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CIO Applications Weekly Brief

Be first to read the latest tech news, Industry Leader's Insights, and CIO interviews of medium and large enterprises exclusively from CIO Applications

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Editor's Pick (1 - 4 of 8)
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Driving Innovation through Business Intelligence

Driving Innovation through Business Intelligence
John Matelski, CIO and Director of Information Technology, DeKalb County Government

Business Intelligence a Top Priority in 2016, Again?

Business Intelligence a Top Priority in 2016, Again?
Ben Stewart, CIO, Stant USA Corporation

Unleashing the Power of Analytics in Academics

Unleashing the Power of Analytics in Academics
Leebrian E Gaskins, AVP-IT & CIO, Texas A&M International University

Why Current Business Intelligence Tools Are Missing the Mark

Why Current Business Intelligence Tools Are Missing the Mark
Steve Brennan, VP, Data Strategy and Analytics, Carhartt

Bust the myths of Self-Service Business Intelligence

Bust the myths of Self-Service Business Intelligence
Raj Polanki, US Head of Analytics & Data Science, Wacker Chemical Corporation

The First Steps to BI Adoption

The First Steps to BI Adoption
Kjersten Moody, Chief Data and Analytics Officer, State Farm

AI-enabled business transformation: Closing the gaps

AI-enabled business transformation: Closing the gaps
Shilpa Yelamaneni, Director of Data Science and Advanced Analytics, Ecolab

Applying Deep Learning to Streamline Healthcare Administration

Applying Deep Learning to Streamline Healthcare Administration
Sanji Fernando, SVP Artficial Intelligence & Analytics Platforms, Optum

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The Latest Business Intelligence Trends

By CIO Applications| Tuesday, May 28, 2019
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FREMONT, CA: The latest Business intelligence (BI) trends are outlined below as it is a staple of a compelling corporate entity.

Analytics Adoption: Analytics is a new tool for stakeholders to get an understanding of data related to their function and obtain insights. The bottom-up strategy is absorbed as a fundamental part of the BI strategy, enabling users to perform analysis independently. 

Cloud-Based Analytics: As the processes are shifting towards cloud-based analytics, data-sharing is streamlined automatically. The various facade of digital transformation, like IoT integration, is easier to initiate without making any additional investments in the infrastructure, while allowing unlimited permission to access raw data.  

Data Privacy: The introduction of the code of ethics will ensure secure data sharing, avoid misuse of data applications, and control overzealous conclusions drawn from insights. The ethics involved are cross-disciplinary and apply to data storage, governance, and data use as well. It ensures compliance with guidelines and aids manager in the lookout for inaccuracies and bias

Explainable AI: Explainable artificial intelligence is helpful while seeking an explanation for the obtained result by increasing transparency in AI processes, encouraging users to dig deep into the data to understand the ultimate conclusions.

Natural Language Interactions with Data: With the progress of natural language processing (NLP), the need for facilitation of new data can be carried out by users both experienced and novice by the use of visualizations, other analytics outputs and focus on the related information without the need of an expert.

Actionable Analytics: Actionable analytics will place data wherever it is most necessary in real-time, with the help of APIs and plug-ins to provide insights to and existing software.

Data Collaborations for Social Good: Shared cloud space is cheaper and efficient for combining forces and sharing data to create an impact none could achieve individually.

The convergence of BI and Data management: The role of BI will be to maintain high-quality data, metadata, and associations between various tools connected. Similarly, data governance is more streamlined as data warehousing and management platforms are integrated into BI tools.

Data Storytelling and Data Democracy: Data storytelling is an avenue to foster a culture of conversation around data in which many partners are involved in understanding results. The drift of democratizing data engrosses data scientists for better application of the data to the businesses in conjuncture to an advanced user for better incorporation of analytics to encourage data interdependency.

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