Technology the programs that direct and control the

 

 

 

 

 

 

 

Technology Terminology

Glossary

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Introduction

With the
rise of tools such as robotic process automation (RPA), Business Process Management
(BPM), Customer Relationship Management
systems (CRM systems),
artificial intelligence (AI) and analytics, operational excellence leaders need
to understand and be well versed in understanding which technology holds the most
potential for their business and what investments will create results.

For the
first steps in learning from the best companies, join us at TEX Week in Nashville, Tennessee 4-6 June.
The event will feature a host of speakers that will unveil their strategies and
toolset for embedding project and enterprise wide technology excellence in
their operational excellence execution, plans and strategy.

This
technology terminology glossary looks at the hottest topics and terms in the
realm of cutting-edge technology that will help businesses stay up to date with
the latest technology trends and help drive operational excellence.

 

 

 

 

 

 

 

 

 

 

 

Application Program Interface (API)

A set of computer programming protocols and tools that
help build programs that enable communication between computers. API’s provide
specifications that describe the exchange of information and aid in the
development of the programs that direct and control the transfer. 

The real value of APIs is that they reduce the amount
of programming that is needed to allow computers to “talk” / exchange data,
while helping provide data transfer quality. 
To do this, the specifications in an API provide the framework for
properly formatting the data that is passed and then helping to control data
transmission and receipt. 

 

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Artificial Intelligence (AI)

There can
be a wide range of capabilities in the support that is provided, AI is
basically computer programs that mimic what a person does based on rules and
associated actions.  At a lower level of
capability, these services are designed following situation/response
interaction.   This may include
inference, situation and response, visual perception, speech recognition, and
complex rule based decision-making.  Here
the computer is performing or directing the performance of tasks normally
performed by a person. 

In practice
AI, when used for more complex situations it can exhibit perception – a type of
thinking based on cognitive learning algorithms where the AI system learns by
answering questions and then inferring answers.

 

 

 

 

 

 

Business
process management (BPM)

A management
and operational change discipline that integrates the strategy and goals of an
organization with the expectations and needs of customers to drive operational
change.  To do this, it focuses on end?to
end processes modeling and analysis, and brings together strategies, goals,
culture, organizational structures, roles, policies, rules, techniques,
methodologies, and IT tools to:

•Look for
opportunities to fundamentally rethink the business operation and its IT
support

•Analyze,
design, implement, control, and continuously improve end?to?end processes

•Establish
process governance around company methodologies and standards

•Implement
new technologies and concepts that allow innovative ‘breakthrough” thinking and
design

 

 

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Lean vs
Agile

Both Lean and Agile are primarily
conceptualized as a means to improve the efficiency of a process. Lean started
in the manufacturing industry, while Agile has been conceptualized and
developed in the software industry.

Lean as a methodology
can be used for any industry, business and provides a holistic approach to
make an organization or process more effective and efficient. Lean is more like
a philosophy which has different dimension of cultural change, mindset change
along-with the toolkit embedded in it. It provides a holistic approach
to make an organization /process more effective and
efficient. Lean has a broad range of tool kit such as value stream map (VSM),
waste analysis, kanban, heijunka, jidoka, multitasking, supermarket, visual
management, empowerment, etc

Agile is specific to increasing the speed
of application development in the software industry. It is based on the philosophy of
efficient project management specific to application development.  Agile uses a set of tool kit primarily taken
from Lean. Some Lean tools being used in Agile development are multitasking, kanban,
empowerment, visual management, etc

 

Digital
Transformation

Is the
redesign of the IT function, including the infrastructure, the hardware,
communications etc.  The business
strategy and new business operating model are drivers in this new design.  The objective is to define a new IT
capability and what will be necessary to deliver the requirements of the
digital strategy and the business strategy/operating model.  The key in designing the new IT environment
is the creation of a very flexible technology model that is capable of absorbing
new technology and blending it seamlessly with existing technology.

This builds
an architecture that is flexible and allows the blending of both new technology
and legacy technology. 

 

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Machine
learning

Also called
self-learning computers.  This is a
subset of the overall AI topic. 

Machine
learning refers to the ability of a computer to learn without being explicitly
programmed.  To a large degree, these
computers are trained by giving them problems to solve. 

They do not
need every specific command to be written covering every possible
situation.  They learn through trial and
error.   Often the computer is given
problems to solve and applies complex algorithms to look for the probability of
an answer being right.  These predictions
and their outcome when applied to situations or problems allows these programs
to try any number of minor changes to the algorithms until the right answer is
obtained. At this point the algorithm that provided the right answer is noted.  In many of these systems, the computer
interacts directly with the person through voice recognition and accesses “big
data” through the internet.

This is
evolving into an important part of robotics and how intelligent, self-learning,
robots may be used in the future.

 

 

 

Model driven

“Model
driven” refers to activities that are guided by graphical models.  An example is in model-driven business
reengineering, where the business activity and workflow are defined and
graphically modeled.   This technique is
at the heart of both BPMS and RPA.  Both
use models that depict the business operation in great detail with supporting
information that can be used to define scenarios that can be used to shortcut
both manual and automated processing. 
These models can be used to directly describe how business changes can
be made or to show how manual and automated activity will align and work in
harmony.  This capability can also be
used to model the business/customer relationship based on both scenarios and on
an evolving interaction using AI.

This
approach is also used in most areas of activity – from architecture to
scheduling.  However, today, this term is
largely use to describe more complex activities like business or digital
transformation.

 

 

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Robotic
process automation (RPA)

Robotic
process automation is a business redesign tool suite that supports the concept
of leveraging special automation software tools to handle repetitive human
tasks that are highly rules based with little variation in the way the tasks
are performed.  These tools are based on
models of the business with defined business rules that will become the logic
behind the activity.  

 

 

 

 

 

 

Chat bots

A chatbot (sometimes referred to as a chatterbot) is a
computer program that attempts to simulate the conversation or
“chatter” of a human being via text or voice interactions. A user can
ask a chatbot a question or make a command, and the chatbot responds or performs
the requested action. Some chatbots use natural language processing systems,
but simpler systems scan for keywords within the input, then pull a reply with
the most matching keywords or the most similar wording pattern from a database.
They use artificial intelligence (AI) technologies that is commonly used in
businesses across sales and service departments and in the consumer app and
device market.

 

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CRM systems

Customer
relationship management systems. In a nutshell, this type of large integrated
application lets you store and manage prospect and customer information, like
contact info, accounts, leads, and sales opportunities, in one central location
but yet decentralized operation.   This
provides scope and resource flexibility while allowing access to vast amounts
of organized information.

The term
customer relationship management (CRM) refers to practices, strategies and
technologies that companies use to manage and analyze customer interactions and
data throughout the customer lifecycle, with the goal of improving business
relationships with customers, assisting in customer retention, and driving
sales growth.

CRM systems
are designed to compile information on customers across different channels —
or points of contact between the customer and the company — which could
include the company’s website, telephone, live chat, direct mail, marketing
materials and social media. CRM systems can also give customer-facing staff
detailed information on customers’ personal information, purchase history,
buying preferences and concerns.

 

 

Enterprise
Architecture

Enterprise
architecture (EA) is a discipline for proactively and holistically leading enterprise
responses to disruptive forces by identifying and analyzing the execution of
change toward desired business vision and outcomes. EA delivers value by
presenting business and IT leaders with signature-ready recommendations for
adjusting policies and projects to achieve target business outcomes that
capitalize on relevant business disruptions.

The goal of enterprise architecture is to create a unified IT
environment (standardized hardware and software systems) across the firm or all
of the firm’s business units, with tight symbiotic links to the business side
of the organization (which typically is 90% of the firm as seen earlier, at
least by way of budget) and its strategy. The goals are to promote alignment,
standardization, reuse of existing IT assets, and the sharing of common methods
for project management and software development across the organization. The
end result, theoretically, is that the enterprise architecture will make IT cheaper,
more strategic, and more responsive.

The purpose of enterprise architecture is to create a map of IT assets
and business processes and a set of governance principles that drive an ongoing
discussion about business strategy and how it can be expressed through IT.

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Lean
Production

Lean production is an
approach to management that focuses on cutting
out waste, whilst ensuring
quality. This approach can be applied to all aspects of a business –
from design, through production to distribution.

Lean production aims
to cut costs by making the business more efficient and responsive to market
needs.

This approach sets
out to cut out or minimise activities
that do not add value to the production process, such as holding of
stock, repairing faulty product and unnecessary movement of people and product
around the business.

The ten rules of lean production can be
summarized:

1.      
Eliminate
waste
2. Minimize inventory
3. Maximize flow
4. Pull production from customer demand
5. Meet customer requirements
6. Do it right the first time
7. Empower workers
8. Design for rapid changeover
9. Partner with suppliers
10. Create a culture of continuous improvement

Internet of Things

The internet
of things (IoT) is the interconnection over the Internet of almost any
devise.  This is made possible by the
computing devices embedded in everyday objects, that enables them to send and
receive information.  Because of imbedded
microchips, almost anything can be connected today – including pets, people,
objects, etc.  This raises issues about
privacy since most devices can be turned on remotely and record what it sees
and hears.

Security in
this environment is of critical importance and new approaches such as block
chain are working to address this issue.

 

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Data
Analytics

Data analytics refers to qualitative and
quantitative techniques and processes used to enhance productivity and business
gain. Data is extracted and categorized to identify and analyze behavioral data
and patterns, and techniques vary according to organizational requirements. It
is also known as data analysis.

Data analytics is primarily conducted in
business-to-consumer (B2C) applications. Global organizations collect and
analyze data associated with customers, business processes, market economics or
practical experience. Data is categorized, stored and analyzed to study
purchasing trends and patterns.

Evolving data facilitates thorough
decision-making. For example, a social networking website collects data related
to user preferences, community interests and segment according to specified
criteria such as demographics, age or gender. Proper analysis reveals key user
and customer trends and facilitates the social network’s alignment of content,
layout and overall strategy.

 

 

 

 

Cloud
Services

Cloud computing
refers to the networking of an unlimited number of computers or computing
devises that together, provides access to theoretically limitless computer
power and storage.  In reality, the cloud
is a lot of independent, networked computers that together provide almost
limitless processing and storage power.

The
technology uses the internet to deliver computing resources to an organization
as a complete service, rather than having the organization purchase each
component separately, integrate them, and internally manage and support them.
Think of it as renting a computing resource instead of buying, building, and
operating your own computing infrastructure.

An issue that
persists with Cloud based computing is security.  However, considerable ongoing research in
this area is ongoing with new, more secure, capabilities being continuously
released.

 

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Blockchain

Blockchain is a critical part of the bitcoin peer-to-peer
payment system. The bitcoin system works using a blockchain ledger to record
transactions. Bitcoin is a global cryptocurrency that can be used as a medium
of exchange. However, while many parties have started to accept bitcoin as a
currency, it is still controversial and poses risks in terms of security and
stability.

The blockchain ledger helps to provide transparency for
transactions. Although many bitcoin transactions are in some ways anonymous,
the blockchain ledger can link individuals and companies to bitcoin purchases
and ownership by allowing individual parties, called miners, to process
payments and verify transactions. Rather than a central company presiding over
the use of bitcoin, these blockchain originators serve central roles in the
management and administration of this alternative currency system.

 

 

 

 

E-currency

E-currency is money that is exchanged on computers. “E”
is the first letter of the word “electronic” and “currency” is a money system.

Buying and selling products and services on
the Internet is possible because of e-currency.  Credit cards are a popular example of
e-currency. E-wallets (online money vendors) is another. Bitcoin may be
considered another – virtual physical money. With e-currency, banks now offer
services where customers can send money from one place to another without using
cash.

 Using
e-currency, customers can pay bills, change money from one account to another,
and pay for things they want to buy such as food, a car, a television or a
service such as forex trading.

 

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Intelligent
Automation

Intelligent
automation is the combination of artificial intelligence (AI) and
automation—including natural language processing, machine learning and machine
vision.  

Applications range from collecting, analyzing and making
decisions about textual information to guiding autonomous vehicles and advanced
robots. It is helping
companies transcend conventional performance tradeoffs to achieve unprecedented
levels of efficiency and quality. Intelligent automation systems sense and
synthesize vast amounts of information and can automate entire processes or
workflows, learning and adapting as they go.

Intelligent automation can be used to streamline business
processes and make complex decisions faster. Commercial examples include a
marketing system that presents offers to customers based on their profile and
market basket analysis, a credit card processing system that identifies and
blocks fraudulent transactions, and an e-discovery system that classifies
documents according to their meaning and relevance to ongoing litigation.

 

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Adaptive
Case Management

Adaptive
Case Management is a productive system that deploys not only the organization
and process structure, but it becomes the system of record for the business
data entities and content involved.

All
processes are completely transparent and fully auditable. It enables non-technical
business users in virtual organisations to seamlessly create, consolidate
structured and unstructured processes from basic predefined business entities,
content, social interactions, and business rules. It moves the process
knowledge gathering from the template analysis, modeling, and simulation phase
into the process execution phase in the lifecycle. It collects actionable
knowledge—without an intermediate analysis phase—based on process patterns
created by business users.

Adaptive
Case Management differs from business process management (BPM) in that the case
information is the focus and the thing around which the other artifacts are
organized. And it is the case information that persists for the long term.

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Big Data

Big data is
a term that describes the large volume of data – both structured and
unstructured – that inundates a business on a day-to-day basis. But it’s not
the amount of data that’s important. It’s what organizations do with the data
that matters. Big data can be analyzed for insights that lead to better
decisions and strategic business moves.

The amount
of data that’s being created and stored on a global level is almost
inconceivable, and it just keeps growing. That means there’s even more
potential to glean key insights from business information.

The
importance of big data doesn’t revolve around how much data you have, but what
you do with it. You can take data from any source and analyze it to find
answers that enable:

1) cost reductions

2) time reductions

3) new product development and optimized
offerings

4) smart decision making