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)

mmace@blue-granite.com

www.blue-granite.com

www.linkedin.com/in/mmace