Monday, December 01, 2014

Big Data Analytics Business Model: Revenue Sharing Strategy and Productized Services

Healthcare companies are living in a threatening environment: costs are increasing at higher rates than inflation rates, and new technologies and therapies are expensive and take too long to bring to market. This situation has moved the marketplace to adopt analytics as a way to prioritize population health strategies, chronic disease therapies, and claims processing efficiencies. A strategy to reduce costs and to accelerate these analytic solutions is to embrace a revenue-sharing strategy with IT and analytics vendors.

Some healthcare business analytics vendors will have a problem adapting to this strategy. Steve Jobs said it best in 1995, when referring to IBM: “Companies get confused. When they start getting bigger they want to replicate their initial success. And a lot of them think, well, somehow there’s some magic in the process of how that success was created. So they start to try to institutionalize process across the company and before very long people get confused that the process is the content. I mean that’s ultimately the downfall of IBM. IBM has the best process people in the world; they just forgot about the content.”[1]

The content in healthcare analytics is a measurable business lift that is timely, reduces costs, and improves clinical outcomes. This measurable business lift is different from the traditional IT ROI. Analytic vendors should be able to stand by their products or solutions, not just sell the software and provide training. This is the reason that fifty percent of all analytic solutions are failing; they do not take into consideration the measurable business results of their solution.

In order for a revenue-sharing analytics solution to work, the analytics vendor and the healthcare companies need to be financially invested in the same business metrics. Large companies like IBM, Microsoft, Oracle, or GE need to change their strategy in the area of big data analytics. This shift from technology and analytics, to solving a business problem with measurable results in a timely manner, is going to be the keystone of success in the 21st century. In the Internet industry we see the revenue sharing model with Google AdSense, and, in my opinion, it would work in the healthcare industry, as well (with some industry-specific modifications). Also, the Banking and Financial industry have seen the benefits of a revenue-sharing model in mutual funds, which resulted in the innovation know as high frequency trading.

We are at an exciting time when technology, big data, and advanced analytics are merging to produce and improve measurable business insights. Let me suggest a business model (which includes technology, analytics and domain expertise) to help streamline solving business problems: a combination of industry-specific predictive models products and services to improve the implementation and accuracy of those models is what I refer to as a Productized Service business model.

I have been in the analytics industry for over twenty years, and in today’s environment we require not only new approaches to big data analytics, but a different business model strategy as well.

First, companies are designing analytics platforms, but there seems to be some disconnect between the analytics platform and the business problems that analytics should help solve. The main reason is that the IT team is used to solve technology problems and not specific business problems. This is a “translation” issue. The business and technology teams have different language and motivation. IT thinks that this bridge is overcome with the position of business analyst, but in reality the BA is mostly collecting requirements but not explaining the business need.

Second, IT and data scientists speak a different language. For example, a predictive analytics segment is a part of the population that has statistically similar characteristics, while a segment (think database indexing/partition) for IT is an optimized procedure to allow queries to run faster.

Third, data scientists and business owners also speak a different language. I am sure that we all have had the experience of putting both groups in the same room and the business owners want to talk about specific issues, while data scientists want to talk about distribution, correlation, and predictive models.

I have found that a way to overcome these translation issues is to have a productized business model. An immediate is to have an organizational alignment from the top down that brings these groups together to start overcoming these language issues at all levels within a company. In other words, the strategy and alignment start from the executive management and moves down throughout the organization. Metrics are extremely important in this step. Companies must include in their metrics the number of revenue sharing programs and how are they performing.

The second step is to create learning opportunities, knowledge transfer, for each group to summarize both their basic skills and motivation. This requires planning and the ability to deal with new information not previously known.

Third, companies need to have analytics products that deal with both domain specific issues and operational efficiency issues. For example, in the healthcare industry there are operational issues (i.e., subrogation, correct payment of claims, and utilization of nurses for care management and disease management programs), as well as clinical issues (i.e., coordination of care, quality of care, treatment, and diagnosis). This is crucial since it allows for companies to talk about specific business issues that can be explained and modified as needed.
Fourth, companies need to develop specific business and technical (IT and statistical) metrics up front to diagnose whether an analytic product or solution is working or not. This process of designing metrics is a good time to start overcoming language barriers among different groups.

Fifth, companies need to develop libraries that allow business, IT, and analytics terms to be captured and disseminated throughout the company. These dictionaries allow people to have a standard way to reference each other’s languages. It is important that these dictionaries have examples using common language for examples and not just technical language. In healthcare, disease specific population health management segmentations should be the same for marketing, claims processing, and CM/DM programs.

Sixth, the productized service needs to be cost efficient. One of the best ways to do this is a revenue sharing arrangement between all the stakeholders that they will share in the costs and revenues arrived through analytics. This is going to be challenging in today’s financial models for some companies, but something that can be experimented and refine as needed.

Seventh, already developed products need to be implemented quickly. Companies want an analytics product to be operationalized and implemented within three months, if possible, to start reaping its benefits. This will involve compromises by all the different groups. Business, IT, or data scientist should not expect the first product that is implemented to be perfect. Nevertheless, they should have high expectations that the analytics product have a measurable impact in their operations.

Lastly, the maintenance, refreshing, and update of any analytic product must be established up front as part of the project plan. These services are expensive and require planning and budgeting, hence requiring time to prepare. Depending on the company fiscal year, a one-year notice may be required.

In conclusion, healthcare companies are going to need to influence these strategic changes for IT and analytics vendors by taking the lead in the way that they do business. Ultimately, revenue sharing strategy and productized services will force the corporate changes necessary to quickly adapt in this challenging environment.



[1] http://www.networkworld.com/article/2454038/data-center/would-jobs-have-made-ibm-deal-not-in-95.html

Monday, December 31, 2012

Business Analytics: Analytics Predictions 2013 by Alberto Roldan

Business Analytics: Analytics Predictions 2013 by Alberto Roldan

Analytics Predictions 2013 by Alberto Roldan



This year I would like to do something different and address trends in the business analytics industry that, in my opinion, will affect all industries in the next 1-3 years.  The purpose of these predictions is to provide some guidance so that the companies we represent today make informed decisions in the area of analytics that could affect their profitability and revenues in the future:

1.     Electric power transmitted through the air. Could you imagine the implications of this technology on all industries?  This will eliminate the need to plug in to restore electric power to any device.  This would include cars, computers, laptops, and mobile devices.  The opportunity for smart-energy solutions is going to transform business analytics with a potential of a ten-fold increase in the amount of business opportunities.  Key skills will be experience in smart-grid analytics for short-term forecasting of electricity utilization, big data, and machine learning algorithms like principal component analysis (PCA).

2.     Embedded semiconductor analytics. This technology is currently used in automobiles as sensors (e.g., check oil, gas gauge).  Could you imagine the implications in the business-to-business (B2B) market for this technology?  Right now alerts that are generated (e.g., retailer fraud, institutional compliance for anti-money laundering, or pharmaceutical utilization) need to be programmed and maintained at a substantial operational cost.  This embedded technology can produce some measurable costs improvement in most industries.

3.     Executives will become more conversant in analytics methodologies and technologies. The larger the investment in analytics, whether technologies or solutions, the more important it will become for executives to have a deeper understanding in this area of investment.  This type of conservation will facilitate moving analytics from the realm of the data scientist to permeate the operational side of any company.

4.     Reliance in machine-learning algorithms for big data analytics. The concept that analytics methodologies that work in small data sets but not in big data analytics will come to the forefront.  Utilization of machine-learning algorithms to provide business insights and forecast future trends that have an impact on revenues or cost levers will become make significant inroads in the next 36 months. 

5.     Operational analytics. The term operational analytics will be used to have a good or bad implication depending on the experience of each person.  It will be bad for those that have spent millions of dollars in data warehousing and technology tools but have not obtained a measurable cost savings as a consequence of their investment.  It will be good for those that have made a prior investment that has had a measurable impact in revenues or costs.  The ability of consulting and analytics companies to explain the measurable value of operational analytics will become the cornerstone of whether a company perceives analytics as a good or bad investment.

6.     Analytics reporting will undergo a transformation to visualizations that can accommodate multiple dimensions involved in big data. Reporting will undergo a transformation that allows end users to simultaneously visualize multiple dimensions in real-time big data situations.  The advent of big data, machine-learning algorithms, and the need to prove measurable business value will require 3D visualizations for reporting purposes so that companies have a 360-degree view that will capture business insights as well as the impact on profitability of multiple what-if scenarios.  

Thursday, April 26, 2012

Sunday, April 22, 2012

Big Data Innovation Conference - April 25 & 26, San Francisco

Big Data Innovation Conference - April 25 & 26, San Francisco

I am the co-chair for the first day. This is an exciting conference for those of us that develop analytics methodologies to get business insights to provide measurable business lift. If you are attending contact me at alberto.roldan@cognizant.com so that we could meet during the conference

Tuesday, January 10, 2012

Big Data and Analytics: Game Theory in Practice

This new year I have decided to concentrate in big data and advanced analytics.  Specifically, I am emphasizing the area of game theory and big data to give business insights in the area of "what if" scenarios.  This article inThe Economist on Modelling Behaviour is a good starting point. 

The objective is how to provide business insights in a rapidly changing world with big data challenges.  One of my proposed solutions is to adapt or use game theory in business analytics.  I had the fortune and privilege to be trained in game theory by Dr. Allen Whiting in the mid-1970s.  Game theory has been around since the 1940s (although there are references for as long as 1713!) and is used in 2007 Hurwiczs, Maskin and Myerson were awarded awarded the Nobel Prize in Economics "for having laid the foundations of mechanism design theory."

I will always be in debt to Dr. Whiting (former Undersecretary of State in the Kennedy and Johnson administrations) for teaching me how to do analysis in a way that has become my livelihood since the 1976. Friendship like imagination has no limits!

Link: http://www.economist.com/node/21527025

Enjoy!

Thursday, January 05, 2012

What's your Algortihm?

This is a good article that describe what do I do for a living and different business analytics applications.

Wednesday, December 28, 2011

Alberto’s Business Analytics Predictions for 2012

Alberto’s Business Analytics Predictions for 2012

2011 was a great year for business analytics across all industries. As business analytics projects proved their measurable value within companies, I believe that in 2012 we will continue to see a mathematical increase in the number of companies that use business analytics. My predictions for 2012 are not in order of importance.

1. Increased utilization of advanced analytics in retail, CPG, healthcare, energy, banking, and healthcare – These industries will continue to lead in incorporating advanced analytics in their day-to-day operations using real-time or near real-time systems. Education and understanding of how advanced analytics can help companies will increase within companies in strategic and tactical areas.

2. 3D visualization techniques used in gaming will become more prevalent in business analytics – Visual analytics will enhance dashboarding and reporting techniques currently used by companies due to its measurable lift in providing better business insights.

3. Decrease in the amount of time that it takes to successfully design, test and implement an advanced analytics project to no more than three months – As companies become more educated in the benefits of advanced analytics, tools, and resources, they will start demanding the streamlining of analytics projects so that they can “turn around in a quarter.”™

4. Enhanced integration of IT and business stakeholders – Business analytics will bring the long- sought goal of better integration between IT and business stakeholders within companies. Business analytics will become the bridge between IT and business stakeholders since it answers specific business questions that are implemented through the enterprise by the IT organization.

5. Resources with analytic skills will continue to be a hot commodity – Companies will seek these resources to incorporate them into their infrastructure. As the demand for these finite resources increases, the marketplace price will increase.

6. Outsourcing and Offshoring of analytic projects and resources – Companies will seek analytic business models that can turn projects around quickly and seamlessly within their infrastructure. They will discover that it takes a combination of analytics, IT, and project management skills to successfully implement an analytics project with measurable business lift. Outsourcing and offshoring models will become a more attractive alternative in terms of pricing and delivery.

7. IT organizations will continue to struggle with analytics projects – The learning curve of the differences and similarities between analytics and IT projects will continue to plague IT organizations, and outsourcing and offshoring delivery models will become more attractive methods to deal with these issues.

8. Analytics tools will continue to thrive in the marketplace – Companies will continue to purchase these tools to give them the ability to predict and segment their big data. Analytics appliances that are industry- and problem-specific will proliferate in the next 2-3 years.

9. Social media integration with transaction data will become a priority for business stakeholders and the IT organizations – Companies have a gut feeling that this data is important and that it will help them to better target their customers and decrease costs. Although companies will struggle with this integration issue, they will ultimately turn to using advanced analytic techniques for successful and measurable business lift integration.

10. Voice recognition and natural language software will become a major data integration issue – As companies increase the use of voice recognition software, integration of these massive amounts of data will become a challenging issue and they will turn to advanced analytic techniques to solve this issue.

11. Big data analytics will become a priority for companies – As companies acquire more and more data, the issue of how to get value of this data will become a priority for many companies. The difference between having big data and getting the most value of this data will become part of the strategic goals for many companies. Big data with actionable and measurable business insights will go hand-in-hand.

12. Continuous improvement and refreshing of predictive models and business segments – As companies implement predictive models and statistical valid segments within their organizations, the ability to improve and the need to know when and how to automatically refresh these models will become an issue for many IT organizations. Initially, companies will move to offshoring and outsourcing these tasks. In the longer term, IT organizations will look to automate these tasks, and they will incorporate techniques such as embedded analytics.
Innovation is alive and thriving in the area of analytics.  Applications in different industries, cross-utilization of techniques in different domains, and new optimization techniques are always improving.  The future belongs to the young generation and the role of my generation is to provide guidance so their dreams are realized.  Let us roll up our sleeves and work for a better and brighter future.  We are unleashing the power of the mind!

Business Analytics

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