So you need some work done that you’ve never had done before or you need to buy something you’ve never bought before. What should you pay? That can be a tough question. What seems reasonable? Sometimes we set arbitrary rules. It’s OK if it’s under $50 or under $100. But that’s just a reassurance that you’re not getting ripped off too badly. Certainly the best way to avoid that outcome is to know how much that service or thing is worth, or at least know what others have paid for the same thing.
Fortunately now, in the age of the customer, that’s easier to find out. Price information for most consumer goods is easier to come by, making the buying process more efficient. But what about governments? We’ve all heard about the $600 toilet seat or the $400 hammer. Stories of government spending excess and mismanagement abound. Some are urban legends or misrepresentations. Others have legs — such as the recent reports of Boeing overcharging the US Army. While these incidents are likely not things of the past, open data initiatives have made significant progress in exposing spending data and improving transparency. Citizens can visit sites such as USAspending.gov for US federal government spending or "Where Does My Money Go?" for details on UK national government spending, and most large cities publish spending as well.
To jump on this R feeding frenzy most leading BI vendors claim that they “integrate with R”, but what does that claim really mean? Our take on this – not all BI/R integration is created equal. When evaluating BI platforms for R integration, Forrester recommends considering the following integration capabilities:
Usually when a product or service shouts about its low pricing, that’s a bad thing but in Google’s case there’s unique value in its Sustained-use Discounts program which just might make it worth your consideration.
A journalist called and asked me today about the market size for wearables. I replied, “That’s not the big story.”
So what is? It's data, and what you can do with it.
First you have to collect the data and have the permission to do so. Most of these relationships are one-to-one. I have these relationships with Nike, Jawbone, Basis, RunKeeper, MyFitnessPal and a few others. I have an app for each on my phone that harvests the data and shows it to me in a way I can understand. Many of these devices have open APIs, so I can import my Fitbit or Jawbone data into MyFitnessPal, for example.
From the story on 9to5mac.com, it is clear that Apple (like with Passbook) is creating a single place for consumers to store a wide range of healthcare and fitness information. From the screenshots they have, it also appears that one can trend this information over time. The phone is capable of collecting some of this information, and is increasingly doing so with less battery burn due to efficiencies in how the sensor data is crunched, so to speak. Wearables – perhaps one from Apple – will collect more information. Other data will certainly come from third-party wearables - such as fitness wearables, patches, bandages, socks and shirt - and attachments, such as the Smartphone Physical. There will always be tradeoffs between the amount of information you collect and the form factor. While I don't want to wear a chubby, clunky device 24x7, it gets better every day.
IBM recently kicked off its big data market planning for 2014 and released a white paper that discusses how analytics create new business value for end user organizations. The major differences compared with last year’s event:
Organizational change. IBM has assigned a new big data practice leader for China, similar to what it’s done for other new technologies including mobile, social, and cloud. IBM can integrate resources from infrastructure (IBM STG), software (IBM SWG), and services (IBM GBS/GTS) teams, although the team members do not report directly to them.
A new analytics platform powered by Watson technology. The Watson Foundation platform has three new functions. It can be deployed on SoftLayer; it extends IBM’s big data analysis capabilities to social, mobile, and cloud; and it offers enterprises the power and ease of use of Watson analysis.
Measurable benefits from customer insights analysis. Chinese organizations have started to buy into the value of analytics and would like to invest in technology tools to optimize customer insights. AmorePacific, a Hong Kong-based skin care and cosmetics company, is using IBM’s SPSS predictive analytics solution to craft tailored messages to its customers and has improved its response rate by more than 30%. It primarily analyzes point-of-sale data, demographic information from its loyalty program, and market data such as property values in the neighborhoods where customers live.