During the week of March 24-28, 2014, Cognizeus arranged for Boston’s first weeklong data and analytics unconference. Each day held a different track starting with Monday and Big Data Analytics, followed by Tuesday with Health Informatics, Wednesday with Finance/Insurance, Marketing on Thursday followed by Workforce Analytics on Friday evening.
In a later post, we walk through the social media analytics from the event
The keynote speakers for each day started with Paul Sonderegger from Oracle, Gil Alterovitz at Harvard med school, Christopher Lynch at Atlas Ventures, Judah Phillips at Smart Current, and finally Greta Roberts from Talent Analytics.
Klurig Analytics was a Silver sponsor of the event with our team responsible for social media and social media analytics. Using the handle @AnalyticsWeek and tweeting both text and images to the community, enabling non-attendees to follow along as well as leaving a historical trail of the event. For instance KDnugget used our tweets for a great post about Monday night where Gregory Piatetsky moderated a panel.
The first night, at District Hall, was all about Big Data Analytics. Paul Sonderegger from Oracle gave a really good keynote address with key points such as
- there is more data being born outside of a company than inside the company, about the company
- there is a difference between data-first and model-first but you need both. The whole is greater than the sum
- predictive analytics do not predict big things; predictive analytics predicts small things
- build a data strategy
- think in terms of data market share
- create proprietary data assets. Take public data and combine it with own data and get better data
- use data to make data, like how Google is using data. Create a big data flywheel
The panel discussion was moderated by Gregory Piatetsky. A selection of comments:
Gregory Piatetsky: Data scientist will be replaced by automated algorithms? Data science in a box? Can data science be automated?
David Jegen: Humans do what humans do really well. Machines do what they do really well. There will be a coexistence. PayPal had problems and put machines on it, realized that you need humans and now they coexists. Machines to curate data. When do you bring in a human to curate, to train the machine. Humans are artists.
Anand Rao. In AI, we have proven that it is really hard to replace a human brain.
Chip Hazard The data preprocessing part will be handled by machine. The key is to ask the right question, the right question is really hard to find and that is where we need a human.
Paul Markovitz: data scientist will be doing less grudge work (machines handles that), instead they will be doing the fun work.
Tuesday night, at the Microsoft NERD center, the discussion centered on Health Informatics and Analytics. Gil Alterovitz gave another great keynote address including an overview and a presentation of SMART. Key pointers are:
- re: cloud computing – the analyst just need the data, doesn’t care of where the data is
- today, if you have a disease, your healthcare provider gives you advice. Using sensors, ques can be given to you automatically. For instance, when the sensor senses that you are low on blood sugar, ques can be given to you to certain food at certain times
- Genomics will be a game changer. For about $1,000, you can get your whole genome sequenced
- There are many genomic standards, we need an adapter that can talk to all standards and we have one named SMART
- create an eco system of SMART apps, compete on SMART apps
- SMART Genomics Advisory Platform – different modules can be re-used in other apps
- China is very interested in SMART because their data is more centralized
- Key is to abstract away the tough parts of building apps, it should be easy to build apps
The panel discussion was moderated by Charlie Schick and consisted of Michael Greeley, Allen Kamer, Kris Joshi and Timothy Andrews.
Charlie Schick: Even though there are hackfests in healthcare programming, but it is too slow. How do we overcome the slow speed? How do we get all together so providers have access to data and systems.
Michael Greely: Many great initiatives and accelerators but they are not adequate yet. Optum Labs is better to teach ecosystem. Optum Labs is great place to learn the ecosystem of healthcare as for analytics and bigdata.
Allen Kamer: We struck partnerships with different groups to partner to help us build products, early adopters such that we can prove. Partnerships are a great way of building products. Helps patients getting help faster and more cost effective. Optum Labs is helping smaller companies to get started. Companies can reach out to Optum Labs with different initiatives to partner around things.
Kris Joshi: The innovation is very local. The flipside is different from other places in the country. It is important to partner. Need to get the right asset built and then a different way of scaling the asset.
Tim Andrews: Healthcare is very regulated. There is no one institution in charge of healthcare. It is very hard to get them to share. Need to get hold of multiple targets to work together, but it is really tough in healthcare.
On Wednesday, at Hack/Reduce in Cambridge, Christopher Lynch gave a keynote address about Finance and Insurance. Key points:
- Big data becomes transformationable when power of analytics is given to the common man
- The cognitive impact of data science – infrastructure has little intelligence
- Deploy technology for actionable outcome – deliver the right data at the right time
- Realtime is old. Intime is more important, as ‘in time’ to make a decision. Time to value!
- How do you impact time to value. That is what is important today
- Big data is to look at all data inside a company, including social media data and financial data and all data in between
- Simplicity is transformationable. Need to deliver quick data in easy to understandable way
- Opportunity for New England to be a center of competence in big data and analytics
- Make sure your technology is giving back to the community
- A great company is the company that cracks the code of delivering big data analytics to the rest of us
The panel was moderated by Bill Fearnley. Sample question:
Bill Fearnley: What is the key challenge that lead to big data solutions?
John Muller: Analytics is the key thing. We create big data and that is a challenge. But the main idea is the analytics and the data visualization
John Raguin: Marketing data analytics have been around for decades. But tools doesn’t mean anything. The real question is: will this customer be a profitable customer for me? Technology is immaterial as long as it gets the job done.
Deborah Cooper: Without big data, my group would not be as responsible as they should be. Big data is key. The challenge is too many data non-integrated data sources, and too expensive.
Michael Schmidt: Important to be able to explain what goes into a prediction. His team created a bracket for March Madness. 75% accuracy with minimal effort. Easy to explain, easy to use.
Back to Microsoft NERD center for an evening discussion analytics in Marketing. Judah Phillips gave a fantastic overview of various marketing concepts related to big data analytics. Sample comments:
- When is the right time to quantify? When you have the data, need information about the data, consistency.
- Right time when the company is cross-functional, when you can improve a situation and when revenue is at risk.
- All data is not actionable.
- Analytics value chain: 1. understanding, 2. collecting data, 3. reporting, 4. analyze and socialize, 5. optimize – 6. demonstrating that the analytics team provide economic value, that the analytics have an economic impact on the bottom line.
- A/B testing yields significant gains if executed correctly
- What does a complete analytics team include? you need the architecture team, the reporting team and the analysis team.
- Analytics can serve marketing, product, sales, executive and IT
The panel was moderated by Cesar Brea. Sample question and comments:
Cesar Brea: what is the undefined problem that you are trying to crack?
Raj Aggarwal: Instead of building apps, Localytics built tools for manage apps. Sell shovels instead of digging for gold.
Rama Ramakrishnan: The average customer only shops twice a year, therefore it is really hard to build a recommendation engine because person who bought product A might not want product B
Bill Simmons: if you build stuff for NASA, ensure it needs to be built in a large number of congressional districts
Ben Clark: Radical about keeping it simple stupid. Prefers A/B testing to MVT (multivariate testing). Business value can be evaluated with simple metrics; traffic, conversion rate and average value. Almost all marketing analytics use the same three data points.
At District Hall, where Greta Roberts, Talent Analytics, CEO, give a really fun keynote address about workforce automation. Key points:
- Insight comes from many data sources. Need to additional value. Torture data until it confesses.
- The important thing is how we mix the data, how it is pulled together.
- Tracking to outcomes matters immensely in data science.
- Workforce analytics is about optimizing organizational layout
- Solve the business challenge (not the HR challenge)
- Using workforce analytics, we can do things like: we can predict that this person will be a top data scientist
- New Deloitte report – HR analytcs is in the top 5 global trends – HR Analytics is huge and it will grow!
- It is a huge trend because employee expense is the top expense in any company
- Start with HR analytics even before you get complete data – worry less about completeness – get started
- If employees are leaving prior to reaching breakeven, they are a loss for the company.
- The key is to use workforce analytics to help predict which employees will stay past the break even point.
- By using prediction, we can also see what makes a good employee good, compared to a not so good employee
- When you make decisions, you need to use both the analytics and the human intuition.
- The most exciting is reducing attrition and increasing performance in HR
- HR analytics helps to put people in the roles where they want to be.
Abby Cashman led the panel as a moderator and among other great question, Abby asked the panel the following question: how do you define ROI, how do you measure this, what are the metrics that you use?
David Parker: We need to keep clean: red, yellow, green – we know what color the banker. Started at 6% green, now 24% green – good trend. Coach to behavior based on metrics.
David Dietrich: Measure to ROI – innovation work – people were volunteering – free and open source tools. Who is creating new IP and who can replicate that – this is really important for ROI
Lisa Croteau: measure metrics by having people using the system that they created. Having people in the company to look at standardized data. Good record keeping system. Good expe
rience working with techies. Measure success by counting phone calls when people asking questions about the system
Melissa Arronte: Measure by customer satisfaction surveys – do you use the data that we provide? A few successes in analytics will pay for the team.