Understanding Structured and Unstructured Data


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. In our modern digital world, proper use of data can play a huge role in a business’s success. Understanding data structure is a key to unlocking its value. Structured data.

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.

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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


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.

Differentiating Between Data Lakes and Data Warehouses

Smart Data Collective

The market for data warehouses is booming. While there is a lot of discussion about the merits of data warehouses, not enough discussion centers around data lakes. We talked about enterprise data warehouses in the past, so let’s contrast them with data lakes.

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


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.

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.

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.

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.

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.

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


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.

Text Analytics – Understanding the Voice of Consumers


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.

Data Lakes on Cloud & it’s Usage in Healthcare


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.

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. Among other issues, the conference proposed reducing healthcare costs through the use of big data and machine learning tools. Many experts presented data provided at the conference.

Mastering Data Variety


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.

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


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. Consequently, many data leaders today are striving to overcome these barriers by streamlining their enterprise knowledge management processes and practices. Enterprises need flexible systems that can evolve as their business and data evolve.

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Quantitative and Qualitative Data: A Vital Combination


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.

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. In this particular case, getting even small things wrong in your data has huge implications when using it. If you start here and believe that trustworthy data is core to any AI applications then you to address this gap first. EHR structured data.

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.

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.

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.

How to Avoid the 10 Big Data Analytics Blunders


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.

NeoBanks – The New Age Tech Revolutionizing AI in Banking


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.”. The rise of new kinds of data and analysis techniques from AI to machine learning that is actually driving the decisions behind the systems that can enable the so-called transformations. Digital transformation could mean different things to different businesses.

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


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.

Do I Need a Data Catalog?


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. Given the value this sort of data-driven insight can provide, the reason organizations need a data catalog should become clearer. This also diminishes the value of data as an asset.

Why Your Data Lineage is Incomplete Without an Automated Business Glossary


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. While some businesses suffer from “data translation” issues, others are lacking in discovery methods and still do metadata discovery manually.

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.

Top 10 Key Features of BI Tools in 2020


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. They prefer self-service development, interactive dashboards, and self-service data exploration. To put it bluntly, users increasingly want to do their own data analysis without having to find support from the IT department. Self-service data preparation.

What are Different Kinds of Data Visualization?


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.

Data Visualization and Visual Analytics: Seeing the World of Data


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. One of the downsides of the role that data now plays in the modern business world is that users can be overloaded with jargon and tech-speak, which can be overwhelming.

AML: Past, Present and Future – Part III


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.

The Data Journey: From Raw Data to Insights


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 organizations tackle the challenges of this new world to help their companies and their customers thrive.

Perficient Discusses Watson Solutions with IBM CEO

Perficient Data & Analytics

If I’m talking to a company that’s done analytics, they’ve probably already covered their structured data. But what are they doing with unstructured data like images, video, notes, and content? Organizations tend to focus on their area of expertise – analytics will focus on structured data and data warehousing; content management will focus on documents and metadata.

Fact or Fiction? Smart Data Visualization Tells the Tale


If you are considering a Business Intelligence solution, you ought to give some consideration to the concept of Smart Data Visualization and review your prospective solution to determine its capabilities in that regard. Smart Data Visualization provides many benefits to the organization and to the business users, who will leverage the selected BI tools to gather, analyze, share and report on data. How do users perceive and use data?