Looking at Business Intelligence - past, present and future

2. PLAN
1. 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. PLAN
1. 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. 1 - INTRODUCTION

5. 1 - INTRODUCTION

6. 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

7. 1 – INTRODUCTION – YOU?

8. 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!

9. PLAN
1. 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

10. 2 - DEFINITIONS
1. Business Intelligence
2. Data vs Information
3. Management Information
4. Data Warehouse
5. Data Mining
6. Analytics
7. Big Data

11. 2 - DEFINITIONS
1 – Business Intelligence

12. 2 - DEFINITIONS
Business
(from www.Dictionary.com)

13. 2 - DEFINITIONS
Intelligence
(from www.Dictionary.com)

14. 2 - DEFINITIONS
Business Intelligence
(from www.Dictionary.com)

15. 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."

16. 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"

17. 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".

18. 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.

19. 2 - DEFINITIONS
3 - MIS

20. 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.

21. 2 - DEFINITIONS
Data Warehouse
(from www.oracle.com)

22. 2 - DEFINITIONS
5 - Data Mining
(from www.saedsayad.com)

23. 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.

24. 2 - DEFINITIONS
7 – Big Data

25. 2 - DEFINITIONS
7 – Big Data

26. 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

27. PLAN
1. 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

28. 3 - PAST
The earliest definition of business intelligence (BI), in an October 1958 IBM Journal article by
H.P. Luhn, A Business Intelligence System

29. 3 - PAST
Earlier….(1869 – Napoleon’s march on Russia in 1812).

30. 3 - PAST
Earlier….(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!

31. 3 - PAST
Even 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)

32. 3 - PAST
Even, even earlier….(Caveman ?).

33. PLAN
1. 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

34. 4 – CURRENT LANDSCAPE
1. Vendors
2. Capabilities
3. Platforms

35. 4 – CURRENT LANDSCAPE
1 - Vendors
(from www.gartner.com)

36. 4 – CURRENT LANDSCAPE
1 - Vendors
(from www.gartner.com)

37. 4 – CURRENT LANDSCAPE
2 - Capabilities

38. 4 – CURRENT LANDSCAPE
2 - Capabilities

39. 4 – CURRENT LANDSCAPE
3 - Platforms

40. PLAN
1. 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

41. 5 – FUTURE ?
1. Hype Cycle
2. Mega Data ?
3. Trends (BI on BI)
4. Crystal Ball

42. 5 - FUTURE
1 – Hype Cycle
(from www.gartner.com)
Technology Trigger
A potential technology
breakthrough kicks things
off
Peak of Inflated Expectations
Early publicity produces
a number of success
stories—often accompanied
by scores of failures
Trough of Disillusionment
Early publicity produces
a number of success
stories—often accompanied
by scores of failures
Slope of Enlightenment
More instances of how the
technology can benefit
the enterprise start to
crystallize and become
more widely understood
Plateau of Productivity
Mainstream adoption
starts to take off

43. 5 - FUTURE
1 – Hype Cycle
(from www.gartner.com)

44. 5 - FUTURE
1 – Hype Cycle
(from www.gartner.com)

45. 5 - FUTURE
1 – Hype Cycle
(from www.gartner.com)

46. 5 - FUTURE
(from meetupmashup.blogspot.com)
2 – Mega Data

47. 5 - FUTURE
(from meetupmashup.blogspot.com)
2 – Mega Data

48. 5 - FUTURE
(from www.google.co.uk/trends)
3 - Trends

49. 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

50. 5 - FUTURE
(from www.ibm.com)
4 – Crystal Ball - Data Federation

51. 5 - FUTURE
(from sql Saturday)
4 – Crystal Ball - Calculation moves onto data storage

52. 5 - FUTURE
© Gary Nuttall 2015
4 – Crystal Ball - Cloud vs Appliances ?

53. 5 - FUTURE
© Gary Nuttall 2015
4 – Crystal Ball - Machine Learning, Machine Intelligence, Artificial Intelligence

54. 5 - FUTURE
© Gary Nuttall 2015
4 – Crystal Ball - Merging of Augmented Reality with Data Visualisation

55. 5 - FUTURE
© Gary Nuttall 2015
4 – Crystal Ball - Integration of
IoT derived data

56. 5 - FUTURE
© Gary Nuttall 2015
4 – Crystal Ball - Increased use of Geospatial Data

57. 5 - FUTURE
© Gary Nuttall 2015
4 – Crystal Ball - Segmentation of One (Marketing Holy Grail)

58. 5 - FUTURE
© Gary Nuttall 2015
4 – Crystal Ball - Cross-discipline development

59. 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

60. 5 - FUTURE
© Gary Nuttall 2015
The future is already here

61. PLAN
1. 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

62. 6 – USEFUL RESOURCES
www.Wikipedia.com
www.LinkedIn.com
www.Gartner.com
MeetupMashup.Blogspot.com

63. PLAN
1. 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

64. 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 ?