Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Semantech Inc. - Mastering Enterprise Big Data - Intro


Published on

This presentation provides the first in a series of briefings examining how Big Data solutions can be leveraged in real-world enterprises.

Published in: Technology
  • Be the first to comment

Semantech Inc. - Mastering Enterprise Big Data - Intro

  1. 1. Mastering Enterprise Big Data Concept Overview March, 2014 Copyright 2014 – Semantech Inc. 1
  2. 2. Introduction “Big Data” may very well be the most over-hyped and misunderstood trend in IT today. The goal of this presentation is to help demystify the topic by gaining a better understanding of how it ought to exploited and managed. This briefing is not meant to address key technical concepts or a “Big Data 101.” We’re going to focus instead on the real-world challenges associated with Big Data implementations in the context of the larger enterprise environment. Copyright 2014 – Semantech Inc. 2
  3. 3. Where Does Big Data Fit ? All Data-related technologies fit within the same Data Continuum – Data must be managed, it can be discovered and then hopefully exploited… Copyright 2014 – Semantech Inc. 3
  4. 4. The Appeal of Big Data The appeal of Big Data is two-fold; 1) it lies in the implied potential of technology being able to keep pace with the exponential growth of data volume and 2) there is an expectation that a leaner approach to data management will ultimately make the enterprise easier to run… These notions are both true and false. Yes, Big Data technology is designed to both increase volume and speed – yet it is not a silver bullet and many fail to recognize that any such capability must reside within already existing enterprise ecosystems. Copyright 2014 – Semantech Inc. 4
  5. 5. What is Big Data, Really? Does it refer to size, to volume to velocity, to flexibility in data types? Does it refer to the types of systems and algorithms loosely confederated under the Big Data umbrella – NoSQL, Hadoop, Key Value Pair Databases, Document Databases, Graph Databases… Is Big Data Operational or Analytic or both? The more people try to define it, the more it’s starting to sound like the all “legacy” data technologies it is supposed to be eclipsing. The truth is that there simply isn’t one standard definition that encompasses the variety of Big Data technology that now exists. It’s evolved past that point already. The main thing in common with all Big Data solutions is that they are looking to shift old paradigms. Copyright 2014 – Semantech Inc. 5
  6. 6. Where Does Big Data Fit? Are traditional Data Management and Architectures now obsolete with the oncoming waves of Big Data technology? Maybe Not – Maybe Big Data isn’t as revolutionary as we think. Copyright 2014 – Semantech Inc. 6
  7. 7. Things to Keep in Mind 1. Existing database technologies are not going away anytime soon. 2. All Data is an enterprise asset, thus all Data must be managed in the context of the larger environment. 3. Technology must serve a purpose – a new technology can help define new applications – however, to justify an investment, a valid Use Case (or Use Cases) must eventually appear. 4. Fast Data and lot’s of Data are of little value without integrity and context. 5. Databases are systems – systems have architectures and themselves form parts of larger architectures. 6. All IT solutions require Governance. Copyright 2014 – Semantech Inc. 7
  8. 8. The Real Challenge with Big Data There are three main challenges that tend to plague every Big Data project today: 1. Lack of a Strategy, Use Cases and a Value Proposition that fits not just in the context of one project, but in the context of the enterprise. 2. Lack of a Design and Governance Framework that can manage the solution lifecycle of any Big Data project. 3. Lack of an Integrated Architecture that properly leverages Big Data capabilities within the larger ecosystem of enterprise Data systems. Copyright 2014 – Semantech Inc. 8
  9. 9. How Do you Design Big Data? Traditional elements of the Data Enterprise are well understood and generally have clear expectations for both design and management. So, can Big Data fit into a picture like this? If not , is it acceptable to operate without such understanding ? 9
  10. 10. “Enterprise Big Data” Defined When Big Data was limited primarily to Hadoop databases focused on one or two internet focused Use Cases driven by Google, the idea of Big Data was much easier to grasp. Now that Big Data has “grown up,” there is no simple or standard way to view Big Data, unless we also expand our scope. Once Big Data jumped from one technology to dozens, from one Use Case to dozens, from one industry to dozens and from one prototype system to a production element within an enterprise – it became something new. It became “Enterprise Big Data” – and it’s never going back. Copyright 2014 – Semantech Inc. 10
  11. 11. “Enterprise Big Data” Defined 2 Enterprise Big Data: The collection of technologies designed to handle the explosion of data associated with 21st century IT systems and Internet applications. This technology is not meant to replace all existing database capability but rather to supplement it in cases where performance of large or complex data sets requires more dynamic and flexible management. Another key aspect is that Enterprise Big Data is just that – it is for the Enterprise, not just Google – it is a collection of technologies designed to serve the enterprise and ultimately reside within it. Copyright 2014 – Semantech Inc. 11
  12. 12. Understanding the Challenge Let’s take a look at some of the challenges associated with deploying Big Data capability in real-world enterprises 12
  13. 13. Challenge 1: Strategy The Challenge: Demonstrating a technology is one thing – a relatively easy thing. Demonstrating the value of that technology within your organization is something entirely different. How do you decide when and how to employ Big Data capability and more importantly how do you make it relevant? Typical Problems that Arise: 1. The typical web-focused Use Cases don’t seem to apply in your org. 2. There isn’t a clear path as to how the technology will improve efficiency or fuel growth. 3. The solution seems to be competing with similar capability The Strategy is missing… (both new and legacy) with no clear plan for reconciliation. Copyright 2014 – Semantech Inc. 13
  14. 14. Challenge 2: Governance The Challenge: Big Data solutions cannot exist separate from the rest of the mission and infrastructure of an enterprise. Yet, there is no standard Big Data management or Governance framework in IT. Typical Problems that Arise: 1. Data Integrity is not evaluated at all (for Big Data). 2. Solution Lifecycle Management is absent. 3. There are conflicting views as to whether it can be governed at all. 4. Big Data solutions are seriously out of touch with business needs or representatives. 5. There is no metrics framework in place to understand value. Copyright 2014 – Semantech Inc. 14
  15. 15. Challenge 3: Enterprise Data Integration The Challenge: Both at the Operational and Analytical level, Big Data represents only part of a larger picture. And it is no longer easy to determine just where Big Data fits in that big picture. In order to actualize any strategy - data discovery, exploitation and management must be integrated. Typical Problems that Arise : 1. There are no standard Big Data Architecture patterns. 2. There are often no clear design strategies for integrating Big Data with ETL, Data Warehouses and Master Data Management systems. 3. No one knows how to model Big Data. Copyright 2014 – Semantech Inc. 15
  16. 16. Top 10 Lists The Do’s and Don’t of Big Data 16
  17. 17. Top 10 Don’ts 1. Don’t initiate Big Data projects without an enterprise strategy. 2. Don’t let your techies run the project without business input. 3. Don’t assume Big Data can’t be designed, modeled or architected. 4. Don’t assume that Big Data ought to be limited to Internet or Social Media data. 5. Don’t assume 1 Big Data technology will support all of your needs. One size doesn’t fit all. 6. Don’t replace exist technologies too soon. 7. Don’t assume that Big Data will be focused on a narrow set of data formats. 8. Don’t assume that Big Data can’t be governed (as data or as systems). 9. Don’t separate management of other data systems from Big Data solutions. (Don’t reinvent the wheel) 10. Don’t start without defining your Use Cases. Copyright 2014 – Semantech Inc. 17
  18. 18. Top 10 Do’s 1. Do adopt Big Data Technology, when you’re ready and if it makes sense – but validate that first. 2. Do integrate Governance and management of Big Data with the rest of your enterprise data architecture. 3. Do design Big Data solutions – both as systems and as data. 4. Do evaluate All of the available Big Data technologies before deciding which one/s are the best fit. 5. Do integrate your existing ETL and ESB / Middleware infrastructure with your Big Data solution from the beginning. 6. Do employ both Semantic modeling and Master Data Management (MDM) for Big Data – and yes it is possible. 7. Do update and revise enterprise processes to accommodate new technology and capability when necessary. 8. Do create a security plan as part of the initial Big Data strategy, especially if that data resides in the Cloud. 9. Do your homework. Do use an Architect to help with your project. 10. Do assess and question your initial assumptions and strategy and amend after gathering lessons learned. Copyright 2014 – Semantech Inc. 18
  19. 19. Taking the First Step Mastering Enterprise Big Data… 19
  20. 20. A Realization, A Foundation The first step towards mastering enterprise Big Data is understanding the realization that regardless of whether data has a formal structure – like Third Normal Form (relational), Hierarchy, Schema Dimensions or little structure (like many Big Data solutions) – all data can be classified through Semantics. Data Classification then facilitates Data Discovery, Data Management and Data Integration. Big Data can be classified and modeled within the context of a larger paradigm. This is the first step – it is the foundation. Copyright 2014 – Semantech Inc. 20
  21. 21. Step 1: Provide the Foundation There needs to be a bridge that can span every data system, data source and element. This is our foundation 21
  22. 22. Big Data in the Enterprise We will explain this high level or Conceptual Architecture in greater depth in our next presentation 22
  23. 23. Conclusion Semantech Inc. has presented this introductory topic as the first in a series of briefings on Enterprise Big Data. The follow-on briefings will include: 1. How to Architect Enterprise Big Data Solutions 2. How to Model Enterprise Big Data 3. How to Secure Enterprise Big Data Systems 4. How to Govern Enterprise Big Data 5. Enterprise Big Data real-world Scenarios and Case Studies. 23
  24. 24. Some of Our Clients 24
  25. 25. Thank You Come visit us at: 25 25