Creating a clearly articulated data strategy—a roadmap of technology-driven capability investments prioritized to deliver value—helps ensure from the get-go that you are focusing on the right things, so that your work with data has a business impact. In this presentation, the experts at Silicon Valley Data Science share their approach for crafting an actionable and flexible data strategy to maximize business value.
Presentation: Big Data – From Strategy to Production - Mario Meir-Huber, Big Data Leader Eastern Europe, Teradata GmbH (AT), at the European Data Economy Workshop taking place back to back to SEMANTiCS2015 on 15 September 2015 in Vienna
This framework helps organizations align Data Strategy with Business Strategy to prioritize goals around the most pressing operational needs. It introduces Data Management & Data Ability Maturity Matrix to visualize the core path of business digital transformation, which is easy to understand and follow. And it provides the standard template for implementation, which can share the flexibility to engage applications of different industries.
Developing a Data Strategy -- A Guide For Business Leadersibi
Data is one of our most valuable assets -- yet we rarely understand how to incorporate it into our business plans. This presentation provides an introduction to data strategy for business leaders and points to more resources.
Data Strategy - Executive MBA Class, IE Business SchoolGam Dias
For today's enterprise Data is now very much a corporate asset, vital to delivering products and services efficiently and cost effectively. There are few organizations that can survive without harnessing data in some way.
Viewed as a strategic asset, data can be a source of new internal efficiencies, improved competitive advantage or a source of entirely new products that can be targeted at your existing or new customers.
This slide deck contains the highlights of a one day course on Data Strategy taught as part of the Executive MBA Program at IE Business School in Madrid.
Deep Neural Networks (DNN), or simply Deep Learning (DL), took Artificial Intelligence (AI) by storm and have infiltrated into business at an unprecedented rate. Access to vast amounts of data, recently made available by the Big Data revolution, extensive computational power and a new wave of efficient learning algorithms, helped Artificial Neural Networks to achieve state-of-the-art results in almost all AI challenges.
The implications of DL supported AI in business is tremendous, shaking to the foundations many industries. However, incorporating this technology in established business is far from obvious: cultural inertia in organizations, lack of transparency in most DL models and the complexity in training these models are some of the issues that will be addressed.
Creating a clearly articulated data strategy—a roadmap of technology-driven capability investments prioritized to deliver value—helps ensure from the get-go that you are focusing on the right things, so that your work with data has a business impact. In this presentation, the experts at Silicon Valley Data Science share their approach for crafting an actionable and flexible data strategy to maximize business value.
Presentation: Big Data – From Strategy to Production - Mario Meir-Huber, Big Data Leader Eastern Europe, Teradata GmbH (AT), at the European Data Economy Workshop taking place back to back to SEMANTiCS2015 on 15 September 2015 in Vienna
This framework helps organizations align Data Strategy with Business Strategy to prioritize goals around the most pressing operational needs. It introduces Data Management & Data Ability Maturity Matrix to visualize the core path of business digital transformation, which is easy to understand and follow. And it provides the standard template for implementation, which can share the flexibility to engage applications of different industries.
Developing a Data Strategy -- A Guide For Business Leadersibi
Data is one of our most valuable assets -- yet we rarely understand how to incorporate it into our business plans. This presentation provides an introduction to data strategy for business leaders and points to more resources.
Data Strategy - Executive MBA Class, IE Business SchoolGam Dias
For today's enterprise Data is now very much a corporate asset, vital to delivering products and services efficiently and cost effectively. There are few organizations that can survive without harnessing data in some way.
Viewed as a strategic asset, data can be a source of new internal efficiencies, improved competitive advantage or a source of entirely new products that can be targeted at your existing or new customers.
This slide deck contains the highlights of a one day course on Data Strategy taught as part of the Executive MBA Program at IE Business School in Madrid.
Deep Neural Networks (DNN), or simply Deep Learning (DL), took Artificial Intelligence (AI) by storm and have infiltrated into business at an unprecedented rate. Access to vast amounts of data, recently made available by the Big Data revolution, extensive computational power and a new wave of efficient learning algorithms, helped Artificial Neural Networks to achieve state-of-the-art results in almost all AI challenges.
The implications of DL supported AI in business is tremendous, shaking to the foundations many industries. However, incorporating this technology in established business is far from obvious: cultural inertia in organizations, lack of transparency in most DL models and the complexity in training these models are some of the issues that will be addressed.
7 Pillars Of Data Strategy - High Five 2018Evan Levy
Data and analytics companies are seemingly everywhere, many claiming to be panaceas for all your data woes. As much as we like to rely on technology, the human factor is still the most important part of the equation. Clear strategy and focused leadership is required to make transformative, meaningful change in data culture, influencing capture, analysis, and presentation of your company’s data.
Data strategy - How & When to Invest (SXSW V2V Core Conversation)Courtney Hemphill
Data strategy is a necessary component of every company but the approach and skills can vary widely as a product and its users grow. Data ensures that each product feature released can be measured as to its impact and effectiveness. Data also surfaces latent market needs that can be leveraged into further product value.
Carbon Five has been using data to solve tricky product problems with companies like Square, Altschool, StitchFix, Prosper, and Fandango for over 15 years. Come join the conversation if you are interested in what skills are necessary to drive data science at your company, how to hire data science talent, and what data strategy looks like for different companies.
- See more at: http://schedule.sxswv2v.com/events/event_V2VP46093#sthash.oPukb2oW.dpuf
Analytics is all about course correcting the future. While this starts with accurate predictions of the future, without resultant actions steering the future toward company goals, knowing that future is academic. Successful companies must be grounded in successful data-based prescription. In this webinar, William will present a data maturity model with a focus on how analytic competitors outdo the competition by looking forward to a data-influenced future.
Data Driven Decisions: Building an Insight Driven CultureAmazon Web Services
Analytics done well can transform the way that decisions are made across an organisation. The proliferation of data, matched with the accessibility of new technologies such as AI and Machine Learning, means answers to more and more business questions are within reach. Having a clear strategy for building a data driven culture, to realise the value in analytics, is now a business imperative. This presentation covered:
• The Amazon Story: A Culture of Innovation and History of Machine Learning
• Deloitte & Amazon: Perspectives on Building a Data Driven Culture
• Customer Discussion: Predictions & practical advice
This was presented in Australia and New Zealand in October 2018
Creating a Data-Driven Organization: an executive summaryCarl Anderson
What does it mean for an organization to be data-driven? It is not about having lots of reports and dashboards or big data but having the right data culture. Learn more about that culture in this executive summary of the key findings in Carl Anderson's new book "Creating a Data-Driven Organization" (2015) from O'Reilly Media.
The Data Driven Enterprise - Roadmap to Big Data & Analytics SuccessBigInsights
The Data Driven Enterprise - Roadmap to Big Data & Analytics Success
Presentation used at the series of Breakfast seminar around Australia hosted by Lenovo/Intel/SAP/EY
How to Monetize Your Data Assets and Gain a Competitive AdvantageCCG
Join us for this session where Doug Laney will share insights from his best-selling book, Infonomics, about how organizations can actually treat information as an enterprise asset.
This slide deck provides an overview to WSO2 Big data platform and discuss some of its customer case studies and applications. It discuss Big Data in general, real time analytics WSO2 CEP, batch analytics WSO2 BAM, and new products like predictive analytics with WSO2 Machine Learner. For more information, please reach us though architecture@wso2.org.
7 Pillars Of Data Strategy - High Five 2018Evan Levy
Data and analytics companies are seemingly everywhere, many claiming to be panaceas for all your data woes. As much as we like to rely on technology, the human factor is still the most important part of the equation. Clear strategy and focused leadership is required to make transformative, meaningful change in data culture, influencing capture, analysis, and presentation of your company’s data.
Data strategy - How & When to Invest (SXSW V2V Core Conversation)Courtney Hemphill
Data strategy is a necessary component of every company but the approach and skills can vary widely as a product and its users grow. Data ensures that each product feature released can be measured as to its impact and effectiveness. Data also surfaces latent market needs that can be leveraged into further product value.
Carbon Five has been using data to solve tricky product problems with companies like Square, Altschool, StitchFix, Prosper, and Fandango for over 15 years. Come join the conversation if you are interested in what skills are necessary to drive data science at your company, how to hire data science talent, and what data strategy looks like for different companies.
- See more at: http://schedule.sxswv2v.com/events/event_V2VP46093#sthash.oPukb2oW.dpuf
Analytics is all about course correcting the future. While this starts with accurate predictions of the future, without resultant actions steering the future toward company goals, knowing that future is academic. Successful companies must be grounded in successful data-based prescription. In this webinar, William will present a data maturity model with a focus on how analytic competitors outdo the competition by looking forward to a data-influenced future.
Data Driven Decisions: Building an Insight Driven CultureAmazon Web Services
Analytics done well can transform the way that decisions are made across an organisation. The proliferation of data, matched with the accessibility of new technologies such as AI and Machine Learning, means answers to more and more business questions are within reach. Having a clear strategy for building a data driven culture, to realise the value in analytics, is now a business imperative. This presentation covered:
• The Amazon Story: A Culture of Innovation and History of Machine Learning
• Deloitte & Amazon: Perspectives on Building a Data Driven Culture
• Customer Discussion: Predictions & practical advice
This was presented in Australia and New Zealand in October 2018
Creating a Data-Driven Organization: an executive summaryCarl Anderson
What does it mean for an organization to be data-driven? It is not about having lots of reports and dashboards or big data but having the right data culture. Learn more about that culture in this executive summary of the key findings in Carl Anderson's new book "Creating a Data-Driven Organization" (2015) from O'Reilly Media.
The Data Driven Enterprise - Roadmap to Big Data & Analytics SuccessBigInsights
The Data Driven Enterprise - Roadmap to Big Data & Analytics Success
Presentation used at the series of Breakfast seminar around Australia hosted by Lenovo/Intel/SAP/EY
How to Monetize Your Data Assets and Gain a Competitive AdvantageCCG
Join us for this session where Doug Laney will share insights from his best-selling book, Infonomics, about how organizations can actually treat information as an enterprise asset.
This slide deck provides an overview to WSO2 Big data platform and discuss some of its customer case studies and applications. It discuss Big Data in general, real time analytics WSO2 CEP, batch analytics WSO2 BAM, and new products like predictive analytics with WSO2 Machine Learner. For more information, please reach us though architecture@wso2.org.
Mobile, Wearables, Big Data and A Strategy to Move Forward (with NTT Data Ent...Barcoding, Inc.
Join NTT Data Enterprise Services, Inc.for a discussion on the Internet of Things (IoT), wearables, augmented reality, predictive analytics, and a strategy for using Big Data effectively in your enterprise. Presented at the Barcoding, Inc. Executive Forum 2014
In this presentation, you will learn how to transform a Big Data initiative into a realized, measurable ROI:
• Understand the complex mix of business expectation, hype, reality, and new information source opportunities in the Big Data space
• Use the Business Case process to help to you identify what you can achieve and what is not yet ready
• Build communities of interest around prototypes and plan for success for your company’s advantage
• Learn how to industrialize your Big Data innovations to achieve measurable, sustainable benefits
RWDG Webinar: The New Non-Invasive Data Governance FrameworkDATAVERSITY
Non-Invasive Data Governance is summarized as the practice of formalizing accountability for data and the application of governance to process. Non-Invasive Data Governance describes how data governance is applied to the organization rather than being forced into the environment. A NIDG framework will be introduced in this webinar.
In this month’s installment of the RWDG webinar series, Bob Seiner will present a new data governance framework that addresses the core components of data governance for each level of the organization. The resulting framework can be used for all approaches to data governance.
In this webinar Bob will discuss:
- The five core components of a data governance effort
- The five levels where the core components will be addressed
- Detailed explanation of each component for each level
- A diagram to complete the framework for your organization
- A framework comparison across approaches
How to Build a Rock-Solid Analytics and Business Intelligence StrategySAP Analytics
http://spr.ly/SBOUC_VP - The key to a successful analytics program is to have the right strategy in place. An effective approach benefits both IT and the core business alike. A solid, well-communicated business intelligence strategy is more than just a good idea. It’s crucial to maximizing ROI, reaching KPIs, and identifying metrics that actually mean something. Take the next step in your journey to a solid BI strategy.
Presenters: Deepa Sankar & Pat Saporito, SAP
Check out this white paper from eInfochips which showcases how energy and utility providers can unlock potential service opportunities using our predictive analytics solution across all stages of the business cycle. Major utility players are set to roll out millions of smart meters with the aim of generating actionable insights even though as per the industry’s own admission, any serious effort toward monetization is being offset by a lack of core IT capabilities, especially in big data technology. Capturing proactive intelligence on consumer behavior is the way to go. In this white paper, eInfochips demonstrates how utility players can predict demand response, generation response and create new revenue models around coincidental peak demands, smart expenditure modeling and other forms of end user data.
How PepsiCo's Big Data Strategy is Disrupting CPG Retail AnalyticsHortonworks
Like all consumer packaged goods (CPG) companies, PepsiCo relies on huge volumes of data to accurately replenish its retailers with the appropriate amount and type of product. Across the CPG industry, most analysts exclusively rely on Excel and Access for data wrangling, but as PepsiCo’s data surpassed the capabilities of those tools, they knew they needed a better way.
https://www.brighttalk.com/webcast/9573/227935
Big Data in Retail - Examples in ActionDavid Pittman
This use case looks at how savvy retailers can use "big data" - combining data from web browsing patterns, social media, industry forecasts, existing customer records, etc. - to predict trends, prepare for demand, pinpoint customers, optimize pricing and promotions, and monitor real-time analytics and results. For more information, visit http://www.IBMbigdatahub.com
Follow us on Twitter.com/IBMbigdata
What are big data in the contacts of energy & utilities, and how/where can the utilities find value in the data. In this C-level presentation we discussed the three prime areas: grid operations, smart metering and asset & workforce management. A section on cognitive computing for utilities have been omitted from the presentation due to confidentiality - but I tell you - it is mind-blowing perspectives on how IBM Watson will help utilities plan and optimize their operations in the near future!
See more on http://www.ibmbigdatahub.com/industry/energy-utilities
Strategizing Big Data in Telco
Big data feels to be a very hot topic nowadays. Some industries depend on it completely, some have opportunities to roll out their strategies and execute, some just considering when it is a right time to hop in.
To my mind, Big Data is not about technology. Big data is about people generating data and data used for the benefit of people.
Big data is a pool of activities intended at processing the data a company owns (internal and external) so that to open new revenue opportunities, minimize costs and enhance UX.
I had some ideas and thoughts on what telecommunication companies may start from in formulating the Big Data Strategy and so packed some of the most important pieces of thoughts into a small presentation.
What is the difference between Small Data and Big Data?
What kind of data is used currently and which is to be relied on a new paradigm?
What kind of products are expected from telcos?
My personal ranking of operators in terms of their Big Data execution
What are the stages telcos should pass through to become a Big Data operator?
Prerequisites for Big Data transformation
Please take a look at the presentation to find answers to these questions and feel free to share your opinion.
Thanks!
Today’s business leaders are seeking answers to questions such as what technology to invest in, who to work with and what technologies will help reshape their industry and operating model over the next 10 years. In this session, industry experts Byron Connolly, Editor-in-Chief of CIO of IDG, and Tech Research Asia’s Tim Dillon, will together discuss the important role Cloud plays in the current state of Australia’s business landscape and to the next-wave technologies such as IOT, Robotics, ML and AI, which are reshaping industries and business sooner than predicted.
Speakers:
Byron Connolly, Editor-in-Chief of CIO, IDG
Tim Dillon, Tech Research Asia
Data Virtualization, a Strategic IT Investment to Build Modern Enterprise Dat...Denodo
This content was presented during the Smart Data Summit Dubai 2015 in the UAE on May 25, 2015, by Jesus Barrasa, Senior Solutions Architect at Denodo Technologies.
In the era of Big Data, IoT, Cloud and Social Media, Information Architects are forced to rethink how to tackle data management and integration in the enterprise. Traditional approaches based on data replication and rigid information models lack the flexibility to deal with this new hybrid reality. New data sources and an increasing variety of consuming applications, like mobile apps and SaaS, add more complexity to the problem of delivering the right data, in the right format, and at the right time to the business. Data Virtualization emerges in this new scenario as the key enabler of agile, maintainable and future-proof data architectures.
Telecom revenues are declining.
Till now, Data revenues have been critical for Telcos which have successfully followed a “walled garden” approach. But the "walled gardens" are fast eroding under threat from integrated players like Google and Apple, and the telco revenues are fast declining.
This presentation presents strategies a Telco to counter this emerging threat from different types of online players and increase or at least retain a share of data revenues.
An insightful and visionary speech about the future of smart city by Mr. Ronald RAFFENSPERGER, Chief Technology Officer, Data Center Solution Sales, Huawei Technologies Company Limited
Frontiers in Alternative Data : Techniques and Use CasesQuantUniversity
QuantUniversity Summer School 2020 (https://qusummerschool.splashthat.com/)
https://quspeakerseries10.splashthat.com/
Lecture 1: Alexander Denev
In this talk, Alexander will introduce Alternative Data and discuss it's uses from his book, The Book of Alternative Data
- What is alternative data?
- Adoption of alternative data
- Information value chain
- Risks associated with alternative data
- Processes required to develop signals
- Valuation of alternative data
Lecture 2: Saeed Amen
In this talk, Saeed will discuss use cases in Alternative Data
-Deciphering Federal Reserve communications
- Using CLS flow data to trade FX
- Geospatial Insight satellite data to estimate retailers' EPS
- Saving "alpha" with transaction cost analysis
- Using Bloomberg News data to trade FX
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
4. Big Data - Big Potential
Not only for Telcos but also for other industries
Telcos Perspective
Issues and challenges
Big Data Strategy Ingredients & Considerations
Center of Excellence (CoE)
Group level platforms
6. • Telcos are facing
severe pressure
from OTTs.
Big Data -Big
Potential
• Facebook,
WhatsAPP, Line,
Tango, Skype,
Twitter.
Big Data -Big
Potential
• Main earnings of
OTTs is coming out
of the Data and
content they
possess.
Big Data -Big
Potential
BIG DATA - BIG POTENTIAL
7. • WhatsApp
acquired by
Facebook in
• USD 19 B.
Big Data -Big
Potential
• Alcatel-Lucent
acquisition by
Nokia in
• USD 16.6 B.
Big Data -Big
Potential
• Shows where the
value is…..
Big Data -Big
Potential
BIG DATA- BIG POTENTIAL
8. Potential of Big Data
Big Data technology and services market will grow at a 26.4%
compound annual growth rate to $41.5 billion through 2018
About six times the growth rate of the overall information
technology market –
IDC Survey
Definitive source of competitive
advantage
Being used across multiple industries
• To improve operational
efficiency
• To Generate new
revenue stream
10. Telcos perspective
(What they need to do)
TelcosNeed
Improve operational efficiency, optimizing costs
Generating new revenue stream
Collaboration/partnership with other industry verticals for many
new opportunities
Enhance Customers Experience for retention and revenue growth
11. Big Data – Telecom Case Studies
Telecom Italia (SKIL) – R&D
Symantec and Knowledge innovation lab
Mobistar and Kabel Deutschland – using
big data to accelerate their fraud
detection efforts
Singtel initiative to use Big Data to tap
into advertising domain
12. Big Data – Telecom Case Studies
Telefonica Dynamic insights a Big Data
initiative
Orange - Research and applications for
mobile "Big Data" towards achieving
socioeconomic development goals
Telstra Health relies on Big Data
Acquired Dr Foster a UK based e-health
advisory
13. Big Data – Telecom Case Studies
Malaysia’s Celcom Axiata leveraged high
performance analytics to optimize its marketing
campaigns & deliver over $50 million in annual
incremental revenue.
Singtel’s DataSpark develops data analytics, which
leverages (anonymized) location analytics to
derive mobility patterns and customer insights.
SK Telecom’s Geovision offers statistical
database/map combination for analyzing
purchasing patterns of customers based on
age/time/location.
14. Important Aspects of Strategy
3 Major Aspects
Business Oriented
Solution and Platform
Processes and Governance
15. Dimensions of Strategy
Internal
Related to internal efficiency,
business improvement, cost
savings
Offers, service improvement,
new products
External
Related to new revenue streams
Partnerships/collaborations
16. Some Example Use Cases
•In Real time, near real time and offline
•While browsing and based on browsing history
•Location, usage and device based
•During a live interactions cross and upselling
•Prepaid to post paid conversion
Personalized offers
•During network fallout
•Based upon analysis on congestion and service degradation
•Field Technician assignment and arrival optimization
Personalized proactive
customer care based upon
•Selling anonymously the insights
•Partnering with other industries for running advertising campaigns
Monetizing CSP data
through analyzing it
Revenue Assurance, Fraud management and Security
These are just a few ------- Tip of the Wealth it has
17. Big Data Strategy
Need to
implement a
compelling
strategy
covering
- Business use case
scenarios
- Reference Architecture
- Assessing existing
capabilities
- Required capabilities
- Assessing Value of
partnerships and
collaborations
Roadmap to
implement
strategy
18. Common Challenges
Relatively new technology, Every one talks about it but very few really know
TCO is relatively high as no one size fits all solution available
Scarce technical expertise and understanding of business cases available in Telcos
Needs lots of R&D, hit & trial approach and Telcos resources are busy in day to
day operations
Since revenues are soaring, it is difficult for Telcos
- to do large investments
- where business cases are not clearly known and difficult to perceive.
19. A Center of Excellence
(CoE)
or a Big Data LAB
is the agile, viable, cost
effective and result
oriented approach to BD
challenges
20. What CENTER OF ECELLENCE will do ???
CENTER OF ECELLENCE WILL
Developing a Big data platform keeping in view Reference Architecture.
Incubating the business use cases identified by the BD Strategy with standard
interfaces.
• Awareness and orientation of BD Strategy.
• Selection and prioritization of use cases
• Implementing Big Data Strategy.
• Implementing Big Data incubated Use cases.
Business
Involvement
Building capabilities to offer BD as a Service to other industries/Governments.
21. CoE: Big Data as a Service
Lots of industries see a great potential.
Do not have expertise and wealth of data as Telcos have.
New revenue generation opportunities through partnerships and
collaborations with other industry verticals
Airline, Hospitality, Tourism, Transportation
Healthcare, Utilities
Government
22. Roadmap
Big Data Strategic
architecture
development
(X Month )
Business Involvement
(Selection and
prioritization of Use case)
Roadmap and plan for
the CoE-
X Month After Approval
of Big Data Strategy
Implementation of CoE
(Big Data Lab)
-X Months Infrastructure
setup.
Start producing use cases
(X months)
Implementing incubated
use cases with partners
and in production
On going - as per
priorities set and
requirements
23. How to develop Big Data Strategy Conclusive Comments
Understanding the opportunity, existing capability of your organization
and overall direction
Identifying and analysing the use cases
Building the business cases
Think big, start small
Value of partnerships