Hadoop in the Microsoft Enterprise

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This document discusses how Hadoop can be used in the Microsoft enterprise to make sense of data fragmentation. It proposes starting simply by using Hadoop (HDInsight) for log storage. Existing ETL processes can be reused by incrementally adding Hadoop. This allows organizations to understand their data in a more complete, contextual way and take a customer-centric view, like seeing all customer interactions across systems. Starting small and getting different departments involved can encourage experimentation with Hadoop and analytics.

Technologie

Hadoop
in the Microsoft Enterprise

@DanRosanova
West Monroe Partners
http://danrosanova.wordpress.com

W hat does the Microsoft
Enterprise Look Like

The reality is slightly more complicated

Client OS Office
Active Directory

System Center
Applications
Server OS

Virtualization

NTFS
ETL OLTP OLAP
SAN / NAS

How do we
make sense of this

Fragmentation
?

Existing and New tools

SSAS

ODBC
For HIVE
+
SSIS

Applied to Customer
Transactional
Social Media
Web Logs Sqoop

Hive Flume

HDFS
IVR Logs Email & Chat
Pig
Pig

Transactional Data
ID Sector Rev Code Acct Amount
01 Q X QW 10000 1200
02 P X AB 20000 1020
03 O X CD 20000 11221
04 N XX QW 50000 2323
05 M X CD 30000 33
06 L XX AB 10000 323231
07 K X ER 20000 12
08 J XXX CD 20000 3233
09 I X QW 40000 5468
10 H X ER 50000 234
11 G O AB 55000 765
12 F X ER 70000 34538
13 E XX ER 25000 3456476
14 D XXX AB 10000 4564
15 C X QW 10000 456
16 B XX YZ 11000 44
17 A X AB 15000 456

Complete information awareness

Transactional
Temporal
Spatial

Contextual

This sounds familiar
How is this different from other
from other consolidation approaches?

Work with departmentalization

Not against it

Agile Analytics
Self Service
Accessible
Easy to use

Start Simple: Storage
$

1000
SAN

HDInsight
(Hadoop)
50

5
TB

5 25 100 1000

Create log storage
• IIS (Web Server) Logs
• Event Logs
• Trace Logs

Incorporate log analysis
• Give users access to HDInsight / Hadoop with tools
they know
• Encourage departmental experiementation
• Development and IT departments are good early
adopters
• So is marketing

Tap into existing ETL
• You already have a TON of ETL
• Reuse as much as you can
• At first aim for replication
• Incrementally add value

How most organizations
view their

Customer

Understand your data

Understand your

@Dan Rosanova
West Monroe Partners
http://danrosanova.wordpress.com

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Background: Some early applications of Computer Vision in Retail arose from e-commerce use cases - but increasingly, it is being used in physical stores in a variety of new and exciting ways, such as: ● Optimizing merchandising execution, in-stocks and sell-thru ● Enhancing operational efficiencies, enable real-time customer engagement ● Enhancing loss prevention capabilities, response time ● Creating frictionless experiences for shoppers Abstract: This talk will cover the use of Computer Vision in Retail, the implications to the broader Consumer Goods industry and share business drivers, use cases and benefits that are unfolding as an integral component in the remaking of an age-old industry. We will also take a ‘peek under the hood’ of Computer Vision and Deep Learning, sharing technology design principles and skill set profiles to consider before starting your CV journey. Deep learning has matured considerably in the past few years to produce human or superhuman abilities in a variety of computer vision paradigms. We will discuss ways to recognize these paradigms in retail settings, collect and organize data to create actionable outcomes with the new insights and applications that deep learning enables. We will cover the basics of object detection, then move into the advanced processing of images describing the possible ways that a retail store of the near future could operate. Identifying various storefront situations by having a deep learning system attached to a camera stream. Such things as; identifying item stocks on shelves, a shelf in need of organization, or perhaps a wandering customer in need of assistance. We will also cover how to use a computer vision system to automatically track customer purchases to enable a streamlined checkout process, and how deep learning can power plausible wardrobe suggestions based on what a customer is currently wearing or purchasing. Finally, we will cover the various technologies that are powering these applications today. Deep learning tools for research and development. Production tools to distribute that intelligence to an entire inventory of all the cameras situation around a retail location. Tools for exploring and understanding the new data streams produced by the computer vision systems. By the end of this talk, attendees should understand the impact Computer Vision and Deep Learning are having in the Consumer Goods industry, key use cases, techniques and key considerations leaders are exploring and implementing today.

Computer Vision: Coming to a Store Near You

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Whole genome shotgun based next generation transcriptomics and metagenomics studies often generate 100 to 1000 gigabytes (GB) sequence data derived from tens of thousands of different genes or microbial species. De novo assembling these data requires an ideal solution that both scales with data size and optimizes for individual gene or genomes. Here we developed an Apache Spark-based scalable sequence clustering application, SparkReadClust (SpaRC), that partitions the reads based on their molecule of origin to enable downstream assembly optimization. SpaRC produces high clustering performance on transcriptomics and metagenomics test datasets from both short read and long read sequencing technologies. It achieved a near linear scalability with respect to input data size and number of compute nodes. SpaRC can run on different cloud computing environments without modifications while delivering similar performance. In summary, our results suggest SpaRC provides a scalable solution for clustering billions of reads from the next-generation sequencing experiments, and Apache Spark represents a cost-effective solution with rapid development/deployment cycles for similar big data genomics problems.

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The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.

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In this session, we will delve into strategic approaches for optimizing knowledge management within Microsoft 365, amidst the evolving landscape of Copilot. From leveraging automatic metadata classification and permission governance with SharePoint Premium, to unlocking Viva Engage for the cultivation of knowledge and communities, you will gain actionable insights to bolster your organization's knowledge-sharing initiatives. In this session, we will also explore how to facilitate solutions to enable your employees to find answers and expertise within Microsoft 365. You will leave equipped with practical techniques and a deeper understanding of how there is more to effective knowledge management than just enabling Copilot, but building actual solutions to prepare the knowledge that Copilot and your employees can use.

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In an era where artificial intelligence (AI) stands at the forefront of business innovation, Information Architecture (IA) is at the core of functionality. See “There’s No AI Without IA” – (from 2016 but even more relevant today) Understanding and leveraging how Information Architecture (IA) supports AI synergies between knowledge engineering and prompt engineering is critical for senior leaders looking to successfully deploy AI for internal and externally facing knowledge processes. This webinar be a high-level overview of the methodologies that can elevate AI-driven knowledge processes supporting both employees and customers. Core Insights Include: Strategic Knowledge Engineering: Delve into how structuring AI's knowledge base is required to prevent hallucinations, enable contextual retrieval of accurate information. This will include discussion of gold standard libraries of use cases support testing various LLMs and structures and configurations of knowledge base. Precision in Prompt Engineering: Learn the art of crafting prompts that direct AI to deliver targeted, relevant responses, thereby optimizing customer experiences and business outcomes. Unified Approach for Enhanced AI Performance: Explore the intersection of knowledge and prompt engineering to develop AI systems that are not only more responsive but also aligned with overarching business strategies. Guiding Principles for Implementation: Equip yourself with best practices, ethical guidelines, and strategic considerations for embedding these technologies into your business ecosystem effectively. This webinar is designed to empower business and technology leaders with the knowledge to harness the full potential of AI, ensuring their organizations not only keep pace with digital transformation but lead the charge. Join us to map a roadmap to fully leverage Information Architecture (IA) and AI chart a course towards a future where AI is a key pillar of strategic innovation and business success.

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Hadoop in the Microsoft Enterprise

1. Hadoop in the Microsoft Enterprise @DanRosanova West Monroe Partners http://danrosanova.wordpress.com

2. W hat does the Microsoft Enterprise Look Like

3. Microsoft Enterprise - Basics

4. The reality is slightly more complicated Client OS Office Active Directory System Center Applications Server OS Virtualization NTFS ETL OLTP OLAP SAN / NAS

5. Data lives here

6. Users live here

7. System Administrators Live HERE

8. Many enterprises are fragmented

9. VERY fragmented

10. How do we make sense of this Fragmentation ?

11.

12. Into this

13.

14. Existing and New tools SSAS ODBC For HIVE + SSIS

15. Applied to Customer Transactional Social Media Web Logs Sqoop Hive Flume HDFS IVR Logs Email & Chat Pig Pig

16. Transactional Data ID Sector Rev Code Acct Amount 01 Q X QW 10000 1200 02 P X AB 20000 1020 03 O X CD 20000 11221 04 N XX QW 50000 2323 05 M X CD 30000 33 06 L XX AB 10000 323231 07 K X ER 20000 12 08 J XXX CD 20000 3233 09 I X QW 40000 5468 10 H X ER 50000 234 11 G O AB 55000 765 12 F X ER 70000 34538 13 E XX ER 25000 3456476 14 D XXX AB 10000 4564 15 C X QW 10000 456 16 B XX YZ 11000 44 17 A X AB 15000 456

17. Transactional + Temporal Data

18. Complete information awareness Transactional Temporal Spatial Contextual

19. This sounds familiar How is this different from other from other consolidation approaches?

20. Work with departmentalization Not against it

21. Agile Analytics Self Service Accessible Easy to use

22. How do you eat an elephant

23. Start Simple: Storage $ 1000 SAN HDInsight (Hadoop) 50 5 TB 5 25 100 1000

24. Create log storage • IIS (Web Server) Logs • Event Logs • Trace Logs

25. Incorporate log analysis • Give users access to HDInsight / Hadoop with tools they know • Encourage departmental experiementation • Development and IT departments are good early adopters • So is marketing

26. Tap into existing ETL • You already have a TON of ETL • Reuse as much as you can • At first aim for replication • Incrementally add value

27. Why do all this?

28. How most organizations view their Customer

29. Some organizations see

30. What we should see

31. Understand your data Understand your @Dan Rosanova West Monroe Partners http://danrosanova.wordpress.com

Hadoop in the Microsoft Enterprise

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