Predictive Analytics on Big Data. DIY or BUY?


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Customer expectations for relevant and individualized experiences are rising and evolving at breakneck speed. This has enterprises working furiously at building data infrastructure to collect and store data. But collecting and storing is only the beginning. The technology and know-how to derive value from data—to do predictive analytics on big data—is fast becoming the critical competitive differentiator for businesses.

Join Apigee’s Abhi Rele and Alan Ho as they discuss the market dynamics of predictive analytics and big data and the key capabilities needed to deliver the adaptive apps and APIs every business needs to remain relevant and be competitive.

Join to Discuss:
- Data lakes, machine learning, unstructured data processors, real-time access, APIs—the capabilities to rapidly deliver predictive analytics on big data
- Getting from data lake to production app - how putting big data to use and deriving real value requires a fresh approach
- Pros and cons for the build vs. buy decision to deliver adaptive apps and APIs

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  • Businesses today operate in a new normal.

    They need to operate differently in order to be relevant and competitive in this digital world that is very competitive and evolving very rapidly.

    There are 3 big trends here
    The proliferation of digital and mobile is causing businesses to adopt an Omni-channel mindset and approach. Its no longer sufficient to just be present on the web, you need to be where your customers are. You need to develop a holistic view of how your customers are interacting with you across all channels. And you need to deliver a contextual journey where each interaction builds upon the prior one.
    Evolving customer expectations is forcing companies to think hard about individualizing each interaction. Our expectations of what technology can and should do is evolving. We expect digital interactions – we want it to be easy to complete our tasks using whatever device we have at hand, we expect to receive recommendations and advice to help us complete our task. But not just any advice – it needs to be relevant and useful otherwise we tune out. We switch brands very easily. So companies need to tailor each interaction to each individual.
    In a competitive digital world where consumers make decisions largely using digital input and JUST in time, companies need to be proactive – be a step ahead of their customers in anticipating what they might want and giving it to them before they even ask…

    It turns out that PA on BD along with APIs are well suited to help companies thrive in this new normal. BD can ensure they are working with all their data, PA can help them individualize and be proactive, and API serve a dual purpose to capture interaction data and also deliver experiences on multiple channels.

    Alan to jump in – its not so easy though…move to next slide and talk to challenges

    They need to be omnichannel – be where their customers are – mobile, web, in store, partner, or newer wearable devices, and so on. They need to be able to analyze and mine all the interactions that their customers have – be it on the web, or mobile, or in store or via personal wearable device - to understand what their customers are trying to accomplish. They need to be able to individualize each interaction so its useful and relevant. They need to anticipate what their customers might do next so they can be a step ahead and delight their customers every single step.

    Businesses have been doing personalization. Are you saying that’s not sufficient?

    In many ways it isn’t. Traditionally, enterprises have developed a few large segments. These would be based on demographic attributes or simple behaviorial attributes such as new vs returning customer, first time vs repeat buyer, recent visitor and so on, and these behavioral attributes would be sourced from just that channel. So the targeting would be very coarse, and the personalization – we’ll we’ve all experienced receiving recommendations and offers and scratching out heads about how that company ever came up with it. So businesses do need to operate differently.

    In the same way its also no longer sufficient to react to an issue after it occurs – the digital world is far too competitive – someone else is likely to veer your customers away with a better offer or experience

  • Enterprises struggling with a number of technical challenges….

    Sounds like businesses are struggling with a number of challenges in a new digital world that they don’t quite fully understand, is very competitive, and and one that’s evolving very rapidly

  • The key conflict is whether they should use OS or buy a product. That is the key question

  • And when they try to evaluate which one to choose, they’re finding there’s no silver bullet

    When they consider DIY with Open source, …. But…

    When they consider BUY product they… but ….

  • Lets take a step back and discuss what it takes to build apps and APIs powered by predictive analytics….
  • Hadoop….Open source winning here…lots of investment across verticals

    Is Hadoop sufficient to analyze and mine entitles and events – essentially the vast amounts of digital interactions?

    You need specialized data structures – relational not sufficient. Too many tables to join, Queries to hard to design, difficult to make it perform well and so on…
  • Descriptive Analytics

    2 types – Simple and complex

    Simple – give example use case. Open source good enough. Give example of open source tools available.

    Complex - give example - understand customer journey and what they did next after receiving an offer

    Connect back to why you need data structure to analyze the journey. No good open source here.

  • Predictive Analytics requires more investigation. There’s no open source winner and businesses really need to make sure they can analyze interaction data effectively otherwise they wont get superior precision.

  • 4 key things to look for….

    Open source?

  • Often overlooked but is where rubber hits the road?

  • If businesses want to build it themselves, there are 4 key considerations
    What is the maturity of the open source offerings they need for their use cases?
    Do they have the right skills and experitise to build and maintain the offering?
    Can they execute flawlessly to deliver the solution that meet the needs of the business?
    Is the TCO acceptable? – the total cost of ownership of building, maintaining and growing the offering
  • On the other hand

    If businesses want to buy a product there are 4 key considerations
    How real is the product? Very important, given all the hype around big data and predictive analytics
    How has the product accelerated time to market for other enterprises?
    How much control and flexibility does the product provide them? Can they deploy multiple use cases?
    What is the true ROI?

  • Predictive Analytics on Big Data. DIY or BUY?

    1. 1. Predictive Analytics on Big Data DIY or BUY?
    2. 2. @karlunho Alan Ho @abhirele Abhi Rele
    3. 3.
    4. 4.
    5. 5. Use BIGDATA10 for 10% off
    6. 6. Agenda • Predictive analytics on big data • Businesses are conflicted • Forging a path forward CC-BY-SA
    7. 7. Why predictive analytics on big data? CC-BY-SA
    8. 8. The new normal • Omni-channel • Individualized • Proactive CC-BY-SA
    9. 9. Challenges • Data lakes: learning to swim • Predictive analytics: in flux • Open source: rapid innovation • Got data scientists? • Point solutions CC-BY-SA
    10. 10. Key conflict DIY with open source OR BUY product CC-BY-SA
    11. 11. Evaluating options CC-BY-SA DIY BUY Pros • Control • Cost savings • Time to market • Market evolution Cons • Expertise • Risk • Hype
    12. 12. CC-BY-SA Data lake Descriptive analytics Predictive analytics Integration Monitoring&mgmt. Mobile Web Kiosk IoT Unstructured & structured data Event & entity data Real-time & batch data Partner Internal & external data
    13. 13. Data lake • Hadoop • Entities and events CC-BY-SA Data lake Descriptive analytics Predictive analytics Integration Monitoring&mgmt.
    14. 14. Descriptive analytics • Simple • Complex CC-BY-SA Data lake Descriptive analytics Predictive analytics Integration Monitoring&mgmt.
    15. 15. Predictive analytics • Summarized vs. fine-grain data • Unstructured data • No open source winner • Difficult to use • Mahout vs. Oryx vs. RHadoop CC-BY-SA Data lake Descriptive analytics Predictive analytics Integration Monitoring&mgmt.
    16. 16. Integration • APIs vs. useful APIs • Real time • Scalability • Security CC-BY-SA Data lake Descriptive analytics Predictive analytics Integration Monitoring&mgmt.
    17. 17. Monitoring & mgmt. • Achilles heel • Model performance • Model deployment • Availability CC-BY-SA Data lake Descriptive analytics Predictive analytics Integration Monitoring&mgmt.
    18. 18. to summarize…
    19. 19. DIY or BUY? CC-BY-SA
    20. 20. CC-BY-SA Data lake Descriptive analytics Predictive analytics Integration Monitoring&mgmt. Mobile Web Kiosk IoT Unstructured & structured data Event & entity data Real-time & batch data Partner Internal & external data
    21. 21. DIY considerations • Maturity of open source • Skills and expertise • Ability to execute • TCO CC-BY-SA
    22. 22. BUY considerations • Hype vs. reality • Time to market • Control & flexibility • True ROI CC-BY-SA
    23. 23. Use BIGDATA10 for 10% off
    24. 24. Questions? @karlunho Alan Ho @abhirele Abhi Rele
    25. 25. Thank you!