Creating real value from the data life cycle

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The big impact of Big Data in the post-modern world is unquestionable, un-ignorable and unstoppable today. While there are certain discussions around Big Data being really big, here to stay or just an over hyped fad; there are facts as shared in the following sections of this whitepaper that validate one thing - there is no knowing of the limits and dimensions that data in the digital world can assume. As of now, there are only predictions, forecasts that form the basis for businesses to decide their own course of action by taking advantage of this new window of opportunity with high-end intricate technology based resources and tools. These are nothing but ways and means to harness the mammoth and draw necessary inferences for making smart, intelligent business decisions.

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iauro systems Pvt Ltd

iauro Systems Pvt ltd focuses on Mobile, Cloud and BigData domains. iauro has delivered projects from single line of concept to finished market ready products. iauro also focuses on new, upcoming, disruptive technologies like IOT, iBeacons, Wearables, Data analytics, etc.

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BIG DATA MANAGEMENT

Why Adaptive AI Can Overtake Traditional AI

Article | July 15, 2022

With the ever-changing technology world, company demands and results are no longer the norm. Businesses in a variety of sectors are using artificial intelligence (AI) technologies to solve complicated business challenges, build intelligent and self-sustaining solutions, and, ultimately, remain competitive at all times. To that aim, ongoing attempts are being made to reinvent AI systems in order to do more with less. Adaptive AI is a significant step in that direction. It has the potential to outperform standard machine learning (ML) models in the near future because of its ability to enable organizations to get greater results while spending less time, effort, and resources. The capacity of adaptive AI to enable enterprises to achieve greater outcomes while investing less time, effort, and assets is why it can overtake traditional AI models. Why Adaptive AI Overtakes Traditional AI Robust, Efficient and Agile Robustness, efficiency, and agility are the three basic pillars of Adaptive AI. The ability to achieve great algorithmic accuracy is referred to as robustness. The capacity to achieve reduced resource utilization is referred to as efficiency (for example, computer, memory, and power). Agility manages the ability to change operational circumstances in response to changing demands. Together, these three Adaptive AI principles provide the groundwork for super-capable AI inference for edge devices. Data-Informed Predictions A single pipeline is used by the adaptive learning approach. With this method, you can use a continually advanced learning approach that maintains the framework up-to-date and encourages it to achieve high levels of performance. The Adaptive Learning method examines and learns new changes made to the information and produces values, as well as their associated attributes. Moreover, it benefits from events that can modify market behavior in real-time and, as a result, maintains its accuracy consistently. Adaptive AI recognizes information from the operational environment and uses it to produce data-informed predictions. Closing Lines Adaptive AI will be utilized to meet changing AI computing requirements. Operational effectiveness depends on algorithmic performance and available computer resources. Edge AI frameworks that can change their computing demands effectively reduce compute and memory requirements. Adaptive AI is robust in CSPs' dynamic software environments, where inputs and outputs alter with each framework revamp. It can assist with network operations, marketing, customer service, IoT, security, and customer experience.

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BUSINESS STRATEGY

Data-Centric Approach for AI Development

Article | July 22, 2022

As AI has grown in popularity over the past decade, practitioners have concentrated on gathering as much data as possible, classifying it, preparing it for usage, and then iterating on model architectures and hyper-parameters to attain our desired objectives. While dealing with all of this data has long been known as laborious and time-consuming, it has typically been seen as an upfront, one-time step we take before entering into the essential modeling phase of machine learning. Data quality concerns, label noise, model drift, and other biases are all addressed in the same way: by collecting and labeling more data, followed by additional model iterations. The foregoing technique has worked successfully for firms with unlimited resources or strategic challenges. It doesn't work well for machine learning's long-tail issues, particularly those with fewer users and little training data. The discovery that the prevailing method of deep learning doesn't "scale down" to industry challenges has given birth to a new "trend" in the area termed "Data-Centric AI." Implementing a Data-Centric Approach for AI Development Leverage MLOps Practices Data-centric AI prioritizes data over models. Model selection, hyper-parameter tuning, experiment tracking, deployment, and monitoring take time. Data-centric approaches emphasize automating and simplifying ML lifecycle operations. Standardizing and automating model-building requires MLOps. MLOps automates machine learning lifecycle management pipelines. An organizational structure improves communication and cooperation. Involve Domain Expertise Data-centric AI development requires domain-specific datasets. Data scientists can overlook intricacies in various sectors, business processes, or even the same domain. Domain experts can give ground truth for the AI use case and verify whether the dataset truly portrays the situation. Complete and Accurate Data Data gaps cause misleading results. It's crucial to have a training dataset that correctly depicts the underlying real-world phenomenon. Data augmentation or creating synthetic data might be helpful if gathering comprehensive and representative data is costly or challenging for your use case.

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BIG DATA MANAGEMENT

A Modern Application Must Have: Multi-cloud Database

Article | July 11, 2022

To function well, modern apps require enormous amounts of diverse data from sensors, processes, interactions, etc. However, these apps cannot understand the unstructured big data and extract commercial value for effective operations unless this data is maintained properly. In today's age of cloud computing, apps gather and analyze data from various sources, but the data isn't always kept in the same database or format. While increasing overall complexity, several formats make it more difficult for apps to retain and use various data. Multi-model databases, a cutting-edge management system, provide a sophisticated approach to handling varied and unstructured data. A multi-model database allows various data models to natively utilize a single, integrated backend, as opposed to combining different database models. Why Has Multi-Model Database Become a Necessity for Modern Applications? Modern applications can store diverse data in a single repository owing to the flexible approach to database management, which improves agility and reduces data redundancy. Improve Reliability Each database might be a single point of failure for a larger system or application. Multi-model databases reduce failure points, enhancing data dependability and recovery time. Such recovery minimizes expenses and maintains customer engagement and application experience. Simplify Data Management Fragmented database systems benefit contemporary applications but complicate development and operations. Multi-model databases provide a single backend that maintains data integrity and fault tolerance, eliminating the need for different database systems, software licenses, developers, and administrators. Improve Fault Tolerance Modern apps must be fault-tolerant and respond promptly to failures promptly. Multi-model databases enable this by integrating several systems into a single backend. The integration provides system-wide failure tolerance. Closing Lines As applications get more complicated, so do their database requirements. However, connecting many databases and preserving consistency between data gathered from various sources is a time-consuming and expensive undertaking. Fortunately, multi-model databases provide an excellent option for generating the data models you want on a single backend.

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BIG DATA MANAGEMENT

Why Increasing Data Maturity Help Businesses Unlock Digital Potential?

Article | July 5, 2022

There is no dispute that brands that harness and invest in data capabilities will be the ones to realize their maximum revenue potential. However, while today's marketers have access to a multitude of data sources, understanding what data to use and how to utilize it are two of the biggest challenges for all. Data utilization in companies is an inconsistent experience. Some businesses have sensibly invested in improving their data maturity. They can pivot quickly to maximize income potential in an unstable economic environment. Others face a cycle of declining returns as they try to reproduce previous achievements with variable outcomes. Importance of Data Maturity for Businesses Understanding your organization's data maturity is critical for five reasons. An understanding of data maturity may assist marketers in: Align Recognize which problems and challenges the wider organization is attempting to solve and modify techniques to support those goals. Appreciate Analyze honestly what the company is good at doing presently and where adjustments are needed to create better data decision-making. Evaluate Measure data literacy levels while implementing training and upskilling resources to facilitate the implementation of an open learning environment to encourage innovative thinking. Anticipate As the company's data capabilities develop, look forward to significantly advanced analytics possibilities. Calibrate Optimize technology and infrastructure to extract maximum value now while also appropriately planning for future resources. Future-Proof Your Business with Data Maturity Data maturity applies to the whole organization. It is a company-wide effort that extends beyond the goals of a single sales or marketing team. As a result, it's critical to bring together diverse internal influencers to determine how improvements to your data strategy can assist everyone in achieving the same objectives. The mix of stakeholders is unique to each organization, so it will be determined by your company's priorities.

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Spotlight

iauro systems Pvt Ltd

iauro Systems Pvt ltd focuses on Mobile, Cloud and BigData domains. iauro has delivered projects from single line of concept to finished market ready products. iauro also focuses on new, upcoming, disruptive technologies like IOT, iBeacons, Wearables, Data analytics, etc.

Related News

BIG DATA MANAGEMENT

Centers for Disease Control and Prevention (CDC)’s Data, Analytics, and Visualization Task Force Subcontract Awarded to Aveshka, Inc.

Aveshka, Inc. | July 12, 2021

Centers for Disease Control and Prevention (CDC) recently awarded Aveshka, a trusted provider of data analytics capabilities and subject matter expertise in the public health realm, a subcontract with Data, Analytics, and Visualization Task Force (DAVTF). Aveshka is currently serving as a subcontractor to Booz Allen Hamilton Program. Under the contract, Aveshka will continue its essential work in supporting the nation’s COVID-19 response. It currently supports agencies to include Departments of Justice, Defense, Health and Human Services, and Homeland Security; as well as federal financial agencies, state and local agencies, and numerous commercial clients on pandemic response. Aveshka's health scientists and scientific data analysts will support the DAVTF in its objective to identify, track, and monitor COVID-19 cases and its spread and containment. Aveshka’s constant work in the Covid-19 pandemic has been solidified with this award. Aveshka has multidisciplinary teams of specialists utilizing a tailored method to every engagement. Many organizations have sought Aveshka’s expertise and discipline to support the return to office efforts operational continuity, workforce protection, and readiness for future waves and other disruptive events. ABOUT AVESHKA, INC. Aveshka is a thought leader that integrates strategy, technology, and innovation to deliver cutting edge, breakthrough solutions that strengthen the nation against security threats. Aveshka accelerates the advantage for its customers' missions, rendering them protected today and prepared for a safer and smarter tomorrow.

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JV with Datavard and Pyramid Analytics Bridge Gap Between SAP and Modern Cloud Platforms

Datavard, Pyramid Analytics | July 28, 2020

Datavard, a recognized global leader in SAP data transformation solutions and services in Heidelberg, Germany, announces a partnership with Pyramid Analytics, an analytics and business intelligence software company headquartered in Amsterdam, Netherlands. The two companies join forces to help their customers bridge the gap between SAP and modern cloud platforms, enabling advanced data analytics, forecasting, AI, and machine learning scenarios. With Pyramid Analytics and Datavard, organizations can fundamentally consolidate and modernize their analytics and data capabilities on a single platform. Under the partnership, Pyramid Analytics gains the rights to re-sell Datavard’s flagship product—Datavard Glue®. Pyramid is an enterprise BI platform that supports the full analytics workflow, from data modeling to dashboards and reports. It provides deep support for organizations with SAP-based data infrastructures. Pyramid lets companies directly query their SAP BW and SAP HANA databases, and it supports interoperability with a vast array of data sources, both in the cloud and on-premises. When combined with Glue from Datavard, customers can blend specialized SAP data sets with external data to create powerful new insights.

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Brinker International Selects Teradata to Facilitate Advanced Analytics, Machine Learning and Data Science Capabilities

Brinker International, Teradata | July 23, 2020

Teradata, the cloud data and analytics company, today announced that after an evaluation of other cloud analytics offerings on the market, Brinker International, Inc. has reinvested with Teradata, leveraging the Teradata Vantage platform – delivered as-a-service, on Amazon Web Services (AWS) – as the core of its data foundation to facilitate advanced analytics, machine learning and data science across the organization. Brinker is one of the world's leading casual dining restaurant companies and has been a Teradata customer for more than two decades. Founded in 1975 and based in Dallas, Texas, Brinker owns, operates, or franchises more than 1,600 restaurants under the names Chili's® Grill & Bar and Maggiano's Little Italy®. Over the past year, Brinker has been working to further increase its capabilities in advanced analytics and data science.

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BIG DATA MANAGEMENT

Centers for Disease Control and Prevention (CDC)’s Data, Analytics, and Visualization Task Force Subcontract Awarded to Aveshka, Inc.

Aveshka, Inc. | July 12, 2021

Centers for Disease Control and Prevention (CDC) recently awarded Aveshka, a trusted provider of data analytics capabilities and subject matter expertise in the public health realm, a subcontract with Data, Analytics, and Visualization Task Force (DAVTF). Aveshka is currently serving as a subcontractor to Booz Allen Hamilton Program. Under the contract, Aveshka will continue its essential work in supporting the nation’s COVID-19 response. It currently supports agencies to include Departments of Justice, Defense, Health and Human Services, and Homeland Security; as well as federal financial agencies, state and local agencies, and numerous commercial clients on pandemic response. Aveshka's health scientists and scientific data analysts will support the DAVTF in its objective to identify, track, and monitor COVID-19 cases and its spread and containment. Aveshka’s constant work in the Covid-19 pandemic has been solidified with this award. Aveshka has multidisciplinary teams of specialists utilizing a tailored method to every engagement. Many organizations have sought Aveshka’s expertise and discipline to support the return to office efforts operational continuity, workforce protection, and readiness for future waves and other disruptive events. ABOUT AVESHKA, INC. Aveshka is a thought leader that integrates strategy, technology, and innovation to deliver cutting edge, breakthrough solutions that strengthen the nation against security threats. Aveshka accelerates the advantage for its customers' missions, rendering them protected today and prepared for a safer and smarter tomorrow.

Read More

JV with Datavard and Pyramid Analytics Bridge Gap Between SAP and Modern Cloud Platforms

Datavard, Pyramid Analytics | July 28, 2020

Datavard, a recognized global leader in SAP data transformation solutions and services in Heidelberg, Germany, announces a partnership with Pyramid Analytics, an analytics and business intelligence software company headquartered in Amsterdam, Netherlands. The two companies join forces to help their customers bridge the gap between SAP and modern cloud platforms, enabling advanced data analytics, forecasting, AI, and machine learning scenarios. With Pyramid Analytics and Datavard, organizations can fundamentally consolidate and modernize their analytics and data capabilities on a single platform. Under the partnership, Pyramid Analytics gains the rights to re-sell Datavard’s flagship product—Datavard Glue®. Pyramid is an enterprise BI platform that supports the full analytics workflow, from data modeling to dashboards and reports. It provides deep support for organizations with SAP-based data infrastructures. Pyramid lets companies directly query their SAP BW and SAP HANA databases, and it supports interoperability with a vast array of data sources, both in the cloud and on-premises. When combined with Glue from Datavard, customers can blend specialized SAP data sets with external data to create powerful new insights.

Read More

Brinker International Selects Teradata to Facilitate Advanced Analytics, Machine Learning and Data Science Capabilities

Brinker International, Teradata | July 23, 2020

Teradata, the cloud data and analytics company, today announced that after an evaluation of other cloud analytics offerings on the market, Brinker International, Inc. has reinvested with Teradata, leveraging the Teradata Vantage platform – delivered as-a-service, on Amazon Web Services (AWS) – as the core of its data foundation to facilitate advanced analytics, machine learning and data science across the organization. Brinker is one of the world's leading casual dining restaurant companies and has been a Teradata customer for more than two decades. Founded in 1975 and based in Dallas, Texas, Brinker owns, operates, or franchises more than 1,600 restaurants under the names Chili's® Grill & Bar and Maggiano's Little Italy®. Over the past year, Brinker has been working to further increase its capabilities in advanced analytics and data science.

Read More

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