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Business Intelligence isn't Big Data, and Big Data isn't BI

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SQL? SQL! SQL Hadoop SQL.. . BI Isn’t Big Data, Big Data Isn’t BI June, 2014 Mark Madsen www.ThirdNature.net @markmadsen


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Summary Common uses and commodity technology lead to Novel practices lead to Different data and different technology needs lead to New architectures Lead to Common uses and commodity technology © Third Nature Inc.


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Our ideas about information and how it’s used are outdated. © Third Nature Inc.


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How We Think of Users Our design point is the passive consumer of information. Proof: methodology ▪ IT role is requirements, design, build, deploy, administer ▪ User role is run reports Self-serve BI is not like picking the right doughnut from a box. © Third Nature Inc. Slide 4


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How We Think of Users How We Want Users to Think of Us Our design point is the passive consumer of information. Proof: methodology ▪ IT role is requirements, design, build, deploy, administer ▪ User role is run reports Self-serve BI is not like picking the right doughnut from a box. © Third Nature Inc.


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How We Think of Users © Third Nature Inc. What Users Really Think


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We think of BI as publishing, an old metaphor. Publishing has value, but may not be actionable. © Third Nature Inc.


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When you first give people access to information that was unavailable… OH GOD I can see into forever © Third Nature Inc.


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After a while it becomes the new normal © Third Nature Inc.


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Planning data strategy means understanding the context of data use so we can build infrastructure We need to focus on what people do with information as the primary task, not on the data or the technology. © Third Nature Inc. Analyze Exceptions Analyze Causes Decide No problem Monitor No idea Do nothing Act


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General model for organizational use of data Analyze Exceptions Analyze Causes Decide No problem Monitor No idea Do nothing Act Act within the process Usually real-time to daily © Third Nature Inc.


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Origin of BI and data warehouse concepts The general concept of a separate architecture for BI has been around longer, but this paper by Devlin and Murphy is the first formal data warehouse architecture and definition published. “An architecture for a business and information system”, B. A. Devlin, P. T. Murphy, IBM Systems Journal, Vol.27, No. 1, (1988) Copyright Third Nature, Inc. © Third Nature Inc. 12 Slide 12


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Origins: in 1988 there was only big hair. ▪ No real commercial email, public internet barely started ▪ Storage state of the art: 100MB, cost $10,000/GB ▪ Oracle Applications v1 GL released; SAP goes public, enters US market ▪ Unix is mostly run by long-haired freaks ▪ Mobile was this This is the context: scarcity of data, of system resources, of automated systems outside core financials, of money to pay for storage. © Third Nature Inc.


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General model for organizational use of data Act on the process Collect new data Usually days/longer timeframe © Third Nature Inc. Copyright Third Nature, Inc. Analyze Exceptions Analyze Causes Decide No problem Monitor No idea Do nothing Act


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Straight line vs closed loop causality BI is suited for stability, analytics for adaptation © Third Nature Inc.


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You need to be able to support both paths Causal analysis, “data science” Collect new data Monitor Act on the process Analyze Exceptions Analyze Causes Decide Act Act within the process Conventional BI, addition of EDM © Third Nature Inc. Copyright Third Nature, Inc.


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The usage models for conventional BI Act on the process Collect new data Usually days/longer timeframe Analyze Exceptions No problem Monitor This is what we’ve been doing with BI so far: static Analyze reporting, dashboards, Act Decide Causes query, OLAP ad-hoc No idea Do nothing Act within the process Usually real-time to daily © Third Nature Inc. Copyright Third Nature, Inc.


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The usage models for analytics and “big data” Analytics and big data is Collect focused on new use new data cases: deeper analysis, causes, prediction, optimizing decisions Analyze Monitor ad-hoc, This isn’t Exceptions reporting, or OLAP. No problem Act on the process Usually days/longer timeframe Analyze Causes Decide No idea Do nothing Act Act within the process Usually real-time to daily © Third Nature Inc. Copyright Third Nature, Inc.


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Over time, processes are (usually) better understood, so there is a movement of decisions from right to left, where support is more automated. Simple Assumption: Order Complicated Assumption: Unorder Complex Assumption: Disorder Cause and effect is repeatable Cause and effect is separated & predictable in time & space, repeatable, learnable Cause and effect is coherent in retrospect only, modelable but changing Known Knowable Unpredictable Standard processes, clear metrics, best practice Analytical techniques to determine options, effects Experiment to create possible options Sense, categorize, respond Sense, analyze, respond Test, sense, respond Like a bike Like a car Like economic systems © Third Nature Inc. Copyright Third Nature, Inc.


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As practices evolve based on new capabilities… A new level of complexity develops over top of the older, now better understood processes, leading to new data and analysis needs. © Third Nature Inc.


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Complexity will continue to increase Technology captures observations. These change our understanding. New understanding changes practices. Practices drive changes to technology, needing more data © Third Nature Inc.


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What do you mean, “Just doughnuts?” © Third Nature Inc. I never said the “E” in EDW meant “everything”…


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It’s going to get a lot worse E Not E Conclusion: any methodology built on the premise that you must know and model all the data first is untenable © Third Nature Inc.


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“Big data is unprecedented.” - Anyone involved with big data in even the most barely perceptible way © Third Nature Inc.


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We’ve been here before © Third Nature Inc. Source: Bill Schmarzo, EMC


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© Third Nature Inc. Source: Noumenal, Inc. “Big” is well supported by databases now


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Orders of magnitude: 20 years ago TB, today PB Shifts in data availability by orders of magnitude necessitate new means of managing and using it. © Third Nature Inc.


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Analytics embiggens the data volume problem Many of the processing problems are O(n2) or worse, so moderate data can be a problem for DB-based platforms © Third Nature Inc.


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Much of the big data value comes from analytics BI is a retrieval problem, not a computational problem. Five basic things you can do with analytics ▪ Prediction – what is most likely to happen? ▪ Estimation – what’s the future value of a variable? ▪ Description – what relationships exist in the data? ▪ Simulation – what could happen? ▪ Prescription – what should you do? Slide 29 Copyright Third Nature, Inc. © Third Nature Inc. Copyright Third Nature, Inc.


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Most people do not need special technology Number of people The distribution of data size is about normal, yet these guys set the tone of the market today. Bigness of data © Third Nature Inc. Copyright Third Nature, Inc.


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Number of jobs Analytics: This is really raw data under storage Microsoft study of 174,000 analytic jobs in their cluster: median size ??? Bigness of data © Third Nature Inc. Copyright Third Nature, Inc.


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Number of jobs Working data for analytics most often not big 14 GB Smallness of data © Third Nature Inc. Copyright Third Nature, Inc.


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Computation Lots A Simple Division of the Problem Space Big analytics, little data Big analytics, big data Specialized computing, modeling problems: supercomputing, GPUs Complex math over large data volumes requires shared nothing architectures Little Little analytics, little data Little analytics, big data The entry point; SAS, SMP databases, even OLAP can work The BI/DW space, for the most part Little © Third Nature Inc. Data volume Lots


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What makes the actual data “big”? Very large amounts Hierarchical structures Nested structures Encoded values Non-standard (for a database) types Deep structure Human authored text “big” is better off being defined as “complex” or “hard to manage” © Third Nature Inc. Copyright Third Nature, Inc.


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Three kinds of measurement data we collect The convenient data is transactional data. ▪ Goes in the DW and is used, even if it isn’t the right measurement. The inconvenient data is observational data. ▪ It’s not neat, clean, or designed into most systems of operation. The difficult and misleading data is declarative data. ▪ What people say and what they do require ground truth. We need to make use of all three. © Third Nature Inc. Copyright Third Nature, Inc.


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“big data” vs. transactions Reference data The classic example of “structured data” Transaction data includes: ▪ quantification details (date, value, count) ▪ reference data for explanation (product, customer, account) ▪ Lots of meaningful information Reference data is usually shared across the organization, hence its importance. There are two parts: ▪ identifier to uniquely identify the subject ▪ descriptive attributes with common or standardized value domains Transaction details © Third Nature Inc.


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Today it’s different data: observations, not transactions Sensor data doesn’t fit well with current methods of collection and storage, or with the technology to process and analyze it. © Third Nature Inc. Copyright Third Nature, Inc.


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Big data as a type of data: Transactions vs. Events Transactions: ▪ ▪ ▪ ▪ Each one is valuable Mutable The elements of a transaction can be aggregated easily A set of transactions does not usually have important ordering or dependency Events: ▪ A single event often has no value, e.g. what is the value of one click in a series? Some events are extremely valuable, but this is only detectable within the context of other events. ▪ Elements of events are often not easily aggregated ▪ A set of events usually has a natural order and dependencies ▪ Immutable © Third Nature Inc.


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Example “big data”: Web tracking data USER_ID SESSION_ID VISIT_DATE SESSION_START_DATE PAGE_VIEW_DATE DESTINATION_URL REFERRAL_NAME REFERRAL_URL PAGE_ID REL_PRODUCTS SITE_LOCATION_NAME SITE_LOCATION_ID IP_ADDRESS BROWSER_OS_NAME © Third Nature Inc. 301212631165031 590387153892659 1/10/2010 0:00 1:41:44 AM 1/10/2010 9:59 https://www.phisherking.com/gifts/store/LogonForm?mmc= link-src-email-_-m100109-_-44IOJ1-_-shop&langId=1&storeId=1055&URL=BECGiftListItemDisplay Google.com http://www.google.com/search?sourceid=navclient&aq=0h& oq=Italian&ie=UTF8&rlz=1T4ACGW_enUS386US387&q=italia n+rose&fu=0&ifi=1&dtd=204&xpc=1KoLqh374s PROD_24259_CARD PROD_24654_CARD, PROD_3648_FLOWERS VALENTINE'S DAY MICROSITE SHOP-BY-HOLIDAY VALENTINES DAY 67.189.110.179 MOZILLA/4.0 (COMPATIBLE; MSIE 7.0; AOL 9.0; WINDOWS NT 5.1; TRIDENT/4.0; GTB6; .NET CLR 1.1.4322)


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Web tracking data has a nested structure USER_ID SESSION_ID VISIT_DATE SESSION_START_DATE PAGE_VIEW_DATE DESTINATION_URL REFERRAL_NAME REFERRAL_URL PAGE_ID REL_PRODUCTS SITE_LOCATION_NAME SITE_LOCATION_ID IP_ADDRESS BROWSER_OS_NAME © Third Nature Inc. 301212631165031 “unstructured” data 590387153892659 embedded in the 1/10/2010 0:00 logged message: 1:41:44 AM complex strings 1/10/2010 9:59 https://www.phisherking.com/gifts/store/LogonForm?mmc= link-src-email-_-m100109-_-44IOJ1-_-shop&langId=1&storeId=1055&URL=BECGiftListItemDisplay Direct PROD_24259_CARD PROD_24654_CARD, PROD_3648_FLOWERS VALENTINE'S DAY MICROSITE SHOP-BY-HOLIDAY VALENTINES DAY 67.189.110.179 MOZILLA/4.0 (COMPATIBLE; MSIE 7.0; AOL 9.0; WINDOWS NT 5.1; TRIDENT/4.0; GTB6; .NET CLR 1.1.4322)


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The missing ingredient from most big data © Third Nature Inc.


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The creation and flow of data is different for observations, interactions and declarations The process for most human-entered data Data entry Store Extract Cleanse Load Use The process for much machine-generated data Data Generation Store Use © Third Nature Inc. Copyright Third Nature, Inc. Cleanse Use


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We collect large volumes of text, a rare practice ten years ago. Today we can turn text into data. Sentiment, tone, opinion Words & counts, keywords, tags Categories, taxonomies Copyright Third Nature, Inc. Entities people, places, things, events, IDs Topics, genres, relationships, abstracts


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You can store this data in an RDBMS, but… © Third Nature Inc.


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Example data: Twitter Message API Payload Looks like: This is really just a record format much like a DB row. Datetime, userID, name, location, description, message, message metadata, etc. But it’s In json or xml.


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A tweet has lots of fields, but one important one The payload is free text but has other elements: @markmadsen Check out: From #MongoDB to #Cassandra: Why The Atlas Platform Is Migrating http://owl.li/cvxFK ‘To’ username Hashtag URL Hashtag From these things you likely want to generate or link to reference data. © Third Nature Inc.


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Internal payload elements form a new graph The @elements point to other records and create a deeply linked structure. You have to assemble the linked structure to see what’s really there, which means repeated scanning some/all of the data. The derived pattern is interesting data, sometimes more than the individual messages. © Third Nature Inc.


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There are many patterns in the data Follower / following networks are easy – they are explicit and independent of the events. Community detection requires looking at patterns of @ communication in addition to follow relationships. What do you do with these after discovery? Follower network © Third Nature Inc. Conversational communities


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More data: patterns emerge from lots of event data Patterns emerge from the underlying structure of the entire dataset. The patterns are more interesting than sums and counts of the events. Web paths: clicks in a session as network node traversal. Email: traffic analysis producing a network The event stream is a source for analysis, generating another set of data that is the source for different analysis. © Third Nature Inc.


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Big changes for data warehousing workloads The results of analytic processing can, often do, feed back into the system from which they originate. Much of the data is being read, written and processed in real time. Our design point was not changing tables and ephemeral patterns. © Third Nature Inc.


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Unstructured is Not Really Unstructured Unstructured data isn’t really unstructured: language has structure. Text can contain traditional structured data elements. The problem is that the content is unmodeled. Slide 51


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THE BIG CHANGE ISN’T TECHNOLOGY, IT’S ARCHITECTURE © Third Nature Inc. Slide 52


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There are really three workloads to consider, not two 1. Operational: OLTP systems 2. Analytic: OLAP systems 3. Scientific: Computational systems Unit of focus: 1. Transaction 2. Query 3. Computation Different problems require different platforms © Third Nature Inc.


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The geography has been redefined The box we created: • not any data, rigidly typed data • not any form, tabular rows and columns of typed data • not any latency, persist what the DB can keep up with • not any process, only queries The digital world was diminished to only what’s inside the box until we forgot the box was there. © Third Nature Inc.


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Layered data architecture The DW assumed a single flat model of data, DB in the center. New technology enables new ways to organize data: ▪ Raw – straight from the source ▪ Enhanced –cleaned, standardized ▪ Integrated – modeled, augmented, ~semi-persistent ▪ Derived – analytic output, pattern based sets, ephemeral Implies a new technology architecture and data modeling approaches. © Third Nature Inc.


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Food supply chain: an analogy for data Multiple contexts of use, differing quality levels © Third Nature Inc.


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Data infrastructure is a platform ▪ ▪ ▪ ▪ © Third Nature Inc. Any data – structures, forms Any latency –in motion, at rest Any process – query, algorithm, transformation Any access – SQL, API, queue, file movement


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The evolution of BI is to a data platform, which means separating application from infrastructure. The platform has to do more than serve queries; it has to be read-write. BI, data extracts, analytics, applications Application layer: Deliver and use Multiple access methods Enhanced data Derived data Raw data Infrastructure layer: Process and analyze Store and manage Multiple ingest methods The new model also encompasses data at rest and data in motion © Third Nature Inc.


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IT reality is multiple data stores and systems Separate, purpose-built databases and processing systems for different types of data and query / computing workloads, plus any access method, is the new norm for information delivery. BI, Reporting, Dashboards, apps Data processing 1 Marge Inovera $150,000 Statistician 1 Marge Inovera $150,000 Statistician 1 Marge Inovera $150,000 Statistician 1 Marge Inovera $150,000 Statistician 1 Marge Inovera $150,000 Statistician 1 Marge Inovera $150,000 Statistician 1 Marge Inovera $150,000 Statistician 1 Marge Inovera $150,000 Statistician 1 Marge Inovera $150,000 Statistician 2 Anita Bath $120,000 Sewer inspector 2 Anita Bath $120,000 Sewer inspector 2 Anita Bath $120,000 Sewer inspector 2 Anita Bath $120,000 Sewer inspector 2 Anita Bath $120,000 Sewer inspector 2 Anita Bath $120,000 Sewer inspector 2 Anita Bath $120,000 Sewer inspector 2 Anita Bath $120,000 Sewer inspector 2 Anita Bath $120,000 Sewer inspector 3 Ivan Awfulitch $160,000 Dermatologist 4 Nadia Geddit $36,000 DBA 3 Ivan Awfulitch $160,000 Dermatologist 4 Nadia Geddit $36,000 DBA 3 Ivan Awfulitch $160,000 Dermatologist 4 Nadia Geddit $36,000 DBA 3 Ivan Awfulitch $160,000 Dermatologist 4 Nadia Geddit $36,000 DBA 3 Ivan Awfulitch $160,000 Dermatologist 4 Nadia Geddit $36,000 DBA 3 Ivan Awfulitch $160,000 Dermatologist 4 Nadia Geddit $36,000 DBA 3 Ivan Awfulitch $160,000 Dermatologist 4 Nadia Geddit $36,000 DBA 3 Ivan Awfulitch $160,000 Dermatologist 4 Nadia Geddit $36,000 DBA 3 Ivan Awfulitch $160,000 Dermatologist 4 Nadia Geddit $36,000 DBA 1 Marge Inovera $150,000 Statistician 1 Marge Inovera $150,000 Statistician 1 Marge Inovera $150,000 Statistician 1 Marge Inovera $150,000 Statistician 1 Marge Inovera $150,000 Statistician 1 Marge Inovera $150,000 Statistician 1 Marge Inovera $150,000 Statistician 1 Marge Inovera $150,000 Statistician Data Warehouse 1 Marge Inovera $150,000 Statistician 2 Anita Bath $120,000 Sewer inspector 2 Anita Bath $120,000 Sewer inspector 2 Anita Bath $120,000 Sewer inspector 2 Anita Bath $120,000 Sewer inspector 2 Anita Bath $120,000 Sewer inspector 2 Anita Bath $120,000 Sewer inspector 2 Anita Bath $120,000 Sewer inspector 2 Anita Bath $120,000 Sewer inspector 2 Anita Bath $120,000 Sewer inspector 3 Ivan Awfulitch $160,000 Dermatologist 4 Nadia Geddit $36,000 DBA 3 Ivan Awfulitch $160,000 Dermatologist 4 Nadia Geddit $36,000 DBA 3 Ivan Awfulitch $160,000 Dermatologist 4 Nadia Geddit $36,000 DBA 3 Ivan Awfulitch $160,000 Dermatologist 4 Nadia Geddit $36,000 DBA 3 Ivan Awfulitch $160,000 Dermatologist 4 Nadia Geddit $36,000 DBA 3 Ivan Awfulitch $160,000 Dermatologist 4 Nadia Geddit $36,000 DBA 3 Ivan Awfulitch $160,000 Dermatologist 4 Nadia Geddit $36,000 DBA 3 Ivan Awfulitch $160,000 Dermatologist 4 Nadia Geddit $36,000 DBA 3 Ivan Awfulitch $160,000 Dermatologist 4 Nadia Geddit $36,000 DBA Databases © Third Nature Inc. Stream processing Documents Flat Files XML Queues Source Environments ERP Applications


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Away from “one throat to choke”, back to best of breed “The extremely specialized nature of mass production raises the costs of product change and therefore slows down innovation.” - Abernathy, 1978 Tight coupling leads to slow changes. In a rapidly evolving market componentized architectures, modularity and loose coupling are favorable over monolithic stacks, single-vendor architectures and tight coupling. © Third Nature Inc.


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Staff and skills are a problem in a build market @BigDataBorat: Give man Hadoop cluster he gain insight for a day. Teach man build Hadoop cluster he soon leave for better job #bigdata © Third Nature Inc.


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Technology Adoption Some people can’t resist getting the next new thing because it’s new and new is always better. Many IT organizations are like this, promoting a solution and hunting for the problem that matches it. Better to ask “What is the problem for which this technology is the answer?” © Third Nature Inc. Copyright Third Nature, Inc.


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Four core capabilities big data technologies add 1. Unlimited scale of storage, processing ▪ Agility, faster turnaround for new data requests (but not a replacement for BI) ▪ Fewer staff to accomplish same goals 2. New data accessibility ▪ More data retained for longer period ▪ Access to data unused due to cost or processing limits ▪ Any digital information becomes usable data 3. Scalable realtime processing ▪ Brings ability to monitor and act on data as events occur 4. Arbitrary analytics ▪ Faster analysis ▪ Deeper analysis ▪ More broadly accessible analytics © Third Nature Inc.


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An important Hadoop + cloud computing benefit Scalability is free – if your task requires 10 units of work, you can decide when you want results: 1 server, 10 units of time 10 servers, 1 unit of time X X Time Cost is the same. Not true of the conventional IT model © Third Nature Inc.


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Hadoop: a summary of the magic 1. Provides both storage and complex processing as part of the same platform 2. Makes parallel programming more accessible 3. Schemaless and file-based, therefore flexible 4. Inexpensive, reliable scale-out 5. Potential for fast, scalable ingest 6. Low-cost © Third Nature Inc.


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As a technology moves from emerging to commodity the nature of acquiring, using and managing it changes Innovation Maturation Saturation Generate options Constrain choices Standardize / minimize choice Innovation Adaptation Acquisition Novel practice Good practice Best practice Maximize value Optimize Minimize costs Agile & open source* methods 6 Sigma & process methods © Third Nature Inc. Copyright Third Nature, Inc.


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Today: repeating the experience of the 80s & 90s Innovation Maturation Saturation This is the turbulent phase of the market as it goes through rapid development, then product and service changes. The Internet combined with commodity computing is forcing a new business and IT structural evolution, already underway. © Third Nature Inc. Copyright Third Nature, Inc.


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How we develop best practices: survival bias We don’t need best practices, we need worst failures. © Third Nature Inc. Copyright Third Nature, Inc.


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TANSTAAFL Technologies are not perfect replacements for one another. When replacing the old with the new (or ignoring the new over the old) you always make tradeoffs, and usually you won’t see them for a long time. © Third Nature Inc.


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“When a new technologyQuestions? you're either part of rolls over you, the steamroller or part of the road.” – Stewart Brand © Third Nature Inc.


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Welcome to the big data revolution, more of an evolution © Third Nature Inc. Be pragmatic, not dogmatic


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CC Image Attributions Thanks to the people who supplied the creative commons licensed images used in this presentation: acorn_blue.jpg - http://www.flickr.com/photos/rogersmith/314324893/ wheat_field.jpg - http://www.flickr.com/photos/ecstaticist/1120119742/ Phone dump - Richard Barnes ponies in field.jpg - http://www.flickr.com/photos/bulle_de/352732514/ straw men.jpg - http://www.flickr.com/photos/robinellis/6034919721/ text composition - http://flickr.com/photos/candiedwomanire/60224567/ girl on cell tokyo .jpg - http://flickr.com/photos/8024992@N06/986538717/ hamadan people mosaic.jpg - http://flickr.com/photos/hamed/225868856/ twitter_network_bw.jpg - http://www.flickr.com/photos/dr/2048034334/ klein_bottle_red.jpg - http://flickr.com/photos/sveinhal/2081201200/ donuts_4_views.jpg - http://www.flickr.com/photos/le_hibou/76718773/ subway dc metro - http://flickr.com/photos/musaeum/509899161/ © Third Nature Inc.


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About the Presenter Mark Madsen is president of Third Nature, a technology research and consulting firm focused on business intelligence, data integration and data management. Mark is an award-winning author, architect and CTO whose work has been featured in numerous industry publications. Over the past ten years Mark received awards for his work from the American Productivity & Quality Center, TDWI, and the Smithsonian Institute. He is an international speaker, a contributor to Forbes Online and on the O’Reilly Strata program committee. For more information or to contact Mark, follow @markmadsen on Twitter or visit http://ThirdNature.net


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About Third Nature Third Nature is a research and consulting firm focused on new and emerging technology and practices in analytics, business intelligence, information strategy and data management. If your question is related to data, analytics, information strategy and technology infrastructure then you‘re at the right place. Our goal is to help organizations solve problems using data. We offer education, consulting and research services to support business and IT organizations as well as technology vendors. We fill the gap between what the industry analyst firms cover and what IT needs. We specialize in product and technology analysis, so we look at emerging technologies and markets, evaluating technology and hw it is applied rather than vendor market positions. © Third Nature Inc.


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