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.”
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
Are you data-flooded, data-driven, data informed? Everyone is searching for ways to monetize data assets. But data is simply a means to an end. The end is not just reports, dashboards, heatmaps, knowledge, or wisdom. The end we seek is fact based decisions and actions.
In other words, what is the use case that shapes the context for “Raw Data -> Aggregated Data -> Intelligence -> Insights -> Decisions -> Operational Impact -> Financial Outcomes -> Value creation.”
Best practice firms (and even political campaigns) can’t operate on anecdotes, opinions and gut instinct. They have to strike a measured balance between opinion vs. scorecards vs. KPI metrics. Rather than data-driven, they need to be data-informed. That’s a big shift.
For instance, high-end cars use telemetry to know that an engine part is likely to break down before it actually does, based on the vibration or temperature patterns, a technique known as predictive maintenance. The idea is that a part does not fail all at once. Instead, it deteriorates over time until it eventually breaks. By monitoring the part all the time, you can spot problems before they become obvious.
Similarly big data analytics will facilitate new scenarios. Some may even be disruptive similar to how MP3 players changed the music industry or electronic readers changed the publishing model. To be competitive, organizations will require new technology with clear implementation strategies, iterative test-and-learn environments and data science talent.
However, despite the rosy predictions, many organizations will flounder in their Big Data efforts not because they lack analytics capability but because they lack clear objectives or multi-year roadmaps in converting noisy data into useful signals.
So the first question is: What do you really want to achieve? Increased customer loyalty? A greater share of wallet via cross-sell? New customers? Lower attrition? In other words, what is the use case? As the old adage goes: if you don’t know where you are going, any road will get you there.
Starting with a clear objective is essential. Big Data Analytics promise: enable “data monetization” through more timely, more accurate, more complete, more granular, more frequent decisions. So, what exactly are the types of business problems big data analytics likely to solve? For this you need a mini-MBA in Big Data Use Cases.
Who doesn’t want to achieve faster “time-to-information” and shorter “time-to-decision” for executives and managers with mobile BI? Who doesn’t want to disseminate insights or KPIs to front-line employees, such as field sales representatives, line of business managers, and field service employees?
The question is not whether Mobile BI is a good idea but how to execute this program in a low-cost way? How to design and deploy eye-popping “wow” apps? How to support, maintain and enhance these apps which are constantly changing? What technology and infrastructure to put in for a national or global deployment? Who is going to fund all this plumbing – corporate, LoB or IT?
Business Analytics solutions for “always-on” 3/4G enabled mobile devices – iPads, iPhones, tablets, smart phones – are becoming prevalent as the form factor becomes appropriate for BI. We are increasingly seeing firms build state-of-the-art dashboard solutions for iPads. The “post-desktop” apps provide senior management with intuitive interactive access to the company’s most important business KPIs and dealing with data overload.
Tablets, 4G Wireless and next gen displays (+gesture based, verbal interfaces) have enabled new productivity improvements and better ways to consume information, perform ad-hoc querying and scenario planning. Dashboard, heatmaps and scorecards on the iPad, iPhones and Androids are intuitive, attractive, powerful, available at any time and any place: a perfect mix for top managers, sales teams and even customers.
BI (and Information Management) is a natural fit for mobile devices. Managers, blue and white workers spend a majority of their time away from their desks. Most are traveling, walking about or driving from site to site. And it’s these mobile workers who need the most up-to-date information. They need mobile BI to retrieve data to make on-the-spot decisions, monitor operational processes and review KPI, and work-in-process dashboards.
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.