data monetization

Your Healthcare Analytics Solution: From Concept to Launch in 100 Days

Below is the video recording of a Juicebox healthcare analytics product webinar. The video is about 45 minutes and includes our tips for successful launches, a quick data product demo, and a Juicebox Q&A session at the end.

Our tips include which project tasks are best done slowly (e.g. getting your data ready) and which tasks need to move fast (e.g putting a working data product prototype in front of customers). Knowing what to do fast and what to do more slowly is a big part of the tips we share on the video.

We also touch on Juicebox strengths and alignment to support fast and slow tasks, such as:

🐇 Design development agility

🐢 Enterprise-grade app development

🐇 Understandable data stories

🐢 Capture your customer needs

Enjoy the webinar. After watching you can schedule a personal demo where we can go into Juicebox or case studies. https://www.juiceanalytics.com/get-started

Data is the Bacon of Business: Lessons on Launching Data Products

Last week was the 4th annual Nashville Analytics Summit. The event has grown from 150 participants three years ago to 470 this year. I took the opportunity within this friendly analytics community to share our latest thinking at Juice. Last year I spoke about "Beyond Data Visualization: What's Next in Communicating with Data”. This year my talk was entitled “Launching Data Products for Fun & Profit”. I started with a simple premise: Data is the bacon of business. I’ll let Jim Gaffigan explain:

His logic works for data, too.

We've had a front-row seat as our clients have transformed their data assets into revenue-generating data businesses. But launching successful data products isn't simple. And it is a far cry from your typical reporting or self-serve BI solutions — the insight-free data delivery vehicles of the past. I’ve posted the slides from my talk here:

Here are a few highlights:

  • Data products are happening now. Big technology companies are making massive investments in pursuit of better data sources for their products. IBM spent billions for The Weather Channel to enhance Watson Analytics. Google bought Waze for crowd-sourced traffic data. Microsoft wanted LinkedIn’s “economic graph” so badly they spent $26 billion.
  • The best data product stories start with a visionary leader. Our clients aren’t just thinking about fancier visualizations. They want to transform their businesses by making their customers smarter and more successful through data.
  • My friend Oli Hayward of Hall & Partners provided some valuable lessons from launching a world-class market research analysis portal. He explained the need to start by selling to internal audiences and targeting only the most innovative clients (we’re in the same boat there).
  • Data is an imperfect reflection of reality. When you present data to customers, prepare to discover exactly how imperfect it is. Which led me to this joke...

If you’d like to hear more about our lessons learned from dozens of data product launches, send us a note at info@juiceanalytics.com.

"Chart" new territory with your data

Amazing discoveries start with an innovative mind willing to look at things differently. Take Columbus, they said he was crazy for sailing the ocean blue in search of the “new world”. Well here’s another outrageous idea for you!  What if you could use your Big Data project as a way to make additional revenue? Here are some ideas so that you can begin to chart this unknown territory with your Big Data, and turn your discoveries into dollars.

3 ways to monetize your data

It is logical to use company data to save money and find cost savings internally. But what if you take another approach with that same data? Check this out-- U.S. News and World Report was able to make their own discovery.  They created the criteria and collected the data on college rankings for decades. And each year universities fight for the top rankings in their region or for a particular education track. They produced these ranking reports geared toward the prospective student. One day they stepped back and took another look at the rich data they had collected over the years, realizing they had another (big) market for this information. If they could package and sell it in a new way, to the colleges and universities, they could provide valuable insight and create new revenue streams!

So here are some tips to help you think outside the box with your Big Data.

1.  Make it unique

Think of ways that you can make the data unique to your audience and their needs. You have data that no one else has, and it can help users make better decisions. Think about who, outside your business, could benefit from this unique information, and how they can benefit. Then apply some additional strategies to really make your data a must have:

Mashup - combine your Big Data with a public data set

What would happen if you combined your data with a data set on data.gov or another public set? Perhaps you work in the public health sector as an executive of a health insurance company. You could overlay your Big Data with government census data to identify healthcare trends that a growing hospital needs to plan for. The hospitals could use your data product to set up their hospital for the future. Here’s a list of companies already using government data in creative ways.

Predictive Analytics - find the treasure in future trends

Can we apply an algorithm to our data to find some special meaning or make the data more helpful? Predikto is one company that has this down in the railroad industry. They have a great product to predict the breakdown of railroad track safety monitors. Their product analyzes a plethora of data from weather to train loads to provide maintenance crews critical yet simple health-check displays, so they can easily see when these monitors are likely to fail and preemptively send a crew out for repairs before any damage is done.

Composite Metrics - if you build it they will come

Sometimes a simple metric isn’t enough if it can’t fully describe a behavior or the performance of a system. That’s when you need to come up with a Franken-measure: a made-up metric that creates a comprehensive composite to capture complex concepts. Think Google’s PageRank or the NFL’s Passer Rating. PageRank combines multiple complex metrics on web traffic and trends in such a way that the end result is something we can understand and use.

2. Put your best efforts into the user experience.

By putting yourself in your user’s shoes, then you can design data products much more effectively. First, like we mentioned earlier, you need to really think about who your audience is and what your audience needs to get from the data. How does this impact the way you tell the story of the data, and how you design the product so that the users can see the value immediately?

More often than not, the heart of the designer’s message is lost among all the metrics and charts. In this flurry of enthusiasm to display tons of data, little attention is paid to the user and guiding them on how to consume the information.

Remember, your data consumers are not the experts in the data like you are.  Your users probably have responsibilities other than analyzing data. Give them the high-level path to follow, and let those users who need more info have the option to drill down into the details. Think about the delivery of data much like the way you tell a story, provide a beginning (starting point), middle (critical details) and end (decision points).

3. Start small, design one product first that solves a real problem easily.  

It’s better to prototype a data product that is ready to put in front of a user in six weeks instead of six months. This allows you to keep it simple and make adjustments quickly based on what’s working and what’s not. Think like Google. Put out a concept or idea as a beta, study the user responses and feedback and add more capabilities as you go. This kind of logic allows for a quick release, less investment in development of the product and the opportunity to grow with the consumer.

Now that you are ready to set sail and chart your own new data territory, here are more helpful leads to help you do more with your data products!

Join us in December for our webinar on Turning Data into Dollars.

Also check out DJ Patil’s (the U.S. Government’s Chief Data Scientist)  free e-book, Data Jujitsu, the art of creating a data product.

Finally, take a look at our own, Zach Gemignani’s slideshare on turning data into dollars.

For a demo of our product, Juicebox, schedule an appointment.

Analytics 3.0 and Data Monetization

When it was published in 2007, Competing Analytics: The New Science of Winning sparked the imagination of many business leaders. It opened eyes to the concept of analytics as a strategic capability, beyond basic reporting, financial analysis, and basic marketing optimization. The authors Thomas Davenport and Jeanne Harris established themselves as leading thinkers in an emerging field. However, the book found its critics among some analytics professionals (like us) who felt it delivered a superficial understanding of the real challenges “on the ground” and offered guidance that was abstract, academic, and anecdotal.

At the time, we said his advice for becoming an analytics competitor was "a good example of condensed misperceptions about what analytics can and should do."

Others were less circumspect:

  • The top review on Amazon states: “This is the glib, anecdotal book built around a basic, almost stereotypic Harvard Business Review five-level model, this one focusing on various levels of use of analytical methods, systems and processes.” 
  • From Neil Raden: "what Davenport is implying is not only centralized control, but also centralized design. This is another naive assumption, because many organizations are not only decentralized—they’re dysfunctional."

Even with mixed feedback, it was clear that Competing on Analytics hit a nerve. Davenport and Harris continued their research and evaluation of the analytics world, and in my opinion, have made progress in reflecting the realities and challenges of analytics practitioners. In 2010, they released a book entitled Analytics at Work that focused more on the front-line realities of information workers. In December 2013, Davenport and Harris published an article in the Harvard Business Review entitled Analytics 3.0.

With this evolution, I feel they have begun to capture the essence of the analytics opportunity ahead. In particular, they have begun to focus on how data can be used to enhance product offerings — shifting the focus from smarter internal decisions to smarter, higher-value product offerings. In their conversations with data savvy companies, they describe seeing "a new resolve to apply powerful data-gathering and analysis methods not just to a company’s operations but also to its offerings—to embed data smartness into the products and services customers buy."

They go on to say:

Today it’s not just information firms and online companies that can create products and services from analyses of data. It’s every firm in every industry. If your company makes things, moves things, consumes things, or works with customers, you have increasing amounts of data on those activities. Every device, shipment, and consumer leaves a trail. You have the ability to analyze those sets of data for the benefit of customers and markets. You also have the ability to embed analytics and optimization into every business decision made at the front lines of your operations.

The Analytics 1.0, 2.0, and 3.0 framework from the International Institute for Analytics

The Analytics 1.0, 2.0, and 3.0 framework from the International Institute for Analytics

In the HBR article, Davenport and Harris describe what it takes for companies to engage in Analytics 3.0, and include some of the standard messages that we’ve become so accustomed to from the era of Big Data: more data, more “data management options" (i.e. Hadoop, NoSQL, in-memory, etc.), faster technologies and faster analysis. Perhaps least compelling for me is a concept of creating "analytics on an industrial scale.” Apparently IBM has created a data model factory and assembly line to make and maintain 5,000 models a year. It isn’t clear whether this kind of approach would be appropriate or useful for most other companies.

However, when they delve into the human and organization challenges, their message is more resonant with our experience. For example, they highlight the need for:

  • Focus on delivering analytics to the front-line decision makers. These are the people who are making everyday decisions that impact customers. The best analytical solutions do a good job of presenting data that helps people within their current workflow in ways that are easy to understand and act upon.
  • Time and resources need to be invested into data discovery to understand data before delivering analytical products. Too often we see analytics and reporting rushed out to an audience without a clear sense of what metrics matter and what information will actually help the recipient.
  • Collaboration must occur between the business, analysts, and IT. This is certainly one of those concepts that is easier said than done. Nevertheless, it is better to recognize this challenge up front than believe a cohort of elite data scientists will be able to bring analytics to the masses.
  • Top-level leadership needs to support the deep embedding of analytics into products and services. While this is true, we have also found that achieving successes at a grassroots level can help convince leadership of the opportunity in analytical products.
  • Prescriptive analytics (the effort to use data to specify optimal behaviors and actions) will be more valuable than descriptive analytics and more common than predictive analytics. We’ve found that the analytics needs to have a clear and strong point of view to guide users to insights and actions.
  • Organizations need to focus on transforming how decisions are made. Davenport and Harris state: "Managers need to become comfortable with data-driven experimentation. They should demand that any important initiative be preceded by small-scale but systematic experimentation.” The challenge is in building a culture of data fluency — something we tackle in our upcoming book.

Every day we talk with companies that view their data as an asset that can help them either 1) augment and enhance their existing solutions; or 2) generate new revenue streams. Like Davenport and Harris, we feel these are the early days. Some companies still view customer reporting as an unfortunate requirement rather than an opportunity to build loyalty. Other companies haven’t had the time or resources to find ways to make their products more powerful with data. That understanding will come. Gartner predicts that 30% of businesses will be monetizing their data by 2016 and McKinsey Consulting is seeing similar evidence that companies are finding ways to turn data into dollars. We are excited to have a front-row seat.