Digital Disruption & Transformation

Digital Disruption & Transformation

- The evolution of technology is the driving factor for digital disruption and we are no
where close to be done

- It is started with Internet->mobile->Social->Cloud->Big Data Analytics->IOT->Dumb
Robots->Cognitive Systems->3D printing and we are heading towards Renewable
Energy->Autonomous Robots, Self driving cars, smart cities, smart homes, connected
healthcare

- Enterprise environment transform to an everchanging, dynamic environment: cloud
application, mobility, private data cloud center, from data to insight, global complexity,
and connectivity

- Five Converging Trends: Mobility (the use of mobile phone really high) , Cloud – Apps
& Data Center (workload will be much in cloud), Data – structured&unstructured
(mostly unstructured), identities (personal and impersonal line are blurring), IoT
(significant increase)

- Threats: frequency and duration are increasing, increased detected IT security incident,
increased theft of intellectual property, almost every incident including human error,
and digital extortion on the rise.

- Disruptive technologies:
• McKinsey Global Institute (2013)  ‘disrupt the status quo, alter the way people live
and work, rearrange value pools, and lead to entirely new products and services’
• Christensen (1997)  technology evolves through quality improvements to inferior but
low-priced products. Disruptors ‘sneak’ into an existing market and compete directly
with incumbents.

- Digital disruption: Rapid advances in computing power, connectivity, mobility and data
storage capacity

- Digital technologies: high investment but can easily replicated at low cost, network
feature make it more widely used, add value by enhancing the gathering, processing,
storage and transmission of data and provide the delivery of information in digital
form, and affect individual interact with each other.

- Fundamental Enabling Technology: Internet, Cloud, and IoT.

- Pillars of Digital disruption components: experience, security, and data.
• Experience -> Design your Digital Disruption based on Customer/Partner Experience.
How they interact with your company through the entire lifecycle, whether it’s web or
support or product. Ensure a design centric approach to the transformation
• Data -> With digital transformation, we are generating more and more data every day.
More people are connecting with each other through various devices, various
platforms, and data is at the center of all this.
• Security -> With massive amount data that we generate, we create an environment
that is easy for thieves / hackers to attack. As a thought leader in Digital disruption, it
is our responsibility to protect our customer / partner’s identity and data. There by
security becomes an extremely strong pillar for the digital disruption

- Top three factors of external competition: increased start up, increased competitor
activities, and partner/customer becoming competitor.

- Transformation views: Internal, External, and Holistic.

- Transfromation tips:
• Ground everything in evidence. Spend where you’ll make the biggest difference,
whether this be a large or small budget.
• Assessment – Do the assessment to understand your firm’s Digital Status
• Take people with you on the journey - You must build relationships and remember,
don’t leave people behind.
• Build solid foundations but keep people interested when it’s not sexy.
• Test and learn as you go. Be flexible and prepare to change your pace/direction if
needed
• Be realistic and don’t get distracted by shiny new toys. It’s easy to feel guilty by not
leveraging all the new technologies. “
• Define benefits as clearly as you can upfront. If you can’t, at least define the
hypothesis.


Data & Analytics
- Data explanation: sets of values or information that contain quantitative and qualitative
variables. Data are measured, collected and reported, and analyzed where can be
visualized using images, graphs, or other analysis tools.

- Data have three characteristics: velocity, system impact, and scope.

- Analytics: is discovery, interpretation, and communication of meaningful pattern of
data.

- Data analytics: is accumulation of raw data captured from various source that can be
used to identify fruitful pattern and relationships.

- Data analytics function: enables quick decisions or help change policies due to trends
observed

- Challenges: organizational ‘buy in’, high cost implementation, regulation and privacy
information, and lack of expertise.

- "Most companies only a fraction of the potential value". Is it a strength or a weakness?
Based on the research in 2011, a company can get five potential benefits from data
and analytics, but company have not use them all, they only use some of the benefits
that they can get from data and analytics. The greatest progress has occurred are in
location-based services and in retail. On the other side, manufacturing, the public
sector, and health care have captured less than 30 percent of the potential value.

- 5 elements of data and analytics transformation
• Uses cases/source of value: Clearly articulating the business need and projected
impact
• Data ecosystem: Gathering data from internal systems and external source.
Appending key external data, creating an analytic "sandbox"
• Modeling insights: Applying linear and nonlinear modeling to derive new insights.
Codifying and testing heuristics across the organization
• Work flow integration: Redesigning process, developing an intuitive user interface
that is integrated into day-to day workflow
• Adoption: Building frontline and management capabilities, proactively managing
change and tracking adoption with performance indicators
- Indicators for potential of disruption:
▪ Assets are underutilized due to inefficient signaling
▪ Supply/demand mismatch
▪ Dependence on large amounts of personalized data
▪ Data is siloed or fragmented
▪ Large value in combining data from multiple sources
▪ R&D is core to the business model
▪ Decision making is subject to human biases
▪ Speed of decision making limited by human constraints

- Large value associated with improving accuracy of prediction

- 6 Disruptive Models
• Business model enabled by orthogonal data: Orthogonal data will rarely replace the
data that are already in use in a domain; it is more likely that an organization will
integrate orthogonal data with existing data.
• Hyperscale, real time matching: Digital platforms provide marketplaces that connect
sellers and buyers using data and analytics in real time and on an unprecedented scale.
This can be transformative in markets where supply and demand matching has been
inefficient.
• Radical personalization: Data and analytics can reveal finer levels of distinctions, and
one of the most powerful uses is micro-segmenting a population based on the
characteristics of individuals.
• Massive data integration abilities: Combining and integrating large stores of data from
all of varied sources has incredible potential to yield insights.
• Data-driven discovery: data and algorithms can support, enhance, or even replace
human ingenuity in some instances. Data and analytics are helping organizations
determine how to structure teams, resources, and workflows.
• Enhanced decision making: Data and analytics can change human incompetency by
bringing in more data points from new sources, breaking down information
asymmetries, and adding automated algorithms to make the process instantaneous.
Data and Analytics resulting insights to make decisions faster, more accurate, more
consistent, and more transparent.

- Predictive Analytics: application of machine learning that effective at spotting fraud. Involves
not only forecasting but also uses such as anticipating fraud and bottlenecks or diagnosing
diseases. Helps classify customers or observations into groups for predicting value, behavior,
risk, or other metrics.

-360 degree view: Refer to their operations and customers. The insight that they gain from
such analyses is then used to direct, optimize, and automate their decision making to
successfully achieve their organizational goals
Big Data

- Definition: high volume, high velocity, and/or high variety information assets that
require new forms of processing to enable enhanced decision making, insight
discovery and process optimization (Gartner, 2013)

- Characteristics: massive quantity of data and various degree of structure in data

- Source of data: Structured (clearly defined data whose pattern make them easily
searchable) and unstructured (not easily searchable)

- Big Data Business Model Maturity Impact:
Business monitoring: monitor business performance (flag under and over
performing areas). Analytics: trending, historical comparison, benchmarking,
indices, and share.
• Business insight: Leveraging new unstructured data sources with advanced
statistics, predictive analytics, and data mining. Complimented with real-
time data.
• Business Optimization: Using embedded analytics to automatically optimize
parts of their business operations (ex: resource scheduling by buying
behavior).
• Data monetization: Looking to leverage big data for new revenue opportunities.
• Business Metamorphosis: Leverage the insights captured (market, product
customer) to transform current business model into new services in the new market.

- Data science process: acquire, store, learn, apply.

- Skills needed: statistics, visualization, data modelling, IT, computer science, and
machine learning.

- Implementation of Big Data: analyze both structured and unstructured data, determine
trends and discover theories, deep analysis to 3P (performance, productivity, and
profit), also prevent breaches and fraud.

Internet of Things (IoT)

- IoT: refers to the networks created through the embedding of sensors and internet
connectivity hardware into consumer goods, public infrastructure and production
machinery.

- Functions: enables the collection of data, the automation and improvement of
production processes and infrastructure management, and the development of new
consumer goods and services

- Information Value Loop: Information gathered by the Internet of Things enables
businesses to create and capture new value by providing insight to optimize actions.
Modified actions in turn give rise to new information, starting the cycle anew. Value
drivers determine how much value is created; their relevance and importance depend
on the specific use case.

- Explanation of Information Value Loop:
• CREATE: The use of sensors to generate information about a physical event or state.
• COMMUNICATE: The transmission of information from one place to another.
• AGGREGATE: The gathering together of information created at different times or from
different sources.
• ANALYZE: predictions, or prescriptions for action.
• ACT: Initiating, maintaining, or changing a physical event or state.

Artificial Intelligence

- Definitions: area of computer sciencee that emphasizes the creation of intelligence
machines that work and react like humans. Some activities that AI are designed to learn
are speech recognition, learing, planning, and problem solving.

- 4 areas of value chain where AI can create value: project (smarter RnD & forecasting),
produce (optimized production and maintenance), promote (targeted sales and
marketing), and provide (increase user experience).
• Projects: Accurately forecast demand, optimize supply, and shape future offerings
for success
• Produce: Get more out of machines while minimizing maintenance and repairs.
• Promote: Charge the right price and deliver the right message to the right target.
• Provide: Give customers a rich, personalized, and convenient experience.
- 5 elements of successful AI transformation: sources of value, data ecosystems,
techniques and tools, workflow integration, and open culture organization.
• Source of value: Find the true source of value, and build a business case for it.
• Data ecosystems: know what data they already have access to and where they can
obtain additional data relevant for their company’s future success.
• Techniques and tools: companies need to build internal capabilities and partner
with or acquire additional know-how from AI startups or leading AI firms to bridge
the technical gap.
• Workflow integration: Once companies’ capabilities are producing AI-powered
insights, they must be integrated to capture the benefits promised in the business
case.
• Open culture organization: an organizational culture open to the collaboration of
humans and machines is required.

- Accountants technology broad problem:
• Providing better and cheaper data to support decision making
• Generating new insight from analysis of data
• Freeing up time to focus on more valuable tasks such as decision-making, problem
solving, advising, strategy development, relationship building and leadership.

-Implementation of AI in accounting sector:
• using machine learning to code accounting entries and improve on the accuracy of
rules-based approaches, enabling greater automation of processes
• improving fraud detection through more sophisticated, machine learning models
of ‘normal’ activities and better prediction of fraudulent activities;
• using machine learning-based predictive models to forecast revenues
• improving access to, and analysis of, unstructured data, such as contracts and
emails, through deep learning models.

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