mailto:nuttall_gary@Hotmail.com

PLAN1. INTRODUCTION & OBJECTIVES

2. DEFINITIONS

3. NOSTALGIA ISN’T WHAT IT USED TO BE

4. CURRENT LANDSCAPE

5. MYSTIC MEG

6. USEFUL RESOURCES

7. Q&A

PLAN1. INTRODUCTION & OBJECTIVES

2. DEFINITIONS

3. NOSTALGIA ISN’T WHAT IT USED TO BE

4. CURRENT LANDSCAPE

5. MYSTIC MEG

6. USEFUL RESOURCES

7. Q&A

1 - INTRODUCTION

1 - INTRODUCTION

DISCLAIMER

ALL VIEWS ARE MY OWN

BASED ON 25 YEARS EXPERIENCE

VENDORS MAY NOT LIKE WHAT I SAY!

MENTION OF PRODUCTS, TOOLS, SERVICES & COMPANIES SHOULD

NOT BE TREATED AS AN ENDORSEMENT (OR A CRITICISM)

NAMES HAVE BEEN CHANGED TO PROTECT THE GUILTY!

IF YOU’D LIKE A COPY OF THE PRESENTATION THEN GET IN TOUCH

1 – INTRODUCTION – YOU?

1 – INTRODUCTION – OBJECTIVE

• Based on experience gained in various industries

• Lessons learned and shared are relevant for both B2B and B2C

• Purpose: To chart the history of Business Intelligence and forecast future capabilities

• Objective: To be educational & provoke thought!

PLAN1. INTRODUCTION & OBJECTIVES

2. DEFINITIONS

3. NOSTALGIA ISN’T WHAT IT USED TO BE

4. CURRENT LANDSCAPE

5. MYSTIC MEG

6. USEFUL RESOURCES

7. Q&A

2 - DEFINITIONS

1. Business Intelligence

2. Data vs Information

3. Management Information

4. Data Warehouse

5. Data Mining

6. Analytics

7. Big Data

2 - DEFINITIONS

1 – Business Intelligence

2 - DEFINITIONS

Business

(from www.Dictionary.com)

2 - DEFINITIONS

Intelligence

(from www.Dictionary.com)

2 - DEFINITIONS

Business Intelligence

(from www.Dictionary.com)

2 - DEFINITIONS

Business Intelligence

(from www.Wikipedia.com)

The earliest definition of business intelligence (BI), in an October 1958 IBM Journal article by

H.P. Luhn, A Business Intelligence System, describes a system that will:

"...utilize data-processing machines for auto-abstracting and auto-

encoding of documents and for creating interest profiles for each of the

'action points' in an organization. Both incoming and internally

generated documents are automatically abstracted, characterized by a

word pattern, and sent automatically to appropriate action points."

2 - DEFINITIONS

1 - Business Intelligence

© Gary Nuttall 2015

A pragmatic definition for this presentation:

"the effective transformation of data

into information

to make better informed decisions"

2 - DEFINITIONS

2 - Data vs Information ?

(from www.Wikipedia.com)

Data, information and knowledge are closely related concepts, but each

has its own role in relation to the other. Data is collected and

analyzed to create information suitable for making decisions, while

knowledge is derived from extensive amounts of experience dealing with

information on a subject. For example, the height of Mt. Everest is

generally considered data. This data may be included in a book along

with other data on Mt. Everest to describe the mountain in a manner

useful for those who wish to make a decision about the best method to

climb it. Using an understanding based on experience climbing mountains

to advise persons on the way to reach Mt. Everest's peak may be seen as

"knowledge".

2 - DEFINITIONS

3 - Management Information (Systems)

(from www.inspiredbusinessintelligence.me)

MI (Management Information) is data collected for the monitoring and

reporting of the business in general. It can be measured and compared

against previously collected data to provide Performance Indicators of

how the business is running. Good examples of MI could be; indicators

or Staff Sickness Levels, previous period(s) sales, production

statistics.

BI (Business Intelligence) is a set of methodologies, processes,

architectures, and technologies that transform raw data into meaningful

and useful information used to enable more effective strategic,

tactical, and operational insights and decision-making.

2 - DEFINITIONS

3 - MIS

2 - DEFINITIONS

4 - Data Warehouse

(from www.oracle.com)

A data warehouse is a relational database that is designed for query and

analysis rather than for transaction processing. It usually contains

historical data derived from transaction data, but it can include data

from other sources. It separates analysis workload from transaction

workload and enables an organization to consolidate data from several

sources.

In addition to a relational database, a data warehouse environment

includes an extraction, transportation, transformation, and loading

(ETL) solution, an online analytical processing (OLAP) engine, client

analysis tools, and other applications that manage the process of

gathering data and delivering it to business users.

2 - DEFINITIONS

Data Warehouse

(from www.oracle.com)

2 - DEFINITIONS

5 - Data Mining

(from www.saedsayad.com)

2 - DEFINITIONS

6 - Analytics

(from www.Wikipedia.com)

Analytics is the discovery and communication of meaningful patterns in

data. Especially valuable in areas rich with recorded information,

analytics relies on the simultaneous application of statistics, computer

programming and operations research to quantify performance. Analytics

often favors data visualization to communicate insight.

2 - DEFINITIONS

7 – Big Data

2 - DEFINITIONS

7 – Big Data

2 - DEFINITIONS

CONCLUSIONS ?

(from www.Wikipedia.com)

Business Intelligence is about presenting information to make better

informed decisions (in whatever “business” the domain is)

A Data Warehouse is an architectural approach to how data is extracted

and stored for the purpose of downstream consumption

Analytics is the application of computation to identify patterns in data

“Big Data” is more, varied, faster, data……and now an accepted term

PLAN1. INTRODUCTION & OBJECTIVES

2. DEFINITIONS

3. NOSTALGIA ISN’T WHAT IT USED TO BE

4. CURRENT LANDSCAPE

5. MYSTIC MEG

6. USEFUL RESOURCES

7. Q&A

3 - PAST

The earliest definition of business intelligence (BI), in an October 1958 IBM Journal article by

H.P. Luhn, A Business Intelligence System

3 - PASTEarlier….(1869 – Napoleon’s march on Russia in 1812).

3 - PASTEarlier….(1869 – Napoleon’s march on Russia).

Charles Minard's map of

Napoleon's disastrous

Russian campaign of 1812.

The graphic is notable for

its representation in two

dimensions of six types of

data: the number of

Napoleon's troops; distance;

temperature; the latitude

and longitude; direction of

travel; and location

relative to specific dates

Data Mashup & Data

Visualization!

3 - PASTEven earlier….(400BC – Roman Census ).

The census was first instituted by Servius Tullius, sixth king of Rome.

After the abolition of the monarchy and the founding of the Republic, the

consuls had responsibility for the census until 443 BC.

(from www.Wikipedia.com)

3 - PASTEven, even earlier….(Caveman ?).

PLAN1. INTRODUCTION & OBJECTIVES

2. DEFINITIONS

3. NOSTALGIA ISN’T WHAT IT USED TO BE

4. CURRENT LANDSCAPE

5. MYSTIC MEG

6. USEFUL RESOURCES

7. Q&A

4 – CURRENT LANDSCAPE

1. Vendors

2. Capabilities

3. Platforms

4 – CURRENT LANDSCAPE

1 - Vendors

(from www.gartner.com)

4 – CURRENT LANDSCAPE

1 - Vendors

(from www.gartner.com)

4 – CURRENT LANDSCAPE

2 - Capabilities

4 – CURRENT LANDSCAPE

2 - Capabilities

4 – CURRENT LANDSCAPE

3 - Platforms

PLAN1. INTRODUCTION & OBJECTIVES

2. DEFINITIONS

3. NOSTALGIA ISN’T WHAT IT USED TO BE

4. CURRENT LANDSCAPE

5. MYSTIC MEG

6. USEFUL RESOURCES

7. Q&A

5 – FUTURE ?

1. Hype Cycle

2. Mega Data ?

3. Trends (BI on BI)

4. Crystal Ball

5 - FUTURE

1 – Hype Cycle

(from www.gartner.com)

Technology TriggerA potential technology

breakthrough kicks things

off

Peak of Inflated ExpectationsEarly publicity produces

a number of success

stories—often accompanied

by scores of failures

Trough of DisillusionmentEarly publicity produces

a number of success

stories—often accompanied

by scores of failures

Slope of EnlightenmentMore instances of how the

technology can benefit

the enterprise start to

crystallize and become

more widely understood

Plateau of ProductivityMainstream adoption

starts to take off

5 - FUTURE

1 – Hype Cycle

(from www.gartner.com)

5 - FUTURE

1 – Hype Cycle

(from www.gartner.com)

5 - FUTURE

1 – Hype Cycle

(from www.gartner.com)

5 - FUTURE

(from meetupmashup.blogspot.com)

2 – Mega Data

5 - FUTURE

(from meetupmashup.blogspot.com)

2 – Mega Data

5 - FUTURE

(from www.google.co.uk/trends)

3 - Trends

5 - FUTURE

© Gary Nuttall 2015

4 – Crystal Ball

• Data Federation

• Calculation moves onto data storage

• Cloud vs Appliances ?

• Machine Learning, Machine Intelligence, Artificial Intelligence

• Merging of Augmented Reality with Data Visualisation

• Integration of IoT derived data

• Increased use of Geospatial Data

• Segmentation of One (Marketing Holy Grail)

• Cross-discipline development

5 - FUTURE

(from www.ibm.com)

4 – Crystal Ball - Data Federation

5 - FUTURE

(from sql Saturday)

4 – Crystal Ball - Calculation moves onto data storage

5 - FUTURE

© Gary Nuttall 2015

4 – Crystal Ball - Cloud vs Appliances ?

5 - FUTURE

© Gary Nuttall 2015

4 – Crystal Ball - Machine Learning, Machine Intelligence, Artificial Intelligence

5 - FUTURE

© Gary Nuttall 2015

4 – Crystal Ball - Merging of Augmented Reality with Data Visualisation

5 - FUTURE

© Gary Nuttall 2015

4 – Crystal Ball - Integration of

IoT derived data

5 - FUTURE

© Gary Nuttall 2015

4 – Crystal Ball - Increased use of Geospatial Data

5 - FUTURE

© Gary Nuttall 2015

4 – Crystal Ball - Segmentation of One (Marketing Holy Grail)

5 - FUTURE

© Gary Nuttall 2015

4 – Crystal Ball - Cross-discipline development

5 - FUTURE

© Gary Nuttall 2015

4 – Crystal Ball

• Data Federation

• Calculation moves onto data storage

• Cloud vs Appliances ?

• Machine Learning, Machine Intelligence, Artificial Intelligence

• Merging of Augmented Reality with Data Visualisation

• Integration of IoT derived data

• Increased use of Geospatial Data

• Segmentation of One (Marketing Holy Grail)

• Cross-discipline development

5 - FUTURE

© Gary Nuttall 2015

The future is already here

PLAN1. INTRODUCTION & OBJECTIVES

2. DEFINITIONS

3. NOSTALGIA ISN’T WHAT IT USED TO BE

4. CURRENT LANDSCAPE

5. MYSTIC MEG

6. USEFUL RESOURCES

7. Q&A

6 – USEFUL RESOURCES

www.Wikipedia.com

www.LinkedIn.com

www.Gartner.com

MeetupMashup.Blogspot.com

PLAN1. INTRODUCTION & OBJECTIVES

2. DEFINITIONS

3. NOSTALGIA ISN’T WHAT IT USED TO BE

4. CURRENT LANDSCAPE

5. MYSTIC MEG

6. USEFUL RESOURCES

7. Q&A

oEMAIL: NUTTALL_GARY@HOTMAIL.COM

oTWITTER: @GPN01

oLINKEDIN: HTTP://WWW.LINKEDIN.COM/IN/GARYNUTTALL

oMEETUP: MEETUP MASHUP LONDON: HTTP://WWW.MEETUP.COM/MEETUP-MASHUP-

LONDON/

oBLOGGER: HTTP://MEETUPMASHUP.BLOGSPOT.CO.UK/

7 – QUESTIONS ?