Fast data: Gaining business impetus and customer value from big data

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© Copyright Ovum. All rights reserved. Ovum is a subsidiary of Informa plc. 1

Fast Data Gaining business impetus and customer value from Big Data

Richard Edwards, Principal Analyst, Ovum IT.

@redwards | [email protected]

2 © Copyright Ovum. All rights reserved. Ovum is a subsidiary of Informa plc.

Agenda

• Catalysts

• The customer-adaptive enterprise

• What is Fast Data?

• New technologies to solve tomorrow’s decision-making challenges

• The application and consumption of Fast Data

• Actions


Catalysts

• Accelerated disruptive change and margin-eroding commoditization are now the norm.

• Power has shifted to the digitally connected consumer.

• Old business models are fast becoming outmoded.

• An enterprise-wide need for a deeper and broader perspective of the customer.


Fundamentals

• Getting the right information to the right people at the right time on the right device is the goal of every business.

• Organizations want information fast and on demand.

• Intelligent information management unites analytics, collaboration, enterprise content management, and information governance.


Adapting at speed is essential for survival

• 87% of the Fortune 500 companies that made the list in 1955 have gone bankrupt.

• Life expectancy of a firm has reduced from 75 years to just 15 Source: S.Denning, Forbes Magazine

• Average CEO tenure has dropped to 6.6 years from 8.1 10 years ago Source: Booz & Co


The customer-adaptive enterprise

• Today’s environment presents major opportunities for enterprises that have the vision to raise their sights, and their game, to become customer-adaptive.

• A customer-adaptive enterprise (CAE) has highly acute 360° vision and is driven by a desire and strategic intent to create and deliver value and be persistently relevant to its customers.

• Continued market relevance is achieved not only by delivering a superior customer experience, but also through continuous innovation born of deep insight into the customer’s context.


The number of channels consumers use to interact


The proliferation of customer channels

• Understanding the customer’s interaction history requires a high degree of cross-channel connectivity and intelligence.

• While some organizations may limit their customer experience management efforts to customer support, more advanced organizations will provide a deeper level of insight to all departments.

• Organizations that reach this stage will have laid a very firm foundation on which to build the customer-adaptive enterprise.


Evolutionary journey to the customer-adaptive model


Closing the loop on value creation and delivery

• The customer-adaptive enterprise is an evolution grounded in a mature understanding of CRM as a strategy to acquire, retain, and develop customers.

• The key difference between CEM and the customer-adaptive enterprise is that the former is often a response to commoditization.

• CAE seeks to avoid commoditization through a far deeper insight into the customer’s changing context; converting it into more relevant innovation.


Attributes of the customer-adaptive enterprise


• Visionary and ethical leadership

• Fully engaged workforce

• Highly collaborative

• Highly developed sensing capabilities

• Delivering a superior customer experience

• Continuously create relevant value

• Optimized enough, connected processes

• An adaptive architecture

Powerful closed-loop sensing capabilities

• “Knowing earlier” requires a powerful set of business and customer-intelligence tools that act in concert like a central nervous system: enter analytics and Big Data.

• Being able to read the signals of change in the context of a customer demands more than simply harvesting social-network chatter or transactional history, i.e. Big Data, it demands Fast Data! – Live customer data – all interactions with the organization – Adjacent data – factors that might impact customer behavior,

such as technology disruptors – Ecosystem data – factors that might impact behavior in the

future


What is “Fast Data”

• Most of the attention from early successes with Big Data has focused on analyzing huge volumes of highly variable data.

• Hadoop emerged as a batch-driven system capable of widening the scope of analytics to all, not just some, of the data.

• Used to optimize Internet search, build personal profiles of customers, and analyse the “digital exhaust” from social networks or point-of-sale terminal transactions.

• What if…these analyses could be delivered in realtime?


Fast Data: Why now? What’s new?

• Proprietary technologies for processing huge torrents of data have been around for a while…

• …but specialized technology, specialized applications; unaffordable outside of an exclusive niche.

• Taking Fast Data into the enterprise mainstream:

– Moore’s Law for processing

– Dropping price of silicon is impacting Dynamic Random Access Memory and Solid State Disk (aka SSD or flash disk)

– The growth and extension of high-bandwidth communication backbones


Long-term storage pricing declines ($)

Source: Abhay Paliwal, In-Memory Enterprise Data Management: Future Trends in Data Processing; Kajashi.com (March 2012) 16

The growing demand for Fast Data

• Big Data: a challenge that requires powerful alternatives beyond traditional SQL database technology.

• Most of the early implementations of Big Data, especially with NoSQL platforms such as Hadoop, have focused more on volume and variety, with results delivered through batch processing.

• The four FIVE faces of Big Data: Volume, Variety, Veracity, Velocity, and Value.

• The need for speed; making existing applications more accurate, responsive, and effective.


Ovum’s definition of Fast Data

• Fast Data is the subset of Big Data implementations that require speed: “The power of now!”

• It enables instant analytics or closed-loop operational support with data that is either not persisted, or is persisted in a manner optimized for instant, ad hoc access.

• Fast Data applications are typically driven by rules or complex logic or algorithms.

• Connectivity is also critical and optimizing connectivity through high-speed internal buses is essential for computation of large blocks of data.


What Fast Data is not:

• “Fast” transaction systems that can be updated interactively but don’t automatically close the loop on how an organization responds to events.

• Conventional OLTP databases, typically designed with some nominal degree of optimization, such as locating “hot” data on the most accessible parts of disk, more elaborate indexes, and/or table designs to reduce the need for joins, etc.

• Video, voice, or imaging.


The application of Fast Data

• Enable more complex queries that could take advantage of approaches such as regression analyses.

• Increase the granularity (level of detail that can be computed) of existing query and analytics applications.

• Generate different views of the data (for example, dynamically generate MOLAP cubes) on demand, allowing more flexibility in running analytics.

• Provide the opportunity to run more ad hoc What-if queries that could in turn be enhanced with data mining.


Examples of Fast Data applications

• Sensory applications that provide snapshots of phenomena or events from a variety of data points that are aggregated and processed in real time or near real time.

• Stream-processing applications that process high-speed data feeds with embedded, rules-driven logic to either alert people to make decisions, or to trigger automated closed-loop operational responses.

• Realtime or near realtime analytics. • Realtime transactional or interactive processing applications

involving large, multi-terabyte, Internet-scale data sets.


Solving tomorrow’s decision-making challenges

• Fast Data is not a single application or platform type.

• There are a wide variety of platforms and approaches for delivering realtime or near-realtime response for complex analytics accommodating widely diverse sources of data.

• In turn, core SQL technology is being reinvented for a new breed of Internet-scale transaction processing.

• Analytic database vendors now offering either all-flash or all in-memory (RAM) models.


The application and consumption of Fast Data

• Employees need to focus on what matters, and for customer-adaptive enterprises this means putting the enterprise social network to work.

• The employee “console” replaces the corporate email Inbox as the primary tool.

• Imagine A Google Now, Apple Siri, or Microsoft Cortana for the workplace - The right personalized information at the right time on the available device(s):

– Next appointment → Next activity or task

– Sports → The competition

– News → Market insights

– Restaurant reservations → Resource scheduling

– Flights → Dispatch & Delivery information

– Nearby attractions → Nearby skills, expertise, and opportunities

– Public transportation → Market trends and opportunities

– Traffic → Personal dashboard

– Weather → Problems ahead


Actions:

• Take part in Ovum’s customer-adaptive research program!

• Continue to focus on CEM initiatives – a solid foundation for a customer-adaptive enterprise capability!

• Fast Data approaches are suited to high-value business processes that are highly volatile – identify them!

• Consider Fast Data as part of a spectrum of approaches that will complement, not replace, traditional interactive or batch analytics or OLTP.


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Fast data: Gaining business impetus and customer value from big data