Going Open: The Perils And Promise Of Open Source BI
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The informal description: The enterprise software market is in the midst of a major structural shift. Open source is a change in the conduct of the market – it’s not about ideology. If we understand why this is happening, we can figure out how to respond. The firms that develop a strategy to deal with the new market realities and open source will be the ones who make money. The others, not so much. The formal one from the conference: Why open source? Why now? What should a software vendor do in a commodifying market? This presentation explains how an open source strategy can work in today's business intelligence market and what values are "better than free." He explains the range of options available to a vendor, the situations where value can develop, the risks and the rewards. Madsen will also give examples of the rationale for and benefits of partnering with or using open source.
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Slides for Briefing Room webcast ( https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=869f964b1380f728cedde802779a1e12 )
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Intro:
Mark Madsen’s exposure to the software industry can be measured in terapoints (that’s 1 million powerpoints). He received his Master’s degree in Science from an unnamed university in 1993 and has been doing what is described in technical jargon as “a lot of stuff” since then. With knowledge as broad as the wide Mississippi, his qualifications need not be divulged, and certainly not investigated. He doesn't want anyone to make a fuss over him—just treat him as you would any great man. Please welcome Mark Madsen
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The big challenge most analytics and IT professionals face today is dealing with complexity. Trends are still not clear. It helps to look at the past and current state to understand what’s really happening in the data technology market – a whole lot of reinvention and some innovation, but not where you expect it.
We have the (well-understood) problems that we have, with their (well-understood) limitations and intractabilities.
We deal with them in the world in which they were first codified and framed. Paradigms (world views) change as a function of political, economic, technological, cultural, use and growth, however, and when the world changes we’ll have a criteria for framing not just the problems/shortcomings/intractabilities of the prior paradigm, but that paradigm itself.
At that point, however, it will have ceased to matter because we’ll be dealing with fundamentally new problems/shortcomings/intractabilities.
Pay no attention to the man behind the curtain - the unseen work behind data ...
Goal: explain the nature of the work of an analytics team to a manager, and enable people on those teams to explain what a data science team needs to a manager.
It seems as if every organization wants to enable analytical-decision making and embed analytics into operational processes. What can you do with analytics? It looks like anything is possible. What can you really do? Probably a lot less than you expect. Why is this? Vendors promise easy-to-use analytics tools and services but they rarely deliver. The products may be easy but the work is still hard.
Using analytics to solve problems depends on many factors beyond the math: people, processes, the skills of the analyst, the technology used, the data. Technology is the easy part. Figuring out what to do and how to do it is a lot harder. Despite this, fancy new tools get all the attention and budget.
People and data are the truly hard parts. People, because many believe that data is absolute rather than relative, and that analytic models produce an answer rather than a range of answers with varying degrees of truth, accuracy and applicability. Data, because managing data for analytics is a nuanced, detail-oriented and seemingly dull task left to back-office IT.
If your goal is to build a repeatable analytics capability rather than a one-off analytics project then you will need to address the parts that are rarely mentioned. This talk will explain some of the unseen and little-discussed aspects involved when building and deploying analytics.
Assumptions about Data and Analysis: Briefing room webcast slides
In many ways, moving data is like moving furniture: it's an unpleasant process dubbed an occasional necessary evil. But as the data pipelines of old decay, a new reality is taking shape: the data-native architecture. Unlike traditional data processing for BI and Analytics, this approach works on data right where it lives, thus eliminating the pain of forklifting, narrowing the margin of error, and expediting the time to business benefit. The new architecture embodies new assumptions, some of which we will talk about here.
Register for this episode of The Briefing Room to hear veteran Analyst Mark Madsen of Third Nature explain why this shift is truly tectonic. He'll be briefed by Steve Wooledge of Arcadia Data who will showcase his company's technology, which leverages a data-native architecture to fuel rapid-fire visualization and analysis of both big data and small.
Everything Has Changed Except Us: Modernizing the Data Warehouse
Keynote, Munich, June 2016
The way we make decisions has changed. The data we use has changed. The techniques we can apply to data and decisions have changed. Yet what we build and how we build it has barely changed in 20 years.
The definition of madness is doing more of what you already do and expecting different results. The threat to the data warehouse is not from new technology that will replace the data warehouse. It is from destabilization caused by new technology as it changes the architecture, and from failure to adapt to those changes.
The technology that we use is problematic because it constrains and sometimes prevents necessary activities. We don’t need more technology and bigger machines. We need different technology that does different things. More product features from the same vendors won’t solve the problem.
The data we want to use is challenging. We can’t model and clean and maintain it fast enough. We don’t need more data modeling to solve this problem. We need less modeling and more metadata.
And lastly, a change in scale has occurred. It isn’t a simple problem of “big”. The problem with current workloads has been solved, despite the performance problems that many people still have today. Scale has many dimensions – important among them are the number of discrete sources and structures, the rate of change of individual structures, the rate of change in data use, the variety of uses and the concurrency of those uses.
In short, we need new architecture that is not focused on creating stability in data, but one that is adaptable to continuous and rapidly changing uses of data.
A Pragmatic Approach to Analyzing Customers
The business market is different today than it was 20 years ago when BI got started. We're just beginning to grasp how to work within the new economic and communication models. Companies can't rely solely on financial and operational metrics any more, and need to analyze customer behaviors in more detail.
The big change in analysis is a move from mass market metrics to individualized data, no longer analyzing or managing by averages. The stream of events and observations available from applications today combined with new platforms for collecting and processing data enables (relatively) easy analysis.
Despite this, many companies struggle to analyze customer data. This talk will describe a handful of customer metrics and models that are (relatively) easy to do, yet are often not done. It's often easier to succeed by stringing together a handful of simple techniques rather than applying advanced techniques.
Expect to come away from this session with:
- a little history of customer data use by marketing and how that has changed in the last 10 years.
- the most common behavioral data sources you have available.
- some of the basic questions that often go unanswered, and data that is not assessed in the proper context.
- some basic analyses you can perform.
Disruptive Innovation: how do you use these theories to manage your IT?
The term disruptive innovation was popularized by Harvard professor Clayton Christensen in his 1997 book “The Innovator’s Dilemma.” Nearly 20 years later “Disrupt!” is a popular leadership mantra that is more frequently uttered than experienced. You can't productize it. You can't always control it – at least what effects it has in practice. You aren't necessarily going to like every product of innovation. So are you sure you want it? If so, how do you promote a culture in which innovation can flower – and, potentially, thrive? Because that's probably the best that you can do.
Perhaps there's a better framing for innovation than just "disruption.“ This session is an overview of commmoditization and innovation theories followed by basic things you can do to apply that theory to your daily job architecting, choosing and managing a data environment in your company.
Briefing room: An alternative for streaming data collection
Knowing what’s happening in your enterprise right now can mark the difference between success and failure. The key is to have a rich view of activity, such that analysts and others can explore in a fully multidimensional fashion. Benefiting from such a detailed perspective can help professionals identify the exact nature of problems or opportunities, thus enabling precise actions that make a difference quickly.
Register for this episode of The Briefing Room to hear veteran Analyst Mark Madsen of Third Nature explain how a nexus of innovations for analyzing network traffic can help companies stay on top of their game. He’ll be briefed by Erik Giesa of ExtraHop, who will showcase his company’s stream analytics technology for wire data, which provides real-time, multidimensional views of network traffic. He’ll share success stories of how ExtraHop has solved otherwise intractable problems and enabled a new level of root-cause analysis.
Building the Enterprise Data Lake: A look at architecture
The topic is building an Enterprise Data Lake, discussing high level data and technology architecture. We will describe the architecture of a data warehouse, how a data lake needs to differ, and show a high level functional and data architecture for a data lake. This webinar will cover:
Why dumping data into Hadoop and letting users get it out doesn't work
The difference between a Hadoop application and a Data Lake
Why new ideas about data architecture are a key element
An Enterprise Data Lake reference architecture to frame what must be built
Briefing Room analyst comments - streaming analytics
Slides for Briefing Room webcast ( https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=869f964b1380f728cedde802779a1e12 )
Organizations worldwide are learning hard lessons these days about the constraints of dated information systems. The time-tested process of Extract-Transform-Load (ETL) is fast losing its ability to cope with the volume, velocity and variety of Big Data coming down the pike. Forward-thinking companies are therefore prepping the battle field by designing on-ramps to the future of streaming analytics. Register for this episode of The Briefing Room to hear Analyst Mark Madsen explain how a new era of data solutions is rising to the challenge of streaming data. He'll be briefed by Steve Wilkes, founder and CTO of the Striim platform. Steve will share how enterprises are turning to streaming data integration, in-memory transformations and continuous processing to achieve the goals of ETL in milliseconds – at a fraction of the cost and complexity of legacy systems. Several case studies will be shared.
Everything has changed except us
The way we make decisions has changed. The data we use has changed. The techniques we can apply to data and decisions have changed. Yet what we build and how we build it has barely changed in 20 years.
The definition of madness is doing more of what you already do and expecting different results. The threat to the data warehouse is not from new technology that will replace the data warehouse. It is from destabilization caused by new technology as it changes the architecture, and from failure to adapt to those changes.
The technology that we use is problematic because it constrains and sometimes prevents necessary activities. We don’t need more technology and bigger machines. We need different technology that does different things. More product features from the same vendors won’t solve the problem.
The data we want to use is challenging. We can’t model and clean and maintain it fast enough. We don’t need more data modeling to solve this problem. We need less modeling and more metadata.
And lastly, a change in scale has occurred. It isn’t a simple problem of “big”. The problem with current workloads has been solved, despite the performance problems that many people still have today. Scale has many dimensions – important among them are the number of discrete sources and structures, the rate of change of individual structures, the rate of change in data use, the variety of uses and the concurrency of those uses.
In short, we need new architecture that is not focused on creating stability in data, but one that is adaptable to continuous and rapidly changing uses of data.
Bi isn't big data and big data isn't BI (updated)
Big data is hyped, but isn't hype. There are definite technical, process and business differences in the big data market when compared to BI and data warehousing, but they are often poorly understood or explained. BI isn't big data, and big data isn't BI. By distilling the technical and process realities of big data systems and projects we can separate fact from fiction. This session examines the underlying assumptions and abstractions we use in the BI and DW world, the abstractions that evolved in the big data world, and how they are different. Armed with this knowledge, you will be better able to make design and architecture decisions. The session is sometimes conceptual, sometimes detailed technical explorations of data, processing and technology, but promises to be entertaining regardless of the level.
Yes, it’s about the data normally called “big”, but it’s not Hadoop for the database crowd, despite the prominent role Hadoop plays. The session will be technical, but in a technology preview/overview fashion. I won’t be teaching you to write MapReduce jobs or anything of the sort.
The first part will be an overview of the types, formats and structures of data that aren’t normally in the data warehouse realm. The second part will cover some of the basic technology components, vendors and architecture.
The goal is to provide an overview of the extent of data available and some of the nuances or challenges in processing it, coupled with some examples of tools or vendors that may be a starting point if you are building in a particular area.
On the edge: analytics for the modern enterprise (analyst comments)
On the Edge: Analytics for the Modern Enterprise
[these are the analyst comments on enterprise data architecture and streaming]
Webcast description: The speed of business today requires new approaches to generating and leveraging analytics. Latencies of a day, an hour or even minutes no longer suffice in many situations. For these use cases, organizations must embrace analytics at the edge: a process that involves targeted number-crunching at the fringe of the enterprise. When designed properly, these systems give companies a leg up on their competitors. Register for this episode of The Briefing Room to hear veteran Analyst Mark Madsen of Third Nature explain how a new era of information architectures is now unfolding, paving the way to much more responsive and agile business models. He'll be briefed by Kim Macpherson of the Cisco Data and Analytics Business Unit, who will explain how her company's platform is uniquely suited for this new, federated analytic paradigm. She'll demonstrate how edge analytics can help companies address opportunities quickly and effectively.
Crossing the chasm with a high performance dynamically scalable open source p...
Many open source projects are seeking commercial backing and growing into businesses – e.g. Spark, Cassandra, Hadoop. How do you market your project or product to investors or to enterprises? What should your pitch deck or customer presentation look like? This talk will show you the archetype (based on watching hundreds of these presentations) and exactly how not to do a presentation like this.
Intro:
Mark Madsen’s exposure to the software industry can be measured in terapoints (that’s 1 million powerpoints). He received his Master’s degree in Science from an unnamed university in 1993 and has been doing what is described in technical jargon as “a lot of stuff” since then. With knowledge as broad as the wide Mississippi, his qualifications need not be divulged, and certainly not investigated. He doesn't want anyone to make a fuss over him—just treat him as you would any great man. Please welcome Mark Madsen
Don't let data get in the way of a good story
Storytelling is not about raising someone’s IQ, it’s about raising their blood pressure. Stories engage emotions rather than intellect, making “storytelling with data” a poor metaphor for data visualization when our goal is to communicate clearly.
People are often confused or misled by “story”, thinking they need a classical story structure with protagonists, action and resolution when the job may be simpler, or more complicated. Some of the storytelling tools and suggestions vendors promote would get you kicked out of your boss’s office you used them without taking into account their goals and context.
Narrative is what we are really talking about, not story. We need to focus our attention on narrative techniques rather than “story” and its forced linear structure. This means understanding why we want to communicate: is it to explain, to build shared understanding, to convince others that our interpretation is the right one?
We use visualization as a tool for many different purposes, communication being one. The idea of narratives with data is a good one, but not all narrative is story. The purpose of this talk is to provide clarity around the goals of communicating with data and to provide a goal-oriented framework that escapes the bad metaphorical frame imposed by “storytelling”.
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