BIG Data Analytics Move to Competitive Advantage

Transcription

1 BIG Data Analytics Move to Competitive Advantage

2 where is technology heading today Standardization Open Source Automation Scalability Cloud Computing Mobility Smartphones/ tablets Internet of Things Wireless App stores Browsers Web-by languages DevOps Big Data No SQL Analytics Scale-out-storage Data vs. Models

3 big question what is big data evolution or revolution of business intelligence who is using big data how should practitioners proceed

4 what is big data Big data isn t just a technology it s a business strategy for capitalizing on information resources Getting started is crucial Success at each entry point is accelerated by products within the Big Data platform Build the foundation for future requirements by expanding further into the big data platform

5 what is big data Cost effectively manage and analyze all available data in its native form unstructured, structured, streaming Website Social Media Billing ERP CRM RFID Network Switches

6 Top 5 Challenges what are big data challenges Unlock big data quickly get a view and understand big data sources. Analyze raw data ingest and analyze data in its native format. Simplify your warehouse optimize your warehouse by offloading deep analytics tasks to purpose-built appliances. Reduce cost with right solution offload workloads and data sets to cost-efficient processing solutions. Analyze data in motion harness streaming data and analyze it.

7 Customer need Understand existing data sources unlock big data Search and navigate data within existing systems No copying of data Value statement Get up and running quickly Discover and retrieve big data Work even with big data sources by business users Solution assurebi, Hadoop, IBM etc.

8 Customer need Ingest data as-is into Hadoop Combine it with data from DWH analyse raw data Process very large volume of data Value statement Gain new insight Overcome the high cost of converting data from unstructured to structured format Experiment with analysis on different data and combine them with other sources Solution assurebi, Hadoop, IBM etc.

9 merging traditional and big data approaches Traditional Approach Structured & Repeatable Analysis Big Data Approach Iterative & Exploratory Analysis Business Users Determine what question to ask IT Delivers a platform to enable creative discovery IT Structures the data to answer that question Business Explores what questions could be asked Monthly sales reports Profitability analysis Customer surveys Brand sentiment Product strategy Maximum asset utilization

10 Web-based analysis and visualization Spreadsheet-like interface Define and manage long running data collection jobs spreadsheet style analysis Analyze content of the text on the pages that have been retrieved

11 big insights and the data warehouse Big Data analytic applications BigInsights Traditional analytic tools Data warehouse Filter Transform Aggregate

12 real-life challenges I need to evaluate the possible relationship between client salary and overdrafts Analyst OK. We have to evaluate a lot of statistics, set the correct db indexes and db partitioning. It will take us 5 days. IT

13 real-life challenges Great. Thanks a lot. I m going to check the results. Done. You can run your analytical query. Analyst IT After 5 days...

14 real-life challenges Great. I can see here some nice Noooo!!! correlations. Now I need to look It s at not it possible to work from the different perspective. here! Ohhh, welcome dear friend. Understand. So, it s. another 5 days of our work Analyst IT After 10 minutes...

15 evolution or revolution of business intelligence It is very common to find data in disparate silos inside organizations, with a strong degree of territorial or technical boundaries around the data. Even seasoned data professionals can find the world of big data overwhelming. A company might be collecting market research interviews, a stream of information from social networks, supply chain data and sales figures from multiple sites. Which source is the most important? How can they be combined to maximum effect?

16 evolution or revolution of business intelligence Companies need to ensure that data-driven thinking is not confined to the IT department. It is about how you use data and add a layer of interpretation to it in order to get to the answer that you are looking for. Skilled data scientists are in short supply and high demand. This sophisticated kind of large data analytic work requires people who are not only capable, but desirous of this kind of work. There is a fairly limited category of professionals who get up in the morning wanting to go to work and do this kind of thing. The right person will possess knowledge of the sector in which that person s company operates, as well as the skills required to work with large data sets. Companies should also think hard about how to communicate the results they derive from their data work to employees with different needs and different levels of expertise.

17 evolution or revolution of business intelligence Forrester says big data encompasses "techniques and technologies that make capturing value from data at an extreme scale economical. Volume of data combined with multiple disparate sources Speed of processing needed Big Data solutions are being leveraged to break existing processing bottlenecks Data mining multiple sources Analyze relationships across many multiple sources of data including structured data from existing legacy systems combined with unstructured data from crowd sourced information High speed data analytics

18 who is using data Executives from a diverse range of sectors, including education and public services, say that their organization plans to or is already collecting many types of data. High-performing companies are more in touch with data than their less-successful rivals. Being able to do things in real time makes people think differently about the problem.

19 how do you get started?: assurebi approach Develop list of opportunities to leverage Big Data Identify Data Source Assess Data Quality Gaps Develop Data Aggregation Approach Do you have the required tools? Create Data Improvement Plan Simulate Data Aggregation on few cases. Assess quality and results Execute in Phase

20 and now with

21 assurebi approach I need to evaluate the possible relationship between client salary and overdrafts. I will use assurebi. Analyst IT

22 Great. I can see here some nice correlations. Now I need to look at it from the different perspective. With assurebi I can run the query immediately. The response will be in the same time Analyst assurebi approach IT IT can do something else much more useful

23 assurebi approach

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