Chief Data Officer Summit 2014 – Day 2 Highlights
Previous post
Highlights from the presentations by Data Governance experts from Visa, Bing, San Francisco County, and RS Investments at Chief Data Officer Summit 2014 in San Francisco, CA.
The role and importance of Chief Data Officer has been increasingly gaining attention across industries. Data is being viewed as the most competitive tool that organizations have in order to maintain relevance and growth in today’s complex environment. While the CDO office holds enormous potential, it is currently faced with a wide variety of issues around data integration, ROI, data quality, etc.
The Chief Data Officer Summit (May 22-23, 2014) organized by the Innovation Enterprise at San Francisco, CA covered the above topics through insightful talks from leading experts across various domains. Along with a deep dive into the role of the Chief Data Officer, the summit covered innovation, data management and data governance. Through real-life business case studies and discussions on major issues, the summit offered solutions and insight from the CDOs and other business leaders.
Despite the great quality of content as well as speakers, it is hard to grasp all the information during the summit itself. KDnuggets helps you by summarizing the key insights from all the sessions at the summit. These concise, takeaway-oriented summaries are designed for both – people who attended the summit but would like to re-visit the key sessions for a deeper understanding and people who could not attend the summit. As you go through it, for any session that you find interesting, check KDnuggets as we would soon publish exclusive interviews with some of these speakers.
Highlights from selected talked on day 1.
Here are highlights from selected talks on day 2 (Fri, May 23):
Philippe De Smedt, Chief Data Architect, Visa described how organizational silos impact our ability to leverage data in his talk titled "Building a Data-Driven Culture". Data is now increasingly being considered one of the most important assets of the organization. He highlighted the top data challenges as: hidden insights due to data locked in silos, investment prioritized by IT (these priorities may not align with business strategy), IT may not have insight into new business opportunities, and often long development lifecycles. The need of the hour is to shift the focus on data from the IT organization to business decision makes. Business leaders understand what value data can bring to making timely, well-informed and impactful decisions, but only if it is managed, understood and governed consistently across the organization.
IT should focus on ease of use, adaptability of analytics applications in order to empower business users. Complementing this, the business needs to clearly articulate its data requirements to IT, and conversely, IT needs to be accepting of the fact that, more and more, requirements will come from the business, rather than being IT driven. In order to focus on fostering a data driven culture we need to develop some fundamental capabilities such as Master Data Management, Metadata documentation, Enterprise Data Modeling, Governance and Stewardship.
Joy Bonaguro, Chief Data Officer, San Francisco City & County explained Open Data movement and its future, in her talk titled "Open Data Grows Up: Institutionalizing an Initiative".
She decomposed the Open Data movement in the following five phases:
Open data has unlocked a data ecosystem - from consumer facing apps to increases in government transparency. And now it’s time for open data to move past initiative and become part of doing business. Citing a survey, she mentioned that the biggest barriers to data use are: data quality, skills and/or capacity to use data, and knowledge/awareness of existing data sets. Within an ecosystem of shared data and technology, the San Francisco country is trying to (1) increase individual skills and capacity, (2) support analytic programs in city departments, and (3) foster a data enabled policy environment in the pursuit towards increased use of data in decision-making. She shared the mission and vision of the San Francisco's CDO office, which focuses on using data to drive government transparency, efficiency and innovation.
Monica Khurana, Chief Information Officer, RS Investments talked about Big Data from a CIO's perspective. In her talk titled "Bridging the gap between Big Data and existing Data Warehouses", she emphasized that data will play a revolutionary role in years to come through changing business models, driving business strategy, etc. The data flow diagram in the investment management sector can be summarized to: Alpha factor data, risk analytics, market data, account/position data, trade cost data and other analytics impacting the trading decision.
The key data challenges include the large size of data sets, diversity of data formats, expectations heading towards real-time analytics and delivering valuable business insights. She envisions the future where data warehouses and Big Data systems will co-exist and mutually benefit from each other. She concluded her talk with the following four key takeaways:
Juan Miguel Lavista, Principal Data Scientist, Bing delivered an insightful talk on "The Importance of Experimentation". Using the standard old medical practice of bloodletting (which killed many including George Washington) as an example, he made a very important point that we can be wrong about something for 2000 years. It is not trivial to know if something really works.
He shared a few real-life A/B testing results around website design and explained that intuition is not reliable. This happens so because we learn from correlations, and quite often, we intuitively assume correlation to be causation. One of the first principles we need to learn in order to be data-driven is that correlation does not imply causation. Thus, there is an immense need for experimentation in order to explore and confirm causation.
The gold standard to prove causalty is the randomized control experiment, however, this is not enough. It is also necessary to understand that to have a culture of experimentation, we first need to understand that when an idea fails, this is not an error and important lessons can be learned. In conclusion, he described the need for the right incentives to experiment and shared the following quote:
It is difficult to get a man to understand something, when his salary depends on his not understanding it. -Upton Sinclair
Related:
The Chief Data Officer Summit (May 22-23, 2014) organized by the Innovation Enterprise at San Francisco, CA covered the above topics through insightful talks from leading experts across various domains. Along with a deep dive into the role of the Chief Data Officer, the summit covered innovation, data management and data governance. Through real-life business case studies and discussions on major issues, the summit offered solutions and insight from the CDOs and other business leaders.
Despite the great quality of content as well as speakers, it is hard to grasp all the information during the summit itself. KDnuggets helps you by summarizing the key insights from all the sessions at the summit. These concise, takeaway-oriented summaries are designed for both – people who attended the summit but would like to re-visit the key sessions for a deeper understanding and people who could not attend the summit. As you go through it, for any session that you find interesting, check KDnuggets as we would soon publish exclusive interviews with some of these speakers.
Highlights from selected talked on day 1.
Here are highlights from selected talks on day 2 (Fri, May 23):
Philippe De Smedt, Chief Data Architect, Visa described how organizational silos impact our ability to leverage data in his talk titled "Building a Data-Driven Culture". Data is now increasingly being considered one of the most important assets of the organization. He highlighted the top data challenges as: hidden insights due to data locked in silos, investment prioritized by IT (these priorities may not align with business strategy), IT may not have insight into new business opportunities, and often long development lifecycles. The need of the hour is to shift the focus on data from the IT organization to business decision makes. Business leaders understand what value data can bring to making timely, well-informed and impactful decisions, but only if it is managed, understood and governed consistently across the organization.
IT should focus on ease of use, adaptability of analytics applications in order to empower business users. Complementing this, the business needs to clearly articulate its data requirements to IT, and conversely, IT needs to be accepting of the fact that, more and more, requirements will come from the business, rather than being IT driven. In order to focus on fostering a data driven culture we need to develop some fundamental capabilities such as Master Data Management, Metadata documentation, Enterprise Data Modeling, Governance and Stewardship.
Joy Bonaguro, Chief Data Officer, San Francisco City & County explained Open Data movement and its future, in her talk titled "Open Data Grows Up: Institutionalizing an Initiative".
She decomposed the Open Data movement in the following five phases:
- The Executive Edict
- The Publishing Scramble
- The Period of Stagnation
- The Resource Rechoning
- The Integration of the Initiative
Open data has unlocked a data ecosystem - from consumer facing apps to increases in government transparency. And now it’s time for open data to move past initiative and become part of doing business. Citing a survey, she mentioned that the biggest barriers to data use are: data quality, skills and/or capacity to use data, and knowledge/awareness of existing data sets. Within an ecosystem of shared data and technology, the San Francisco country is trying to (1) increase individual skills and capacity, (2) support analytic programs in city departments, and (3) foster a data enabled policy environment in the pursuit towards increased use of data in decision-making. She shared the mission and vision of the San Francisco's CDO office, which focuses on using data to drive government transparency, efficiency and innovation.
Monica Khurana, Chief Information Officer, RS Investments talked about Big Data from a CIO's perspective. In her talk titled "Bridging the gap between Big Data and existing Data Warehouses", she emphasized that data will play a revolutionary role in years to come through changing business models, driving business strategy, etc. The data flow diagram in the investment management sector can be summarized to: Alpha factor data, risk analytics, market data, account/position data, trade cost data and other analytics impacting the trading decision.
The key data challenges include the large size of data sets, diversity of data formats, expectations heading towards real-time analytics and delivering valuable business insights. She envisions the future where data warehouses and Big Data systems will co-exist and mutually benefit from each other. She concluded her talk with the following four key takeaways:
- Data is the business currency of the future
- Companies that take a comprehensive approach to data stand to realize an additional 60% return on their data assets
- Poor Data Management can cost upto 35% of a business’s operating revenue
- 90% of Fortune 500 companies will have at least one Big Data initiative in the next two years
Juan Miguel Lavista, Principal Data Scientist, Bing delivered an insightful talk on "The Importance of Experimentation". Using the standard old medical practice of bloodletting (which killed many including George Washington) as an example, he made a very important point that we can be wrong about something for 2000 years. It is not trivial to know if something really works.
He shared a few real-life A/B testing results around website design and explained that intuition is not reliable. This happens so because we learn from correlations, and quite often, we intuitively assume correlation to be causation. One of the first principles we need to learn in order to be data-driven is that correlation does not imply causation. Thus, there is an immense need for experimentation in order to explore and confirm causation.
The gold standard to prove causalty is the randomized control experiment, however, this is not enough. It is also necessary to understand that to have a culture of experimentation, we first need to understand that when an idea fails, this is not an error and important lessons can be learned. In conclusion, he described the need for the right incentives to experiment and shared the following quote:
It is difficult to get a man to understand something, when his salary depends on his not understanding it. -Upton Sinclair
Related: