3. Scaleable
CONTEXT
Data is growing larger at an exponential rate
Data is more diverse encompassing a broad range from formal structure to bit-blobs
Dense-data-change over time is expected to be captured, archived and mined
Increasingly organizations want horizontal reach to bridge their data assets with other
internal, external, and reference data resources
The revolution of the Internet-of-Things will dramatically change the nature of data
management and data analysis in lab operational contexts
4. Scaleable
PRODUCT OBJECTIVES
Cloud-hosted - to accommodate scale, sufficient on-demand performance and HP/HA (High
Performance / High Availability) - in-house hosting will not achieve these goals cost effectively
Elastic Resources - to accomodate small startup adoptions up to large engagements, you need to utilize
common established infrastructure (IaaS) where capacity can to be expandable on-demand thus
minimizing operational costs and maximizing control and administration
Global Compute-Power - compute and store resources should migrate closer to the edge of maximum
use for shortest latency hops - this requires adoption of global-scale IaaS partners
Operational Readiness - Software consumed as a service requires 24x7 operations and architectures
that accommodate revisions with no downtime - this requires design and development for continuous
deployment / continuous integration / continuous operations
5. Secure
CONTEXT
Data is recognized as a critical competitive asset
Clients must trust your ability to protect, defend, and remove both their data, content,
knowledge and applications
Some content needs to also be shared across consortiums, affiliations and partner networks
Attacks on compute resources is proportional to the value to those resources
The nature of attacks on secured resources changes strategy as protections improve
Industry-stipulated regulatory compliance will often dictate the “how” of security measures
6. Secure
PRODUCT OBJECTIVES
Encryption - strong encryptions must be applied to data-at-rest as well as to data-in-transit
Administration - Data access administration by fine-grained, login-controlled role and organization
delineations must be fundamental in the design and operations of software services
Custom - Open-designs are needed to ensure custom security accommodations can be implemented
as well as managed efficiently and non-disruptively
Cloud - IaaS cloud security systems now exceed in-house capacities for protecting data / application
assets
Multi-tenant - Hosting content and services across multiple clients with the same app-stack requires a
top-to-bottom multi-tenancy approach to ensure data and application resource boundaries are respected
7. Easy
CONTEXT
The problems our clients are trying to solve are intrinsically complex, exploratory and difficult
They are high-valued workers so their time and attention is the greatest resource
Users increasingly expect software systems to encode know-how intelligence
Knowledge workers do not work in isolation. Sharing and communication is fundamentally valuable
The best software product applications feel like a natural and better extension of what they used to do
Unnecessary or nonessential features get in the way of optimal use
Best-practices and logical sequencing should be obvious automated checklist-driven steps
Simple is hard, simple is best
8. Easy
PRODUCT OBJECTIVES
Design - the best user interfaces are simple, clean, adaptive and created with the involvement of the customer.
That should be the Osthus style-brand. Simple-power-harnessed.
Landscape - Increasingly people are doing work across a wide variety of devices, from small to large screen
footprints, while away from the office. UIX should be designed to work well across diverse settings with minimal
effort
Help - learning to use the system should happen in 4 phases: first-use instructions, as-needed help, in-depth
tutorials per topic area, searchable reference resources
Templates - common use patterns should be templatized to simplify and direct best-practice activity
Collaberation - working with others, across teams, across organizational boundaries, with ties to common
social-exchange tools should be intrinsic to the design and implementation
9. Adaptive
CONTEXT
Not all clients have the same needs at the same time
The rate of technical innovation is accelerating so capabilities should be expected
to expand and change to maintain the most usefulness
Clients are innovators that use your products to create unique (proprietary)
solutions
A highly flexibly and extensible product capability empowers consulting and
solution service business models
10. Adaptive
PRODUCT OBJECTIVES
LBE - Having a low-barrier to entry model seeks to remove friction from clients getting
value from the product ASAP. This involve demos, trial offerings, per-seat / per-use
pricing, opt-out monthly licensing, template-driven use cases, online tutorials, help-desk
resources, etc.
Custom - if the product is delivered as a broad suite of capabilities with the ability to
adapt the flow of data and analysis across custom processing chains then service
offerings are enabled and entrenchment is stronger
Up-Sell - the system should monitor use so as to be able to make up-sell
recommendations on-the-fly
11. Smart
CONTEXT
Data >> to information >> to knowledge are value step-functions. “Knowing” has
the premium value.
Every product will be expected to be “smart” over the next 10 years
Smart products help you do your job better that you could alone
Smart products automatically make or enable you to make decisions
Smart products capture, contain and share knowledge
12. Smart
PRODUCT OBJECTIVES
Smart - Artificial intelligence, automated decisions & reasoning, pattern
recognition, cognitive and semantic contextual computing, language processing
are now building-block software components that should be part of any next-
generation product line
Expert - Products should know you and what you are doing, projecting the aura of
an expert helper
Improve - Smart products should be able to improve, adapt and accumulate
knowledge overtime - knowledge stores and rule-based / pattern recognition
reasoning should be laced through the use-pipeline
13.
14. Karl Seiler | President
karl@piviting.com
Piviting.com
@pivitguru
SMARTER CHANGE