How Will The Cloud Impact Data Warehousing Technologies?

Smart Data Collective

sThe recent years have seen a tremendous surge in data generation levels , characterized by the dramatic digital transformation occurring in myriad enterprises across the industrial landscape. The amount of data being generated globally is increasing at rapid rates.

Research quality data and research quality databases

Simply Statistics

When you are doing data science, you are doing research. You want to use data to answer a question, identify a new pattern, improve a current product, or come up with a new product. The common factor underlying each of these tasks is that you want to use the data to answer a question that you haven’t answered before. That is why the key word in data science is not data, it is science. The data is the substrate you use to get the answers you care about.


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Big Data Ingestion: Parameters, Challenges, and Best Practices


Businesses are going through a major change where business operations are becoming predominantly data-intensive. quintillions of bytes of data are being created each day. This pace suggests that 90% of the data in the world is generated over the past two years alone. A large part of this enormous growth of data is fuelled by digital economies that rely on a multitude of processes, technologies, systems, etc. Data has grown not only in terms of size but also variety.

Introduction To The Basic Business Intelligence Concepts


“Without big data, you are blind and deaf and in the middle of a freeway.” – Geoffrey Moore, management consultant, and author. In a world dominated by data, it’s more important than ever for businesses to understand how to extract every drop of value from the raft of digital insights available at their fingertips. Your fast thinking system can take in massive amounts of data at once. They prevent you from drowning in data. quintillion bytes of data produced daily.

Seize The Power Of Analytical Reports – Business Examples & Templates

Datapine Blog

In recent years, analytical reporting has evolved into one of the world’s most important business intelligence components, compelling companies to adapt their strategies based on powerful data-driven insights. Our next data analysis report example comes in the form of our FMCG dashboard.

From Data Silos to Data Fabric with Knowledge Graphs


What Makes a Data Fabric? Data Fabric’ has reached where ‘Cloud Computing’ and ‘Grid Computing’ once trod. Data Fabric hit the Gartner top ten in 2019. This multiplicity of data leads to the growth silos, which in turns increases the cost of integration.

On the Hunt for Patterns: from Hippocrates to Supercomputers


The entire history and practice of modern medicine, argues Fry, is built on finding patterns in data. These are the so-called supercomputers, led by a smart legion of researchers and practitioners in the fields of data-driven knowledge discovery.

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. Semi-structured.

Perficient Discusses Watson Solutions with IBM CEO

Perficient Data & Analytics

IBM CEO, Ginni Rometty, hosted a panel with top Watson solution providers to discuss partner experiences with the Watson platform. 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?

Artificial Neural Networks: Solving Challenges in Health Sciences

Perficient Data & Analytics

Think of it as a computing system, structured as a series of layers, with each layer composed of one or more neurons. In an ANN the data is fed forward through the network layer-by-layer, until it reaches the final layer, and it is only when the data reaches the final layer’s activations that the network’s predictions are made. Because of the amount of data and learning involved, this method of training the network can take days or weeks to fully train the model.

Conversational AI: Design & Build a Contextual Assistant – Part 2

Sirius Computer Solutions

In this post, we’ll look at structuring happy and unhappy conversation paths, various machine learning policies and configurations to improve your dialogue model, and use a transfer learning-based language model to generate natural conversations. While this is pretty exciting, training the NLU model to identity generic entities like location is a time-consuming process that requires a lot of data. Once you’re done, you can save the training data and retrain your models.

Conversational AI: Design & Build a Contextual Assistant – Part 1

Sirius Computer Solutions

Level 5 and beyond : at this level, contextual assistants are able to monitor and manage a host of other assistants in order to run certain aspects of enterprise operations. Natural Language Understanding (NLU) is a subset of NLP that turns natural language into structured data. First, we feed an NLU model with labeled data that provides the list of known intents and example sentences that correspond to those intents.