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Innovative data integration in 2024: Pioneering the future of data integration

CIO Business Intelligence

In the age of big data, where information is generated at an unprecedented rate, the ability to integrate and manage diverse data sources has become a critical business imperative. Traditional data integration methods are often cumbersome, time-consuming, and unable to keep up with the rapidly evolving data landscape.

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Key Data Trends And Forecasts In The Energy Sector

Smart Data Collective

Corporations need data to forecast the market’s future and the recent drop in the price of fossil fuels have invigorated alternative energy projects globally. The increased amount of equipment it takes for wind turbines and solar PV panels requires analytical software to maintain it. Effects of Analytics.

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Bridging the gap between mainframe data and hybrid cloud environments

CIO Business Intelligence

A high hurdle many enterprises have yet to overcome is accessing mainframe data via the cloud. Data professionals need to access and work with this information for businesses to run efficiently, and to make strategic forecasting decisions through AI-powered data models.

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Better Budgeting, Planning, and Forecasting With Bizview D365BC Connector

Jet Global

This applies to collaborative planning, budgeting, and forecasting, which, without the right tools, can be daunting on its best day. What holds us back from working smarter is the risk of integrating better tools that, although the tool is seemingly an improvement, runs the risk of throwing off your whole process. Bizview Smarts.

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Common Problems With CPM Software

Jet Global

And an entire industry of software solutions has cropped up to help companies achieve this. Let’s take a closer look at what corporate performance management is, why it’s important, and the common problems presented by many CPM software solutions. Budgeting, planning, and forecasting in finance. Forecasting. The problem?

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Top 10 Analytics And Business Intelligence Trends For 2020

datapine

The development of business intelligence to analyze and extract value from the countless sources of data that we gather at a high scale, brought alongside a bunch of errors and low-quality reports: the disparity of data sources and data types added some more complexity to the data integration process.

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Becoming a machine learning company means investing in foundational technologies

O'Reilly on Data

Here are some typical ways organizations begin using machine learning: Build upon existing analytics use cases: e.g., one can use existing data sources for business intelligence and analytics, and use them in an ML application. Modernize existing applications such as recommenders, search ranking, time series forecasting, etc.