Data-Driven Marketing Roadshow Attivio - March 27, 2014
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Presentation by Attivio General Manager Europe Peter Philipp. Data-Driven Marketing Road Show at DCU The Helix, Dublin, March 27 2014
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Data-Driven Marketing Roadshow Attivio - March 27, 2014
- 1. Delivering Big Content Insight for Marketing
- 2. © 2014 ATTIVIO | PROPRIETARY AND CONFIDENTIAL Avoid data theft and downtime by extending the security perimeter outside the data-center and protect from increasing frequency, scale and sophistication of web attacks. What is Big Data, really?
- 3. © 2014 ATTIVIO | PROPRIETARY AND CONFIDENTIAL The Data Universe Big Bang The Data Universe Structured Unstructured Unstructured Data Unstructured Content Business Intelligence Enterprise Search
- 4. © 2014 ATTIVIO | PROPRIETARY AND CONFIDENTIAL The (more) Complete Data Universe The Data Universe Structured Unstructured Unstructured Data Unstructured Content
- 5. © 2014 ATTIVIO | PROPRIETARY AND CONFIDENTIAL Can you really use Big Data tools for Big Content? Big Data Big Content Many small records needing reduction into something meaningful Many large documents often needing individual decomposition into multiple data points Often very high rates of change at machine speeds, usually net-new records Rates of change at human speeds, often in the form of updates Many different varieties of sources (social, web logs, sensors, etc.) Dozens of languages, hundreds of file types. Ultimately very “jagged” data results VolumeVelocityVariety
- 6. © 2014 ATTIVIO | PROPRIETARY AND CONFIDENTIAL Understanding your customer’s sentiment Will the word ‘sick’ be good or bad?
- 7. © 2014 ATTIVIO | PROPRIETARY AND CONFIDENTIAL Avoid data theft and downtime by extending the security perimeter outside the data-center and protect from increasing frequency, scale and sophistication of web attacks. Let’s look at Big Content a bit closer To: X Airline Service <csr@x.com> From: Joe Customer jcust@xyz.com Subject: My recent experience with your phone agent Date: 3-1-2013 01:35:00 I travelled on your airline from San Francisco to Boston and had a terrible experience with your phone agent. I had recently been granted frequent flyer status on your airline matching my status on airline Y and when I checked in for my flight was able to select a window upgraded seat. However, when I arrived at the airport, I was in a middle seat. I called the customer help desk and was told that “no seats are guaranteed” and there was nothing that could be done for me. The agent spoke in a very rude tone, not the way I would expect you to treat your frequent flyers.
- 8. © 2014 ATTIVIO | PROPRIETARY AND CONFIDENTIAL Avoid data theft and downtime by extending the security perimeter outside the data-center and protect from increasing frequency, scale and sophistication of web attacks. What does this single email tell us? To: X Airline Service <csr@x.com> From: Joe Customer jcust@xyz.com Subject: My recent experience with your phone agent Date: 3-1-2013 01:35:00 I travelled on your airline from San Francisco to Boston and had a terrible experience with your phone agent. I had recently been granted frequent flyer status on your airline matching my status on airline Y and when I checked in for my flight was able to select a window upgraded seat. However, when I arrived at the airport, I was in a middle seat. I called the customer help desk and was told that “no seats are guaranteed” and there was nothing that could be done for me. The agent spoke in a very rude tone, not the way I would expect you to treat your frequent flyers. Sentiment Competitor Key terms for my industry Relevant Airport Locations and Route Customer emailCustomer name
- 9. © 2014 ATTIVIO | PROPRIETARY AND CONFIDENTIAL What could the value of a lot of emails be? • Now we can answer new questions like: • “Which routes are driving negative customer experiences and why are they unhappy with those routes?”
- 10. © 2014 ATTIVIO | PROPRIETARY AND CONFIDENTIAL Building Bridges from Big Content to Big Data • Leverage Text Analytics routines for interpreting content • Entity Extraction: detection of people, places, things • Key Phrase Detection: identifying topics and concepts • Classification: sentiment analysis, categorization • Lean on metadata layers • Corporate taxonomies (e.g. product hierarchies, sales territories, etc.) • Controlled vocabularies (e.g. Medical Subject Headings) • It’s all about tying the Content back to a Business Entity • For example: identifying that the email is a complaint about Product XYZ
- 11. © 2014 ATTIVIO | PROPRIETARY AND CONFIDENTIAL Big Content is everywhere! Verticals Information Sources for Analysis Financial Services / Insurance News, research reports, email, company filings, claims Pharma / Life Sciences Patents and applications, clinical trials annotations, electronic lab notebook, technical publications Healthcare Patient records, care management systems, surveys Government Analyst reports, news, communications, intelligence Manufacturing / High Tech Parts catalogs, manuals, purchase orders, maintenance comments, shift logs, field reports, safety reports Media / eCommerce Social media, news, blogs, reviews
- 12. © 2014 ATTIVIO | PROPRIETARY AND CONFIDENTIAL Case StudyMajor Retailer • Leverage data from all sources to drive actionable insights in real time • Understand the impact of posts, sentiment on product revenues and promotions • Optimize to supply to demand • Provide search and text analysis for Facebook, Twitter, Databases and internal chat Yammer integrated into BI tool • Key phrase and entity extraction, document classification according to customer product hierarchy, sentiment analysis • Delivery in 4 weeks • Maximize revenue from promotions – tap into trends, events, etc as they happen • Execute 1-1 marketing & merchandising strategies in real-time • Quickly understand the ‘why’ behind the ‘what’ – tap into positive revenue trends
- 13. © 2013 ATTIVIO | ACTIVE INTELLIGENCE© THANK YOU Contact Us General Manager, Europe Peter Philipp pphilipp@attivio.com Regional Director Germany Austria & Switzerland Matthias Frye mfrye@attivio.com
Notes de l'éditeur
- Big Data is simply about recognizing that there are valuable sources of information in the enterprises that for various reasons, do not fit nicely into the traditional data warehousing and BI technology stack. The dimensions of Big Data we’ve all heard so much about are really just a way to classify the reasons a particular source of data does not fit. For example, the volume dimension usually refers to sources of data with low-value per record which make it cost prohibitive to load those sources into the warehouse. For example, web click logs or system logs – this type of data needs to be stored cheaply and reduced to meaningful data for end user consumption. Technologies like Hadoop do a great job of this. Similarly High Velocity data often requires technology like CEP to take a large noisy stream and glean meaningful insight from it. On the Variety and Complexity side we are looking at data sources such as text and documents that either live in hard to access repositories or require advanced text analytics to produce meaningful data points.
- This is the beginning of the great divide in businesses today.
- Taking taxonomical look at where Big Content comes from, it’s easily visible as a sibling of true Big (Unstructured) Data and lives within the same family tree. While the Unstructured Data world describes mostly log files, sensor data, and other machine generated outputs, Unstructured Content describes the human generated information in the form of communication, documents, etc.
- Let’s look at Big Content in the lens of the 3 V’s. Should you tackle Big Content the same way as Big Data? In short, no. They tend to be inverses of one another in their extremes. Volumetrically, Big Data is about reducing petabytes to meaningful gigabytes. Big Content tends to deal with taking gigabytes or terabytes of human generated information and often creating a bit MORE data that was originally being managed. For instance, a document may contain hundreds of entities, entity relationships, sentiment scores, key phrases, etc. Big Content is about expanding that dense, single document into many data points.Speaking with respect to Velocity, the key difference resides in the way information is created in Big Data vs Big Content. While human behavior will drive some Big Data generation, ultimately it’s happening at machine speeds recording vast amounts of small pieces of information on what often seems like a single human interaction. Big Content is generated directly by humans in the form of documents, emails, reports, etc. (Though this is one area where dimensionally speaking with enough humans your Big Content can approach a Big Data problem: e.g. Twitter fire hose.)Lastly, the Variety dimension sheds some light on often overlooked differences. While Big Data comes in a multitude of forms since it’s typically stored in flat file compatible formats, Big Content systems need to deal with HUNDREDS of file types combined with DOZENS of languages. Complexity aside, when you layer in text analytics routines as part of best practices, the result is often extremely jagged data forms. While Big Data is designed to support variety, variety is a definite constant even with the same input types in Big Content.
- Take an example of email. A single communication with a customer often reveals lots of information on products, services, and more.
- What does a single email tell us? Quiet often they can be quite rich!
- If we get an even LARGER pile of content, truly Big Content, it starts to look just like the data you’re probably used to working with today.
- How do you start to connect the dots? Tried and true text analytics is our friend here! Plus, some established metadata practices from the content-world such as taxonomies and ontologies. They may already be in use by your organization! In the end, it’s all about using routines to derive the data points from the content and providing a way to connect those dots back to the business.
- Easy to look around and see Big Content in any organization. What systems even within IT do you rely on to contribute to your role? SharePoint? Documentum? File systems?