Competing with analytics: Rethinking on a large scale
Term Time Frame Specific Meaning
Corporate analytics 1954-1970 UPS created the first corporate analytics
group in 1954
Decision support 1970-1985 Use of data analysis to support decision
making
Executive support 1980-1990 Focus on data analysis for decisions by
senior executives
Online analytical processing 1990-2000 Software for analyzing multi-dimensional
data tables
Business Intelligence 2000-2010 Tools to support data driven decisions with
emphasis on “dashboards”
Big Data 2010-Today Focus on very large, unstructured, fast
moving, internal and external data
Making Sense of Data through the Years
Source: Big Data at Work, Thomas Davenport
Better, faster decision making through the large-scale capture of internal data, and analysis of that data with high-speed queries and reports
Manual, Paper-Based Digital, Collaborative
Fast Facts
Client: Life Care Centers
Industry: Skilled Nursing Facilities
Location: Cleveland, TN
Market: Healthcare CAM
Solution: Enterprise DW
Dashboard & Reports
Users: Human Resources
Finance, Operations
Platform: SQL Server Enterprise
SQL Analysis Services
SharePoint Enterprise
Office Excel
Business intelligence dashboards for 650+ managers & executives
Insights into key metrics such as clinical, census, expenses, and labor
Saved millions of dollars through better decision-making for labor and expenses
Case Study: Life Care Centers of America
“Without dramatic improvements, data volumes
from next-generation sensors and the complexity
of integrated systems will far outpace the ability for
analytics to consume it.”
Dr. Carey Swartz
Data to Decisions Team Lead
Office of Naval Research
Pentagon
“The hidden trap with business intelligence occurs when…
looking for current patterns of business activity and
strengthen those patterns. This is the essence of driving
forward by looking in the rearview mirror. That leads to
stagnation and creating core rigidities that will eventually
bring the company down. ”
Mark P. McDonald
VP, Head of Research at Gartner
Exponential Growth in
Data, to 40ZB by 2020
Varied Nature of our Data,
with 80% of the world’s
data Unstructured
Value at High Volume,
finding patterns in
historical data
1 Megabyte: A small novel OR A 3.5 inch floppy disk
2 Megabytes: A high resolution photograph
1 Gigabyte: A movie at TV quality
10 Terabytes: The collection of the US Library of Congress
2 Petabytes: All US academic research libraries
5 Exabytes: All words ever spoken by human beings
42 Zettabytes: Storage requirements for all human speech
ever spoken at if digitized as 16 kHz 16-bit audio as of 2003
Modern Sources Driving Growth in Data Volumes
20% Structured 80% Semi-Structured
Combining data from external systems, the Internet, sensors, public data, audio/video and more
Business Intelligence Big Data
Type of data Formatted in rows and columns Unstructured/semi-structured
Volume of data 10’s of terabytes or less 100’s of terabytes to petabytes
Flow of data Static collection of data Constant flow of data
Analysis methods Hypothesis-based Machine learning
Primary purpose Internal decision support Data-based products, services
Differences between conventional analytics and big data
Source: Big Data at Work, Thomas Davenport
U.S. agriculture and livestock firms support: $140B dairy industry with 9 million cows (13 million fewer than 1950)
$68B beef industry with 97 million cattle
Embedding sensors in cow stomachs, noses; sensor “pills” last for 80-100 days inside stomach
Measure temperature, bacteria, blood, heart rate, stomach acidity, GPS location, and more
Data transmitted via Bluetooth to neck collar, then WIFI transmission to server in the Cloud; analysis available on any mobile device
Combined with traditional data from weight scales, milk production, beef production, operations/sales/marketing
Data collected and analyzed at high rates to: Maximize milk production for “precision dairy farming”
Catch digestive problems early
Immediate response to sickness or pregnancy
Adjust diet and environment conditions, continuously test results
Improve animal health, wellbeing, and profitability
The Digital Cow
Scenario: You are the CIO of a mid-market organization and are intrigued by the prospect of big data.
You’ve assembled a team of senior leaders from across the organization to do some heavy thinking about where big data fits, what it could do for the company, and where to begin.
3 Minutes per Section
How to begin the journey for your organization: Developing a Big Data Strategy
Industry and Organization
Describe your industry and business that you’ll use for developing your big data strategy.
Discuss quickly as a group and pick a common business/industry that you are most comfortable with.
Business Objective
Big data can help with cost reductions, effective decision making, or product/service improvement.
What do you want from big data?
Where will you focus? What is your primary goal?
Modern Data Sources
What untapped, unstructured data sources could be used for your big data initiative?
What traditional, structured data will you combine with non-traditional, unstructured sources to find new value for the business?
Big Data Analytics
Big data analytics are typically created with machine learning tools, in high-performance environments.
What story should your data tell you?
What specific metrics/analytics are you looking?
How might big data integrate with your existing business intelligence environment?
Telecomm
Retail Industry
Manufacturing
• Operational dashboards
• Customer scorecards
• Proactive maintenance
• Infrastructure investment
• Bandwidth allocation
• Recommendation engine
• Brand health
• Price sensitivity
• Product mix
• Web path optimization
• A/B testing
• Quality control
• Supply chain management
• Proactive equipment maintenance
• Yield maximization
• Crowd sourced quality assurance
Matthew Mace
BlueGranite, Inc.
877.817.0736 ext. 701 (voicemail)
269.312.7479 (office)
269.760.8314 (mobile)
www.blue-granite.com
www.linkedin.com/in/mmace
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