The Intersection of Big Data and Analytics Philip Russom TDWI Research Director for Data Management May 5, 2011
Sponsor 2
Speakers Philip Russom TDWI Research Director, Data Management Francois Ajenstat Director of Product Management, Tableau Software 3
Today s Agenda Background Definitions Advanced Analytics Big Data Advanced Analytics and Big Data Why put them together? Use Cases and Requirements Departments, tools, data preparation, visualization Recommendations 4
Today In 3 Yrs 85% Background According to a recent TDWI survey, 38% of organizations surveyed are practicing advanced analytics today. But 85% say they ll do it within 3 years! 38% Why the rush to advanced analytics? Change is rampant in business We ve been through multiple economies in recent years Analytics helps discover what changed & how to react Business pace keeps accelerating Analytics, with Big Data, is pressing closer to real time There are still many opportunities to leverage Advanced analytics is still the best way to find new customer Got analytics? segments, best suppliers, products of affinity, sales seasonality, etc. And these analyses are best with all your data hence Big Data 5
Multiple Analytic Methods There s a cross-road intersection where you choose an analytic method or multiple methods! 1. Online Analytic Processing (OLAP) 2. Extreme SQL 3. Predictive Analytics 4. Other 6
Defining Advanced Analytics OLAP & its Variants Users have this Will keep it Won t go away Advanced Analytics Discovery oriented Works with Big Data Experiencing massive adoption by users Online Analytic Processing (OLAP) It s somewhat rudimentary, but required. Demands multidimensional data modeling, but works well with most EDWs. There are multiple approaches to OLAP. Extreme SQL Uses well-known SQL-based tools & techniques. Relies on long, complex SQL statements to define recent business events. Predictive Analytics Uses data mining and/or statistics to anticipate future events. Requires special tools and training. Other Analytic Methods Visualization, artificial intelligence, natural language processing. 7
What is the status of your organization s advanced analytics program? Advanced analytics is already mainstream & will become more so. Deployed and mature Deployed, but new In technical development Under consideration No plans for advanced analytics 14% 21% 16% 17% 32% Half of organizations surveyed (51%) are committed to a program for advanced analytics, whether it s currently under development or already deployed. Another third (32%) are considering a program, which should make advanced analytics even more commonplace. Relatively few organizations have no plans (17%). Source TDWI. Based on 140 responses, August 2010 8
Defining Big Data The simple definition: multi-terabyte datasets Big Data s not just big. It s also: Complicated, coming from many data sources Big Data comes from: Traditional applications, transactional data, customer interactions, Web logs, click streams, sensor data, social media, mobile devices Data types are increasingly unstructured or semi-structured Many data sources are streaming = big data in tiny time frames Big data keeps getting bigger, sometimes unpredictably Big data will soon involve petabytes, not terabytes Storing Big Data is a bit of a problem Processing and integrating Big Data is a bigger problem Big data certainly has its challenges, but it also presents useful advantages you can leverage. 9
What s the approximate total data volume that your organization manages specifically for advanced analytics, both today and in three years or so? Users conduct adv d analytics with growing analytic datasets. <500GB 500GB-1TB 1-3TB 3-10TB >10TB 3% 8% 10% 10% 14% 16% 16% 16% 17% 33% Small-to-medium size analytic datasets (3Tb and smaller) will get less prominent. Very large datasets (10Tb and larger) will become much more common. Don't know Today In 3 Yrs 27% 30% Advanced analytics is definitely a Big Data affair. Source TDWI. Based on 141 responses, August 2010 10
Advanced Analytics and Big Data: Why put them together? To satisfy business and technology requirements for a new wave of analytic applications. Advanced Analytics Discovery Analytics works best with a large data sample. Have Big Data? Leverage it. Analytic tools and databases can handle the demanding load. Big Data 11
Use Cases for Analytics with Big Data Customer base segmentation Planning and forecasting Price optimization Production yield in manufacturing Workforce management Fraud detection Risk calculations Loan approvals Facility monitoring Mobile asset mgt 12
Analytics is a Departmental Requirement Analytic applications are, by nature, focused on tasks, data domains, and opportunities. These are strongly associated with specific departments. For example: Customer base segmentation should be owned and executed by marketing and sales departments The actuarial department does risk analysis The procurement department does supply & supplier analysis Users face a tough decision: Use enterprise BI platforms, designed for reports & OLAP? Acquire & build a departmental analytics infrastructure? TDWI sees more organizations deploying dep t BI & analytics. 13
Analytic Tool Complexity is Potential Barrier For advanced analytics, does a department: Hire people with Ph.D.s in statistics; hand coders capable of Extreme SQL; designers for predictive models? Buy complex, expensive tools for advanced analytics? Spend a year developing analytic models? Argue over data samples, analytic algorithms? To keep it simple and practical, many departments: Side step barriers inherent in complex tool deployments Acquire a straightforward analytic tool that s usable by a wide range of business and technology people in the department Adopt analytic methods that leverage advanced data visualization 14
Data Management Adjustments for Analytics Analyze data first Later, improve it for a more polished analysis Analytic discovery depends on data nuggets Both query-based and predictive analytics need: Big data, raw data Data quality for analytic databases Do discovery work before addressing data anomalies and standardization E.g., fraud is often revealed via non-standard or outlier data Data modeling for analytic databases Modeling data can speed up queries and enable multidimensional views But it loses details & limits queries Do only what s required, like flattening and binning Data for post-analysis use in BI Apply best practices of DI, DQ, modeling 01101 00100 10110 10010 10100 10011 15
Trends in Data Visualization Mega Trends Size As the user interfaces of dashboards, scorecards, analyses, reports, and portals become increasingly visual, data visualization becomes ever more important. Drivers More users demand dashboards. Big data is now the norm. Analytics is booming. Speed Dashboards, scorecards, and portals need frequent refresh. Ad hoc queries need speed, especially for analytics. Interop. As report/analysis varies, users need to access new data easily. Need for in-line analytics to guide customer facing apps, etc. Economics In the current down economy, capital budgets for enterprise BI are frozen or cut. Dep t budgets relatively liquid. Trends Data visualization supports growing user communities. Visualizations must scale to data size Analytic relations are best viz d. Visualization tools are optimized for fast queries, even when queries are distributed, multidimensional, ad hoc, and repetitive. Viz tools have optimized interfaces to go directly at source data. Visualizations tend to be Web or service based; hence easy to embed. Data viz tools are inexpensive compared to large multi-tool platforms for business intelligence. Data viz adapts well to dep t use. 16
Recommendations Choose analytic approaches you need. Select analytic tools that are appropriate to methods chosen Assume that analytics and Big Data go together Discovery Analytics works best with a large data sample. Have Big Data? Leverage it. Analytic tools and databases can handle the demanding load. Note that analytics is a departmental affair Decide whether to use enterprise BI platforms or acquire tools strictly for departmental use Select tools that are appropriate for dept use Give the business what it needs Reporting and OLAP continue to serve us well Complement them with discovery analytics 17
All rights reserved. 2011 Tableau Software Inc.
Tableau Software, Inc. Tableau makes rapid-fire business intelligence software Headquartered in Seattle, WA Fastest growing business intelligence company in the world Stanford Professor Pat Hanrahan and Dr. Chris Stolte invented visualization technology Customers Apple Microsoft Wells Fargo Zynga Bank of America Wal*Mart Safeway Pfizer Merck Ferrari GM CBS + 1000 s more All rights reserved. 2011 Tableau Software Inc.
All rights reserved. 2011 Tableau Software Inc.
All rights reserved. 2011 Tableau Software Inc. Vision is our most powerful sense
The human visual system is powerful How many 9s? All rights reserved. 2011 Tableau Software Inc.
The human visual system is powerful All rights reserved. 2011 Tableau Software Inc.
Accountants exploit pop-out All rights reserved. 2011 Tableau Software Inc.
The human visual system is powerful All rights reserved. 2011 Tableau Software Inc.
Tableau helps people see and understand data. All rights reserved. 2011 Tableau Software Inc.
Additional Resources Web Seminar Resources + For a copy of the presentation workbook and to hear the web seminar on-demand go to http://www.tableausoftware.com/tdwi-big-data Q & A + If you have a question, please type it in the panel for an immediate reply or contact us via email or phone. Philip Russom + TDWI + Twitter @prussom + prussom@tdwi.org Francois Ajenstat + Tableau Software + Twitter @ajenstat + fajenstat@tableausoftware.com + (206) 633-3400 x5483 All rights reserved. 2011 Tableau Software Inc.
Questions?? 28
Contact Information If you have further questions or comments: Philip Russom, TDWI prussom@tdwi.org Francois Ajenstat, Tableau fajenstat@tableausoftware.com 29