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 Layman’s Guide to Data Science: How to Become a (Good) Data Scientist


How simple is Data Science? Sometimes when you hear data scientists shoot a dozen of algorithms while discussing their experiments or go into details of Tensorflow usage you might think that there is no way a layman can master Data Science.

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.

Building tools for enterprise data science

O'Reilly on Data

The O’Reilly Data Show Podcast: Vitaly Gordon on the rise of automation tools in data science. In this episode of the Data Show , I spoke with Vitaly Gordon , VP of data science and engineering at Salesforce. Continue reading Building tools for enterprise data science

How Artificial Intelligence Will Disrupt the Financial Sector


Artificial intelligence thrives with data. The more data you have, the better your algorithms will be. However, just having a lot of data is not sufficient anymore. More data beats clever algorithm, but better data beats more data." - Peter Norvig - Director of Research, Google.

Snowflake: A New Blueprint for the Modern Data Warehouse

Sirius Computer Solutions

Companies today are struggling under the weight of their legacy data warehouse. These old and inefficient systems were designed for a different era, when data was a side project and access to analytics was limited to the executive team. Data migration and integration.

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. That is why the key word in data science is not data, it is science.

Glossary of Digital Terminology for Career Relevance

Rocket-Powered Data Science

AGI (Artificial General Intelligence): AI (Artificial Intelligence): Application of Machine Learning algorithms to robotics and machines (including bots), focused on taking actions based on sensory inputs (data). Analytics: The products of Machine Learning and Data Science (such as predictive analytics, health analytics, cyber analytics). pattern detection and pattern recognition in data). NLG is a software process that transforms structured data into human-language content.

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.

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. Your fast thinking system can take in massive amounts of data at once. They prevent you from drowning in data. The data warehouse.

Investment Company Reporting Modernization Goals & Expectations

Perficient Data & Analytics

Ease of access, aggregation, and analysis of the reported data by the Commission and the public. New forms you must submit: Form N-PORT: Requires investment companies to report portfolio information monthly in a structured data format. Form N-CEN: Requires investment companies to report census-type information annually in a structured data format.

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. The power of the data lake lies in the fact that it often is a cost-effective way to store data. Deploying Data Lakes in the cloud.

Interview with Dominic Sartorio, Senior Vice President for Products & Development, Protegrity


Ahead of the Chief Data Analytics Officers & Influencers, Insurance event we caught up with Dominic Sartorio, Senior Vice President for Products & Development, Protegrity to discuss how the industry is evolving. Why should Chief Data & Analytics Officers care about data security?

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. These data-driven, self-learning business processes improve automatically over time and as people use them.

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. Benefits of big data analytics.

Deep automation in machine learning

O'Reilly on Data

We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline. We won’t be writing code to optimize scheduling in a manufacturing plant; we’ll be training ML algorithms to find optimum performance based on historical data.

Here’s How To Implement Manufacturing Analytics Today

Smart Data Collective

Big data is everywhere , and it’s finding its way into a multitude of industries and applications. One of the most fascinating big data industries is manufacturing. In an environment of fast-paced production and competitive markets, big data helps companies rise to the top and stay efficient and relevant. Manufacturing innovation has long been an integral piece of our economic success, and it seems that big data allows for great industry gains.

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.

Evolution of AI in Sales (Part 3)


Traditional algorithmic solutions around structured data have gained and continue to gain traction. A strong case study where a global MNC used AI enabled deal classification, data input enhancement, account analytics, and external data feeds to transform their sales organization in Nigeria resulting in “a substantial increase in sales productivity and forecasted Y-o-Y revenue growth.” Voice data quality). Examples of AI Solutions Usage.

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Evolution of AI in Sales (Part 2)


Victor Antonio, in his book, “Sales Ex Machina: How Artificial Intelligence is Changing the World of Selling”, writes that today’s AI algorithms can look at structured data points like size of the deal, product specifications, compliance, number of competitors, company size, territory/region, client’s industry, client’s annual revenues, public or private company, level of decision-makers and influencers involved, timing etc.

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Different Types of Databases for Modern Data Challenges


We live in an era of Big Data. The sheer volume of data currently existing is huge enough without also grappling with the amount of new information that’s generated every day. It’s no wonder, then, that NoSQL databases are seeing a lot of use in Big Data and real-time web apps.

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.

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.

A Guide to CCPA Compliance and How the California Consumer Privacy Act Compares to GDPR


California Consumer Privacy Act (CCPA) compliance shares many of the same requirements in the European Unions’ General Data Protection Regulation (GDPR). Data governance , thankfully, provides a framework for compliance with either or both – in addition to other regulatory mandates your organization may be subject to. Collects, sells or shares the personal data of 50,000 or more consumers, households or devices. Clinical trial data.

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

Sirius Computer Solutions

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.

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.

Why You Need a Data Catalog & How to Choose One


If the point of Business Intelligence (BI) data governance is to leverage your datasets to support information transparency and decision-making, then it’s fair to say that the data catalog is key for your BI strategy. At least, as far as data analysis is concerned. The right data catalog tool can be a powerful complement to your existing BI processes. The Benefits of Structured Data Catalogs. Choosing a Data Catalog. Smart Data Catalog Tools.

Intel and Cloudera collaborate to bring improved performance to customers with Optane DC Persistent Memory


Cloudera and Intel have a long history of innovation, driving big data analytics and machine learning into the enterprise with unparalleled performance and security. When in memory mode, the data is not saved in the event of a power loss.

Using Artificial Intelligence to Make Sense of IoT Data


IoT is basically an exchange of data or information in a connected or interconnected environment. As IoT devices generate large volumes of data, AI is functionally necessary to make sense of this data. Data is only useful when it is actionable for which it needs to be supplemented with context and creativity. Traditional methods of analyzing structured data are not designed to efficiently process these large amounts of real-time data that is collected from IoT devices.

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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. Let us begin with data warehouse. What is Data Warehouse?

Themes and Conferences per Pacoid, Episode 7

Domino Data Lab

Paco Nathan covers recent research on data infrastructure as well as adoption of machine learning and AI in the enterprise. Welcome back to our monthly series about data science! Evolving Data Infrastructure: Tools and Best Practices for Advanced Analytics and AI (Jan 2019).

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. Provide audit and data lineage information to facilitate regulatory reviews.

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?

In-depth with CDO Christopher Bannocks

Peter James Thomas

Today I am talking to Christopher Bannocks , who is Group Chief Data Officer at ING. As stressed in other recent In-depth interviews [1] , data is a critical asset in banking and related activities, so Christopher’s role is a pivotal one. Part of the In-depth series of interviews.

Big Data Fabric Weaves Together Automation, Scalability, and Intelligence


Today’s data landscape is characterized by exponentially increasing volumes of data, comprising a variety of structured, unstructured, and semi-structured data types originating from an expanding number of disparate data sources located on-premises, in the cloud, and at the edge.

Cloudera + Hortonworks, from the Edge to AI


Google built an innovative scale-out platform for data storage and analysis in the late 1990s and early 2000s, and published research papers about their work. They, too, saw the enormous potential for data at scale in the enterprise.

On procedural and declarative programming in MapReduce

The Unofficial Google Data Science Blog

by SEAN GERRISH and AMIR NAJMI To deliver the services our users have come to rely upon, Googlers have to process a lot of data — often at web-scale. There are many examples of declarative programming constructs out there for data gathering, SQL being one of the most obvious.

Why CU Anschutz Medical Campus Migrated to Google Cloud

Perficient Data & Analytics

Our healthcare team has been working with the University of Colorado over the past couple of years to overcome critical data challenges in healthcare… and the results are exciting. On-premises data warehouse was costly and non-scalable. Integrating vast amounts of clinical data.

Six Strategies for Advancing Customer Knowledge: Bringing Data Together


For them, they may understand that they need a data-driven strategy or the culture may aim to take a shift to being guided by data. “Bringing together as much of this data and information as possible will help organizations gain a richer, more detailed picture of customers.