Another day, another data breach. Just received another “We’re sorry you got hacked”…letter.
This is the fifth letter I have received in the past 3 months: Forbes.com, Target, Neiman Marcus, credit card company and a previous employer. What is going on?
Why aren’t firms investing in beefing up their predictive ability to spot the cyber-security intrusion threats? What’s taking them so long to identify? Why is the attack signature - sophisticated, self-concealing malware - so difficult to spot? Do firms need to invest in NSA PRISM type threat monitoring capabilities?
The three impediments to discovering and following up on attacks are:
- Volume, velocity and variety – Not collecting appropriate security data
- Immaturity and not identifying relevent event context (event correlation)
- lack of system awareness and vulnerability awareness
Obviously… where there is pain…there is opportunity for entrepreneurs. There is a growing focus on big data use case for security analytics after all the breaches we are seeing.
Here are three recent examples that i was personally affected by – Forbes, Target, Neiman Marcus.
The cost of data generation is falling. The cost of collection and storage is also falling. The speed of insight-to-action is increasing. The bottleneck has clearly shifted from transaction processing to Analytics & Insight.
Take for instance, how Lufthansa is innovating around revenue management – pricing and capacity management. It is running a POC with SAP’s HANA. HANA’s in-memory technology allows Lufthansa to load more data and application functionality into memory—rather than from disk—and thus to dramatically improve application performance.
Lufthansa is able to do revenue optimization calculations in real time, rather than in batch mode. The system aggregates considerably more data than previous models, including all relevant passenger data over two years. The system calculates and optimizes the revenue for origin/destination itineraries, and bases pricing on passenger profiles. It also estimates the likelihood of cancellation and no-shows on flights, and thus how much overbooking to allow.
To meet demand for faster innovation around analytics, CFOs and CIOs are rethinking their silo’d sourcing strategies and looking at new way of doing things via Outsourcing Analytics.
The “should we or shouldn’t we outsource data science” discussion is heating up in board-rooms and executive suites as analytics becomes core to the firm, C-level execs have to consolidate efforts for delivering the same services to different groups within an organization.
Salesforce.com has a new tagline… “Welcome to the Internet of Customers. Behind every app, every device, and every connection, is a customer. Billions of them. And each and every one is speeding toward the future.”
And every customer interaction is generating a growing trail of data (“data exhaust”). Every machine that services the customer is generating data. Every conversation, transaction, engagement, touchpoint location, offer, response is a potential digital bread-crumb of opportunity.
Now let’s flip the context. A typical mobile user check their phone interface 150 times a day for updates. A Gen Y or Millenial user obviously much more than a Gen X user. The consumption patterns for information are changing continuously. Facebook style real-time updates which were revolutionary 5 years ago seem outdated in the mobile world. We live in an “attention deficit economy” where attention is the new basis for competition. The firms that create the evolving experience using data which can grab/hold your attention will attract marketing and ad $$s.
As a result, the buzz and hype around data…small data, big data, machine data, social data, mobile data….is relentless. As a result there are a lot of new initiatives and companies. I have been asked repeatedly by a lot of entrepreneurs and strategy teams about analytics market size and opportunity size. Product and services firms are also interested in opportunity sizing as they create new offerings in this area.
I thought i would share a mashup of industry and market sizing data i have collected so far.
- How big is the market for Analytics, Big Data?
- How big is the market for Digital Customer Interaction or Engagement?
- How big is the market for Mobile and Social Intelligence?
- How big is the market for Wearables?
- What is growing fast, faster and fastest?
All good questions as services firms think about digital strategy, analytics and future state. You always want to be in the “hot” area… selling is easier, valuations are richer, revenue growth percentages exponential.
Consumerization’s new frontier is Personalized Big Data. This is really becoming a viable idea around wearable and sensor computing and the basis for new data platform wars.
The new platforms — that collect, aggregate and disseminate — will cover a wide range of User Experience 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 sophisticated data visualization.
The foundation for personalized big data is Predictive Analytics in the form of predictive search (automated deduction or augmented reality). Predictive Search is now entering the mainstream. A wide range of start-ups - Cue, reQall, Donna, Tempo AI, MindMeld, Evernote, Osito, and Dark Sky - and big companies like Apple, Google and Samsung are working on predictive search applications — aimed at enabling new tools that act as personal valets, anticipating what you need before you ask for it.
The following eight secular disruptive themes are what Goldman Sachs believe have the potential to reshape their categories and command greater investor attention in the coming years.
The Eight Themes:
- E-cigarettes – The potential to transform the tobacco industry
- Cancer Immunotherapy – The future of cancer treatment?
- LED Lighting – A large, early-stage and multi-decade opportunity
- Alternative Capital – Rise of a new asset class means growing risk for reinsurers
- Natural Gas Engines – Attractive economics drive strong, long-term penetration
- Software Defined Networking (SDN) – Re-inventing networking for the cloud era
- 3D Printing – Disruption materializing
- Big Data – Solutions trying to keep up with explosive data growth and complexity (Industrial Big Data and Personalized Big Data)
These eight themes – through product or business innovation – Goldman claims are poised to transform addressable markets or open up entirely new ones, offering growth insulated from the broader macro environment and creating value for their stakeholders.
Goldman focuses on the impact of creative destruction – a term made famous by the Austrian economist Joseph Schumpeter, which emphasized the fact that innovation constantly drives breeding of new leaders and replacement of the old.
Health expenditures in the United States neared $3.0 trillion in 2013 which is over ten times the $256 billion spent in 1980. Almost 15% of U.S GDP is estimated to be spent on healthcare.
In 2012, the average annual cost of health coverage per employee was $10,558, compared to $4,924 in 2001 – a 106% increase in 11 years. (Source: Mercer)
The focus in Healthcare today is to reduce the amount of waste in the health care system through the implementation of new forms of health IT… that reduces inefficiencies, redundancies and administrative costs. According the CEO of Aetna…”the health care system wastes more than $765 billion each year – that’s 30 percent of our health care spending.”
As a mega-vertical, healthcare covers several major segments (the 7 Ps)
- Payers (Health Insurance and Plans),
- Providers (Hospital Systems, Labs and IDNs),
- Pharmacy (retail distribution networks), and
- Pharmaceutical and medical equipment manufacturers,
- Prescribers (Physicians and clinics)
- Police (regulators)
While spending on health care is dominating headlines, the health care industry (7Ps) is in a state of flux. Stakeholders across the health care sector are running hard to reduce costs. The drivers impacting cost of healthcare include:
- Aging population – 100% are aging
- Rise in Chronic Disease – 75% of cost
- Demand for technology continues
- Drug cost – better, but still bad (Generics exchanged for biological drugs)
- Waste – estimated at 30%, but depends on definition
The healthcare ecosystem is being reshaped by two powerful counter economic forces at work: (1) Improve quality of care and (2) drive the cost of care down. Basically spend less and get more. As a result, the entire healthcare ecosystem is changing into a “information-driven”, “evidence-based” and “outcome-driven” model.
The target healthcare transformation goals are:
- align economic incentives between payers and providers,
- digital engagement…create a simpler, more transparent consumer experience, and
- connected health….technologies that seamlessly connect our healthcare system.
In this posting we look at Health Care use cases and how data and analytics are being slowly but sure being adopted in the form of informatics. All this change is being driven under the guise of Health Reform.
The “real meat and potatoes” use cases behind big data actual adoption might be around B2B machine data management and Industrial analytics enabled by wireless, battery-free sensor platforms.
While social, consumer, retail and mobile big data get a lot of PR, the big data business cases around industrial machine data analytics or “things that spin” actually make economic sense. These projects tend to show tangible Return on Investment (ROI).
The concept of Internet-connected machines that collect telemetry data and communicate, often called the “Internet of Things or M2M” has been marketed for several years:
- I.B.M. has its “Smarter Planet” initiative
- Cisco has its “Internet of Everything” initiative
- GE has its “Industrial Internet” initiative.
- Salesforce.com has its “Internet of Customers” theme
To compete with GE….Hitachi, United Technologies, Siemens, Phillips and other industrial giants are all getting on the band-wagon as the vision of M2M is now viable with advances in microelectronics, wireless communications, and microfabricated (MEMS) sensing enabling platforms of rapidly diminishing size.
Industrial Internet – making smart use of sensors, networked machines and data analytics - is the big vision, but the business driver is in no unplanned downtime for customers.