Business Intelligence – It’s Better Here

Business intelligence has come a long way in the past 20 years, with easy-to-use and easy-to-adapt dashboards on iPads replacing hand-coded COBOL reports that took months to write. However, the merger-and-acquisition-scarred BI marketplace tends to forget that business users don’t need more tools – they need better information.

Spotlight

Striim

Striim’s unified data integration and streaming platform connects clouds, data and applications with unprecedented speed and simplicity drive data to decisions in real time. Striim specializes in integration from a wide variety of data sources – transaction/change data capture events, log files, application and IoT sensor data – and real-time correlation across multiple streams. With a full blown SQL streaming engine, you can add structure, logic and rules to streaming data. Define time windows for analysis. Detect outliers, visualize events of interest, and trigger alerts and automated workflows – all within milliseconds.

OTHER WHITEPAPERS
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Securing Data In Motion: Working at the Speed of the Mission

whitePaper | August 25, 2022

Everyday user experiences are being completely reimagined. Requesting a ride through an app is better than calling a cab. Buying online is changing the way we go shopping. And gone are the days of waiting in long lines at your local bank. Data within these modern businesses is in constant motion, flowing, in real-time, across systems, environments, and applications every time a user interacts with the system.

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Enhancing data mesh: How distributed ledger solutions empower decentralized governance

whitePaper | June 27, 2023

Data mesh is an innovative approach to data management that offers a solution to the most common challenges organizations face when dealing with large-scale data management. Data mesh concept consists of four key principles for large-scale data management in a multi-tiered, multi-domain organizational structure: domainoriented ownership, data as product, federated computational governance and a self-service data platform. These all promote a more agile, efficient and scalable data architecture. However, one of the central challenges to implementing data mesh is designing decentralized governance mechanisms that align with its core principles.

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Implementing a Modern Data Catalog to Power Data Intelligence

whitePaper | August 9, 2022

This report shows you how a data catalog can help you easily find and then use the data you need.

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SaaS and The Evolution of Cloud-Based Data Management

whitePaper | December 21, 2022

The evolution of cloud-based technology is continuing to reshape the data management landscape. As data volumes increase, users in different industries are demanding flexible, scalable processes that enable them to leverage a wider variety of sources, uncover new insights and confidently make decisions that will drive their businesses forward.

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Better together: AVEVA™ Predictive Analytics and the AVEVA™ PI System™ maximize mining plant ROI

whitePaper | December 23, 2022

From volatile markets and tough competition to the increasingly stringent demands of government regulators and customers alike, there’s no shortage of challenges ahead for the mining industry. To navigate these obstacles and others all while improving profitability, mining companies are seeking innovative ways to optimize the reliability, efficiency, and safety of their operations. Many industry leaders are already finding the answers they are looking for in their operations data.

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Data Analytics Integrity Challenges to Implementation of the Automated Data Collection Processes

whitePaper | March 12, 2020

In recent months, my company (Baron Consulting) has been proactively involved in setting up Data Collection Systems for a range of Private and Public Organisations we are servicing. Some of the data collection challenges have already been discussed in our recent Raw Data Collection 2020: Principles and Challenges White Paper. While the RDC (Raw Data Collection) paper analysed the current state of the everchanging Data Collection requirements, it did not have the scope to address technicalities of the RDC processes along with the specific Data Collection tools and methods. The purpose of this paper is to fill the void by looking into implementation of the automated data collection processes.

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Spotlight

Striim

Striim’s unified data integration and streaming platform connects clouds, data and applications with unprecedented speed and simplicity drive data to decisions in real time. Striim specializes in integration from a wide variety of data sources – transaction/change data capture events, log files, application and IoT sensor data – and real-time correlation across multiple streams. With a full blown SQL streaming engine, you can add structure, logic and rules to streaming data. Define time windows for analysis. Detect outliers, visualize events of interest, and trigger alerts and automated workflows – all within milliseconds.

Events