Name: The Rise of the Data Artist and Their New Big Data Canvas
Start: 2016-02-11T17:00:00Z
End: 2016-02-11T17:49:13.000Z
Location: BrightTALK
Rating: 4.5

Zoomdata develops the world’s fastest visual analytics solution for big data. Using patented Data SharpeningTM and micro-query technologies, Zoomdata empowers business users to visually consume data in seconds, even across billions of rows of data. Zoomdata Fusion enables interactive analysis across disparate data sources, bridging modern and legacy data architectures, blending real-time streams and historical data, and unifying enterprise data with data in the cloud.  Delivered in a micro-services architecture for elastic scalability, Zoomdata runs on premises, in the cloud or embedded in an application. 

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In modern information security, we have come to understand that just as many organizations are being exploited through external threat vectors as internal. The relative ease in which an attack can be "customized" or at least "optimized" for a specific target is ever increasing.

What we would like to explore is the ability of an existing Security analytics team to use existing logs, in conjunction with a "Big Data" back end to detect threats that may be evolving.

Looking at existing/known attack techniques and using logs and near-time analysis we will look for how and where a known TTP chain may deviate to circumvent a Kill Chain/Alert or other active counter measures.

Chris has more than 25 years working in the Security industry. Most Recently, Chris was in charge of product management for Cylance and charged with machine learning in their product development. Chris's experience has been derived from working within Healthcare, Financial Services, Government and a variety of compliance oriented industries.

Branched Execution, The Evolution of a Kill Chain

Exploring big data, live data, and unstructured text fields is exciting to a business analyst, but can generate significant anxiety for the data protection officer. Learn how to open up data to those who need it and protect what needs protecting.

The New Basics of BI Lesson 6: Data Security

Learn how everything we offer is available for your organization to embed, customize, extend, and white label as your own. We know it's Zoomdata under the covers. But your customers don't need to know.

The New Basics of BI Lesson 5: Embedded Analytics

You're in for a treat when you first learn about Search Data. Think of it as a mashup of Google and your traditional BI tool. You could use this technology to learn more from customer surveys, performance reviews, or anything else that has an open text field.

The New Basics of BI Lesson 4: Search Data

Silicon valley venture capitalist Jake Flomenberg gets to see, track and make investments in the evolution of big picture technology trends across areas such as big data analytics, machine learning and artificial intelligence, and emerging modern data platforms and data types that are enabling organizations to be data and analytics-driven.  Data driven companies make more effective information backed decisions and tend to significantly outcompete their peers operating on guesses and gut feel. Some companies are now even selling ‘data products’, for example aircraft engine manufacturers tracking and analyzing huge volumes of engine performance data to enable predictive maintenance, fixing things before failure to avoid costs and negative impact on customers.

Jake will discuss trends he’s seeing related to the data analytics market, such as the growing need for self-service data discovery on very large volumes of data, analytics on streams of data, analytics on unstructured data, and contextual analytics embedded inside of software vendor and enterprise applications. He’ll discuss the characteristics of these markets, where he sees these markets going in the future, and why his firm chose to invest in a company like Zoomdata.

Zoomdata CTO Ruhollah Farchtchi will then discuss industry trends he has forecast for 2018 including the eclipsing of relational databases by modern data platforms for doing analytics, how the cloud has changed the game for application development, and how working with streaming data is becoming the new normal. He’ll also discuss trends that are more specifically relevant to Zoomdata such as how to leverage the value of company’s investments in modern elastically scalable back-end data infrastructure and how to get corresponding value on the front-end, such as the ability to analyze and get insights from huge volumes of data, streaming data, and unstructured data types.

Trends in the Modern Data and Analytics Market and Industry Forecasts for 2018

Your organization invests in many data technologies. With multi-source analysis, you don't just use them, you leverage them for a complete picture of your full business.

The New Basics of BI Lesson 3: Multi Source Analysis

Understanding live data that streams at you constantly is extremely difficult and can be enormously expensive if you don't know how to do it. We'll show you how we do it, and show off our Data DVR so you never miss a byte.

The New Basics of BI Lesson 2: Real Time Data Visualization

Today's most successful companies are data-driven, making informed decisions instead of guesses. Traditionally data-driven insights have been delivered in the form of standalone business intelligence applications, but in the past few years we’ve seen a big shift towards delivering contextual analytics embedded directly into other software applications and business workflows. For software vendors this means meeting high end-user expectations by infusing compelling data visualizations and analytics into every application you create and sell. Modern data visualization and analytics applications, therefore, need to be designed from the ground-up to be quick and easy to embed for a web and mobile-first world. They also must capable of handling today's rapidly evolving modern data platforms so that your analytics technology does not lock you into using yesterday's
data stores.

If you're a CTO, product manager, or software engineering leader, you may be curious about whether to build or buy an integrated analytics solution, how to evaluate the available offerings for embedded analytics, and how to most efficiently embed data visualization and analytics into your application. In this webcast, we will cover the following topics:

● Assessing the current state of your application's integrated analytics
● Approaching the build vs. buy decision
● Styles of embedding, from light to deep
● Considerations for deployment flexibility
● Security integration considerations
● Avoiding data platform lock-in

The Rise of Embedded Analytics, How To Do It Right

Start with the 1-2-3's of Big Data:

Using modern data platforms
Making sense of massive volumes of data
End-user adoption

The New Basics of BI Lesson 1: Big Data Exploration

Watch this video for information about the importance of big data distributions.

There are four dominant Hadoop distributions: Cloudera, Hortonworks, Amazon, and MapR. All four are gaining increased interest as the level of big data adoption grows. The market leader right now is Cloudera although high tech prefers Amazon as do smaller organizations -- up to 1,000 employees.


Content and Images Source: Dresner Advisory Services Big Data Analytics Market Study;  Copyright 2017 -- Dresner Advisory Services

Four Dominant Hadoop Distributions

In this video, we explore the best way to data about your products and services into a valuable product on its own.

As Edd Wilder-James once said, “Data products are the reason data scientists are lately treated like rock stars.” Data products operationalize analytical insight. And that’s what monetizing data is all about. The automobile is a good example for the potential of monetizing data. It illustrates that data about a product can be just as valuable -- if not more valuable -- than the product itself for generating revenue and increasing customer loyalty.

What Are You Doing With Your Data?

This video explores how applications connect to data sources and what that means to an embedded application.

Database query languages like Structured Query Language (SQL) and the Open Database Connectivity Protocol (ODBC) have been around a long time. SQL since the early 1970s and ODBC since 1986. And for as long as people have been querying data, reducing the length of time it took to get answers back -- query latency -- has been a problem. As databases have changed and new types emerged, solving the problem has become even more complex. Custom data source connectors are a solution.

Accessing Data Using Custom Connectors

In this video, you’ll learn why businesses of all sizes are investing so heavily in big data.

Big data and big data analytics are big business. IDC projects the market to grow to $50 billion by 2019. So what do organizations that invest in big data expect to achieve. Is there still a role for intuition in decision-making? Essentially, businesses pursue three objectives with big data: understanding the past, improving the present, and predicting the future. We’re also increasingly surrounded by data-driven smart systems that are reshaping the way we work and the economy we work in.

Big Data Drives Businesses of All Sizes

Watch this video to find out where most organizations are right now in terms of data monetization maturity -- and how to move past that.

A five-stage model describes the path to data monetization maturity. But did you know that although many organizations have invested heavily in data and analytics since the 1990s, most have not moved beyond the first stage of the model -- distributing analytics internally? The next four stages chart the development of analytics from a cost center to a profit center. And each stage has its own requirements.

The Data Monetization Maturity Model

In this video, you’ll find out how iFrames extends the customization of embedded analytics beyond what’s possible with white labeling.

There are a lot of cases when white labeling isn't sufficient,especially when you want to have data analytics alongside other functionality. A good example would be a customer portal where you have several columns with different widgets. You might have a news feed, a weather map, and other features plus analytics tools.

One way to do this would be embed an analytics web application into the portal via an iFrame. Of course, like white labeling, iFrames have limits.

Embedding Analytics with iFrames

This video goes beyond customization via white labeling and iFrames to the use of a software development kit (SDK).

Using iFrames and white labeling for customization, there's always a tradeoff between security,  interconnectivity, and the integrity of the user experience. You have more control and more connectivity with an SDK. You can build a truly custom application without starting from scratch. A robust SDK should include the ability to embed out-of-the-box charts and new charts as well as their accompanying data and metadata. It should also allow you to embed just data that can feed pre-built visualizations and, very important, integrate using REST APIs.

Embedding Analytics with a Software Development Kit

Watch this video to learn, how using REST APIs alongside of an SDK can multiply the power of both.

An API is to software code what a UI is to users. It helps different bits of software interact with each other. And can help you turn a data application into a data platform. If you have access to them, you can do a lot with REST APIs that weren’t built into a particular SDK. For example, you could accept user inputs and pass them to the platform to be stored. Or acquire data and metadata from the application or from users. This can be very powerful when building portals.

Building Your Own Data Platform with REST APIs

Watch this video to learn how administration and automation via REST APIs makes life easier when a SaaS company or large enterprise is embedding a BI platform for use by tens or hundreds of thousands of users.

In that scenario, you want to look for ways to automate the provisioning of new users and groups. So, it's important for a BI platform to offer administrative APIs that can be scripted from your application. These are usually REST APIs, and they really help ease the administrative load when users want to sign up for the embedded service from the parent application.

Embedded Applications: Administration and Automation

Map Analysis - Review of Free Qlik Tools and Tips

Webinar: Diagnosing Opportunity in Healthcare’s Big Data

Data-driven: Customer-obsessed results through analytics

How To Persuade and Inspire with Data

Web Content: Insights from User Interests

If You Can’t Tell the Story, You Can’t Justify the Investment in Analytics.

Big Data Statistical Visualisation: can everyone be a Data Scientist?

Improving time to insight with intelligent data visualizations

How Data Storytelling Can Enhance the Way You Communicate

Infographics for Data Visualization and Storytelling

A Truthful Art: Why Visualization Will Become an Universal Language

3D Immersive Data Visualisation

Challenges of large scale BI deployments

Data Visualization and Storytelling

There’s always been a divide between business and IT. And now the demands of big data have widened that gap. To close it, we’re seeing the emergence of a new role—the data artist. Data artists go beyond traditional analysis to create visualizations that portray facts, relationships, flow, and patterns in ways that the average business decision maker can quickly understand. They literally paint with data—transforming terabytes of often obscure data into meaningful information. Of course, data artists need flexible, extensible tools that help them understand and visually communicate the living, breathing nature of data. Interactive, multi-dimensional tools that offer real-time visibility into data from all types of data sources can help data artists reach their full potential.

The Rise of the Data Artist and Their New Big Data Canvas

Data

Visual

Analytics

embedded

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The Rise of the Data Artist and Their New Big Data Canvas

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