Technology (preventative apps like Apple Health and HealthKit; EHR, claims and reimbursement analytics; Physician Practice management etc.) will reinvent healthcare as we know it. I expect the healthcare transformation to start incrementally and develop slowly in sophistication. Though the early changes will appear clumsy and underwhelming, by 2030 they will seem obvious, inevitable and well beyond the changes we might envision today.
Why change? Consider this:
- Honeywell, a Fortune 100 technology and manufacturing company, needed to manage the ever-escalating cost of insuring its 130,000 employees and their dependents. Honeywell has reported that health care costs were growing approximately 8-10% per year.
- Self-insured employers like Wal-Mart want to make health care cost and quality information available to their 1.2 Million employees. Useful information that can be used by employees to select physicians based on how their rank, or how much they cost, resulting in savings for both the employee and the employer. Decision support enabler.
Here are just a few examples of analytics at work
- Target predicts customer pregnancy from shopping behavior, thus identifying prospects to contact with offers related to the needs of a newborn’s parents.
- Tesco (UK) annually issues 100 million personalized coupons at grocery cash registers across 13 countries. Predictive analytics increased redemption rates by a factor of 3.6.
- Netflix predicts which movies you will like based on what you watched.
- Life insurance companies can predicts the likelihood an elderly insurance policy holder will die within 18 months in order to trigger end-of-life counseling.
- Con Edison predicts energy distribution cable failure, updating risk levels that are displayed on operators’ screens three times an hour in New York City.
Now you are interested. So what about your organization. Do you have the right toolset, dataset, skillset and mindset for analytics? Do you want to enable end users to get access to their data without having to go through intermediaries?
The challenge facing managers in every industry is not trivial… how do you effectively derive insights from the deluge of data? How do you structure and execute analytics programs (Infrastructure + Applications + Business Insights) with limited budgets?
The exploding demand for analytics professionals has exceeded all expectations, and is driven by the Big Data tidal wave. Big data is a term commonly applied to large data sets where volume, variety, velocity, or multi-structured data complexity are beyond the ability of commonly used software tools to efficiently capture, manage, and process.
To get value from big data, ‘quants’ or data scientists are becoming analytic innovators who create tremendous business value within an organization, quickly exploring and uncovering game-changing insights from vast volumes of data, as opposed to merely accessing transactional data for operational reporting.
This EMC infographic summarizing their Data Scientist study supports my hypothesis – Data is becoming new oil and we need a new category of professionals to handle the downstream and upstream aspects of drilling, refining and distribution. Data is one of the most valuable assets within an organization. With business process automation, the amount of data being generated, stored and analyzed by organizations is exploding.
Following up on our previous blog post – Are you one of these — Data Scientist, Analytics Guru, Math Geek or Quant Jock? — I am convinced that future jobs are going to be centered around “Raw Data -> Aggregate Data -> Intelligence ->Insight -> Decisions” data chain. We are simply industrializing the chain as machines/automation takes over the lower end of the spectrum. Also Web 2.0 and Social Media are creating an interesting data feedback loop – users contribute to the products they use via likes, comments, etc.
CIOs are faced with the daunting task of unlocking the value of their data efficiently in the time-frame required to make accurate decisions. To support the CIOs, companies like IBM are attempting to become a one-stop shop by a rapid-fire $14 Bln plus acquisition strategy: Cognos, Netezza, SPSS, ILog, Solid, CoreMetrics, Algorithmics, Unica, Datacap, OpenPages, Clarity Systems, Emptoris, DemandTec (for retail). IBM also has other information management assets like Ascential, Filenet, Watson, DB2 etc. They are building a formidable ecosystem around data. They see this as a $20Bln per year opportunity in managing the data, understanding the data and then acting on the data. Read more
- How to convert Lookers to Bookers…
- How to create unique and effective Digital Experiences that impact probability of purchase or likelihood of return.
- What offers might result in higher “take rates”
The change in consumer behavior and expectations that e-commerce, mobile and social media are causing is hugely significant – big data and predictive analytics will separate brand/retail winners from losers. This won’t happen overnight but the transformation is for real.
Retail Industry makes up a sizable part of the world economy (6-7%) and covers a large ecosystem – E-commerce, Apparel, Department Stores, Discount Drugstores, Discount Retailers, Electronics, Home Improvement, Specialty Grocery, Specialty Retailers and Consumer Product Goods suppliers.
Retail is increasingly is looking like a barbell – a brand oriented cluster at the high-end, a very thin middle, and a price sensitive cluster at the low end. The consumerization of technology is putting more downward pricing pressure in an already competitive “middle” retail environment. The squeeze is coming from e-commerce and new “point, scan and analyze” technologies that give shoppers decision making tools — powerful pricing, promotion and product information, often in real-time. Applications in iPhones and Droid, like Red Laser can scan barcodes and provide immediate price, product and cross-retailer comparisons. They can even point you to the nearest retailer who can give you free shipping (total cost of purchase optimization). This will lead to further margin erosion for retailers that compete based on price (a sizable chunk of the market in the U.S, Europe and Asia).
Data analytics is not new for retailers. Point of sale transactional data obtained from bar-codes first appeared in 1970s. A pack of Wrigley’s chewing gum was the first item scanned using Universal Product Code (UPC) in a Marsh Supermarket in Troy, Ohio in 1974. Since then, retailers have been applying analytics to get even smarter and speedup the entire industry value chain.
More recent use cases of retail analytics include: Read more