Ian  Robinson

Ian Robinson

1623970020

Operational Analytics: Building a Real-time Data Environment for Business

Disruptive technologies, cloud computing and IoT devices continue to evolve and proliferate. As a result, businesses are generating and collecting more data than ever before. However, the challenge here is not gathering the data, but using it in the right way. Businesses are leveraging futuristic analytics features to better understand the data. One such solution is operational analytics.

Data is exponentially increasing every movement. Every time a customer interacts with a website or device, an unimaginable amount of data is generated. Meanwhile, when employees use a company-issued tablet or device to do their jobs, they add more data to the company’s data house. The data goes useless if it is not utilized properly. Henceforth, businesses are adopting operational analytics to increase workplace efficiency, driving competitive advantages, and delighting customers. Operational analytics is at the beginning of gaining ground in the business industry. A survey conducted by Capgemini Consulting on around 600 executives from the US, Europe and China suggests that over 70% of organizations now put more emphasis on operations than on consumer-focused processes for their analytics initiatives. However, only 39% of organizations in the survey said they have extensively integrated their operational analytics initiatives with their business processes and barely 29% of them have successfully achieved their desired objectives from their initiatives.

#big data #data management #latest news #operational analytics: building a real-time data environment for business #operational analytics #building a real-time data environment for business

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Operational Analytics: Building a Real-time Data Environment for Business
Ian  Robinson

Ian Robinson

1623970020

Operational Analytics: Building a Real-time Data Environment for Business

Disruptive technologies, cloud computing and IoT devices continue to evolve and proliferate. As a result, businesses are generating and collecting more data than ever before. However, the challenge here is not gathering the data, but using it in the right way. Businesses are leveraging futuristic analytics features to better understand the data. One such solution is operational analytics.

Data is exponentially increasing every movement. Every time a customer interacts with a website or device, an unimaginable amount of data is generated. Meanwhile, when employees use a company-issued tablet or device to do their jobs, they add more data to the company’s data house. The data goes useless if it is not utilized properly. Henceforth, businesses are adopting operational analytics to increase workplace efficiency, driving competitive advantages, and delighting customers. Operational analytics is at the beginning of gaining ground in the business industry. A survey conducted by Capgemini Consulting on around 600 executives from the US, Europe and China suggests that over 70% of organizations now put more emphasis on operations than on consumer-focused processes for their analytics initiatives. However, only 39% of organizations in the survey said they have extensively integrated their operational analytics initiatives with their business processes and barely 29% of them have successfully achieved their desired objectives from their initiatives.

#big data #data management #latest news #operational analytics: building a real-time data environment for business #operational analytics #building a real-time data environment for business

Ian  Robinson

Ian Robinson

1621644000

4 Real-Time Data Analytics Predictions for 2021

Data management, analytics, data science, and real-time systems will converge this year enabling new automated and self-learning solutions for real-time business operations.

The global pandemic of 2020 has upended social behaviors and business operations. Working from home is the new normal for many, and technology has accelerated and opened new lines of business. Retail and travel have been hit hard, and tech-savvy companies are reinventing e-commerce and in-store channels to survive and thrive. In biotech, pharma, and healthcare, analytics command centers have become the center of operations, much like network operation centers in transport and logistics during pre-COVID times.

While data management and analytics have been critical to strategy and growth over the last decade, COVID-19 has propelled these functions into the center of business operations. Data science and analytics have become a focal point for business leaders to make critical decisions like how to adapt business in this new order of supply and demand and forecast what lies ahead.

In the next year, I anticipate a convergence of data, analytics, integration, and DevOps to create an environment for rapid development of AI-infused applications to address business challenges and opportunities. We will see a proliferation of API-led microservices developer environments for real-time data integration, and the emergence of data hubs as a bridge between at-rest and in-motion data assets, and event-enabled analytics with deeper collaboration between data scientists, DevOps, and ModelOps developers. From this, an ML engineer persona will emerge.

#analytics #artificial intelligence technologies #big data #big data analysis tools #from our experts #machine learning #real-time decisions #real-time analytics #real-time data #real-time data analytics

Ian  Robinson

Ian Robinson

1625011740

Real-Time Data Analytics: Guiding and Improving Business Decisions

Real-time data analytics help in improving business operations by analyzing and processing data chunks to provide instant insights.

Data, also known as the digital currency, is the fuel for modern businesses. The present-day enterprises are constantly bombarded with a humongous amount of data, which needs to be collected, processed, and analyzed. Hence, it is difficult to deliver useful business outcomes instantly. Real-time data analytics resolves the time lag between data collection and processing.

Gartner defines real-time analytics as, “the discipline that applies logic and mathematics to data to provide insights for making better decisions quickly. For some use cases, real-time simply means the analytics is completed within a few seconds or minutes after the arrival of new data.”

Accuracy and speed are crucial in data analytics. The modern business world needs real-time data analytics to efficiently deliver information, minimize costs and downtimes, and improve business decisions.

Benefits of Real-Time Data Analytics

#big data #latest news #real-time data analytics #improving business decisions #guiding #real-time data analytics: guiding and improving business decisions

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Big Data Analytics: Unrefined Data to Smarter Business Insights - TopDevelopers.co

For Big Data Analytics, the challenges faced by businesses are unique and so will be the solution required to help access the full potential of Big Data.
Let’s take a look at the Top Big Data Analytics Challenges faced by Businesses and their Solutions.

#big data analytics challenges #big data analytics #data management #data analytics strategy #business solutions by big data #top big data analytics companies