Data Champions Online

Removing analytics backlogs by using a data catalogue: 

Data is core to Genentech’s digital transformation efforts. And with the ultimate goal of better connecting data and analytics to business outcomes, Genetech’s gRED Early Clinical Development team has reduced the time needed for a single analysis from weeks to a day or less. Learn how the team leveraged a data catalog to remove the need for IT and data management involvement in data discovery and troubleshooting, empowered analysts of a wide-range of backgrounds to self-serve, and unlocked the potential of previously difficult to use data.

Kelly Barnes is a data operations and data integrity leader at Genentech’s Early Research and Development Division. She is a data evangelist, influencer and champion of data quality fundamentals and is responsible for end-to-end Operational Data Management oversight.

What made you chose this topic?

I didn't choose the topic, the topic chose me! I am a former business user of the data we surface in the gRED instance of the Alation, and I have been pushing for better data management practices and tools in my business unit for many years. Alation has been a game changer for us in terms of enabling productivity and agility across the data management spectrum - from standardization and data quality monitoring, to sharing and use of the data. I think that our lessons learned both pre-and-post Alation are useful to any organization that has data that needs to be managed and shared easily across the business, and I'm happy to share them.

What is the greatest success that you have had with this topic in the last 12 months that make this pertinent to you right now?

Now everyone in ECDi can access data and write their own queries even if the only SQL they know is "Select *", which has eliminated data request bottlenecks with IT.

What do you feel would be the #1 takeaway from your session?

How a data catalogue is a unique solution that addresses multiple data management problems experienced in every organization that collects, stores, and uses data.

What made you chose this topic?

When I started with GE in 1993, I was given training on what was called “Change Acceleration” which focused on how people Accept change, overcome resistance, and balance out the Technical/Political/Cultural aspects of the change.  I have used it ever since and the aspects of it are so critical because all change begins and ends with people.

What is the greatest success that you have had with this topic in the last 12 months that make this pertinent to you right now?

I have spent the past 6 months building the performance data and metric trees for a manufacturing site.  The data has proven to be clear, accurate, and SIMPLE.  Previously, there was so much data that you could make two choices when looking at it:  1. You could find something wrong and 2. You could find something right.  This meant that the data was not performing its purpose – to be able to make a decision from it.  We now have the right data that is used by everyone in every function which gives direction and knowledge of current state.

What do you feel would be the #1 takeaway from your session?

That data is used to make informative decisions up and down the organization and should be used to identify areas to help, not to punish.

ML and analytics in the Cloud

Analytics has been one of the last enterprise systems to move to the cloud, but the situation has changed fundamentally in just the last year or two. There is suddenly a proliferation of cloud-based databases, and of open-source development frameworks like SageMaker and TensorFlow — all of them now being heavily promoted by the major cloud vendors. If you know your way around all the available components, it can be easy to build even the most sophisticated machine learning models for everything from image recognition to fraud detection.

In this webinar, we review the array of technologies that are available for machine learning in the cloud, and answer the question of what to use and when and how. We then review a set of case studies that show how easy it can be to build applications based on sophisticated AI and machine learning

What made you chose this topic?

In each of the roles I’ve had over the last decade, designing an organization and then building the team has been paramount to getting the work done in an efficient and effective manner.  In particular this last year required both building an team from scratch, as well as influencing a reorganization to support our Enterprise Data strategy.

What is the greatest success that you have had with this topic in the last 12 months that make this really pertinent to you right now?

I’m incredibly proud of the team that I’ve assembled and how the hiring of my management team was integral to the speed at which we’ve recruited both internal and external colleagues while building processes to support our Enterprise Data strategy and beginning the execution against our multi-year roadmap.

What do you feel will be the #1 takeaway from your session?

Hiring for talent that is interested in being part of a start-up has been key to our progress.  But maybe even more important than an individual’s experience and expertise is engaging colleagues to share their knowledge while providing them a vision for our collective success.

Unleash the value from external data (alternative data)

In this session you will discover how forward-thinking organizations are leveraging alternative data sources, including how you can use employment data, satellite data across departments and how Aramark’s Chief AI Officer uses alternative data to underpin their overall strategy.

Speakers include:

Emmett Kilduff, Founder & Owner, Eagle Alpha
Toby Dayton, CEO, Linkup
Brian Yates, MD EMEA, Orbital
Pavan Arora, Chief AI Officer, Aramark

You can sign up for the session below.

What made you chose this topic?

The need to balance oversight and innovation is difficult in most industries, and many organizations are currently struggling to maintain that balance. In regulated industries, these challenges are even more persistent. Therefore it important to discuss different approaches.

What is the greatest success that you have had with this topic in the last 12 months that make this really pertinent to you right now?

A couple of areas:

1. Implementing a consistent data use framework to accelerate access to data
2. Development of a Analytics Sandbox environment to support innovation

What do you feel would be the top #3 takeaways from your session?

1. It is difficult to maintain the balance between innovation and governance
2. Key considerations to accelerate innovation with oversight
3. Process and Technology can serve as enablers

How do you build assurance in the accuracy and completeness of your data?

Data is the foundation of the analytics value chain. Whilst the growth in AI and real-time data processing have created new opportunities for companies to use data to drive competitive advantage, seizing these opportunities is only possible if companies have an accurate and complete data set to begin with. Developing assurance in the quality and completeness of a data set though is hard: both because data provides the “truth” against which measurements are performed (so measuring data quality is inherently hard), and because developing quality data requires a systematic approach to data capture and quality control across the business. (So requires a cultural change in the business towards data and data collection in particular.)

In this talk, Snowplow Co-Founder and Chief Product Officer, Yali Sassoon, will explore why the need for accurate and complete data is growing and only going to continue to grow, what happens when organisations lose confidence in the validity of their data, before exploring a practical range of approaches and techniques employed by companies to build assurance in their data sets, with a particular focus on customer data.

What made you chose this topic?

I’m passionate about all things data and analytics. When we take the time to be strategic in our approach, we visualize success. By positioning the organization to effectively measure benefits in terms of customer service, growth and retention, and operational improvement, we truly come to realize success. I chose this topic because I get excited when I can see what is possible with data and analytics.

What is the greatest success that you have had with this topic in the last 12 months that makes this really pertinent to you right now?

Our focus continues to be on ensuring we are building and consuming advanced analytic solutions at a faster pace. In the two years I’ve been with State Farm®, we’ve been very deliberate about working collaboratively across business areas to accelerate model development. We are using a consistent approach designed to produce measurable business results through work that is enhancing customer and operational experiences. The consistent approach is also enabling us to deliver and consume advanced analytic solutions at a faster pace. Measuring, validating and reporting to stakeholders has also been a critical component of our success.

What do you feel will be the #1 takeaway from your session?

Understanding that the level of success achieved is directly attributable to how well disciplined you are in your approach to the data and analytics work at hand and your team’s ability to maintain strategic focus to deliver the stated business objective.