Saturday, August 06, 2016

How Can Product Managers Add Value In A Data Centric Enterprise Software Company

All enterprise software companies that I have worked in and have had the opportunity to interact with have mainly four groups of experts managing different aspects of their data. There are data driven products. These products are enabled by data intelligence products. Data intelligence products are enabled by an Enterprise Data Warehouse that organized and stores the data. The enterprise data ware house is fed by a data management team that monitors, received, prepares and audits data.




The Role of Product Managers In A Data Centric Enterprise Software Provider
My workplace is a data centric organization. The value and quality of data we have determine the kind of innovation we can build and deliver to our customers. At my workplace my colleagues and I have designed and applied product management tools such as design thinking and agile development to data driven products development and data intelligence products. We are not there yet on applying product management tools, practices and techniques to data management.

My hypothesis is that data management has become a critical foundational capability for enterprise software companies building data driven products. Today, it is managed like a information technology (IT) support service that is called upon when needed or yelled upon when something goes wrong. It is treated like a utility that is supposed to work.  Instead  it should be treated like a product organization whose capabilities could become a unique advantage for a data centric organization. It should be supported with product management skills, tools and techniques.

Product managers can add value to data management organizations by doing the following.


  • Identifying the capabilities that will add value to the business and build a barrier to competition.
  • Define and document the capabilities a data management team is building including acceptance criteria.
  • Convey the value created by a data capability for the business to executives.  For example, making a new data set available might enable the creation of new innovation. Making a data set available sooner and more frequently might be a market differentiator. It might keep competition away or may enable the business to charge more for current services. Building a file monitoring and audit capability might make the business more reliable, scalable and might help the business expand into new markets that were otherwise not financially feasible to operate in.


There are some hurdles and risks. 
Data management teams traditionally have operated as an information technology organization that did a few projects with longer term milestones. They may not understand or appreciate operating like a product development organization. However every product is going to be data driven and to succeed, every company needs to manage their data management organization like they manage a product organization. My belief is that those who master this will succeed. Those who don't will fail. If you are a product manager or a data analyst, I recommend that you join an organization that understands and appreciate this insight. Ask questions about how an organization operates before joining them. Interview data teams in a company and understand their acceptance or resistance to such a direction. Such due diligence will increase you chances of success in your job. It will also increase the success of the business.

If you are playing the role of data product manager and are working with a data management team, please share you experiences. I would love to know your thoughts. If all this conversation got you excited, consider joining me at Castlight. We are hiring for the Director, Data Intelligence product management role you see above. 

Friday, July 15, 2016

How can a product manager help a data science team be successful

I have been working with data scientists to build data products for over a year now. The first product we build is Castlight Action. Castlight Action is now being used by several large US employers. During this time I learned a lot about the role product managers can play while working with data scientists building a data or data intelligence product. Since the successful release of the Action product, we are taking some of the lessons learned in Action and applying them to other problems and products. These are my observations from both the experiences.

The Pre-requisite
Product managers can prepare themselves by getting some formal education on data science. I did this a couple of years back by getting certified on courses meant for data scientists. However, I believe that may not be necessary and may not be feasible for most product managers.

Last year, John Hopkins University released a certification in data science for executives. This is a good course for product managers to take. I took it this year and found it very valuable. Since then, I tried out some of the concepts in the real world. The concepts taught in this course work in the real world. I highly recommend this course for all product managers planning to or aspiring to work with data scientists.

The Key Responsibilities
I want to outline some of the key responsibilities of a product manager working on a data science product. This may not be a comprehensive list. I am listing the things I have observed so far. I may continue to update this list.

1. Define the purpose of the data science project
The product managers needs to define the capability your company will have once the data science project is complete. I can give you two examples from the work of my teams at Castlight Health. a) We wanted to identify segments of people who are similar and predict their healthcare needs for a certain period in the future. b) We wanted to look at two doctors with the same name in a directory of doctors and determine if the doctors are the same person or not. This is important to create a reliable directory of doctors for our users. Keep the capability description to one page. There is no need to write a long requirements document.

2. Break down the milestones and deliverables for the data scientists
Because any data science project is a research project, it is harder to breakdown work that falls into the cadence of a scrum team. This is an area where a product manager can play a useful role. The product manager need not tell the data scientist what to do. Most product managers will not have the skill to do so. How ever a product manager can specify what needs to be accomplished.  To do this a product manager needs to understand how data scientist works to solve a problem. The course on Data Science for executives from Johns Hopkins can help you with that. Don't just browse through the course. Get the certification.

3. Temper the expectations of your colleagues and business leaders
A product manager needs to explain a data science project and the expected deliverables in simple language to executives, who may or may not be educated about how data scientists work. It is important to explain that the results of a data science project are not always predictable. After a month of work the data science team may come to the conclusion that a particular model does not work as expected and may have to go down  a different path. It is the product managers responsibility to set reasonable expectations and communicate results.

4. Product Managers can perform tasks such as creating test data sets
Data product managers can even perform tasks in a data science project. For example, they can create a test data set to evaluate the efficacy of a machine learning model. Product managers do not need engineering backgrounds or have knowledge of programming to do so. I created a test data set recently for our doctor directory matching project using the tools, my data science colleagues created. They are command line tools. So some familiarity with command line tools and some curiosity about data are pre-requisites. Participating in such tasks will help product managers understand the data and the business problem intimately. It will also help you build credibility with the data scientists.

I plan to write more about the role of product managers in a data or data intelligence product. If you have played the role of a product manager for a product involving data scientists, please share your thoughts. You may have noticed that I use the terms data products and data intelligence products. I believe that data product are different from data intelligence products. More about that later.





Saturday, April 16, 2016

Data Science Can Empower Benefits Leaders Communicate Less and Yet Accomplish More

In many companies today, internal communications overload is a big problem. Internal communication teams including human resources teams are wary of sending out more emails or direct mail than necessary. They also know that most employees ignore internal communications because such communications are poorly written, poorly designed and not personalized. For critical communications such as benefits enrollment, many human resource departments use electronic mail, paper mail, and posters to ensure that the message gets across. Most employees have learned to ignore internal communications unless that message is repeated multiple times.

In this scenario, if a benefits leader wants to educate specific segments of people about health benefits that are relevant to them, there were limited tools available to them. My team at Castlight has taken significant steps to address this need among other things. My purpose here is not to promote the products we build. Instead, I want to talk a bit about how we are planning to use the lessons learned while building Castlight Action, our predictive analytics and personalized recommendations product,  to empower benefits leaders in all our customer organizations, make employees aware of their benefits, to enable employees become healthier and even help them save some money in the process.

Since we have data about the behavior of millions of users and billions of claims, we use data mining techniques such as cluster analysis to identify segments of people with similar attributes who behave the same way. We may not understand why certain segments of people with similar attributes behave the same way, but we know that they do. Then we identify the people with such attributes who are not behaving the same way and tell them they might want to consider such behavior.

The above paragraph is very abstract. So, let me share an example. From our data, we know that for some reason people in their twenties care a lot about dental health. In other words, people in their twenties get dental checkups every year. Once they reach their thirties their lives probably get busier with family and children and they do not get as many dental checkups. So, let us say they were a 1000 people in their twenties in a company with 10,000 people. 800 out of those 1000 people get dental checkups every year. So we wonder about why those 200 people in their twenties are not behaving like the others. Maybe they just joined the company. Maybe they don't know any good dentists near their home. Maybe they think it would be very expensive to go get a dental check up. We really  don't know why. However, we know that if we tell them about the dental benefits they have via their employer and enable them to find high quality, reasonably priced dentists near them, there is a very good chance that they will get their dental checkup done. This leads to benefits awareness, benefits satisfaction, better health, better productivity and in many cases some cost savings.

The interesting thing is that we can accomplish this by communicating to just those 200 people rather than communicating to the 800 people who already get their dental checkups and the 9000 people who are not in their twenties and don't care about dental health. I took a fairly simple example to explain how we reduce the need for mass communication about benefits and significantly increase personalized communications that engage employees and slowly but steadily change behavior, improve benefits satisfaction, make employees healthier, and even save some money for the employer and the employee. Most segments are not this simple to build or comprehend.

Building such an infrastructure and assembling a product, data science and communications team that can do this can cost employers tens of millions of dollars, even if they have a clear idea about how to do this.

Today, Castlight has several such segments and serve many employers in the US. Every month, we keep adding more segments that make personalized highly relevant and valuable recommendations to employees. Every piece of communication we send out makes one person a bit more knowledgeable about his or her benefits, has the chance to make him or her a bit healthier, makes him or her a bit more productive and saves him or her and their employer a little bit of money. We do this several million times every year. Knowing this is what motivates me to get up every day morning and go to work. If you are a physician, data scientist, product designer, content strategist, or a product manager and think this is fun to do, give me call. I am on LinkedIn.




Saturday, March 05, 2016

The Innovator's Prescription by Clayton Christensen

When I joined Castlight Health about 10 months ago, I looked for a good book that provides a good overview of the problems facing American healthcare. While I heard about many specific problems, I wanted to understand the history of today's problems, the reasons those problems arose, the players who have a vested interest in solving those problems and the possible directions to solve those problems. My wife got me the book, The Innovator's prescription by Clayton Christensen, written in 2009. It gave me the required perspective. It answered questions such as who should we build healthcare solutions for and who has the motivation to change healthcare.

If you are new to healthcare technology and want to get a clear idea, this is a good book to read. I posted a video of Clay Christensen summarizing the book below. He summarizes ten years of research in 60 minutes.

Friday, February 26, 2016

Castlight Action was on Fox Business News

It is a product manager's dream to have his or her product covered by the Wall Street Journal and Fox Business News in the same month. I am living that dream this month.  Castlight Action sends personalized health care recommendations to employees at the right time so that they get better heath outcomes and better financial outcomes. Leading the team that designed and built Action and launching it for some of the largest employers in the world will rank among the top five highlights of my career building software products.

My colleague Kristin Torres Mowat, explains Action to the Fox Business team. You can see the video here.

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