Big data and digital transformation

General Management Course

A.A. 2014/2015

Digital Transformation –

Big Data

Matteo Cristofaro General Management Course 2

Digital Darwinism;

Digital transformation;

What is Big Data?;

Sources;

Big Data and insights;

Ethics in Big Data;

Analyzing Big Data;

Culture-Staffing-Processes-Governance framework;

Analytics funcion;

References.

Summary

Digital Darwinism

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We are now in the middle of the third industrial revolution, driven by the omnipresent digitalization.

For this reason, it is again all about a fight for survival, which companies successful for centuries such as mailorder houses and publishing houses have lost their dominant positions.

It thus becomes apparent that it is no longer about size, it is not necessarily about speed, and it is not only about the extent of conforming or strength. Today, it is more about “The survival of the smartest!” One should keep in mind what Charles Darwin stated:

“It is not the strongest of the species that survives, nor the most intelligent that survives. It is the one that is most adaptable to change”

Matteo Cristofaro General Management Course

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The most relevant developments could be characterized as the DiSoLoMo-trend. This trend covers the following dimensions: digital, social, local and mobile. In the first instance, the challenges for companies lie in the trend to digitalized more and more products and services (“digital”). An additional trend is the increased use of social media.

Based on the trend of digitalisation more and more objects lose significant constrations cause by their physical appearence (e.g. books, newspaper, songs, etc.). This development is called Zero Gravity thinking: objects lose the physical restrictions they had in the real world when these objects are transmitted into cyberspace.

Digital transformation (1) - Trends


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Digital transformation (2) – Trends (b)


Digital transformation (3) – Mobility

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We are at the beginning of a development that is allowing the smartphone to become a smart sevice terminal. This process is renforced by the fact that more and more information of relevance to purchasing processes is becoming available by mobile (by QR code, RedLaser App, etc.).

Mobility has eliminated the boundaries of space and time. Customers are always connected, and companies can interact with them at any time. The implications cannot be overstated. With information about products becoming as important as the products themselves, almost every company is now in the business of creating and delivering “content” – information that is personal, relevant and timely when accessed by the customer.

Partnership IBM-Apple http://www.apple.com/pr/library/2014/07/15Apple-and-IBM-Forge-Global-Partnership-to-

Transform-Enterprise-Mobility.html


http://www.apple.com/pr/library/2014/07/15Apple-and-IBM-Forge-Global-Partnership-to-Transform-Enterprise-Mobility.html



















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Therefore competition in many businesses is no longer based on mere product or services.

More and more companies undesrand that they have to create comprehensive and

integrated ecosystem in order to make the customer stay.

But in ecosystems, organizations realize value through the engagement with the

system as a whole, where “value” is defined by participants’ willingness to pay for access to

the ecosystem. Once access occurs, specific transactions may occur within the ecosystem.

Total value created reflects the value of access to and engagement within the system as a

whole.

Digital transformation (4) –

Integrated ecosystem


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elements of digital transformation What do businesses need to do to get ahead of the widespread forces for change in our digital age?

Key areas include reconfiguring the customer value proposition (what is being offered) and

reshaping the operating model (how it is delivered).


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Strategic paths to transformation


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Digital transformation (7) – value

proposition and operating model


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Businesses aiming to generate new customer value propositions or transform their operating models

need to develop a new portfolio of capabilities for flexibility and responsiveness to fast-

changing customer requirements

Digital transformation (8) – A new portfolio

of capabilities


What is Big Data? (1)

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The proliferation of digital products and services results in

consumers generating huge volumes of personal data across

all aspects of their lives, which is captured by organisations

through digital channels or devices.

Data about a person’s friendship networks, hobbies and

interests is captured on Facebook, while details of their

shopping patterns are captured by online retailers. In

addition to these relatively established data sources,

technological developments will continue to generate new sources of

consumer data that could offer unique insights into consumer

behaviour.


What is Big Data? (2)

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Organizations are exploring how large-volume data can usefully be deployed to create and capture value for individuals, businesses, communities, and governments (McKinsey Global Institute, 2011). big data is fast becoming a tool that not only analyzes patterns, but can also provide the predictive likelihood of an event.

The challenge for businesses lies in how to harness, make sense of, and use this information to generate some form of competitive advantage. Businesses that can access these new sources of data, analyse them and convert them into insight on consumer behaviour, will be able to make their products and services more relevant to their customers.


What is Big Data? (Example)

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Participant in a Formula 1 car race generates 20 gigabytes of data from the 150 sensors on the car that can help analyze the technical performance of its components, but also the driver reactions, pit stop delays, and communication between crew and driver that contribute to overall performance.

The emphasis thus moves away from outcomes (win/lose race), to instead focus on each proximal, contributory element for success or failure mapped for every second during the race.

Or…We could analyze the social networks and social engagement behaviors of individuals by mapping mobility patterns onto physical layouts of workspaces using sensors, or the frequency of meeting room usage using remote sensors that track entry and exit patterns, which could provide information on communication and coordination needs based on project complexity and approaching deadlines.


Sources (1)

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Big data is generated from an increasing plurality of sources:

Internet clicks, mobile transactions, user-generated content, and social media as

well as purposefully generated content through sensor networks or business

transactions such as sales queries and purchase transactions.

These data require the use of powerful computational techniques to unveil trends and

patterns within and between these extremely large socioeconomic datasets. “Big”

is no longer the defining parameter, but, rather, how “smart” it is—that is, the

insights that the volume of data can reasonably provide.

The defining parameter of big data is the fine-grained nature of the data itself,

thereby shifting the focus away from the number of participants to the granular

information about the individual.


Sources (2)

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Big data is also a wrapper for different types of granular data. Below, we list five key sources of high volume data:

Public data: held by governments, governmental organizations, and local communities (e.g. energy use).

Private data: held by private firms, non-profit organizations, and individuals (e.g. consumer transactions).

Data exhaust: ambient data that are passively collected, non-core data with limited or zero value to the original data collection partner these data were collected for a different purpose, but can be recombined with other data sources to create new sources of value (e.g. Internet searches)

Community data: is a distillation of unstructured data—especially text into dynamic networks that capture social trends (e.g. Twitter feeds).

Self quantification data: are revealed by the individual through quantifying personal actions and behaviors (e.g. wristbands that monitor exercise and movement).


Big data and insights

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Ethics in Big Data

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Data sharing agreements need to be linked into the

mechanisms for data protection and privacy, including

anonymization for open data, access control, rights

management, and data usage control. Issues such as imputed

identity, where individual identity can be inferred through

data triangulation from multiple sources, will need to be

carefully considered and explicitly acknowledged and

permitted.


Analyzing Big Data(1)

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The typical statistical approach of relying on “p” values to establish the significance of a finding is unlikely to be effective because the immense volume of data means that almost everything is significant.

Using our typical statistical tools to analyze big data, it is very easy to get false correlations.

However, this doesn’t necessarily mean that we should be moving toward more and more complex and sophisticated econometric techniques to deal with this problem; indeed, such a response poses a substantial danger of over-fitting the data.


Analyzing Big Data (2)

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We use the term analytics as the process that extracts value from

data through creating and distributing reports, building and

deploying statistical and data-mining models, exploring and

visualizing data, sense-making, and other related techniques.

Data may be internal or external to the organization;

processing may be realtime, near real-time, or batch; and any

combination of these is possible.


Analyzing Big Data (3)

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There is a range of techniques that draw from several disciplines, including statistics, computer science, applied mathematics, and economics:

Cluster analysis; Data fusion and integration; Data mining; Genetic algorithms; Machine learning; Natural language processing; Neural

networks; Network analysis; Signal processing; Spatial analysis; Simulation; Time series analysis.

The challenge, though, is to shift away from focusing on p values to focusing, rather, on effect sizes and variance explained. A pitfall of big data-again, amplified by our commonly used statistical techniques-lies in focusing too much on aggregates or averages and too little on outliers.


Analysing Big Data (4)

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Once promising leads have been identified, the next challenge of analyzing big data is to then move beyond identifying correlational patterns to exploring causality.

Given the unstructured nature of most big data, causality is not built into their design and the patterns observed are often open to a wide range of possible causal explanations.

There are two main ways to approach this issue of causality.

1. Recognize the central importance of theory. An intuition about the causal processes that generated the data can be used to guide the development of theoretical arguments, grounded in prior research and pushing beyond it.

2. A complementary way is to then test these theoretical arguments in subsequent research-ideally, through field experiments.


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Our organizational framework seeks to integrate analytics, business knowledge, and information technology, and it is based on four main questions:

1. Does the organization view data and analytics as a key function of the organization?

2. Is there a critical mass of data scientists?

3. Are there data scientist with sufficiently deep knowledge of the business unit domains?

4. Is there an adequate analytics governance structure?

Culture-Staffing-Processes-Governance

framework (1) - Integration


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The CsPg framework orients the organization designer to establishing a culture for big data and analytics: hiring, training, and organizing a group of analytics staff; developing the required analytics processes; and setting up a robust analytics governance structure.

Starting with culture, corporate-level executives must recognize the need to organize big data and analytics as an organizational function that is given broad responsibility and authority for data assets and which is analogous to other major functions in the organization.

Analytics staff must be able to obtain and manage data; build statistical, predictive, and data-mining models; and deploy those models.

The more sophisticated the analytics processes become, the more opportunities that can be pursued.

Culture-Staffing-Processes-Governance

framework (2) – Culture and Staff


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There are three basic models for locating the analytics function within the organization, all

of which involve well-known tradeoffs between centralization and decentralization.

1. One model centralizes analytics by placing the data scientists in a single unit.

When analytics is centralized, however, the data scientists may be far away from the

business units they are supposed to support. the challenge in such a structure is for the

data scientists to understand the various business units and their needs. In addition,

there is the issue of where the analytics department should report within the

organization.

2. One model decentralize analytics and place a group of data scientists in each

business unit. this approach makes it easier for data scientists to collaborate with their

respective business units and to tailor their models to each unit’s needs. the main

tradeoff is difficulty in achieving critical mass on enterprise-wide problems and

opportunities.

3. One model is a hybrid approach in which a critical mass of data scientists is housed

in a central unit, and the remaining data scientists are distributed

throughout the organization. One common hybrid model is to set up an analytics

or big data “center of excellence” that the distributed data scientists can draw on as

appropriate. another is to centralize the data scientists that interact with the IT

organization, or those that manage the data, or those that deploy the models.

Culture-Staffing-Processes-

Governance framework – 3 Models


Analytics function (1) – 3 components

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The analytics function is composed of 1) analytics models, 2) analytics infrastructure, and 3) analytics operations.

1) A key analytics operation is building models, this is usually done by statisticians, modelers, or, to use the new name, data scientists. In addition to building models over data, analytics also includes summarizing data in reports (now called descriptive analytics), ad hoc querying of data.

2) Analytics infrastructure refers to the software components, software services, applications, and platforms for managing data, processing data, producing models, and using models to generate alerts, take actions, and make decisions. the key processes associated with analytics infrastructure are managing the data required by the organization and deploying the models and other analytics that are incorporated into the organization’s products, services, and operations.

3) Analytics operations refers to the various processes that result in the outputs of models being used to make decisions and to take actions that bring business value. Analytics operations ensures that the results of models are integrated into an organization’s products, services, and operations.


Analytics function (2) – Organizational

effects

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An organization requires a critical mass of data scientists so that their expertise as a whole extends across these three analytics processes. The team as a whole must be able to:

Identify relevant data (both internal and external), manage the data required for analytics, build the needed analytics models, and deploy the models that

are built into products, services, and internal systems.

Multiple parts of an organization can be involved in analytics processes, typically, a business unit sponsors the model, an analytics department builds the model, an IT unit supplies and manages the data, and an operations unit deploys the model. With so many diverse pieces of an organization involved, an analytics governance structure is critical.


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Challenges for most organizations since the modeling group

must work with other components of the organization to

identify 1) analytics opportunities, 2) obtain the necessary data,

and 3) deploy the resulting models.

The role of an analytics governance structure is to put

in place an analytics leader with sufficient authority to

overcome these three challenges.


effects


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A complete set of parameters for designing a governance structure does

not exist. Grossman and Siegel (2014) suggest the following

preliminary parameters:

1. Ensure that sound long-term decisions about analytics are reached and

that investments in analytics generate business value.

2. Operate in such a way that data, derived data, and analytics products

are protected and managed in a secure and compliant fashion.

3. Operate in such a way as to make sure that there is accountability,

transparency, and traceability to those who are funding analytics

projects, to those who are developing and supporting analytics

resources, and to those who are making use of analytics resources.

4. Provide an organization structure to ensure that the necessary analytics

resources are available.


effects


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These design parameters can be achieved by using governance

committees:

an analytics governance committee that includes senior management and

representatives from the IT organization and various business stakeholders.

This committee helps prioritize analytics opportunities; obtain resources for

analytics projects; and ensure that those building the models get the data

required, that the models that are built get deployed, and that deployed

models measure the business value that they generate.

an analytics technical policy committee that determines what data, analytics

applications, processes, best practices, and standards are used across the

organization.

an analytics security and compliance committee that oversees the security and

compliance of data and analytics processes and applications.

an analytics data management and data quality committee that ensures the

organization’s data and metadata are accurate, complete, and consistent.


effects


References

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George, G., Haas, M.R., Pentland, A.(2014). Big Data and Management. Academy of Management Journal. Vol. 57, No. 2, Pp. 321–326.

Grossman, R.L., Siegel, K.P. (2014). Organizational Models for Big Data and analytics. Journal of Organization Design. Vol. 3, Pp. 20-25.

E&Y UK. Digital data opportunities using insight to drive relevance in the digital world. Web page: Advisory services, Performance improvement-Digital transformation. http://www.ey.com/UK/en/Services/Advisory/Performance-Improvement/Advisory_Digital-Transformation . Accessed on 2nd October 2014.

Kreutzer, R.T., (2014). Digital Darwinism and the need for a a digital transformation.4TH Annual conference on business strategy and Organizational Behaviour.

IBM Global Business Services, (2011), Digital transformation: Creating new business models where digital meets physical, IBM Institute for Business Value Executive Report, Pp. 1-20.

IBM Global Business Services, (2013), Digital reinvention Preparing for a very different tomorrow , IBM Institute for Business Value Executive Report, Pp. 1-24.


http://www.ey.com/UK/en/Services/Advisory/Performance-Improvement/Advisory_Digital-Transformation











Big data and digital transformation

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