Big Data for Executives 2014: Day 2 Highlights
Highlights from the presentations by Big Data experts from McKinsey Solutions, SAP, Techfetch, Weather Analytics on Day 2 of Big Data for Executives 2014.
To help its readers succeed in their Analytics pursuits, KDnuggets provides concise summaries from selected talks at the event. These concise, takeaway-oriented summaries are designed for both – people who attended the event but would like to re-visit the key talks for a deeper understanding and people who could not attend the event. As you go through it, for any session that you find interesting, check KDnuggets as we would soon publish exclusive interviews with some of these speakers.
Highlights from Day 1 (Monday, May 5)
Here are highlights from Day 2 (Tuesday, May 6):
- Often 100 data points are more than enough
- Often value comes from combining data
- In operations, often granularity matters
He stressed that we need to think dynamically forward. Referring to velocity, volume, variability and sophistication as the four major technological and architecture challenges of Big Data, he said that all of these cannot be solved by the same solution patterns. He demonstrated the key components of operational data architecture and analytical data architecture, focusing on the seamless integration of both to deliver advanced capabilities.
Data warehousing (DW) appliances have matured into enterprise-grade DW platforms. Business Analytics thrives on cheap horsepower offered by DW appliances, which are 20 times faster for less than half the total cost of patchwork systems. The increasing need for speed and exponentially decreasing DRAM prices are leading to the rise of in-memory analytics. Next, he discussed the importance of unstructured data warehousing, as nearly 95% of enterprise data is not structured. After discussing other components of architecture, he emphasized that there is a great need to:
- Change mind-sets
- Have a clear strategy
- Have a killer plan
He concluded stating “Machine Learning is great but keep the humans involved”.
- Use cases
- Information and application architecture
- Standards and processes
- People, skills and culture
- Governance
Discussing about identifying business outcomes, he gave the following steps:
- Define/Refine Use Cases
- Data Discovery
- Initial Evaluation. Get started, experiment, and learn along the way
- Information Architecture Design
It is important to assess and identify capability gaps through Architecture Gap Analysis and Comprehensive Gap Analysis. Finally, talking about building a business case, he mentioned that Advanced Analytics provides answers that cannot be provided by traditional business intelligence. He also stated that shareholder impact and value of Advanced Analytics grows with ambitiousness of use cases.
- Shorter Product Development Cycle
- Global Team
- Level Playing Field (SaaS, PaaS, IaaS)
Emphasizing on the immense demand for Big Data professionals, he shared the report from Gartner stating that Big Data will create 1.9 million IT jobs in US by 2015. Sharing his thoughts on how to attract IT workforce, he suggested company professionals/recruiters to aggressively network, attend conferences, and create webinars & white papers. He concluded by giving the following tips for entrepreneurs:
- Come out of comfort zone
- Take calculated risk
- Create opportunity for you & others
- advanced technological integrations,
- intuitive and smart visualizations,
- robust and relevant convergence algorithms,
- user-friendly APIs
He concluded his talk with use cases of intelligent weather data for engineering, insurance, telematics and national intelligence.
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