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.
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?
- 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)
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.
In this posting we look at Health Care use cases and how analytics is being slowly but sure being adopted under the guise of Health Reform in the form of informatics.
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.
As a data engineer and scientist, I have been following the NSA PRISM raw intelligence mining program with great interest. The engineering complexity, breadth and scale is simply amazing compared to say credit card analytics (Fair Issac) or marketing analytics firms like Acxiom.
Some background… PRISM - ”Planning Tool for Resource Integration, Synchronization, and Management” - is a top-secret data-mining “connect-the-dots” program aimed at terrorism detection and other pattern extraction authorized by federal judges working under the Foreign Intelligence Surveillance Act (FISA). PRISM allows the U.S. intelligence community to look for patterns across multiple gateways across a wide range of digital data sources.
PRISM is unstructured big data aggregation framework — audio and video chats, phone call records, photographs, e-mails, documents, financial transactions and transfers, internet searches, Facebook Posts, smartphone logs and connection logs – and relevant analytics that enable analysts to extract patterns. Save and analyze all of the digital breadcrumbs people don’t even know they are creating.
The whole NSA program raises an interesting debate about “Sed quis custodiet ipsos custodes.” (“But who will watch the watchers.”) Read more
- How do I monetize my data? How do we turn data into dollars?
- What small data or big data monetization strategies should I adopt?
- Which analytical investments and strategies really increase revenue?
- What pilots should I run to test data monetization ideas out?
Data Monetization is the process of converting data (raw data or aggregate data) into something useful and valuable – help make decisions (such as predictive maintenance) based on multiple sources of insight. Data monetization creates opportunities for organizations with significant data volume to leverage untapped or under-tapped information and create new sources of revenue (e.g., cross-sell and upsell lift; or prevention of equipment breakdowns).
But, data monetization requires a new IT clock-speed that most firms are struggling with. Aberdeen Research found that the average time it takes for IT to complete BI support requests, with traditional BI software, is 8 days to add a column to a report and 30 days to build a new dashboard. For an individual information worker trying to find an answer, make a decision, or solve a problem, this is simply untenable. For an organization that is trying to differentiate itself on information innovation or data-driven decision making, it is a major barrier to strategy execution.
To speed up insight generation and decision making (all elements of data monetization) business users are bypassing IT and investing in data visualization (Tableau) or data discovery platforms (Qlikview). These platforms help users ask and answer their own stream of questions and follow their own path to insight. Unlike traditional BI that provides dashboards, heatmaps and canned reports, these tools provide a discovery platform rather than a pre-determined path.
Also companies like Marketo which create marketing automation software are getting into the customer engagement and data monetization game. Their focus is to enable marketing professionals find more future customers; to build, sustain, and grow relationships with those buyers over time; and to cope with the sheer pace and complexity of engaging with customers in real time across the web, email, social media, online and offline events, video, e-commerce storefronts, mobile devices and a variety of other channels. And in many companies, marketing knits these digital interactions together across multiple disconnected systems. The ability to interact seamlessly with customers across multiple fast-moving digital channels requires an engagement strategy enabled by data and analytic insights.