NoSQL, Hadoop and MapReduce: Building a Modern Data Infrastructure that Works

Regardless of whether you call it "business intelligence", "big data", "analytics" or just plain old "math", we have many tried and true techniques for dealing with uncertainty. But ambiguity is a separate matter and, at least in my experience, is the hardest part of creating value from data.

During this talk, I will illustrate how the design process can be used to solve ambiguous problems by drawing on projects we've done at Datascope.

About 2/3 of all organizations are investing in Big Data and Hadoop but less than 10% have production applications. Given the broad acknowledgement that Big Data is a huge competitive differentiator, it’s likely that the companies that have not yet deployed Big Data applications are struggling to get their arms around this new technology.

Join this webinar to hear Michael Cote of 451 Group and Joe Goldberg from BMC Software discuss the state of this exciting technology, the challenges companies like yours are facing and how you can accelerate Big Data development and implementation projects with enterprise management solutions such as building Hadoop environments through self service provisioning and reducing the scripting required to create fully operational applications with enterprise workload automation.

Unless you’ve been living under a rock for the past few years, you are well aware that some organizations are already using Big Data analytic tools and technologies as part of their Business Intelligence (BI) applications to increase revenue or cut costs – or both.

These early adopters are no longer drowning in data, or suffering from analysis paralysis; they are getting the information they need, when they need it, and using it to make better business decisions.

Whether you are trying to find the Big Data analytics/BI value proposition that will work in your own organization, or looking for better ways to substantiate that value proposition for your sales targets, you’ll want to tune in to this webinar. Sandy delivers a clear, unbiased explanation of how Big Data analytic tools and technologies are reviving and expanding the utility of BI, and what you can do to apply this knowledge in your own (or your customer’s) BI applications.

There’s no place for ambiguity in the modern datacenter. People have increasingly difficult jobs to do, with no time for costly mistakes. When studying workflow, it becomes obvious very quickly that being glanceable, clear and understandable are the keys to efficient display and communication. This talk focuses on lessons learned and best practices for delivering clarity in any of your information displays, visualizations and communications.

Big Data solutions are often designed and built in a hurry and subsequently turn out to be difficult to maintain, scale and evolve after going into production. Further problems may occur when multiple initiatives “bump up” against each other, proving to be incompatible due to the way they’ve been engineered. This overview-level presentation distils some of the engineering best practices that are surfacing in the industry and suggests how a company can develop a consistent architecture that will underpin a wide range of different business requirements, yet can be implemented incrementally depending upon the company’s priorities and budget. Specifically, it outlines how to complement existing ETL processes and enterprise data warehouses during data ingestion; how to make the data storage invulnerable to change regardless of unforeseen business needs; how to prepare consistent pre-calculated data for consumption by all business communities; the role of in memory products; processing near real-time data streams; the increasing need for accurate operational dashboards.

In our highly data-driven environment, businesses are essentially becoming semi-autonomous agents, constantly competing for resources, customers and talent.

How can they adapt and remain competitive in an ever-changing market? What techniques could leverage real-time data to boost their customer acquisition process? How can they learn about their competition, when the competition is learning them? Can the effects of business decisions on the eco-system be anticipated?

To answer those questions, I will present machine learning and text mining techniques, that mine data from heterogeneous sources, to predict in such multi-agent temporal environments.

Business and IT are facing the challenge of getting real and urgent value from ever-expanding information sources. Building independent silos of big data analytics is no longer enough. True progress comes only by integrating data from traditional operational and informational sources with the new sources that are becoming available, whether from social media or interconnected machines.

In this webinar, Dr. Barry Devlin describes the thinking, architecture, tools and methods needed to achieve a new joined-up, comprehensive data environment.

Laws of nature are simple and Occam’s Razor suggests that simple solutions are the best to complex problems. But, real life data sets are very high dimensional and complex. For instance movie choices, restaurant check-ins, message engagements are all reflections of human choices in hundreds of thousands of options. Indeed, they are all triggered by latent personality and it is important to find these latent dynamics. In this presentation we will talk about how a real human rating data set can be represented in a low dimensional space and how this representation can be used to build a recommendation system as well as to extract insights about your users.

Businesses want everything now, and often fast isn’t fast enough. In-memory computing attracts a ton of interest as a solution, but as the curiosity grows, so does the confusion about what it can actually do and where it should be leveraged. As the cost of RAM continues to drop, an array of products are adopting in-memory technology as a means of getting things done, usually orders of magnitude faster.

Join this live roundtable to learn about data science from a wide variety of angles, from the infrastructure to the visualization.

When solving problems, data scientists often encounter added layers of complexity when the problems to be solved are not well defined, and their solutions unclear. In these cases, standard, more straightforward approaches fall short, as they are not amenable to vague problems, and are thus not guaranteed to reliably produce useful results. At Datascope Analytics, we adopt methodologies from the design community and use a "continuous feedback loop" to iteratively improve dashboards, algorithms, and data sources to ensure that the resulting tool will be useful and well received. During this talk, I will illustrate our approach by sharing a detailed example from one of our projects.

Data science borrows tools from many related fields: statistics and machine learning, "big data," software development, etc. Together, they open new opportunities for finding insight, building data products, and creating value. But the practical work of bringing together diverse tools from multiple disciplines presents new challenges as well. Many organizations---even those with talented teams and good infrastructure---still struggle to effectively unlock the power of data.

Borrowing from experiences at cutting-edge data companies including Jawbone, LinkedIn, and Concurrent, this talk shares strategies and examples for aligning data science workflows to create more value in less time. The Sidekick Pattern---using small, carefully curated data sets to multiply the power of big data---gets special attention as a versatile and often-overlooked technique for streamlining data workflows.

Understanding data science workflows is the key to making good use of day-to-day development time. It is also a prerequisite for making smart investments about hiring, building infrastructure, and prioritizing projects.

If you are a data scientist, this talk will help you accelerate your workflow and build more high-value things faster.

If you are a specialist in a related field, this talk will help you understand what's new about data science, so that you can borrow tips, tricks, and tools for your own practice.

If you are a manager of data scientists, this talk will help you to train, support, evaluate, defend, align, and inspire your team.

Over the past eight years, Trulia has amassed a number of huge datasets related to real estate, geography, and the activity of its users. We have successfully built out our data science team with the goal of turning this data into new product offerings and improved monetization.

This talk with look at our decisions in assembling this team, including what the core competencies should be and tradeoffs between engineering and analysis skill sets. We will also look at the projects the team has undertaken.

The Social Data firehose is now flooding us all with a deluge of potentially enormously valuable insight on our customers, our patients, our partners, our constituents, our audience. But data on its own can be as much a liability as well as an asset and the new Big Data paradigm brings up new challenges as well as new opportunities.

In this webinar we will cover how to gain deep customer insight that's valuable as well as actionable from Social Data. Using a series of real life practical examples of using advanced analytic techniques on these new unstructured data sources we'll show you how to extract this insight and the issues you need to address to get value out of the data.

Having collected Big Data, organizations are now keen on data science and “Big Learning."

Much of the focus has been on data science as exploratory analytics, offline, in the lab. However, building a production-ready large-scale operational analytics system remains a difficult and ad-hoc endeavor, especially when real-time answers are required. Design patterns for effective implementations are emerging, which take advantage of relaxed assumptions, adopt a new tiered "lambda" architecture, and pick the right scale-friendly algorithms to succeed.

Drawing on experience from customer problems, this session will present a reference architecture and algorithm design choices for a successful implementation.

Big Data and Data Science are multi-faceted concepts. In all of their guises, they are driving revolutionary changes in nearly all social, public, and institutional settings. One of most significant and far-reaching developments is the democratization of Data Science. Now everyone and every organization is "doing it".

In this presentation, I will describe some of the most prevalent characteristics and concepts surrounding the current activities associated with Data Science. I will then describe the importance of Data Science as a research discipline and as an application methodology for making advances in Big Data Analytics.

Following the discussion of these concepts (steps toward making discoveries from Big Data), I will then describe some of the misconceptions (missteps on the road to discovery) that can afflict current Big Data efforts.