What’s on Santa’s List for Analytics?

 It’s that time of year again, when stathead’s thoughts turn to sugar plums and new gadgets to make their jobs more interesting and productive.  It’s amazing how technology keeps getting better and cheaper.  Recall that Moore’s law predicted that chip performance would double every two years, which would increase processing speed, memory capacity, sensors, and even the pixels in digital cameras proportionately. For example, comparing the IBM PC released in August 1981 with the Apple iPhone 4 released in June 2010, the CPU clock speed of the PC was 4.77MHz compared to iPhone at 1GHz; the processor instruction size was 16 bits for the PC and 128 bits for the iPhone; the storage capacity of the PC was 160KB and that of the iPhone (base model) was 16GB; and the installed memory (RAM) was 64KB for the PC and 512MB for the iPhone.⁹ Additionally, the list price on release of the PC was $3,000 (or about $7500 adjusted for inflation) and the iPhone was $199, or about 2.5% of the cost of the original PC. This exponential growth in computing performance has driven the impact of digital devices from computers to household appliances in every segment of the world economy.

So, what’s in the Holiday Gift Catalogues for Analytics this year?  Here is a sampler of my five favorites.

NoSQL (Not Only SQL) databases are an alternative to traditional, relational databases and are especially suited for unstructured big data, Web 2.0, and mobile applications. It uses open source software that supports distributed processing. It scales “out” to the cloud, rather than “up” with more servers. It has fewer data model restrictions than relational databases management systems, which allows more agile changes and less need for database administrators. It can use low cost commodity hardware. The bottom line is that it is faster and much cheaper. Examples of popular NoSQL databases include Cassandra, Hadoop, and BigTable. Companies that use it include Facebook, Netflix, LinkedIn, and Twitter. For more information see the NoSQL website, which touts itself as “your ultimate guide to the non-relational universe.” 

High performance computing (HPC) allows users to solve complex science, engineering, and business problems using applications that require high bandwidth, low latency networking, and very high compute capabilities. This is the computing capability needed for mining mountains of data. This capacity can be provided by dedicated computer clusters or by cloud clusters. Dedicated, custom-built, supercomputer infrastructure requires significant capital investments, long procurement times, long queues, and extensive database management. Buying HPC services from the cloud provides definite cost advantages, short lead teams, access to the scale required for a given project, and on-demand capacity. An example of such an offering is from Amazon Web Services called Cluster Compute Instances.  In healthcare, the biopharma sector uses HPC for genome analysis. Other industries, including oil and gas, financial services, and manufacturing, use it for modeling.

  

The idea that machines could replace humans for certain functions has been around a long time. And it certainly has become commonplace in industries such as automotive with robots on the assembly line. But can the machines actually “learn” and improve functioning on their own beyond being explicitly programmed? There are good examples of this with Google Search and Amazon purchasing recommendations, and with voice and facial recognition applications. In healthcare, IBM demonstrated a compelling use of machine learning (and natural language processing and predictive analytics) with its Watson technology by beating two grand champions on the Jeopardy! TV quiz show. IBM is working on healthcare solutions.  It has partnered with is Memorial Sloan-Kettering Cancer Center to have the technology gather and assimilate information from the research literature and from the Center’s clinical experience documented in its medical records and other files to “bring up-to-date knowledge to the bedside of every cancer patient.” Watson might be able to do this through its capabilities to read and understand language, interact with humans, remember everything, and provide answers to real-time questions. How the information will be delivered to the physician, how it might transform the practice of medicine, and whether physicians will embrace the technology are all important, open questions.

The Internet has transformed the way businesses communicate, market, do commerce with customers, and collect data about them. In retail, clicks are challenging the bricks. What could be more indicative of shifting paradigms than the collapse of the structures in which people do business (stores). One example is the capability to do virtually instantaneous randomized trials of alternative Web site features, e.g., how to get the most contributions during a political campaign. Another is Web page “scraping” in which all types of data about people’s Web wanderings are turned into ratings about their suitability for a job, a loan, and a date.

More than half of the adult population in the United States have smartphones.  Facebook has more than 1 billion monthly users.  The hot combination of these clicks and mobile produces a platform for easy, convenient, and quick communications that also enable e-commerce, uber-targeted marketing, location monitoring, and much more. An opportunity going forward in healthcare is to create closer relationships with people to help them get healthier by tapping into data that are freely exchanged and by supporting the continual, fast evolution of new applications to support health.

   

Technology has been awesome in increasing computing capacity with hardware (speed, memory, storage, access, etc.) and with software (to manage all the data and make sense of it). But if the technology is so great, why is the uptake of healthcare analytics so low in comparison to its potential and relative to the performance of other industries? The answer is complicated but one compelling reason is that the technology of making change happen, of getting from a good idea to its being embedded in operations, is unappreciated and untapped.  And analysts are enamored with computing technology and may take their eyes off the prize…making behavioral changes to improve clinical and business outcomes.

This holiday gift idea is cheaper than all the others and may be more consequential.  A guide is available in my book, A Framework for Applying Analytics in Healthcare:  What Can be Learned from the Best Practices in Retail, Banking, Politics and Sports.