Now companies are in the middle of a renovation that forces them to be analytics-driven to
continue being competitive. Data analysis provides a complete insight about their business. It
also gives noteworthy advantages over their competitors. Analytics-driven insights compel
businesses to take action on service innovation, enhance client experience, detect irregularities in
process and provide extra time for product or service marketing. To work on analytics driven
activities, companies require to gather, analyse and store information from all possible sources.
Companies should bring appropriate tools and workflows in practice to analyse data rapidly and
unceasingly. They should obtain insight from data analysis result and make changes in their
business process and practice on the basis of gained result. It would help to be more agile than
their previous process and function.
Closing the data source discovery gap and accelerating data discovery comprises three steps: profile, identify, and unify. This white paper discusses how the Attivio
platform executes those steps, the pain points each one addresses, and the value Attivio provides to advanced analytics and business intelligence (BI) initiatives.
Now companies are in the middle of a renovation that forces them to be analytics-driven to
continue being competitive. Data analysis provides a complete insight about their business. It
also gives noteworthy advantages over their competitors. Analytics-driven insights compel
businesses to take action on service innovation, enhance client experience, detect irregularities in
process and provide extra time for product or service marketing. To work on analytics driven
activities, companies require to gather, analyse and store information from all possible sources.
Companies should bring appropriate tools and workflows in practice to analyse data rapidly and
unceasingly. They should obtain insight from data analysis result and make changes in their
business process and practice on the basis of gained result. It would help to be more agile than
their previous process and function.
Closing the data source discovery gap and accelerating data discovery comprises three steps: profile, identify, and unify. This white paper discusses how the Attivio
platform executes those steps, the pain points each one addresses, and the value Attivio provides to advanced analytics and business intelligence (BI) initiatives.
Suburbia, Alternative Data Expert (FinTech), asked me to design their sales booklet. This is the outcome. The booklet was meant for their stakeholders.
Data Science is in high demand, the melting pot
of complex skills requires a qualified data scientist have made them the unicorns in today's data-driven landscape.
I volunteered my time to share about big data to those looking to understand the space.
This was for Networking with Grace, a group that is focused on helping those get back to work. I put this presentation together to help people learn about big data and how to transition their skill sets to the space.
Big data why big data is huge for CPG manufacturersJanet Dorenkott
CPG manufacturers need to understand big data and understand the value of big data. This presentation explains big data, the evolution of big data and how big data can be used.
What’s The Difference Between Structured, Semi-Structured And Unstructured Data?Bernard Marr
There are three classifications of data: structured, semi-structured and unstructured. While structured data was the type used most often in organizations historically, artificial intelligence and machine learning have made managing and analysing unstructured and semi-structured data not only possible, but invaluable.
This project is about "Big Data Analytics," and it provides a comprehensive overview of topics related to Data and Analytics and a short note on Cognitive Analytics, Sentiment Analytics, Data Visualization, Artificial intelligence & Data-Driven Decision Making along with examples and diagrams.
Suburbia, Alternative Data Expert (FinTech), asked me to design their sales booklet. This is the outcome. The booklet was meant for their stakeholders.
Data Science is in high demand, the melting pot
of complex skills requires a qualified data scientist have made them the unicorns in today's data-driven landscape.
I volunteered my time to share about big data to those looking to understand the space.
This was for Networking with Grace, a group that is focused on helping those get back to work. I put this presentation together to help people learn about big data and how to transition their skill sets to the space.
Big data why big data is huge for CPG manufacturersJanet Dorenkott
CPG manufacturers need to understand big data and understand the value of big data. This presentation explains big data, the evolution of big data and how big data can be used.
What’s The Difference Between Structured, Semi-Structured And Unstructured Data?Bernard Marr
There are three classifications of data: structured, semi-structured and unstructured. While structured data was the type used most often in organizations historically, artificial intelligence and machine learning have made managing and analysing unstructured and semi-structured data not only possible, but invaluable.
This project is about "Big Data Analytics," and it provides a comprehensive overview of topics related to Data and Analytics and a short note on Cognitive Analytics, Sentiment Analytics, Data Visualization, Artificial intelligence & Data-Driven Decision Making along with examples and diagrams.
Difference B/w Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data
The most popular and rapidly evolving technologies in the world are Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data. All firms, large and small, are increasingly looking for IT experts who can filter through the data and help with the efficient implementation of sound business decisions. In light of the current competitive environment, Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data are essential technologies that drive company growth and development. In this topic, “Difference Between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, And Big Data,” we will examine the key definitions and skills needed to obtain them. We will also examine the main differences between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data. So let’s start by briefly introducing each concept.
Data Analysis vs Data Analytics
Data Analysis is the process of analyzing, organizing, and manipulating a collection of data to extract relevant information. An “Analytics platform” is a piece of software that enables data and statistics to be generated and examined systematically, whereas a “business analyst” is a person who applies an analytical method to a collection of information for a specific goal. As this is becoming increasingly popular the corporate sector has started to broadly accept it. Data Analysis makes it easy to understand the data. It provides an important historical context for understanding what has occurred recent past. To master Power BI check out Power BI Online Course
Data Analytics includes both decision-making processes and performance enhancement through relevant forecasts. Businesses may utilize data analytics to enhance business decisions, evaluate market trends, and analyze customer satisfaction, all of which can lead to the creation of new, enhanced products and services. Using Data Analytics, it is possible to make more accurate forecasts for the future by examining previous data. To master Data Analytics Skills visit Data Analytics Course in Pune
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Data Analytics
Data Analysis
Data Analytics is analytics that is used to make conclusions based on data.
Data Analysis is a subset of data analytics that is used to analyze data and derive specific insights from it.
Using historical data and customer expectations, businesses may develop a solid business strategy.
Making the most of historical data helps organizations identify new possibilities promote business growth and make more effective decisions.
The term “data analytics” refers to the collecting and assessment of data that involves one or more users.
This talk is an introduction to Data Science. It explains Data Science from two perspectives - as a profession and as a descipline. While covering the benefits of Data Science for business, It explaints how to get started for embracing data science in business.
Whether you believe into the hype around Big Data's affirmation to transform business, it is true that learning how to use the present deluge of data can help you make better decisions. Thanks to big data technologies, everything can now be used as data, giving you unparalleled access to market determinants. Contact V2Soft's Big Data Solutions if you wish to implement big data technology in your business and need help getting started. https://bit.ly/2kmiYFp
Big data refers to the vast amount of structured and unstructured data that inundates organizations on a daily basis. This data comes from various sources such as social media, sensors, digital transactions, mobile devices, and more.
Data is not consistent, sometimes searches or general interest in certain topics, say social media or other types of data experienced peaks and valleys. Data analysis techniques allow the data scientist to mine this type of unstable data and still draw meaningful conclusions from it.
March Towards Big Data - Big Data Implementation, Migration, Ingestion, Manag...Experfy
Gartner, IBM, Accenture and many others have asserted that 80% or more of the world’s information is unstructured – and inherently hard to analyze. What does that mean? And what is required to extract insight from unstructured data?
Unstructured data is infinitely variable in quality and format, because it is produced by humans who can be fastidious, unpredictable, ill-informed, or even cynical, but always unique, not standard in any way. Recent advances in natural language processing provides the notion that unstructured content can be included in data analysis.
Serious growth and value companies are committed to data. The exponential growth of Big Data has posed major challenges in data governance and data analysis. Good data governance is pivotal for business growth.
Therefore, it is of paramount importance to slice and dice Big Data that addresses data governance and data analysis issues. In order to support high quality business decision making, it is important to fully harness the potential of Big Data by implementing proper Data Migration, Data Ingestion, Data Management, Data Analysis, Data Visualization and Data Virtualization tools.
Check it out: https://www.experfy.com/training/courses/march-towards-big-data-big-data-implementation-migration-ingestion-management-visualization
Most of what companies know is typically held
in a data warehouse – a database that collects transactions and looks at customer transaction activity over time to understand who is buying what through which channel.
What is big data?
Big data is a mix of structured, semi-structured, and unstructured data gathered by organizations that can be dug for data and used in machine learning projects, predictive modeling, and other advanced analytics applications.
Systems that process and store big data have turned into a typical part of data the board architectures in organizations, joined with tools that support big data analytics uses. Big data is regularly portrayed by the three V's:
the enormous volume of data in numerous environments; • the wide variety of data types regularly stored in big data systems, and
the velocity at which a significant part of the data is created, gathered and processed.
These characteristics were first recognized in 2001 by Doug Laney, then, at that point, an analyst at consulting firm Meta Group Inc.; Gartner further promoted them after it gained Meta Group in 2005. All the more as of late, several other V's have been added to various descriptions of big data, including veracity, value and variability.
Albeit big data doesn't liken to a specific volume of data, big data deployments frequently involve terabytes, petabytes, and even exabytes of data made and gathered over time.
Big data is a mix of structured, semistructured, and unstructured data gathered by organizations that can be dug for data and used in machine learning projects,
Small data vs. Big data : back to the basicsAhmed Banafa
Small data is data in a volume and format that makes it accessible, informative and actionable.
The Small Data Group offers the following explanation:
Small data connects people with timely, meaningful insights (derived from big data and/or “local” sources), organized and packaged – often visually – to be accessible, understandable, and actionable for everyday tasks.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Generating a custom Ruby SDK for your web service or Rails API using Smithy
What is big data
1. What Is Big Data ?
By Ashwin Pednekar
Email : ashwinpednekar@gmail.com
2. Agenda
• Introduction to Big Data ( What’s so Big about Big Data ? )
• Understanding Big Data
• Use and Benefits of Big Data
• Technologies used in Big Data
• Famous Quotes about Big Data
3. What is so Big about Big Data ?
• We have heard big data defined in many, many
different ways, and so, I’m not surprised there’s
so much confusion surrounding the term.
Because of all the misunderstanding and
misperceptions
• Big data is a collection of data from traditional
and digital sources inside and outside your
company that represents a source for ongoing
discovery and analysis
5. Enterprise need to Fully understand Big Data
• what it is to them,
• what is does for them
• what it means to them
Understand Data Itself :
• Structured Data
• Unstructured Data
6. Structured Data :
Structured data refers to information with a high degree of
organization, such that inclusion in a relational database is
seamless and readily searchable by simple, straightforward search
engine algorithms or other search operations
Unstructured Data :
Unstructured data usually refers to information that doesn't
reside in a traditional row-column database and not organized or
Structured Logically
Examples include e-mail messages, word processing documents,
videos
7. The management of unstructured data is recognized as one of the
major unsolved problems in the information technology (IT)
industry, the main reason being that the tools and techniques that
have proved so successful transforming structured data into
business intelligence and actionable information simply don't
work when it comes to unstructured data. New approaches are
necessary.
8. Many organizations are missing out on what data experts agree is an opportunity to derive significant business
value from properly harnessing unstructured data. IDC, estimates that unstructured content already accounts
for a staggering 90 percent of all digital data, much of which is locked away across a variety of different data
stores, in different locations and in varying formats.
Unstructured data can help companies gain a better understanding of their customers, products, services and
business in general. For example, data from Twitter streams, social media networks and web logs can help a
company gauge customer sentiment toward a product or service, or help identify and address a potential service
or quality issue before it becomes a full-fledged problem. Combining existing data about customers from
transactional systems with data gathered about them from other sources can help an organization get closer to a
360-degree view of its customers.
And an Answer to achieve this is “Big Data” Technologies and Methods
10. Today’s consumers are a tough nut to crack. They look around a lot before they buy, talk to their entire social
network about their purchases, demand to be treated as unique and want to be sincerely thanked for buying
your products. Big Data allows you to profile these increasingly vocal and fickle little ‘tyrants’ in a far-reaching
manner so that you can engage in an almost one-on-one, real-time conversation with them. This is not
actually a luxury. If you don’t treat them like they want to, they will leave you in the blink of an eye.
Just a small example: when any customer enters a bank, Big Data tools allow the clerk to check his/her profile
in real-time and learn which relevant products or services (s)he might advise. Big Data will also have a key role
to play in uniting the digital and physical shopping spheres: a retailer could suggest an offer on a mobile
carrier, on the basis of a consumer indicating a certain need in the social media
11. Big Data can also help you understand how others perceive
your products so that you can adapt them, or your marketing,
if need be. Analysis of unstructured social media text allows
you to uncover the sentiments of your customers and even
segment those in different geographical locations or among
different demographic groups.
Success not only depends on how you run your company.
Social and economic factors are crucial for your
accomplishments as well. Predictive analytics, fueled by Big
Data allows you to scan and analyze newspaper reports or
social media feeds so that you permanently keep up to speed
on the latest developments in your industry and its
environment. Detailed health-tests on your suppliers and
customers are another goodie that comes with Big Data. This
will allow you to take action when one of them is in risk of
defaulting.
12. The insights that you gain from analyzing your market and its consumers with Big Data are not just valuable to
you. You could sell them as non-personalized trend data to large industry players operating in the same segment
as you and create a whole new revenue stream.
One of the more impressive examples comes from Shazam, the song identification application. It helps
record labels find out where music sub-cultures are arising by monitoring the use of its service, including
the location data that mobile devices so conveniently provide. The record labels can then find and sign
up promising new artists or remarket their existing ones accordingly.
Previously, if business users needed to analyze large amounts of varied data, they had to ask their IT colleagues
for help as they themselves lacked the technical skills for doing so. Often, by the time they received the
requested information, it was no longer useful or even correct. With Big Data tools, the technical teams can do
the groundwork and then build repeatability into algorithms for faster searches. In other words, they can
develop systems and install interactive and dynamic visualization tools that allow business users to analyze, view
and benefit from the data
14. Hadoop :
An open source (free) software framework for processing huge datasets on
certain kinds of problems on a distributed system. Its development was
inspired by Google’s MapReduce and Google File System. It was originally
developed at Yahoo! and is now managed as a project of the Apache
Software Foundation
R Programming:
An open source (free) programming language and software
environment for statistical computing and graphics. The R
language has become a de facto standard among statisticians for
developing statistical software and is widely used for statistical
software development and data analysis. R is part of the GNU
Project, a collaboration that supports open source projects.
15. Spark :
Apache Spark is a fast and general-purpose cluster computing
system designed for processing data in parallel at a large scale
Python NLTK : is a leading platform for building Python
programs to work with human language data. It provides easy-to-
use interfaces to over 50 corpora and lexical resources, along with
a suite of text processing libraries for classification, tokenization,
stemming, tagging, parsing, and semantic reasoning.
MongoDB : is a cross-platform document-oriented database that
stores data into JSON-like documents.
There are many such tools used for Data Analytics , Data mining , Visual and Statistical Analysis .
Big Data is huge ecosystem of such tools and technologies