Understanding Structured and Unstructured Data

Sisense

We live in a world of data: there’s more of it than ever before, in a ceaselessly expanding array of forms and locations. Dealing with Data is your window into the ways Data Teams are tackling the challenges of this new world to help their companies and their customers thrive.

PAN card fraud detection using computer vision

Analytics Vidhya

ArticleVideo Book This article was published as a part of the Data Science Blogathon What is Computer Vision? Advanced Computer Vision Image Image Analysis Programming Project Python Structured Data Unstructured Data blogathon PAN card fraud detection

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What is Big Data? Introduction, Uses, and Applications.

Analytics Vidhya

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction We produce a massive amount of data each day, whether. The post What is Big Data? Beginner Big data Structured Data Unstructured Data 5 vs of big data Applications blogathon

Creating a Big Data Platform Roadmap

Perficient Data & Analytics

One of the most frequently asked questions by our customers is the roadmap to deploying a Big Data Platform and becoming a truly data-driven enterprise. Just as you can’t build a house without a foundation, you can’t start down a big data path without first establishing groundwork for success. There are several key steps to prepare the organization to realize the benefits of a big data solution with both structured and unstructured data.

How to Gain Valuable Insights from Untapped Data Using AI

Perficient Data & Analytics

You probably know your organization needs to invest in artificial intelligence (AI) solutions to take advantage of the deluge of data that mobile and digital users are creating, but do you know why or how? LEGACY ANALYTICS METHODS AREN’T EQUIPPED TO PROCESS ALL DATA TYPES. The majority of data is unstructured (around 80%) which means it isn’t clearly defined or easily searchable the way that structured data is. LEVERAGE YOUR DATA WITH AI.

A Comprehensive Guide to Natural Language Generation

DataFloq

In its essence, it automatically generates narratives that describe, summarize or explain input structured data in a human-like manner at the speed of thousands of pages per second. The part of NLP that reads human language and turns its unstructured data into structured data understandable to computers is called Natural Language Understanding.

NLP vs. NLU: from Understanding a Language to Its Processing

DataFloq

They both attempt to make sense of unstructured data, like language, as opposed to structured data like statistics, actions, etc. However, NLP and NLU are opposites of a lot of other data mining techniques. NLP is an already well-established, decades-old field operating at the cross-section of computer science, artificial intelligence, and increasingly data mining.

Text Analytics – Understanding the Voice of Consumers

BizAcuity

Text analytics helps to draw the insights from the unstructured data. . The key factor for the prosperity of the Hotel is service, online reviews & experience, using the information technology organizations are capturing the data to develop the latest techniques using data analytics to survive the competition. Text Analytics – is a process of turning unstructured text – available in the form of tweets, comments, reviews, etc.

Key Differences between a Traditional Data Warehouse and Big Data

Perficient Data & Analytics

Traditional data warehouse solutions were originally developed out of necessity. The data captured from these traditional data sources is stored in relational databases comprised of tables with rows and columns and is known as structured data. So how do you make the data gathered more useful? This process begins with data consolidation tools like Informatica or Oracle Data Integrator. What is Big Data? Multi-Structured Data.

Big Data Analytics Has Potential to Massively Disrupt the Stock Market

Smart Data Collective

Big data is changing the nature of the financial industry in countless ways. The market for data analytics in the banking industry alone is expected to be worth $5.4 However, the impact of big data on the stock market is likely to be even greater.

How a Discovery Data Warehouse, the next evolution of augmented analytics, accelerates treatments and delivers medicines safely to patients in need

Cloudera

The challenges Matthew and his team are facing are mainly about access to a multitude of data sets, of various types and sources, with ease and ad-hoc, and their ability to deliver data-driven and confident outcomes. . Protect data and create trust in providers.

Deep Learning Would Be Crucial Under Sanders’s Medicare for All System

Smart Data Collective

He should elaborate more on the benefits of big data and deep learning. A lot of big data experts argue that deep learning is key to controlling costs. Health IT Analytics wrote an article on the cost benefits of using big data in healthcare.

A Look at Data Entities and BYOD for Accountants

Jet Global

Financial and operational reports retrieve master data and transactional information from your ERP databases using something called “SQL.” That stands for “structured query language.” (Don’t We refer to the first as “data entities.” Introducing Data Lakes.

Knowledge Graphs and Healthcare

Ontotext

A) The healthcare industry has the advantage of an enormous amount of data upon which to create hypotheses for new treatments. B) The healthcare industry has the disadvantage of an enormous amount of data upon which to create hypotheses for new treatments.

New Software Development Initiatives Lead To Second Stage Of Big Data

Smart Data Collective

The big data market is expected to be worth $189 billion by the end of this year. A number of factors are driving growth in big data. Demand for big data is part of the reason for the growth, but the fact that big data technology is evolving is another. New software is making big data more viable than ever. As new software development initiatives become more mainstream, big data will become more viable than ever. Structured. Unstructured.

Data Lakes on Cloud & it’s Usage in Healthcare

BizAcuity

Data lakes are centralized repositories that can store all structured and unstructured data at any desired scale. Data can be stored as-is, without first structuring it, and different types of analytics can be run on it, from dashboards and visualizations to big data processing, real-time analytics, and machine learning to improve decision making. The power of the data lake lies in the fact that it often is a cost-effective way to store data.

Text Analytics – Understanding the Voice of Consumers

BizAcuity

Text analytics helps to draw the insights from the unstructured data. The key factor for the prosperity of the Hotel is service, online reviews & experience, using the information technology organizations are capturing the data to develop the latest techniques using data analytics to survive the competition. Text Analytics – is a process of turning unstructured text – available in the form of tweets, comments, reviews, etc.

AI Summit: Tagging Medical Records Create Vital Data and Analytics

Perficient Data & Analytics

Doug Kemp of Innodata spoke about getting key medical data into a form that can be understood and used. Like many things, components of AI can parse and understand this data but it can’t be done by itself. 85% of those that will fail due to data issues.

Ontotext Knowledge Graph Platform: The Modern Way of Building Smart Enterprise Applications

Ontotext

According to an article in Harvard Business Review , cross-industry studies show that, on average, big enterprises actively use less than half of their structured data and sometimes about 1% of their unstructured data.

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Complexity Drives Costs: A Look Inside BYOD and Azure Data Lakes

Jet Global

The Data Security Problem: How We Got Here. You can extract data from relational databases, including Microsoft’s SQL Server using the SQL query language. In addition to reading data, however, you can also use the SQL language to insert, update, or delete records from a database.

Throwing Your Data Into the Ocean

Ontotext

‘Don’t Reinvent the Wheel’ Data analysis is an example where time and effort are being spent over and over only for the data and development to be chucked into the ocean after the work is done. After that, the data needs to be cleaned.

How to get powerful and actionable insights from any and all of your data, without delay

Cloudera

Today’s data tool challenges. A large oil and gas company was suffering over not being able to offer users an easy and fast way to access the data needed to fuel their experimentation. They were not able to quickly and easily query and analyze huge amounts of data as required.

The Differences Between Data Warehouses and Data Lakes

Sisense

The amount of data being generated and stored every day has exploded. Companies of all kinds are sitting on stockpiles of data that could someday prove valuable. Instead, businesses are increasingly turning to data lakes to store massive amounts of unstructured data.

Mastering Data Variety

Tamr

Data variety — the middle child of the three Vs of Big Data — is in big trouble. . It’s in the critical path of enterprise data becoming an asset. And it’s been slow to benefit from the kind of technology advancements experienced by its “easier” siblings, data volume and data velocity. Meanwhile, most enterprises have unconsciously built up extreme data variety over the last 50+ years. Structured data” may sound like it’s organized.

Do I Need a Data Catalog?

erwin

If you’re serious about a data-driven strategy , you’re going to need a data catalog. Organizations need a data catalog because it enables them to create a seamless way for employees to access and consume data and business assets in an organized manner.

Top 10 Analytics Trends for 2019

Timo Elliott

We’ve reached the third great wave of analytics, after semantic-layer business intelligence platforms in the 90s and data discovery in the 2000s. They bring insights to users rather than forcing users to unearth elusive trends, and provide more intuitive interfaces that make it easier to get the data people need to do their jobs. These data-driven, self-learning business processes improve automatically over time and as people use them.

NeoBanks – The New Age Tech Revolutionizing AI in Banking

bridgei2i

The firm has a new personal finance app Mimo, which uses open-banking application programming interfaces, artificial intelligence (AI) and data analytics to create a social feed that helps customers manager their money.”.

Governance in Healthcare: Big Data is Table Stakes

Perficient Data & Analytics

Big data itself does not alter the approach to governance nor its framework. And big data isn’t just about data – it’s also concerned with managing and governing vast amounts of content of varying types such as video, images, voice, etc. However, big data, by its sheer volume as well as its predominately unstructured nature, requires governance to adjust and adapt the actual components that are put in place to both effectively control it and extract value out of it.

Quantitative and Qualitative Data: A Vital Combination

Sisense

Digging into quantitative data Why is quantitative data important What are the problems with quantitative data Exploring qualitative data Qualitative data benefits Getting the most from qualitative data Better together. Digging into quantitative data.

How to Avoid the 10 Big Data Analytics Blunders

Tamr

Leading organizations are leveraging an analytics-driven approach—fueled and informed by data—to achieve marketplace advantages and create entirely new business models. Blunder #3: Not solving your real data science problem: dirty data.

Why Your Data Lineage is Incomplete Without an Automated Business Glossary

Octopai

Like NASA’s Spacecraft Needed The Right Combination of Capabilities to Achieve Full Power, BI Teams Need Automated Data Lineage, Data Discovery and an Automated Business Glossary All Working Together for Better Collaboration, Context & Understanding of How You’re Using Your Data.

Approaches to Embrace Big Data

Perficient Data & Analytics

Not every organization starts its big data journey from the same place. However, in order to drive efficiencies, support expected future growth and to continue its evolution to a data-driven company, most organizations are reviewing their current suite of software solutions, platforms and documenting processes and areas of improvement along with devising and executing a strategy to deploy modern business intelligence capabilities. Baby Steps from EDW to Big Data.

Doing a 180 on Customer 360 – The Preferred Path to Customer Insights

Cloudera

The abundant growth of data, maturation of machine algorithms, and future regulatory compliance demands from the European Union’s General Data Protection Regulation (GDPR) will shift the landscape for creating a single source of the truth for customer data. Structured data from operational data stores now provides a small slice of the overall data needed to improve customer experience.

2020 Data Impact Award Winner Spotlight: Merck KGaA

Cloudera

The Data Security and Governance category, at the annual Data Impact Awards, has never been so important. Finally, throw in the constant stream of cyberthreats out there and it’s clear that protecting your enterprise’s data is vital.

Navigating Data Entities, BYOD, and Data Lakes in Microsoft Dynamics

Jet Global

Consultants and developers familiar with the AX data model could query the database using any number of different tools, including a myriad of different report writers. Data Entities. Currently, over 1,700 data entities are available and counting. The Data Warehouse Approach.

Top 10 Key Features of BI Tools in 2020

FineReport

Both the investment community and the IT circle are paying close attention to big data and business intelligence. Overall, as users’ data sources become more extensive, their preferences for BI are changing. Explore and analyze data with a series of common and special charts.

What is Big Data Analytics?

Mixpanel on Data

Companies use big data analytics to uncover new and exciting insights in large and varied datasets. It helps them forecast market trends, identify hidden correlations between data flows, and understand their customers’ preferences in fine detail. But big data requires special infrastructure and a respect for the data science process. Big data analytics isn’t a synonym for data science, though the two are often confused. Big data also moves fast.

Data Visualization and Visual Analytics: Seeing the World of Data

Sisense

Everyone wants to get more out of their data, but how exactly to do that can leave you scratching your head. In a world increasingly dominated by data, users of all kinds are gathering, managing, visualizing, and analyzing data in a wide variety of ways.

Transforming Big Data into Actionable Intelligence

Sisense

Attempting to learn more about the role of big data (here taken to datasets of high volume, velocity, and variety) within business intelligence today, can sometimes create more confusion than it alleviates, as vital terms are used interchangeably instead of distinctly.

Building Better Data Models to Unlock Next-Level Intelligence

Sisense

You can’t talk about data analytics without talking about data modeling. The reasons for this are simple: Before you can start analyzing data, huge datasets like data lakes must be modeled or transformed to be usable. What is data modeling?

What are Different Kinds of Data Visualization?

FineReport

What is Data Visualization? Data visualization provides clear, fast and effective communication according to graphical means. From the user’s point of view, data visualization allows users to quickly grasp the key points of information, which can help them make better and wiser decisions. A good data visualization generally has the following characteristics: Accuracy. 5 Data Visualization Methods Commonly Used. a: What is Unstructured Data?

Test principles – Data Warehouse vs Data Lake vs Data Vault

Perficient Data & Analytics

Understand Data Warehouse, Data Lake and Data Vault and their specific test principles. This blog tries to throw light on the terminologies data warehouse, data lake and data vault. It will give insight on their advantages, differences and upon the testing principles involved in each of these data modeling methodologies. Let us begin with data warehouse. What is Data Warehouse? The Reporting layer helps users retrieve data.

AML: Past, Present and Future – Part III

Cloudera

The system must: Ingest, process, analyze, store, and serve all types of AML data, be it structured (database tables), unstructured (contracts, e-mails, etc.), Handle increases in data volume gracefully. Support machine learning (ML) algorithms and data science activities, to help with name matching, risk scoring, link analysis, anomaly detection, and transaction monitoring. Provide audit and data lineage information to facilitate regulatory reviews.