, it’s worth taking the time to rethink that old model, starting with the dichotomy between operational reporting (OR) and strategic analytics.

Operational Reporting 52

Operational Reporting Reporting Analytics Dashboards 52

Jet Global

SEPTEMBER 30, 2022

What are the best practices for analyzing cloud ERP data? How can we respond in real time to the company’s analytic needs? Data Management. How do we create a data warehouse or data lake in the cloud using our cloud ERP? How do I access the legacy data from my previous ERP? Self-service BI.

Reporting 52

Reporting Data Warehouse Operational Reporting Enterprise 52

Jet Global

NOVEMBER 15, 2022

Although many companies run their own on-premise servers to maintain IT infrastructure, nearly half of organizations already store data on the public cloud. The Harvard Business Review study finds that 88% of organizations that already have a hybrid model in place, see themselves maintaining the same strategy into the future.

Reporting 52

Reporting Finance Software Data Warehouse 52

Peter James Thomas

AUGUST 18, 2018

That was the Science, here comes the Technology… A Brief Hydrology of Data Lakes. Even back then, these were used for activities such as Analytics , Dashboards , Statistical Modelling , Data Mining and Advanced Visualisation. This is the essence of Convergent Evolution. In Closing.

Data Lake 63

Data Lake Data Warehouse Data mining Statistics 63

Peter James Thomas

MAY 3, 2019

The flip side is that making the necessary investments to provide even basic information has been at the heart of the successful business turnarounds that I have been involved in. The bulk of Business Intelligence efforts would also fall into this area, but there is some overlap with the area I next describe as well.

Strategy 55

Strategy Data Architecture Data Quality Data Strategy 55

Jet Global

MARCH 23, 2023

Organizations are promised a ‘one size fits all’ tool that will allow users to ‘drag n drop’ their way to data fluency. For them, self-service must cover tasks like data exploration, modeling, and deploying a sandbox environment for special use cases. These are the users with the most advanced BI skills and requirements.

Dashboards 52

Dashboards Reporting Key Performance Indicator Interactive 52

Jet Global

OCTOBER 24, 2022

Traditional business intelligence platforms offer another alternative, but full-stack BI solutions tend to be difficult to use and maintain, typically requiring a team of full-time specialists, and little or no self-service capabilities. Live demo tailored to your business requirements. Interested in Data Warehousing/BI Cubes.

Analytics 52

Analytics Dashboards Software Reporting 52

Jet Global

DECEMBER 22, 2022

Enable Users With Self-Service Reporting: Views can be used with any business intelligence tool to eliminate the need for technical assistance to create custom analysis. Increase your team’s inherent value and allow them to delve deeper into the data than they ever thought possible. Interested in Data Warehousing/BI Cubes.

Operational Reporting 52

Operational Reporting Reporting Manufacturing Enterprise 52

Jet Global

SEPTEMBER 26, 2023

She previously held roles at Vista Equity Partners and served as SVP of Business Operations at Mediaocean. Peter van Tiggelen, CFO, FE fundinfo: Joining FE fundinfo in February 2022, van Tiggelen oversees finance, legal, and business intelligence.

Marketing 52

Marketing Finance Reporting Operational Reporting 52

Domino Data Lab

JULY 22, 2019

data science’s emergence as an interdisciplinary field – from industry, not academia. why data governance, in the context of machine learning is no longer a “dry topic” and how the WSJ’s “global reckoning on data governance” is potentially connected to “premiums on leveraging data science teams for novel business cases”.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Andrew White

MARCH 22, 2019

I jest here; what I mean to say is that economics and rational, smart models should trump perfectly designed ideas. This ties into the failure of data governance and MDM (see first item in this list). This ties into the failure of data governance and MDM (see first item in this list). Economics over architecture.

Analytics 68

Analytics Metadata Data Governance Management 68

O'Reilly on Data

SEPTEMBER 11, 2019

Our call for speakers for Strata NY 2019 solicited contributions on the themes of data science and ML; data engineering and architecture; streaming and the Internet of Things (IoT); business analytics and data visualization; and automation, security, and data privacy. An ML-related topic, “models,” was No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

Occam's Razor

DECEMBER 3, 2008

If you stick to web analytics your title might tap out at one of the titles mentioned above (say Sr. If you really want to have your Job Title grow a lot more then you'll have to gradually move to the world of Business Analytics (not web) and Business Intelligence roles in IT. Manager, Website Analytics.

Analytics 115

Analytics Consulting Metrics Reporting 115