The document summarizes the presentation given by Dr. Andreas Both on challenges related to big data and the data economy. It discusses Unister's evolution from an internet startup in 2002 to a large company with over 1500 employees in a decade. Unister succeeded by integrating diverse data sets to improve user experience, developing data analysis processes, and defining descriptive analysis processes to handle increasing data volumes, though analysis capabilities reached limits due to many business segments. Overall the presentation addressed the steps a startup must take regarding data access, integration and analysis, and emphasized that managing data is key for data-driven companies but challenging given the volume, variety and velocity of big data.
Bi isn't big data and big data isn't BI (updated)mark madsen
Big data is hyped, but isn't hype. There are definite technical, process and business differences in the big data market when compared to BI and data warehousing, but they are often poorly understood or explained. BI isn't big data, and big data isn't BI. By distilling the technical and process realities of big data systems and projects we can separate fact from fiction. This session examines the underlying assumptions and abstractions we use in the BI and DW world, the abstractions that evolved in the big data world, and how they are different. Armed with this knowledge, you will be better able to make design and architecture decisions. The session is sometimes conceptual, sometimes detailed technical explorations of data, processing and technology, but promises to be entertaining regardless of the level.
Yes, it’s about the data normally called “big”, but it’s not Hadoop for the database crowd, despite the prominent role Hadoop plays. The session will be technical, but in a technology preview/overview fashion. I won’t be teaching you to write MapReduce jobs or anything of the sort.
The first part will be an overview of the types, formats and structures of data that aren’t normally in the data warehouse realm. The second part will cover some of the basic technology components, vendors and architecture.
The goal is to provide an overview of the extent of data available and some of the nuances or challenges in processing it, coupled with some examples of tools or vendors that may be a starting point if you are building in a particular area.
The way we make decisions has changed. The data we use has changed. The techniques we can apply to data and decisions have changed. Yet what we build and how we build it has barely changed in 20 years.
The definition of madness is doing more of what you already do and expecting different results. The threat to the data warehouse is not from new technology that will replace the data warehouse. It is from destabilization caused by new technology as it changes the architecture, and from failure to adapt to those changes.
The technology that we use is problematic because it constrains and sometimes prevents necessary activities. We don’t need more technology and bigger machines. We need different technology that does different things. More product features from the same vendors won’t solve the problem.
The data we want to use is challenging. We can’t model and clean and maintain it fast enough. We don’t need more data modeling to solve this problem. We need less modeling and more metadata.
And lastly, a change in scale has occurred. It isn’t a simple problem of “big”. The problem with current workloads has been solved, despite the performance problems that many people still have today. Scale has many dimensions – important among them are the number of discrete sources and structures, the rate of change of individual structures, the rate of change in data use, the variety of uses and the concurrency of those uses.
In short, we need new architecture that is not focused on creating stability in data, but one that is adaptable to continuous and rapidly changing uses of data.
The Essential Toolkit for Your: EDRM Renovation Australia 2017Steven Oest
The Essential Toolkit for Your: EDRM Renovation Australia 2017
A One-Day Hands-On Repair Workshop Module
1st February 2017, Novotel Sydney Central #edrmaus
http://www.arkgroupaustralia.com.au/events/edrm-renovation-australia-2017/
Hear practical hands - on and case study presentations from:
ActewAGL
University of Sydney
NSW Department of Finance, Services and Innovation
Johnson and Johnson Medical
Optus
AvePoint
The modern user is technologically savvy. As Records and EDRM professionals – if we are not giving them the functionality they are used to in their daily life, they will quickly not adopt or adapt.
What can we do as professionals to not only keep up with the technology trends but also embed information governance, effective change management, user updates and get IT on board?
In this renovation day forum, our case study based presentations will help attendees to break down their problems with systems, strategies and demonstrate how to build them back up.
Feedback from the last EDRM Essentials:
Great and vast experience (MIKTYSH)
Very informative with lots of great innovations to think about (RACGP)
Good engagement (Maroondah City Council)
Great collaboration of experiences in IM (RACGP)
Lots of great innovative information (HUNTER TAFE, TAFE NSW)
Variety of speakers and opportunities for networking (Optus Business)
Great workshops driven from these experiences, some great stories provided (SAI Global)
An estimated 85 percent of companies allow employees to bring their own computers, tablets and smartphones to work and sync them with the organization’s email, file servers and databases. Bring your own device (BYOD) can be advantageous for both employees and corporations but it doesn’t come without risk. 4imprint’s newest Blue Paper®, podcast and infographic, Bring Your Own Device (BYOD) to Work: How It Can Be a Thirst Quencher for Your Company, explores the benefits of BYOD including increased productivity and improved responsiveness and also discusses how organizations can mitigate the associated technology and security risks.
Data lakes, data exhaust, web scale, data is the new oil. Vendors are throwing new terms and analogies at us to convince us to buy their products as the market around data technologies grows. We change data persistence and transaction layers because "databases don't scale" or because data is "unstructured". If data had no structure then it wouldn't be data, it would be noise. Schema on read, schema on write, schemaless databases; they imply structure underlying the data. All data has schema, but that word may not mean what you think it means.
This presentation will describe concepts of data storage and retrieval from technology prehistory (i.e. before the 1980s) and examine the design principles behind both old and new technology for managing data because sometimes post-relational is actually pre-relational. It is important to separate what is identical to things that were tried in the past from new twists on old topics that deliver new capabilities.
Directly related to these topics are performance, scalability and the realities of what organizations do with data over time. All of these topics should guide architecture decisions to avoid the trap of creating technical debts that must be paid later, after systems are in place and change is difficult.
Bi isn't big data and big data isn't BI (updated)mark madsen
Big data is hyped, but isn't hype. There are definite technical, process and business differences in the big data market when compared to BI and data warehousing, but they are often poorly understood or explained. BI isn't big data, and big data isn't BI. By distilling the technical and process realities of big data systems and projects we can separate fact from fiction. This session examines the underlying assumptions and abstractions we use in the BI and DW world, the abstractions that evolved in the big data world, and how they are different. Armed with this knowledge, you will be better able to make design and architecture decisions. The session is sometimes conceptual, sometimes detailed technical explorations of data, processing and technology, but promises to be entertaining regardless of the level.
Yes, it’s about the data normally called “big”, but it’s not Hadoop for the database crowd, despite the prominent role Hadoop plays. The session will be technical, but in a technology preview/overview fashion. I won’t be teaching you to write MapReduce jobs or anything of the sort.
The first part will be an overview of the types, formats and structures of data that aren’t normally in the data warehouse realm. The second part will cover some of the basic technology components, vendors and architecture.
The goal is to provide an overview of the extent of data available and some of the nuances or challenges in processing it, coupled with some examples of tools or vendors that may be a starting point if you are building in a particular area.
The way we make decisions has changed. The data we use has changed. The techniques we can apply to data and decisions have changed. Yet what we build and how we build it has barely changed in 20 years.
The definition of madness is doing more of what you already do and expecting different results. The threat to the data warehouse is not from new technology that will replace the data warehouse. It is from destabilization caused by new technology as it changes the architecture, and from failure to adapt to those changes.
The technology that we use is problematic because it constrains and sometimes prevents necessary activities. We don’t need more technology and bigger machines. We need different technology that does different things. More product features from the same vendors won’t solve the problem.
The data we want to use is challenging. We can’t model and clean and maintain it fast enough. We don’t need more data modeling to solve this problem. We need less modeling and more metadata.
And lastly, a change in scale has occurred. It isn’t a simple problem of “big”. The problem with current workloads has been solved, despite the performance problems that many people still have today. Scale has many dimensions – important among them are the number of discrete sources and structures, the rate of change of individual structures, the rate of change in data use, the variety of uses and the concurrency of those uses.
In short, we need new architecture that is not focused on creating stability in data, but one that is adaptable to continuous and rapidly changing uses of data.
The Essential Toolkit for Your: EDRM Renovation Australia 2017Steven Oest
The Essential Toolkit for Your: EDRM Renovation Australia 2017
A One-Day Hands-On Repair Workshop Module
1st February 2017, Novotel Sydney Central #edrmaus
http://www.arkgroupaustralia.com.au/events/edrm-renovation-australia-2017/
Hear practical hands - on and case study presentations from:
ActewAGL
University of Sydney
NSW Department of Finance, Services and Innovation
Johnson and Johnson Medical
Optus
AvePoint
The modern user is technologically savvy. As Records and EDRM professionals – if we are not giving them the functionality they are used to in their daily life, they will quickly not adopt or adapt.
What can we do as professionals to not only keep up with the technology trends but also embed information governance, effective change management, user updates and get IT on board?
In this renovation day forum, our case study based presentations will help attendees to break down their problems with systems, strategies and demonstrate how to build them back up.
Feedback from the last EDRM Essentials:
Great and vast experience (MIKTYSH)
Very informative with lots of great innovations to think about (RACGP)
Good engagement (Maroondah City Council)
Great collaboration of experiences in IM (RACGP)
Lots of great innovative information (HUNTER TAFE, TAFE NSW)
Variety of speakers and opportunities for networking (Optus Business)
Great workshops driven from these experiences, some great stories provided (SAI Global)
An estimated 85 percent of companies allow employees to bring their own computers, tablets and smartphones to work and sync them with the organization’s email, file servers and databases. Bring your own device (BYOD) can be advantageous for both employees and corporations but it doesn’t come without risk. 4imprint’s newest Blue Paper®, podcast and infographic, Bring Your Own Device (BYOD) to Work: How It Can Be a Thirst Quencher for Your Company, explores the benefits of BYOD including increased productivity and improved responsiveness and also discusses how organizations can mitigate the associated technology and security risks.
Data lakes, data exhaust, web scale, data is the new oil. Vendors are throwing new terms and analogies at us to convince us to buy their products as the market around data technologies grows. We change data persistence and transaction layers because "databases don't scale" or because data is "unstructured". If data had no structure then it wouldn't be data, it would be noise. Schema on read, schema on write, schemaless databases; they imply structure underlying the data. All data has schema, but that word may not mean what you think it means.
This presentation will describe concepts of data storage and retrieval from technology prehistory (i.e. before the 1980s) and examine the design principles behind both old and new technology for managing data because sometimes post-relational is actually pre-relational. It is important to separate what is identical to things that were tried in the past from new twists on old topics that deliver new capabilities.
Directly related to these topics are performance, scalability and the realities of what organizations do with data over time. All of these topics should guide architecture decisions to avoid the trap of creating technical debts that must be paid later, after systems are in place and change is difficult.
A sample of my book "Business unIntelligence - Insight and Innovation beyond Analytics and Big Data", published by Technics Publications, 2013.
Chapter 5 shows the evolution of the Data Warehouse architecture and provides a description of some aspects of a modern Information architecture.
The book can be ordered in hard and softcopy formats at http://bit.ly/BunI-TP1
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How are machine learning and artificial intelligence revolutionizing insurance?
This presentation explains it briefly, including current trends and effects on the business.
The 3 Key Barriers Keeping Companies from Deploying Data Products Dataiku
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- Identifying, defining, and prioritizing valuable problems
- Building the right teams
- Leveraging the proper tools and platforms
- Iterating and deploying effectively
- Reaching end-users to generate value
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It's part of a Data Science Corner Campaign where I will be discussing the fundamentals of DataScience, AIML, Statistics etc.
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A sample of my book "Business unIntelligence - Insight and Innovation beyond Analytics and Big Data", published by Technics Publications, 2013.
Chapter 5 shows the evolution of the Data Warehouse architecture and provides a description of some aspects of a modern Information architecture.
The book can be ordered in hard and softcopy formats at http://bit.ly/BunI-TP1
Challenges & Opportunities the Data Privacy Act BringsRobert 'Bob' Reyes
My slide deck used in People Management Association of the Philippines' (PMAP) Data Privacy Act Forum held last 18 SEP 2017 at Ace Hotel & Suites, Pasig City.
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Opening Keynote by Stefan Wrobel, Institute Director, Fraunhofer IAIS / Member of the board of BITKOM working group Big Data at the European Data Forum 2014, 19 March 2014 in Athens, Greece: Value of Big Data - From Data-Driven Enterprises to a Data-driven Economy
How are machine learning and artificial intelligence revolutionizing insurance?
This presentation explains it briefly, including current trends and effects on the business.
The 3 Key Barriers Keeping Companies from Deploying Data Products Dataiku
Getting from raw data to deploying data-driven solutions requires technology, data, and people. All of which exist. So why aren’t we seeing more truly data-driven companies: what's missing and why? During Strata Hadoop World Singapore 2015, Pauline Brown, Director of Marketing at Dataiku, explains how lack of collaboration is what is keeping companies from building and deploying data products effectively. Learn more about Dataiku and Data Science Studio: www.dataiku.com
20 Emerging influencers in 2020 for big dataRiver11river
You might have not heard most of these names yet, but you surely will soon. This list is designed to recognize emerging talent in the fields of data and analytics – mostly entrepreneurs and up-and-coming talent who are informing, educating and inspiring others through data. They come from different sectors and backgrounds – from data architecture to visualization. The one thing that unites them is their passion for data.
Dr. Martin Mocker, Scientist, MIT, über Erkenntnisse der Spitzenforschung in der Entwicklung von IT-Organisationen, die helfen werden, über mögliche Modelle der Zukunft nachzudenken.
Best Practices for Scaling Data Science Across the OrganizationChasity Gibson
Effective data science in the enterprise is about aligning the right model, data, and infrastructure with the right outcomes. Most organizations today struggle to unlock the potential of data science to enhance decision-making and drive business value.
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- Identifying, defining, and prioritizing valuable problems
- Building the right teams
- Leveraging the proper tools and platforms
- Iterating and deploying effectively
- Reaching end-users to generate value
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It's part of a Data Science Corner Campaign where I will be discussing the fundamentals of DataScience, AIML, Statistics etc.
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This edition features a handful of Backup Solution Providers in several sectors that are at the forefront of leading us into a digital future.
Read More: https://insightssuccess.com/the-05-best-backup-solution-providers-to-watch-in-2022-july2022/
LinkedIn Executive Summit: From Data Driven to the Data RevolutionLinkedIn D-A-CH
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EDF2012 Andreas Both - From data-driven startup to large company in a decade
1. From data-driven startup to large company in a decade
Dr. Andreas Both
Head of Research and Development
Unister GmbH
Germany
European Data Forum 2012
Copenhagen, June 6-7, 2012
2. Claim
. . . challenges of Big Data and the emerging Data Economy
and to develop suitable action plans for addressing these
challenges . . .
Slide 2 Dr. Andreas Both, Head of R&D, Unister
3. Claim
. . . challenges of Big Data and the emerging Data Economy
and to develop suitable action plans for addressing these
challenges . . .
Slide 2 Dr. Andreas Both, Head of R&D, Unister
4. Claim
. . . challenges of Big Data and the emerging Data Economy
and to develop suitable action plans for addressing these
challenges . . .
Slide 2 Dr. Andreas Both, Head of R&D, Unister
5. Claim
. . . challenges of Big Data and the emerging Data Economy
and to develop suitable action plans for addressing these
challenges . . .
Slide 2 Dr. Andreas Both, Head of R&D, Unister
7. Personal Retrospective . . .
. . . joining Unister, April 2010
eCommerce is challenging
complexity of business
everything has to work smoothly
punishment comes quickly
Slide 3 Dr. Andreas Both, Head of R&D, Unister
8. Personal Retrospective . . .
. . . joining Unister, April 2010 some lessons
eCommerce is challenging improve your business model
complexity of business care about our customers
everything has to work smoothly
punishment comes quickly
Slide 3 Dr. Andreas Both, Head of R&D, Unister
9. Personal Retrospective . . .
. . . joining Unister, April 2010 some lessons
eCommerce is challenging improve your business model
complexity of business care about our customers
everything has to work smoothly rapide impact of decisions
punishment comes quickly increasing data complexity
Slide 3 Dr. Andreas Both, Head of R&D, Unister
10. It’s about the business activities, stupid.
Should we really talk about data?
Why should we talk about data processes?
Why should we talk about the data analytics?
Slide 4 Dr. Andreas Both, Head of R&D, Unister
11. It’s about the business activities, stupid.
Should we really talk about data?
Why should we talk about data processes?
Why should we talk about the data analytics?
Is it just about the business?
Is data a core value?
Slide 4 Dr. Andreas Both, Head of R&D, Unister
12. IT companies vs. traditional business
classic business e-business
need natural ressource need data
to establish processes to establish processes
depending on location independent from location
Slide 5 Dr. Andreas Both, Head of R&D, Unister
13. IT companies vs. traditional business
classic business e-business
need natural ressource need data
to establish processes to establish processes
depending on location independent from location
SME will find many niches within the market.
Slide 5 Dr. Andreas Both, Head of R&D, Unister
14. IT companies vs. traditional business
classic business e-business
need natural ressource need data
to establish processes to establish processes
depending on location independent from location
SME will find many niches within the market.
. . . establish large companies too!
Slide 5 Dr. Andreas Both, Head of R&D, Unister
15. Unister’s Success Story
Internet startup mainly located in Leipzig, Germany
private limited company (German GmbH)
founded in 2002, 5 founders
managing director: Thomas Wagner
a
Slide 6 Dr. Andreas Both, Head of R&D, Unister
16. Unister’s Success Story
Internet startup mainly located in Leipzig, Germany
private limited company (German GmbH)
founded in 2002, 5 founders
managing director: Thomas Wagner
eCommerce company, B2C
national and international activities
> 40 web-portals and services
> 13.22 mio. unique user / month in Germanya
a
AGOF e.V. / internet facts 2012-01
Slide 6 Dr. Andreas Both, Head of R&D, Unister
17. Unister’s Success Story
Internet startup mainly located in Leipzig, Germany
private limited company (German GmbH)
founded in 2002, 5 founders
managing director: Thomas Wagner
eCommerce company, B2C
national and international activities
> 40 web-portals and services
> 13.22 mio. unique user / month in Germanya
IT-driven approach
a
AGOF e.V. / internet facts 2012-01
Slide 6 Dr. Andreas Both, Head of R&D, Unister
19. Unister’s Success Story
Number of Employees
1530
1157
Employees
701
372
185
106
7 38
1
2003 2004 2005 2006 2007 2008 2009 2010 2011
Slide 7 Dr. Andreas Both, Head of R&D, Unister
20. IT companies
Data is the foundation
getting data is tough
having data is not enough
managing data is challenging
Slide 8 Dr. Andreas Both, Head of R&D, Unister
21. IT companies
Data is the foundation
getting data is tough
having data is not enough
managing data is challenging
Next parts of the talk
Data Access
Data Integration
(Big) Data Analyses
Slide 8 Dr. Andreas Both, Head of R&D, Unister
22. IT companies
Data is the foundation
getting data is tough
having data is not enough
managing data is challenging
Next parts of the talk
Data Access
← Steps a start-up has to tackle!
Data Integration
(Big) Data Analyses
Slide 8 Dr. Andreas Both, Head of R&D, Unister
23. IT companies
Data is the foundation
getting data is tough
having data is not enough
managing data is challenging
Next parts of the talk
Data Access
← Steps a start-up has to tackle!
Data Integration
Steps Unister had tackled.
(Big) Data Analyses
Slide 8 Dr. Andreas Both, Head of R&D, Unister
24. IT companies
Data is the foundation
getting data is tough
having data is not enough
managing data is challenging
Next parts of the talk
Data Access
← Steps a start-up has to tackle!
Data Integration
Steps Unister had tackled.
(Big) Data Analyses Steps Unister has to tackle.
Slide 8 Dr. Andreas Both, Head of R&D, Unister
26. Data Access
Observations
business models need access
of data
support, description,
enrichment, . . .
Slide 10 Dr. Andreas Both, Head of R&D, Unister
27. Data Access
Observations Unister’s Success (step 1)
business models need access was capable of integrating
of data many data sets
support, description,
user-focussed data
enrichment, . . .
Slide 10 Dr. Andreas Both, Head of R&D, Unister
28. Data Access
Observations Unister’s Success (step 1)
business models need access was capable of integrating
of data many data sets
support, description,
user-focussed data
enrichment, . . .
eCommerce demand
local information
link to local events
(legal) contraints
→ such data not available
Slide 10 Dr. Andreas Both, Head of R&D, Unister
29. Data Access
Observations Unister’s Success (step 1)
business models need access was capable of integrating
of data many data sets
support, description,
user-focussed data
enrichment, . . .
Challenges eCommerce demand
Open Data should be local information
established link to local events
Standards have to be defined (legal) contraints
and followed
→ such data not available
Slide 10 Dr. Andreas Both, Head of R&D, Unister
30. Data Access: Needed Actions
Open Data initiatives need support
enhanced tool support
political commitment
Slide 11 Dr. Andreas Both, Head of R&D, Unister
31. Data Access: Needed Actions
Open Data initiatives need support
enhanced tool support
political commitment
will establish (local) data economics
locally connected companies could grow
Slide 11 Dr. Andreas Both, Head of R&D, Unister
33. Data Integration
Observations
bread and butter of
data-driven companies
needs much effort
Slide 13 Dr. Andreas Both, Head of R&D, Unister
34. Data Integration
Observations Unister’s Success (step 2)
bread and butter of fusion of different data sets
data-driven companies leads to good user experience
needs much effort
Slide 13 Dr. Andreas Both, Head of R&D, Unister
35. Data Integration
Observations Unister’s Success (step 2)
bread and butter of fusion of different data sets
data-driven companies leads to good user experience
needs much effort
eCommerce demand
matching processes
integration tools
Slide 13 Dr. Andreas Both, Head of R&D, Unister
36. Data Integration
Observations Unister’s Success (step 2)
bread and butter of fusion of different data sets
data-driven companies leads to good user experience
needs much effort
Challenges eCommerce demand
establish distributed matching processes
knowledge base
integration tools
Linked Data paradigm
NOSQL + SQL
Slide 13 Dr. Andreas Both, Head of R&D, Unister
37. Data Integration: Needed Actions
support Linked Data Cloud
companies need sound and solid data sets
Slide 14 Dr. Andreas Both, Head of R&D, Unister
38. Data Integration: Needed Actions
support Linked Data Cloud
companies need sound and solid data sets
research on scalable data integration
processes
Cloud Computing → Big Data challenge
Slide 14 Dr. Andreas Both, Head of R&D, Unister
39. Data Analyses and Big Data
Slide 15 Dr. Andreas Both, Head of R&D, Unister
40. Data Analyses and Big Data
Observations
disseminate, understand and
ultimately benefit from
increasing volumes of data
example: social networks
Slide 16 Dr. Andreas Both, Head of R&D, Unister
41. Data Analyses and Big Data
Observations Unister’s Success (step 3)
disseminate, understand and defining data analysis
ultimately benefit from processes with impact
increasing volumes of data pareto-optimal processes lead
example: social networks to good coverage
analysis came to a limit
because of many segments
Slide 16 Dr. Andreas Both, Head of R&D, Unister
42. Data Analyses and Big Data
Observations Unister’s Success (step 3)
disseminate, understand and defining data analysis
ultimately benefit from processes with impact
increasing volumes of data pareto-optimal processes lead
example: social networks to good coverage
analysis came to a limit
because of many segments
eCommerce demand
descriptive analyses processes
higher-level process interfaces
good developers
Slide 16 Dr. Andreas Both, Head of R&D, Unister
43. Data Analyses and Big Data
Observations Unister’s Success (step 3)
disseminate, understand and defining data analysis
ultimately benefit from processes with impact
increasing volumes of data pareto-optimal processes lead
example: social networks to good coverage
analysis came to a limit
because of many segments
Challenges eCommerce demand
... descriptive analyses processes
higher-level process interfaces
good developers
Slide 16 Dr. Andreas Both, Head of R&D, Unister
44. Data Analyses and Big Data: Global Movement
source: ucsd.edu
Slide 17 Dr. Andreas Both, Head of R&D, Unister
45. Data Analyses and Big Data: Challenges
source: hadapt.com
Slide 18 Dr. Andreas Both, Head of R&D, Unister
46. Data Analyses and Big Data: Challenges
The 3 V
Volume
Varity
Velocity
Slide 19 Dr. Andreas Both, Head of R&D, Unister
47. Data Analyses and Big Data: Challenges
The 3 V
Volume
Varity
Velocity
The +2 V
Virality
Viscosity
Slide 19 Dr. Andreas Both, Head of R&D, Unister
48. Data Analyses and Big Data: Needed Actions
handle this is all about knowledge . . .
Slide 20 Dr. Andreas Both, Head of R&D, Unister
49. Data Analyses and Big Data: Needed Actions
handle this is all about knowledge . . .
levels of challenge
Slide 20 Dr. Andreas Both, Head of R&D, Unister
50. Data Analyses and Big Data: Needed Actions
handle this is all about knowledge . . .
levels of challenge
need systems for big data analyses
Slide 20 Dr. Andreas Both, Head of R&D, Unister
51. Data Analyses and Big Data: Needed Actions
handle this is all about knowledge . . .
levels of challenge
need systems for big data analyses
good support: Cloud Computing, . . .
Slide 20 Dr. Andreas Both, Head of R&D, Unister
52. Data Analyses and Big Data: Needed Actions
handle this is all about knowledge . . .
levels of challenge
need systems for big data analyses
good support: Cloud Computing, . . .
need people to operate on the systems
Slide 20 Dr. Andreas Both, Head of R&D, Unister
53. Data Analyses and Big Data: Needed Actions
handle this is all about knowledge . . .
levels of challenge
need systems for big data analyses
good support: Cloud Computing, . . .
need people to operate on the systems
→ ok: some experience available
Slide 20 Dr. Andreas Both, Head of R&D, Unister
54. Data Analyses and Big Data: Needed Actions
handle this is all about knowledge . . .
levels of challenge
need systems for big data analyses
good support: Cloud Computing, . . .
need people to operate on the systems
→ ok: some experience available
need people to develop applications
Slide 20 Dr. Andreas Both, Head of R&D, Unister
55. Data Analyses and Big Data: Needed Actions
handle this is all about knowledge . . .
levels of challenge
need systems for big data analyses
good support: Cloud Computing, . . .
need people to operate on the systems
→ ok: some experience available
need people to develop applications
→ bad: rarely teached
Slide 20 Dr. Andreas Both, Head of R&D, Unister
56. Data Analyses and Big Data: Needed Actions
handle this is all about knowledge . . .
levels of challenge
need systems for big data analyses
good support: Cloud Computing, . . .
need people to operate on the systems
→ ok: some experience available
need people to develop applications
→ bad: rarely teached
need people to think about using the network effect
Slide 20 Dr. Andreas Both, Head of R&D, Unister
57. Data Analyses and Big Data: Needed Actions
handle this is all about knowledge . . .
levels of challenge
need systems for big data analyses
good support: Cloud Computing, . . .
need people to operate on the systems
→ ok: some experience available
need people to develop applications
→ bad: rarely teached
need people to think about using the network effect
→ very bad: Talent Gap
Slide 20 Dr. Andreas Both, Head of R&D, Unister
58. Data Analyses and Big Data: Potential
Big data – The next frontier for innovation, competition,
and productivity
(McKinsey May 2011)
Slide 21 Dr. Andreas Both, Head of R&D, Unister
59. Data Analyses and Big Data: Potential
Big data – The next frontier for innovation, competition,
and productivity
(McKinsey May 2011)
Plenty of possibilities!
Slide 21 Dr. Andreas Both, Head of R&D, Unister
60. Summary: Most important activities
Open Data will give a push
well-developed tools are crucial for SME
talent gap has to be tackled
Slide 22 Dr. Andreas Both, Head of R&D, Unister
61. Conclusion
Data Access, Data Integration, Data Analyses
Slide 23 Dr. Andreas Both, Head of R&D, Unister
62. Conclusion
Data Access, Data Integration, Data Analyses
Big Data
Slide 23 Dr. Andreas Both, Head of R&D, Unister
63. Conclusion
Data Access, Data Integration, Data Analyses
Big Data successful companies
Slide 23 Dr. Andreas Both, Head of R&D, Unister
64. From data-driven
startup to large company
in a decade
Dr. Andreas Both andreas.both@unister.de
Head of R&D +49 341 65050 24496
Unister GmbH http://www.unister.de
Slide 24 Dr. Andreas Both, Head of R&D, Unister