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1 May 28, 2013© Kalido I Kalido Confidential I May 28, 2013Rapid Data IntegrationTools and Methodology
2 May 28, 2013© Kalido I Kalido Confidential I May 28, 2013AgendaWhat is Data Science?Why Data Integration mattersWhy Iter...
3 May 28, 2013© Kalido I Kalido Confidential I May 28, 2013What Is Data Science?Data Science is about analytics– Different...
4 May 28, 2013© Kalido I Kalido Confidential I May 28, 2013Data ScienceAnalytical tools– Slicing and dicing, notreportingR...
5 May 28, 2013© Kalido I Kalido Confidential I May 28, 2013Why Data Integration MattersTo be successful, datascientists ne...
6 May 28, 2013© Kalido I Kalido Confidential I May 28, 2013The Importance Of Iterative MethodologyChange– Market driven ch...
7 May 28, 2013© Kalido I Kalido Confidential I May 28, 2013Iterative Data IntegrationIntegration model– Map sources to ana...
8 May 28, 2013© Kalido I Kalido Confidential I May 28, 2013Business Focused ModelStart with a model– Design logically, dep...
9 May 28, 2013© Kalido I Kalido Confidential I May 28, 2013Automation Using Best PracticesMaster Data Governance and Stewa...
10 May 28, 2013© Kalido I Kalido Confidential I May 28, 2013What Is The Payoff?Better quality data– Better analysis– Bette...
11 May 28, 2013© Kalido I Kalido Confidential I May 28, 2013SummaryData Science offers new insights into businessData Scie...
12 May 28, 2013© Kalido I Kalido Confidential I May 28, 2013What Are Your Next Steps?Attend the next how-to webinar on how...
13 May 28, 2013© Kalido I Kalido Confidential I May 28, 2013PollingI have a clear understanding of how data integrationaff...
14 May 28, 2013© Kalido I Kalido Confidential I May 28, 201314Thank you
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Rapid Data Integration: Tools & Methodology

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  • Recall that I said there are many steps in the process of turning data into information. This is just a partial list of the tasks involved in a best practices data warehouse operation. These tasks don’t go away in a model-driven approach. But, being well-governed requires repeatable processes that deliver consistent results. There is no way to achieve that if these remain manual tasks or are driven from different perspectives. Manual processes are often prone to human error, are inconsistent, and take too much time – especially when requirements change frequently. In a model-driven approach, information policies that support business requirements emerge directly from the business information model. These information policies form a reference against which all of these tasks can be executed. Because of this, you can automate a very large number of these tasks and achieve consistent results. Anybody who’s built data warehouses using Kalido would never go back to what one of our customers call “stick build”. Because you’d never want to have to design and write code that does all this from scratch again. We handle ragged and variable depth hierarchies. We handle data exceptions. We have a built-in match engine for identity resolution. We build slowly changing dimensions without you having to do a single thing. We generate data marts. And all of this is driven by the business information model.
  • Transcript of "Rapid Data Integration: Tools & Methodology"

    1. 1. 1 May 28, 2013© Kalido I Kalido Confidential I May 28, 2013Rapid Data IntegrationTools and Methodology
    2. 2. 2 May 28, 2013© Kalido I Kalido Confidential I May 28, 2013AgendaWhat is Data Science?Why Data Integration mattersWhy Iterative Methodology is criticalHow do we get there?What is the payoff?
    3. 3. 3 May 28, 2013© Kalido I Kalido Confidential I May 28, 2013What Is Data Science?Data Science is about analytics– Different from Business Intelligence– Looks forward– Statistical analysis, predictiveanalysis, trending, etc.
    4. 4. 4 May 28, 2013© Kalido I Kalido Confidential I May 28, 2013Data ScienceAnalytical tools– Slicing and dicing, notreportingRequirements– By definition, you don’t knowwhat you need up front– Traditional datamanagement tools fall short Data profiling, modeling,ETL, etc., all depend upondefined requirements
    5. 5. 5 May 28, 2013© Kalido I Kalido Confidential I May 28, 2013Why Data Integration MattersTo be successful, datascientists need good data– Key question is “why?”– Are we seeing a trend, or isthe data bad?One question will lead toanotherExperimentationNew requirements, moredata, new subject areasHow to keep up?
    6. 6. 6 May 28, 2013© Kalido I Kalido Confidential I May 28, 2013The Importance Of Iterative MethodologyChange– Market driven change – quarterly– Business driven change – monthly– Analysis driven change – dailyTo deal with change, iterative methodologies succeed– Absorb– Implement– Deploy– Test– Repeat
    7. 7. 7 May 28, 2013© Kalido I Kalido Confidential I May 28, 2013Iterative Data IntegrationIntegration model– Map sources to analytical model– Revise physical schema as model evolves– Automation Reduce error, speed deployment and track changesUsability– Make the data available – now!
    8. 8. 8 May 28, 2013© Kalido I Kalido Confidential I May 28, 2013Business Focused ModelStart with a model– Design logically, deploy physically– Support iterations
    9. 9. 9 May 28, 2013© Kalido I Kalido Confidential I May 28, 2013Automation Using Best PracticesMaster Data Governance and StewardshipSchema ManagementModel and Metadata ManagementHierarchy Management Workflow and SecurityData Profiling and Validation Data AuthoringControlled PublicationIdentity ManagementAuto-generated ApplicationBrowse and Search Full History and Audit TrailsAuto Match and MergeOperationsData IntegrationData ValidationSuspense and Exception HandlingData Sourcing and Field MappingDelta DetectionSurrogate Key ManagementCode Management and LookupCurrency and UoMGraphical ModelingModel FederationMulti-GranularitySub-typing and InheritanceComposite EntitiesRagged Hierarchies Change ManagementKPI ManagementBusiness Metadata Classification HierarchiesStar and Snowflake SchemaPhysical Schema ManagementSlowly Changing DimensionsData Mart and AggregatesData Load and Index ManagementRollup Path AwarenessIncremental Summary GenerationProcess AutomationTask Execution and MonitoringDeployment and MigrationArchivingRestore for Model and DataUndo LoadsAudit and LoggingPresentationMetadata Management for BOBJNative XLS Pivot Table GenerationNative QlikView GenerationMetadata Management for MSASMetadata Management for COGNMDM Consumer InterfaceReport-Time Formula ManagementAutomated
    10. 10. 10 May 28, 2013© Kalido I Kalido Confidential I May 28, 2013What Is The Payoff?Better quality data– Better analysis– Better decisions– Better business resultsMore timely data– Reduce guesswork andestimations– Act with confidenceMore efficient– Conserve your resources– Keep pace with change
    11. 11. 11 May 28, 2013© Kalido I Kalido Confidential I May 28, 2013SummaryData Science offers new insights into businessData Science demands current accurate dataRequirements difficult to pre-determineFlexible, agile data platform is a mustKalido Information Engine supports…– Business focused modeling– Rapid integration– Process automation– Accurate data – fast!
    12. 12. 12 May 28, 2013© Kalido I Kalido Confidential I May 28, 2013What Are Your Next Steps?Attend the next how-to webinar on how to use theBusiness Information Model for rapid data integrationSchedule a 1:1 Discovery Session with one of ourexperts and get hands on time with the toolSchedule a more in depth session on rapid dataintegration for you and your colleagues
    13. 13. 13 May 28, 2013© Kalido I Kalido Confidential I May 28, 2013PollingI have a clear understanding of how data integrationaffects my ability to improve insightsI would like to attend a “how-to” webinar on the BusinessInformation Modeler and how it is usedI would like to schedule a Discovery Session with a KalidoExpert and get some experience with the toolI am unsure as to how rapid data integration affects myability to improve insights
    14. 14. 14 May 28, 2013© Kalido I Kalido Confidential I May 28, 201314Thank you
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