Big data gets all the hype today, but enterprises around the globe continue to be run by big spreadsheets – spreadsheets with few controls, analytics or simple ways of testing, validating or detecting patterns
Join our #DataTalk on Thursdays at 5 p.m. ET. This week, we tweeted with Dr. Michael Wu, the Chief Scientist at Lithium, where he applies data-driven methodologies to investigate the complex dynamics of the social web.
Michael works with big data and has developed many predictive and prescriptive social analytics with actionable insights. His R&D won him the recognition as a 2010 Influential Leader by CRM Magazine.
You can see all tweets and resources here:
http://www.experian.com/blogs/news/about/data-scientists/
Extended discourse on the importance of data science governance for production ML and how GDPR can become the catalyst but also generate value for organizations!
Big Data, Data Science, Machine learning creating tremendous value in the education sector. Combination of open source with IBM value adds create compelling value. Artificial intelligence will revolutionize the sector with making education more relevant with Cognitive capabilities of students.
Moving Data Science from an Event to A Program: Considerations in Creating Su...Domino Data Lab
The exponential growth of Big Data and Analytics has outpaced the ability of organizations to govern their data appropriately. The ability to reuse the work done by data scientists work is becoming an economic necessity. The mix of data sources is changing from tradition transactional and ERP systems to include a mix of structured, semi-structured and unstructured data. Data Governance needs to adapt to these changes. This session discusses these data changes and proposed how to adapt current data governance processes. These include, how the concept of a stakeholder has changed and the need for expansion of communications and content management. We look at need to consolidate data from disparate systems and how it governed. Lastly we will investigate how context is emerging as an important factor in governance and how it can be leveraged to provide for accurate, reliable data reuse.
Focus on Your Analysis, Not Your SQL CodeDATAVERSITY
Analysts in the line of business deal with a myriad of time-consuming data preparation and analytic challenges that often require IT or DBA intervention to deliver a requested dataset. Others have taught themselves “enough SQL to be dangerous”, learning the necessary code to extract the data needed to answer their business question. Self-service data analytics empowers these business analysts to take control of the entire analytics process, delivering the necessary results for better business decisions.
Join us to learn how self-service data analytics allows analysts to:
- Utilize a drag-and-drop workflow for data and analytic processes without writing code
- Minimize data movement and ensure data integrity through in-database capabilities
- Easily work across relational and non-relational databases to deliver faster business results
Self-service data analytics delivers a repeatable process that is transparent to not only business analysts, but also SQL coders and decision makers across the organization.
Are you your company’s chief data officer? Given the scarcity of the official role, it’s likely that you’re not — at least in title. But that doesn't mean that you shouldn't operate like one. Do you approach data leadership as a C-level executive or a senior data head? Is your team’s output strategic or just operational? In this interactive keynote, one of the Windy City’s foremost data leaders will lead an interactive discussion on what it takes to lead like a chief, what it looks like, and how to get there and get it done.
Data-Ed Webinar: Demystifying Big Data DATAVERSITY
We are in the middle of a data flood and we need to figure out how to tame it without drowning. Most of what has been written about Big Data is focused on selling hardware and services. But what about a Big Data Strategy that guides hardware and software decisions? While virtually every major organization is faced with the challenge of figuring out the approach for and the requirements of this new development, jumping into the fray hastily and unprepared will only reproduce the same dismal IT project results as previously experienced. Join Dr. Peter Aiken as he will debunk a number of misconceptions about Big Data as your un-typical IT project. He will provide guidance on how to establish realistic Big Data management plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers without getting lost in the hype.
Takeaways:
- The means by which Big Data techniques can complement existing data management practices
- The prototyping nature of practicing Big Data techniques
- The distinct ways in which utilizing Big Data can generate business value
- Bigger Data isn’t always Better Data
Join our #DataTalk on Thursdays at 5 p.m. ET. This week, we tweeted with Dr. Michael Wu, the Chief Scientist at Lithium, where he applies data-driven methodologies to investigate the complex dynamics of the social web.
Michael works with big data and has developed many predictive and prescriptive social analytics with actionable insights. His R&D won him the recognition as a 2010 Influential Leader by CRM Magazine.
You can see all tweets and resources here:
http://www.experian.com/blogs/news/about/data-scientists/
Extended discourse on the importance of data science governance for production ML and how GDPR can become the catalyst but also generate value for organizations!
Big Data, Data Science, Machine learning creating tremendous value in the education sector. Combination of open source with IBM value adds create compelling value. Artificial intelligence will revolutionize the sector with making education more relevant with Cognitive capabilities of students.
Moving Data Science from an Event to A Program: Considerations in Creating Su...Domino Data Lab
The exponential growth of Big Data and Analytics has outpaced the ability of organizations to govern their data appropriately. The ability to reuse the work done by data scientists work is becoming an economic necessity. The mix of data sources is changing from tradition transactional and ERP systems to include a mix of structured, semi-structured and unstructured data. Data Governance needs to adapt to these changes. This session discusses these data changes and proposed how to adapt current data governance processes. These include, how the concept of a stakeholder has changed and the need for expansion of communications and content management. We look at need to consolidate data from disparate systems and how it governed. Lastly we will investigate how context is emerging as an important factor in governance and how it can be leveraged to provide for accurate, reliable data reuse.
Focus on Your Analysis, Not Your SQL CodeDATAVERSITY
Analysts in the line of business deal with a myriad of time-consuming data preparation and analytic challenges that often require IT or DBA intervention to deliver a requested dataset. Others have taught themselves “enough SQL to be dangerous”, learning the necessary code to extract the data needed to answer their business question. Self-service data analytics empowers these business analysts to take control of the entire analytics process, delivering the necessary results for better business decisions.
Join us to learn how self-service data analytics allows analysts to:
- Utilize a drag-and-drop workflow for data and analytic processes without writing code
- Minimize data movement and ensure data integrity through in-database capabilities
- Easily work across relational and non-relational databases to deliver faster business results
Self-service data analytics delivers a repeatable process that is transparent to not only business analysts, but also SQL coders and decision makers across the organization.
Are you your company’s chief data officer? Given the scarcity of the official role, it’s likely that you’re not — at least in title. But that doesn't mean that you shouldn't operate like one. Do you approach data leadership as a C-level executive or a senior data head? Is your team’s output strategic or just operational? In this interactive keynote, one of the Windy City’s foremost data leaders will lead an interactive discussion on what it takes to lead like a chief, what it looks like, and how to get there and get it done.
Data-Ed Webinar: Demystifying Big Data DATAVERSITY
We are in the middle of a data flood and we need to figure out how to tame it without drowning. Most of what has been written about Big Data is focused on selling hardware and services. But what about a Big Data Strategy that guides hardware and software decisions? While virtually every major organization is faced with the challenge of figuring out the approach for and the requirements of this new development, jumping into the fray hastily and unprepared will only reproduce the same dismal IT project results as previously experienced. Join Dr. Peter Aiken as he will debunk a number of misconceptions about Big Data as your un-typical IT project. He will provide guidance on how to establish realistic Big Data management plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers without getting lost in the hype.
Takeaways:
- The means by which Big Data techniques can complement existing data management practices
- The prototyping nature of practicing Big Data techniques
- The distinct ways in which utilizing Big Data can generate business value
- Bigger Data isn’t always Better Data
Successfully Kickstarting Data Governance's Social Dynamics: Define, Collabor...Stijn (Stan) Christiaens
Learn how to launch your data governance program, by answering three questions:
- What does my data mean: collect and manage business definitions and relations, taxonomies and classifications, business rules and ontologies;
- How can I involve all stakeholders: engage them across business units and geographies, with stewards, data owners, … in a guiding workflow;
- How do I operationalize data governance: link MDM, DQ and BI to the business, use business-driven semantic modelling, achieve end-to end traceabilitiy. During this session we will use examples from different verticals: Finance, Government, Utilities,… .
We discuss their main drivers for starting a Data Governance initiative, as well as their pragmatic approach in moving from gradual roll out to support and sustain their Data Governance program.
Becoming (Big) Data Driven presentation at BusinessMeetsIt Big Data seminar M...Geert Van Landeghem
How to become big data driven? Wat does it mean to be data-driven and how data science and big data help to become more data-driven. Finally, how to build a near real-time (big) data platform.
Key Elements for a Successful Service Analytics ProgramData Con LA
Data Con LA 2020
DescriptionThis talk will focus on providing the key elements that enable the successful roll out of a self service analytics program at any organization. I'll discuss my tenure at Qualcomm where I led a self service program there for 10 years and grew it to 500 developers, 3000 applications and 15000 end users. I'll also go over other client case studies like the California Department of Public Health and Illumina where we are developing similar self service programs and go over what works and what does not work.
Speaker
Steve Rimar, Analytica Consulting, LLC, CEO & Founder
First, we will explore the power of a compounding insight machine (as opposed to an ad hoc insight machine):
-Human time is focused on improving logic, rather than executing outcomes
-Less dependent on human biases or frailty
-Robust to and tested by a huge collection of scenarios
Second, we will explore the anatomy of such a machine:
-The roles you need to cast on your team and who to fill them with
-The key processes required for generating and capturing insight and, more importantly, for building upon those insights
-The technology required to enable this approach
Idiots guide to setting up a data science teamAshish Bansal
Some nuggets of how I started the data science practice at Gale Partners on a budget. Presented at the Toronto Hadoop Users Group (THUG) in April, 2015.
AI can give your organization the competitive advantage it needs, but the alarming truth is that only 1 in 10 data science projects ever make it into production. To be successful, organizations must not only correctly design and implement data science, but also raise the data, numerical, and technology literacy across the business.
Attend this webinar to learn what common pitfalls you need to avoid to keep your data science projects from failing. Then Data Scientist Gaby Lio will engage with the audience about project dos and don’ts and leave you with a checklist to ensure your projects success.
As a manager, what do you need to know in order for the data-science project you are leading to be successful?
This presentation looks into a data-science project lifecycle, points out common failures and gives some hints on how to avoid common pitfalls. Examples included.
The target audience is managerial - half technical.
Whitepaper: Thriving in the Big Data era Manage Data before Data Manages you Intellectyx Inc
Paper Overview -
Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone.
Data comes from everywhere and we are generating data more than ever before.
This white paper will explain what Big Data is and provide practical examples, concluding with a message how to put data your data to work.
ADV Slides: What Happened of Note in 1H 2020 in Enterprise Advanced AnalyticsDATAVERSITY
Reassessing the information management marketplace for your enterprise direction on an annual basis is too infrequent. The technology is changing too fast. Data and analytic maturity levels rapidly evolve. What is advanced today may be entry-level in two years. Let’s look at the high points for 1H 2020 in information management developments and how that may change what you are doing now. This can also be a strong data point for preparing 2021 budgets.
Trends in Enterprise Advanced AnalyticsDATAVERSITY
If you missed out on all the trends for 2019 published in
December, or even if you caught some of them, this one merits your time. We’ll be going into 2019 and beyond, since the winners will have an eye on the long view for the source of competitive advantage that is analytics.
It is a fascinating, explosive time for enterprise
analytics.
It is from the position of analytics leadership that the
mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data and projects that will deliver analytics.
After all, no matter what business you’re in, you’re in the business of analytics.
The coming years will be full of big changes in enterprise data architecture. William will kick off the Advanced Analytics 2019 series with a discussion of the trends winning organizations should build into their plans, expectations, vision and awareness now.
7 Big Data Challenges and How to Overcome ThemQubole
Implementing a big data project is difficult. Hadoop is complex, and data governance is crucial. Learn common big data challenges and how to overcome them.
DataOps - The Foundation for Your Agile Data ArchitectureDATAVERSITY
Achieving agility in data and analytics is hard. It’s no secret that most data organizations struggle to deliver the on-demand data products that their business customers demand. Recently, there has been much hype around new design patterns that promise to deliver this much sought-after agility.
In this webinar, Chris Bergh, CEO and Head Chef of DataKitchen will cut through the noise and describe several elegant and effective data architecture design patterns that deliver low errors, rapid development, and high levels of collaboration. He’ll cover:
• DataOps, Data Mesh, Functional Design, and Hub & Spoke design patterns;
• Where Data Fabric fits into your architecture;
• How different patterns can work together to maximize agility; and
• How a DataOps platform serves as the foundational superstructure for your agile architecture.
Introduction to big data for the EA course at Solvay MBAWim Van Leuven
Introduction to what is big data, what can it do and not do, the importance of datascience and how to architect big data solutions (lambda architecture)
Successfully Kickstarting Data Governance's Social Dynamics: Define, Collabor...Stijn (Stan) Christiaens
Learn how to launch your data governance program, by answering three questions:
- What does my data mean: collect and manage business definitions and relations, taxonomies and classifications, business rules and ontologies;
- How can I involve all stakeholders: engage them across business units and geographies, with stewards, data owners, … in a guiding workflow;
- How do I operationalize data governance: link MDM, DQ and BI to the business, use business-driven semantic modelling, achieve end-to end traceabilitiy. During this session we will use examples from different verticals: Finance, Government, Utilities,… .
We discuss their main drivers for starting a Data Governance initiative, as well as their pragmatic approach in moving from gradual roll out to support and sustain their Data Governance program.
Becoming (Big) Data Driven presentation at BusinessMeetsIt Big Data seminar M...Geert Van Landeghem
How to become big data driven? Wat does it mean to be data-driven and how data science and big data help to become more data-driven. Finally, how to build a near real-time (big) data platform.
Key Elements for a Successful Service Analytics ProgramData Con LA
Data Con LA 2020
DescriptionThis talk will focus on providing the key elements that enable the successful roll out of a self service analytics program at any organization. I'll discuss my tenure at Qualcomm where I led a self service program there for 10 years and grew it to 500 developers, 3000 applications and 15000 end users. I'll also go over other client case studies like the California Department of Public Health and Illumina where we are developing similar self service programs and go over what works and what does not work.
Speaker
Steve Rimar, Analytica Consulting, LLC, CEO & Founder
First, we will explore the power of a compounding insight machine (as opposed to an ad hoc insight machine):
-Human time is focused on improving logic, rather than executing outcomes
-Less dependent on human biases or frailty
-Robust to and tested by a huge collection of scenarios
Second, we will explore the anatomy of such a machine:
-The roles you need to cast on your team and who to fill them with
-The key processes required for generating and capturing insight and, more importantly, for building upon those insights
-The technology required to enable this approach
Idiots guide to setting up a data science teamAshish Bansal
Some nuggets of how I started the data science practice at Gale Partners on a budget. Presented at the Toronto Hadoop Users Group (THUG) in April, 2015.
AI can give your organization the competitive advantage it needs, but the alarming truth is that only 1 in 10 data science projects ever make it into production. To be successful, organizations must not only correctly design and implement data science, but also raise the data, numerical, and technology literacy across the business.
Attend this webinar to learn what common pitfalls you need to avoid to keep your data science projects from failing. Then Data Scientist Gaby Lio will engage with the audience about project dos and don’ts and leave you with a checklist to ensure your projects success.
As a manager, what do you need to know in order for the data-science project you are leading to be successful?
This presentation looks into a data-science project lifecycle, points out common failures and gives some hints on how to avoid common pitfalls. Examples included.
The target audience is managerial - half technical.
Whitepaper: Thriving in the Big Data era Manage Data before Data Manages you Intellectyx Inc
Paper Overview -
Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone.
Data comes from everywhere and we are generating data more than ever before.
This white paper will explain what Big Data is and provide practical examples, concluding with a message how to put data your data to work.
ADV Slides: What Happened of Note in 1H 2020 in Enterprise Advanced AnalyticsDATAVERSITY
Reassessing the information management marketplace for your enterprise direction on an annual basis is too infrequent. The technology is changing too fast. Data and analytic maturity levels rapidly evolve. What is advanced today may be entry-level in two years. Let’s look at the high points for 1H 2020 in information management developments and how that may change what you are doing now. This can also be a strong data point for preparing 2021 budgets.
Trends in Enterprise Advanced AnalyticsDATAVERSITY
If you missed out on all the trends for 2019 published in
December, or even if you caught some of them, this one merits your time. We’ll be going into 2019 and beyond, since the winners will have an eye on the long view for the source of competitive advantage that is analytics.
It is a fascinating, explosive time for enterprise
analytics.
It is from the position of analytics leadership that the
mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data and projects that will deliver analytics.
After all, no matter what business you’re in, you’re in the business of analytics.
The coming years will be full of big changes in enterprise data architecture. William will kick off the Advanced Analytics 2019 series with a discussion of the trends winning organizations should build into their plans, expectations, vision and awareness now.
7 Big Data Challenges and How to Overcome ThemQubole
Implementing a big data project is difficult. Hadoop is complex, and data governance is crucial. Learn common big data challenges and how to overcome them.
DataOps - The Foundation for Your Agile Data ArchitectureDATAVERSITY
Achieving agility in data and analytics is hard. It’s no secret that most data organizations struggle to deliver the on-demand data products that their business customers demand. Recently, there has been much hype around new design patterns that promise to deliver this much sought-after agility.
In this webinar, Chris Bergh, CEO and Head Chef of DataKitchen will cut through the noise and describe several elegant and effective data architecture design patterns that deliver low errors, rapid development, and high levels of collaboration. He’ll cover:
• DataOps, Data Mesh, Functional Design, and Hub & Spoke design patterns;
• Where Data Fabric fits into your architecture;
• How different patterns can work together to maximize agility; and
• How a DataOps platform serves as the foundational superstructure for your agile architecture.
Introduction to big data for the EA course at Solvay MBAWim Van Leuven
Introduction to what is big data, what can it do and not do, the importance of datascience and how to architect big data solutions (lambda architecture)
Copy of presentation delivered at the CHASS 2015 National Forum in Melbourne (October 2015), The Council for Humanities, Arts and Social Sciences in Australia is the peak body supporting more than 75 member organisations in their relationships with Federal and State Government policy makers, Academia and the broader community within Australia.
Defining Data Science
• What Does a Data Science Professional Do?
• Data Science in Business
• Use Cases for Data Science
• Installation of R and R studio
Lecture 1.13 & 1.14 &1.15_Business Profiles in Big Data.pptxRATISHKUMAR32
The presentation contain the business profiles in big data analytics. through this ppt user can learn about the different case studies such as facebook and walmart. This ppt contain the information and seven characteristics that are required to learn the basics of big data.
It is a fascinating, explosive time for enterprise analytics.
It is from the position of analytics leadership that the enterprise mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics.
The coming years will be full of big changes in enterprise analytics and data architecture. William will kick off the fifth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.
Data Lake Architecture – Modern Strategies & ApproachesDATAVERSITY
Data Lake or Data Swamp? By now, we’ve likely all heard the comparison. Data Lake architectures have the opportunity to provide the ability to integrate vast amounts of disparate data across the organization for strategic business analytic value. But without a proper architecture and metadata management strategy in place, a Data Lake can quickly devolve into a swamp of information that is difficult to understand. This webinar will offer practical strategies to architect and manage your Data Lake in a way that optimizes its success.
Visit this link to complete the quiz - https://mix.office.com/watch/ays9xktksvjb The Data Asset Introduction - Databases, Business Intelligence, Analytics, Big Data, and Competitive Advantage
Visit this link to complete the quiz - https://mix.office.com/watch/ays9xktksvjb The Data Asset Introduction - Databases, Business Intelligence, Analytics, Big Data, and Competitive Advantage
Usama Fayyad talk in South Africa: From BigData to Data ScienceUsama Fayyad
Public talk by Barclays CDO Usama Fayyad in South Africa: both at University of Pretoria (GIBS) - Johannesburg and at Workshop17 in Capetown July 14-15, 2015
Big data is still relatively new and it is very exciting. The opportunities, if not necessarily endless, are are at least incredibly rich and varied. Aiming to bridge the link between Big Data as a Technology and Big Data as Business Value, we hope our presentation will help frame some of your thinking on how to use and benefit from this topical development.
DataEd Slides: Approaching Data Management TechnologiesDATAVERSITY
Our architecturally solid stool requires three legs: people, process, and technologies. This webinar looks at the most misunderstood of these three components: technology. While most organizations begin with technologies, it turns out that technologies are the last component that should be considered. This webinar will survey a range of Data Management technologies that can be used to increase the productivity of Data Management efforts.
Similar to Never Mind Big Data: We're Still Living in the Era of Big Spreadsheet (20)
DataEd Slides: Approaching Data Management Technologies
Never Mind Big Data: We're Still Living in the Era of Big Spreadsheet
1. Data Analytics for Microsoft Excel
Microsoft Excel is a trademark of Microsoft Corporation
www.InformationActive.com
2. Big Data – Projected to Drive IT Growth
• Gartner Inc. projects that “big data” will
drive $232 Billion in IT spending – 2016
• Includes spending on
• Storage
• Business intelligence
• Database
• Middleware platforms
www.InformationActive.com
3. Big Data – Top Sources Today?
• Machine-generated information
• Sensors, geo-enabled mobile devices
• User-generated information
• Social media, web transactions, social
networks, apps
• Have defined how we talk about
information and content management in
2012-13
www.InformationActive.com
4. But…What About In-House Data?
• Externally generated data is often a shiny
object
• Pundits and vendors look at big data as
way to transform business, get better
insights into customer behavior and
buying patterns
• But what about internal patterns and data
sources? Often neglected.
www.InformationActive.com
5. Spreadsheets: The Original User-
Generated Content
• Spreadsheets may be unglamorous, but
are the original user-generated content to
have a material impact on business
• Spreadsheets in mainstream use for
over 30 years
• Few organizations have applied data
analytics or mined the intelligence held
in spreadsheets
www.InformationActive.com
6. Risks of Neglecting Data Held in
Spreadsheets
1. Risk of ignoring the information needed to
make consistent decisions
2. Risk of allowing errors and non-
compliance proliferate in the dark of
untested formulas, rows and columns
www.InformationActive.com
7. Big Data May Get All the Hype Today…But
Business Are Run by Big Spreadsheets
8. Spreadsheets By the Numbers
• 99.7% of businesses use spreadsheets
• 70% of businesses have “heavy” reliance
on spreadsheets for critical business
activities
• Yet…only 42% of companies paid
attention to spreadsheets as part of risk
reporting and assessment
Source: (Deloitte 2009 Study)
www.InformationActive.com
9. Spreadsheets By the Numbers
• Estimated 90 million computer users in the
US workplace…
• 60% - 55 million – will use spreadsheets
or simple databases for work
Source: Carnegie Mellon University
www.InformationActive.com
10. Spreadsheets By the Numbers
• Spreadsheets have an average 5 year
lifespan
• Used by 13 different business analysts
• Financial analysts spend average 3 hours
a day in spreadsheets
Source: Delft University of Technology
www.InformationActive.com
11. Spreadsheets By the Numbers
• Errors persist in most spreadsheets and
can be hidden sources of risk
• 2-5% of formulae are incorrect
• Consistent for both beginner and expert
users
• Up to 5% of errors can be material to
business operations and financial health
Source: Professor R. Panko
www.InformationActive.com
12. How to Reduce the Risks?
• Recommendation #1:
• Understand where critical business data
is held – odds are it is in a spreadsheet
• Learn how it is being exported,
crunched, analyzed
• Inventory and apply information
management principles to essential
spreadsheets
www.InformationActive.com
13. How to Reduce the Risks?
• Recommendation #2:
• Invest in analytic tools that can be
understood by typical business workers
• Seek out easy-to-use spreadsheet
governance tools that don’t require big
IT investment
• ActiveData for Excel may be the right
solution for your firm
•
www.InformationActive.com
14. How to Reduce the Risks?
• Recommendation #3:
• Don’t pin hopes on big data alone for
improved productivity and operational
insights
• Do the analysis and quality assurance
on your existing “big spreadsheet” data
• Find historical context to supplement
new real-time understanding
www.InformationActive.com
15. Read More on This Topic
• FierceContentManagement
• Article – Nov 5, 2012 “Never Mind Big
Data, We’re Still Coping with the Era of
Big Spreadsheets”
• InformationActive Network
• Blog Post – Oct 20, 2012 “Big Data
Gets the Hype, But We Still Live in the
Era of Big Spreadsheet”
www.InformationActive.com
16. Who is InformationActive Inc.?
Founded in 2003, flagship product
ActiveData has
Tens of Thousands of users
in over 70 countries.