Self-tracking, Seamless Engagement and Personal Efficiency improvement’s new frontier is Personalized Big Data and Digital Health. This is really becoming a viable idea around wearable and sensor computing and the basis for new data platform wars.
The new platforms for digital life or data driven life — that collect, aggregate and disseminate — will cover a wide range of new User Experience (UX) use cases and end-points… medical devices, sensor-enable wristwear, headset/glasses, tech-sensitive clothing. All of them are going to collect a lot of data, low latency analytics, and enable data visualization. Several new firms are entering the activity tracker market LG (Life Band Touch), Sony (the Core), Garmin (Vivofit), Glassup, Pebble, JayBird Reign etc.
Data collection is just one piece of the solution. The foundation for personalized big data is Descriptive and Predictive Analytics. Ok…What do i next? what is the suggestion? in the form of predictive search (automated deduction or augmented reality).
How do i discover useful patterns, analyze, visualize, share, query and mobilize the collected data? A wide range of start-ups – Cue, reQall, Donna, Tempo AI, MindMeld, Evernote, Osito, and Dark Sky – and big companies like Apple, Google, Microsoft, LG and Samsung are working on predictive apps — aimed at enabling new robo-assistants that act as personal valets, anticipating what you need before you ask for it.
At the Analytics Executive Forum, I facilitated a session on Omni-channel analytics. It struck me how every leading consumer facing firm seems convinced that mobile is becoming the dominant B2C interaction channel. Mobile is the gateway to insight based marketing and the “always addressable customer”….
Insight-based interactions – The company knows who you are, what you prefer, and communicates with relevant, timely messages, using the power of analytical intelligence to detect patterns, decode strands of information and create meaningful offers and value.
The “always addressable customer.” This is a consumer who fits the bill on three fronts simultaneously: (1)
- Owns and personally uses at least three connected devices; (2)
Goes online multiple times throughout the day; (3)
- Goes online from at least three different physical locations
The opposite of insight-based is “spray-and-pray” marketing – The company has very limited knowledge about who you are, forgets what you prefer, and tries to reach you with off-target communications that alienate you – based on fragmented data, poor data quality and inadequate integration, resulting in confusing, chaotic interactions. A good example: “I have 2 million frequent flyer miles with your airline and still do not get any recognition, respect or value from this loyalty.”
As companies architect new insight based mobile use cases I suggest that they look at what is coming next. With IOS 7, Apple is delivering several new features – Passbook, Beacon.
Retailers, banks and other customer facing firms/brands better pay attention. 100+ million iPhones are automatically getting this feature with the new OS upgrade making this a mega-disruptor in the coveted target segment everyone is chasing. Read more
Apple with its iCloud offering is attacking the consumer facing digital content big data problem. Big Data is challenging on many fronts from the insights (e.g., analytics and query optimization), to the practical (e.g., horizontal scaling), to the mundane (e.g., backup and recovery).
On June 6th, 2011 Apple Inc. launched its new purpose built digital locker service called iCloud for its 225 million iTunes accounts that frees the end-user from the tyranny of the device. The iCloud service is a cloud offering that would allow users to store digital files such as photos, MP3 music, videos and documents in the cloud and access them from Internet-connected devices like iPhones, iPads, iPods, iMacs and others.
So, what’s the big deal? They are addressing a classic BI data management problem: How to free up data trapped in “device and application jails” in a user-friendly way. The “scan and match” concept is quite applicable to large scale Enterprise Datawarehouses which suffer from data integrity issues as edge data capture and consumption devices proliferate.
Data ingestion, governance and management is a huge problem facing large organizations. As data volumes double every year, not having a basic data management strategy will become an Achilles heel. Most organizations unfortunately don’t know what data assets they have, where these assets are, how they are organized and how well they are secured. Apple shows a neat way to address the Big Data problem in personal cloud management.