The utility of Business Analytics lies in its ability to extract value out of stored data. The value may be tactical or strategic. What are the best process for such value discovery? What are the pitfalls? read about them here.
We propose a new needs driven framework for managing data with Data Lakes - Scalable Metrics Model. Salient features are modularity, extensiblity, flexibility and scalablity. We want to have self-contained modules which can either feed Reporting/Decision engines themselves with the capability of connecting across various other modules for Deep dive Analytics/Mining.
This will be presented at a Global Big Data Conference at Santa Clara on Sep 2nd. Come join us for a fun and learning event.
Marketing analytics
PREDICTIVE ANALYTICS AND DATA SCIENCECONFERENCE (MAY 27-28)
Surat Teerakapibal, Ph.D.
Lecturer, Department of Marketing
Program Director, Doctor of Philosophy Program in Business Administration
Predictive Analytics for Customer Targeting: A Telemarketing Banking ExamplePedro Ecija Serrano
A comparison of classification methods to predict buyers in banking telemarketing. Overcoming class imbalance and gaining insight on what customers are likely to buy a particular financial product.
How AI is transforming Pricing_EPP Monetized_July2019Felix Krohn
AI is here and here to stay. Dozens of AI-related use cases, such as Predictive Analytics, Machine Learning, NLP, RPA and others, have developed from early proof of concept and select success stories to almost mainstream adoption. But which role does Pricing play among the widespread use cases around AI and Advanced Analytics, and where is it in the hype cycle? I am exploring and discussing in this presentation where and how far we are with AI Pricing, what showcases are emerging in retail and what it takes for a successful implementation of AI Pricing.
We propose a new needs driven framework for managing data with Data Lakes - Scalable Metrics Model. Salient features are modularity, extensiblity, flexibility and scalablity. We want to have self-contained modules which can either feed Reporting/Decision engines themselves with the capability of connecting across various other modules for Deep dive Analytics/Mining.
This will be presented at a Global Big Data Conference at Santa Clara on Sep 2nd. Come join us for a fun and learning event.
Marketing analytics
PREDICTIVE ANALYTICS AND DATA SCIENCECONFERENCE (MAY 27-28)
Surat Teerakapibal, Ph.D.
Lecturer, Department of Marketing
Program Director, Doctor of Philosophy Program in Business Administration
Predictive Analytics for Customer Targeting: A Telemarketing Banking ExamplePedro Ecija Serrano
A comparison of classification methods to predict buyers in banking telemarketing. Overcoming class imbalance and gaining insight on what customers are likely to buy a particular financial product.
How AI is transforming Pricing_EPP Monetized_July2019Felix Krohn
AI is here and here to stay. Dozens of AI-related use cases, such as Predictive Analytics, Machine Learning, NLP, RPA and others, have developed from early proof of concept and select success stories to almost mainstream adoption. But which role does Pricing play among the widespread use cases around AI and Advanced Analytics, and where is it in the hype cycle? I am exploring and discussing in this presentation where and how far we are with AI Pricing, what showcases are emerging in retail and what it takes for a successful implementation of AI Pricing.
Analytics has evolved from a support function into a Core Decision making tool. It provides unique capability of connecting the dots across organization & outside and leverage best practices/insights into making Decisions more actionable and outcomes predictable. With a top-down strategic view, iterative Test & Learn framework, hybrid team structure, context based User Experience Design, dual objective (Business & Learning) & recommendation/business case storytelling takes the Analytics deliverables into next level.
A refresher guide about Data Science and Statistics in the domain of Business Intelligence. This presentation not only covers the data science basics but also the way business intelligence industry is headed.
Competition is getting more intense. Globalisation has finally arrived in every country of the world. It is crucial to know your own strengths
and weaknesses as well as these of your „enemies.“
Companies that want to be “ahead of the competition“ must have a
well-performing radar system in order to analyse their competitors and market developments, and to be able to identify relevant opportunities or threats on time .
Competitive Intelligence is the art of always staying one step ahead of
the competition.
Day 1 (Lecture 4): Data Science in the Retail Marketing and Financial ServicesAseda Owusua Addai-Deseh
Lecture on "A Practical Exposition of Data Science in the Retail Marketing and Financial Services" delivered by Delali Agbenyegah, Director of Data Science and Analytics, Express, Columbus OH, USA.
This whitepaper is geared to help
bank marketing professionals
understand the scope of marketing
analytics and also on how it can
contribute value to the various
factions of a bank’s marketing
activities.
Content will range start with why does Text Analytics need a special session on convincing boss, followed by a role play summarizing current mistakes, a sample elevator pitch for your boss and a proposed execution plan. The content is tailored for Mid to Senior Level Managers trying to convince Leaders/Executives/Heads. It doesn’t provide any technical details –methodologies, tools, vendors or hardware investments.
This was presented at Text Analytics West Summit 2014 at San Francisco. Questions? Reach out at Ramkumar Ravichandran @ Linkedin.
Give the People What They Want: An Approach to Thoughtful KM TechnologyEnterprise Knowledge
Presented by Todd Fahlberg, Knowledge Management Consultant on May 19th, 2020.
Implementing a meaningful Knowledge Management technology brings many levels of challenges, even in the most innovative, user-centric organizations. In this session, Todd Fahlberg and Madison Jaronski will share proven practices on how to approach KM Technology broken in four phases: Gathering Requirements & Defining Personas, Leveraging Data-Driven Evaluations, Combining Quantitative and Qualitative Data to Make Holistic Decisions, and Crafting an Implementation Strategy for Success & Adoption. Lastly, Todd and Madison will offer recommendations based on experiences with past and current clients how organizations can make better, people-focused decisions when it comes to Knowledge Management technology.
Data Science - Part I - Sustaining Predictive Analytics CapabilitiesDerek Kane
This is the first lecture in a series of data analytics topics and geared to individuals and business professionals who have no understand of building modern analytics approaches. This lecture provides an overview of the models and techniques we will address throughout the lecture series, we will discuss Business Intelligence topics, predictive analytics, and big data technologies. Finally, we will walk through a simple yet effective example which showcases the potential of predictive analytics in a business context.
I volunteered my time to share about big data to those looking to understand the space.
This was for Networking with Grace, a group that is focused on helping those get back to work. I put this presentation together to help people learn about big data and how to transition their skill sets to the space.
Analytics has evolved from a support function into a Core Decision making tool. It provides unique capability of connecting the dots across organization & outside and leverage best practices/insights into making Decisions more actionable and outcomes predictable. With a top-down strategic view, iterative Test & Learn framework, hybrid team structure, context based User Experience Design, dual objective (Business & Learning) & recommendation/business case storytelling takes the Analytics deliverables into next level.
A refresher guide about Data Science and Statistics in the domain of Business Intelligence. This presentation not only covers the data science basics but also the way business intelligence industry is headed.
Competition is getting more intense. Globalisation has finally arrived in every country of the world. It is crucial to know your own strengths
and weaknesses as well as these of your „enemies.“
Companies that want to be “ahead of the competition“ must have a
well-performing radar system in order to analyse their competitors and market developments, and to be able to identify relevant opportunities or threats on time .
Competitive Intelligence is the art of always staying one step ahead of
the competition.
Day 1 (Lecture 4): Data Science in the Retail Marketing and Financial ServicesAseda Owusua Addai-Deseh
Lecture on "A Practical Exposition of Data Science in the Retail Marketing and Financial Services" delivered by Delali Agbenyegah, Director of Data Science and Analytics, Express, Columbus OH, USA.
This whitepaper is geared to help
bank marketing professionals
understand the scope of marketing
analytics and also on how it can
contribute value to the various
factions of a bank’s marketing
activities.
Content will range start with why does Text Analytics need a special session on convincing boss, followed by a role play summarizing current mistakes, a sample elevator pitch for your boss and a proposed execution plan. The content is tailored for Mid to Senior Level Managers trying to convince Leaders/Executives/Heads. It doesn’t provide any technical details –methodologies, tools, vendors or hardware investments.
This was presented at Text Analytics West Summit 2014 at San Francisco. Questions? Reach out at Ramkumar Ravichandran @ Linkedin.
Give the People What They Want: An Approach to Thoughtful KM TechnologyEnterprise Knowledge
Presented by Todd Fahlberg, Knowledge Management Consultant on May 19th, 2020.
Implementing a meaningful Knowledge Management technology brings many levels of challenges, even in the most innovative, user-centric organizations. In this session, Todd Fahlberg and Madison Jaronski will share proven practices on how to approach KM Technology broken in four phases: Gathering Requirements & Defining Personas, Leveraging Data-Driven Evaluations, Combining Quantitative and Qualitative Data to Make Holistic Decisions, and Crafting an Implementation Strategy for Success & Adoption. Lastly, Todd and Madison will offer recommendations based on experiences with past and current clients how organizations can make better, people-focused decisions when it comes to Knowledge Management technology.
Data Science - Part I - Sustaining Predictive Analytics CapabilitiesDerek Kane
This is the first lecture in a series of data analytics topics and geared to individuals and business professionals who have no understand of building modern analytics approaches. This lecture provides an overview of the models and techniques we will address throughout the lecture series, we will discuss Business Intelligence topics, predictive analytics, and big data technologies. Finally, we will walk through a simple yet effective example which showcases the potential of predictive analytics in a business context.
I volunteered my time to share about big data to those looking to understand the space.
This was for Networking with Grace, a group that is focused on helping those get back to work. I put this presentation together to help people learn about big data and how to transition their skill sets to the space.
Data analysis has transformed the way organizations and individuals make decisions. As the volume of data continues to grow exponentially, the need for data-driven insights has become increasingly critical. However, raw data, no matter how extensive, can often be overwhelming and challenging to interpret. This is where the concept of data storytelling comes into play. In this comprehensive exploration, we will delve into the essence of data storytelling, its significance in data analysis, the key elements that constitute an effective data story, and practical tips for implementing data storytelling techniques.
Understanding and winning your customers in the big data era ( retail industry)Kim Ming Teh
Section 1: Understand & winning your customer in big data era
1) The age of customer & Key trends in retail
2) Big data analytic and use case
Section 2: Interactive Case study
Offline vs Online Retail & Omni-Channel Future
Big Data LDN 2018: EXPERIAN: MAXIMISE EVERY OPPORTUNITY IN THE BIG DATA UNIVERSEMatt Stubbs
Date: 14th November 2018
Location: Data-Driven Ldn Theatre
Time: 10:30 - 11:00
Speaker: Anna Matty
Organisation: Experian
About: Today there is a widespread focus on the 'how' in relation to problem solving. How can we gain better knowledge of what consumers want, or need? How can we be more efficient, reduce the cost to serve, or grow the lifetime value of a customer? But, how do you move to a place where you are not only solving a problem, you are redesigning the entire strategic potential of that problem? You are being armed with insight on what the problem is.
Data and innovation offer huge potential to revolutionise all markets. There is an opportunity to be one step ahead of the need, to redesign journeys and enhance enterprise strategies. To do this you need access to the most advanced analytics but also the best quality, including variations and types of data, and then the technology that can act on this insight. Data science can present a unique opportunity for uncovered growth and accelerate your business through strategic innovation – fast. In this session you will hear more about how today's analytics can move from a single task, to an ongoing strategic opportunity. An opportunity that helps you move at the speed of the market and helps you maximise every opportunity.
Marcus Baker: People Analytics at Scale
People Analytics Conference 2022 Winter
Website: https://pacamp.org
Youtube: https://www.youtube.com/channel/UCeHtPZ_ZLZ-nHFMUCXY81RQ
FB: https://www.facebook.com/pacamporg
Operationalizing Customer Analytics with Azure and Power BICCG
Many organizations fail to realize the value of data science teams because they are not effectively translating the analytic findings produced by these teams into quantifiable business results. This webinar demonstrates how to visualize analytic models like churn and turn their output into action. Senior Business Solution Architect, Mike Druta, presents methods for operationalizing analytic models produced by data science teams into a repeatable process that can be automated and applied continuously using Azure.
6 Key best practices to enhance Marketing with AISophie LEHMANN
Artificial Intelligence has been around for decades, but has
seen a recent resurgence in interest as data size and diversity
continue to grow and the cloud becomes a popular option for
quickly and economically scaling compute power and data storage.
This Checklist explores how AI can be used to enhance marketing
analytics and to help companies both better understand their
customers and deliver a great customer experience.
Overview of Data and Analytics Essentials and FoundationsNUS-ISS
As companies increasingly integrate data across functions, the boundaries between marketing, sales and operations have been blurring. This allows them to find new opportunities that arise by aligning and integrating the activities of supply and demand to improve commercial effectiveness. Instead of conducting post-hoc analyses that allow them to correct future actions, companies generate and analyze data in near real-time and adjust their operations processes dynamically. Transitioning from static analytics outputs to more dynamic contextualized insights means analytics can be delivered with increased relevance closer to the point of decision.
This talk will cover the analytics journey from descriptive, predictive and prescriptive analytics to derive actionable and timely insights to improve customer experience to drive marketing, salesforce and operations excellence.
Webinar - Expert Tips for Crafting Your Best Compensation Data StrategyPayScale, Inc.
Join Payscale’s Chief Product Evangelist, Ruth Thomas; Director of Product Management - Data Products, Vicky Peakman; and Director of Data Science, Sara Hillenmeyer as they share expert tips for crafting your best compensation data strategy.
The dynamic synergy between data analytics and cognitive process automation embodies the very essence of data-driven decision-making. In this latest piece from the E42 Blog, we dive deep into the very synergy that has firmly established itself as the bedrock of modern business strategy, ushering in precision, efficiency, and growth for enterprises.
Strategic planning has been disrupted. The evolution of digital has forced us to re-think the very fabric of how agencies create marketing experiences. From channels to evolvable worlds, from disciplines to hacking, agencies need to change and embrace change. This 4A's presentatio, a collaboration between Laura McFarlane and myself, was presented by Laura at the Planning conference in 2013.
Smart and proactive decision making is not only an advantage but a key to success of any business. This requires quantitative insights into various business operations and influences. The recent advancements in technology and a significant reduction in data storage costs have given rise to vast and versatile sources of data that provide a wealth of such insights. Harnessing them to accelerate and optimize your business is cardinal for keeping up with the emerging trends.
This presentation is an Introduction to the importance of Data Analytics in Product Management. During this talk Etugo Nwokah, former Chief Product Officer for WellMatch, covered how to define Data Analytics why it should be a first class citizen in any software organization
The issues of Income Inequality, urban migration and rural urban divide are interlinked. Dr.Abdul Kalam and Dr.P.V. Indiresan presented the PURA model to address these issues. A concerted and focused effort is needed to deliver success of the PURA Model in select geographies. It can serve as a Proof of concept for subsequent scaling up across the nation.
Intelligent and Smart Systems define the cutting edge of information technology now. They are invisible yet ubiquitous. From identifying individual student’s lack of attention to suggesting remedial measures, from predicting financial failures to preventing future fraud, and from assisting noninvasive surgery to guiding missiles to moving targets, the Artificial Intelligence based applications are stepping into every domain.
Numerous concerns have emerged in parallel. Should they be permitted to run a completely human less system? Can they be assigned all cognitive non routine tasks that humans are good at? Are they effective communicators and consensus builders? What role should they play in decision making? How good are they in picking up data compared to human senses? These and many other questions have surfaced in many fora.
Data used in model building adds another dimension. How unbiased are the data sets used in training? Can a data set be ever unbiased? What are the consequences of data bias in models and algorithms?
This talk explores the issues of setting the boundary for use of AI technology. Areas of concern are delineated, and principles of restraint advocated. It aims to inspire researchers to keep the boundary in mind as they explore new frontiers in AI and to design stable boundary line interfaces.
Values and Beliefs are specific to each culture and their impact on decision choice and decision processes differ from one country to another. This presentation explores various dimensions of this issue and and illustrates how Cultural Factors can be addressed in System Design through examples.
An integrating framework that reconciles the gaps of supply and demand side initiatives and fuses together numerous GOI programs is the need of the hour. Model of such a framework is proposed here.
( Tasc One members are Parasuram Balasubramanian, Padmanabhan Jayasimha, T.R. Sankaranarayanan and Hariharan Shankar. All are alumni of IIT Madras)
An abridged version of this article was published in "Report: IITMAA Sangam 2019 - Reimagining India in 2030"
Digital innovations can play a big role in SCM. We select four examples and amplify in this presentation. They are Competitors to Collaborate, Smart Systems to reduce Product Variety, Demand Sensing and Digital Twins. They can be customised to suit a given business segment or scenario and have the potential to be disruptive innovations.
Graduating students are endowed with two Oars to navigate their way through the ocean of life. First one is about learning to learn. Second oar is the attitude and belief they carry. Through numerous examples from my life and from that of well known people I convey the thought that they need to use these two oars to move through turbulent waters. The students are also advised to grab the career opportunities through technologies known as SMAC and embark on a journey of self discovery.
Tracking money and fund flows from one financial entity to another will lead to a long chain or network of entities spread all over the world. Along with the funds financial risks also flow across the network. They can have a devastating cascading effect when one entity collapses. The financial melt down of global markets in 2007-08 was precipitated by failure in such networks. We present the dimensions and complexity in modelling fund flows in these networks.
No two projects are alike in converting a technical innovation to a market facing solution. Hence the road map for a given invention has to be custom designed. Yet basic concepts are common and we can learn to build the road map through case studies. One such study on urethral strictures is presented here.
Information sharing is a major challenge in SCM due to the geographical spread of partners and monumental paper work involved across countries and regions. Digitisation impacts the flow of goods, funds and information. It is at the threshold of introducing the Smart Factory where all flows are automated. How relevant are these technologies for India? What can be the Smart Approach for India in sequencing the adoption of these technologies? We present a suggested approach here.
Supply Chain Management has evolved over time with frequent inputs from strategic innovations, technology changes and connectivity paradigms. It will continue to be so in coming decades when IIoT, Machine Learning , 3D Printing and Blockchain technologies mature. As the market place moves towards mass customising SCM professionals need to adopt more and more of Gray thinking rather than the conventional black or white approach.
AI and its allied technolgies present an exciting scenario of job changes in coming decades. So are the concerns about loss of traditional jobs. What would be the net impact? We explore the economic models and concepts that allay unfound fears; yet warn us to be ready for constant changes and need for continuous skill rebuilding.
Soft skills such as Empathy, Assertiveness,Proactiveness, Passion and Ability to construct win win solutions play a critical role in career development. They need to be cascaded on top of the technical expertise that one has to build. These are illustrated with many role play examples for effective teaching in a class room environment.
Emerging technologies such as Artificial Intelligence, IIoT and Blockchain are threatening to take away millions of conventional jobs over the next three decades. They have the potential to create even more jobs for he future. But the structural changes in job markets would be painful and would vary from country to country. This presentation suggests a macro model for India to be ready to face the challenges.
The career opportunities emerging, due to technology, in coming decades, is amazing. So do entrepreneurial opportunites. Every student has to be either an entrepreneur or intrapreneur to stay employed.
Both the industry and academia are keen to derive synergy from their relationship; in particular in research partnership. Yet many a time they fall short in what can be achieved. We present a collaboration framework that can enhance the effectiveness.
Sampling is a powerful tool to obtain valuable information about a population quickly and at a fraction of the cost. But the sample size and sampling plan have to be proper to yield scientifically valid and acceptable conclusions. We describe this challenge in understandable terms for all and back it up with sufficient statistical concepts for the benefit of students.
it is a remarkable story of birth and penetration. The Indian IT Services industry used innovation in vendor partnering to enter the global markets in mid 70s. The subsequent growth has been built on the foundations of cost effective service, unrelenting focus on quality, execution excellence and Human Resource supply chain management. It is a success story of globalisation.
Numerous new financial products are created by bundling mortgages, credit card dues etc. They are tranched to create sub products with varying risks and rewards. The financial crisis of 2007-08 owes its origin to these products.
IT Service Firms employ hundreds of thousands of technical staff. At any given time more than 25000 sit on bench in large firms. The decision to keep them on bench,versus train them on new skills or let go can be modeled using mathematical programming to arrive at the best decision.
Significant and continuous productivity gains and system effectiveness can be achieved by applying Queuing Theory to Application software Maintenance in Information Technology Services.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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).
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.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Show drafts
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
1. Invited Session Delivered by
Parasuram Balasubramanian Ph.D,
Founder & CEO,Theme Work Analytics
Bangalore, India 560041
Business Analytics: Processes
and Pitfalls in Value Discovery
Copyrights : PB
ORSI International conference &
47th Annual Convention
S.V.University, Tirupati, India 1-3 December 2014
2. Power of Analytics
Inventory
Bench Staff
Unutilized
funds
Lost Sales
Opportunity
Retail
Firm
Bank
I.T.
Services
Firm
The most significant problem for any firm is to sit on unsold
inventory while a sales opportunity is lost.
3. Power of Analytics
Inventory
Bench Staff
Unutilized
funds
Lost Sales
Opportunity
Retail
Firm
Bank
I.T.
Services
Firm
It arises due to issues of demand estimation, price and promotion
determination and product specifications. (in short, lack of market
knowledge)
5. Power of Analytics
Analytics
Behavioural
Science
Computer
Science
Management
Science ❖ Management Science grew with the field
of Operations Research. Data issues
inhibited its growth till 1970s.
❖ Explosive growth of Computer Science
till 2000 acted as a fodder for growth of
Analytics.
❖ Internet, POS, Mobility, Big Data and
Location Aware Technologies are fueling
the embedding of Behavioural Science
into Analytics.
History reveals that…..
6. Power of Analytics
Analytics
Behavioural
Science
Computer
Science
Management
Science
❖ Credit card services: Capital One segregated the
customer base into those taking large loans and
repaying them slowly versus those taking small
loans and repaying them quickly.
❖ Mortgage Securitization: Chase discovered an
arbitrage opportunity where there is a gap between
market price of MBS and their true worth by
knowing the higher probability of a large number
of loans being prepaid. Mortgages were rebundled
to take advantage.
❖ Netflix came up with differentiated incentives for
profitable customers who were active customers
but rented moves infrequently. all customers paid a
fixed monthly rental
❖ American Airlines and Marriott implemented yield
management concepts in the travel and hospitality
segments by segmenting the customer base
dynamically.
History reveals that….. Analytics facilitates the refinement of
market segmentation
7. Power of Analytics
Analytics
Behavioural
Science
Computer
Science
Management
Science ❖ American Airlines and Marriott implemented yield
management concepts in the travel and hospitality
segments. YM is an initiative that is best suited for
Price Discovery as much as for finer segmentation.
History reveals that….. Analytics facilitates Price Discovery
8. Power of Analytics
Analytics
Behavioural
Science
Computer
Science
Management
Science
❖ Determining Price elasticity coefficients and
characteristics
❖ Developing refined market segments
❖ Dynamic allocation of capacity to maximize
revenue or yield.
❖ Optimal Product functionality for a given
segment.
❖ Identifying the most cost effective
promotions.
❖ Selecting the best retention strategies.
❖ Optimizing supply chain design and logistics
❖ Attraction, retention and motivation of human
resources
❖ Understanding the complex scenario with
respect to diverse risk characteristics of
financial products.
Analytics plays a key role in
9. Power of Analytics
Firm
Customer
Vendor
Knowledge Zone
Knowledge is found where unknowns are probed. The Firm’s interaction
with the Market (consisting of Customers and Vendors) is the fertile ground
10. Power of Analytics
Firm
Customer
Vendor
Knowledge
Zone
❖ Firm designs the product with many
assumptions; Market place is where these
assumptions are validated with the product
release
❖ Firm can solve many factory and field issues
of the product only by tapping the higher
knowledge of the vendors
Product functionality, pricing, promotion strategy etc. need constant
validation. So is product design.
11. Power of Analytics
ERP
CRM
SCM
Knowledge
Zone
ERP,CRM and SCM systems create (or capture) the data and generate
usable information. Knowledge extraction however happens only by
Overlapping these information in multiple layers.
❖ Why does this product not appeal to the
young affluent?
❖ Should our promotional tactics be different in
this geography?
❖ How can our product be fine tuned for proper
balance between price and functionality in
every market segment
❖ Which vendor can work with us to effect
significant product innovations?
O.R. models help to fuse together multiple
layers of information
12. Culture
ThoughtsAction Words
culture manifests itself in the form of arts, literature,
history, architecture, food , socializing or purchasing
habits etc.
(Visible) (Visible)(Invisible)
Values & Beliefs
(define)
Power of Analytics
Human intent can only be inferred. Words are ambiguous. Action
is definitive.
13. Power of Analytics
Internet and the Online Store has enabled a better understanding of human
intent than ever before.
Brick and Mortar Store
Online Store
What was he looking for and did not find ?
Why didn't he buy what he found ?
We don’t know
We
know
Click stream analysis
reveals this
The challenge is to determine how
representative of the target population he
is
14. Prospect Active
Service
Category
not found
AL cost
too high
Service
Provider
not found
Service
Category
not found
SP cost
too high
Unfriendly
User
Interface Unfriendly
User
Interface
Why do members
leave ?
Click Stream Analysis of P and A state members will provide required data
and insight.
15. ❖ Selecting patients for clinical trials for new drug testing
❖ Distribution of stored/Dam water to conflicting interest groups of
farmers, industries, cities for potable water etal
❖ Selecting transplant patients for donated organs
❖ Closure of unviable public facilities
❖ Allocation of runways for planes/airlines when there is fog
❖ Allocation of projects to employees
Hard Choice Problems to tackle : Examples
Power of Analytics
Large volume of data of different types need to be
analyzed; offline or real time
Further
16. Power of Analytics
Big Data Analytics
Big data refers to large data sets exceeding the limits
of normal data base management software.
Vast amounts of data in single or few data sets with
volume, variety and velocity
Data includes text, image, audio and video outputs.
Data source can be a POS Device, Sensor, phone,
ATM or a computer.
17. Power of Analytics
❖ Analytics and particularly the Math Model is the ship
with
which one can navigate through the ocean of Big Data.
❖ Model lends it a purpose and a focus.
❖ Provides reliable results in Predictive analysis
❖ Most cost effective platform to run numerous “what if”
simulations.
Big Data Analytics
18. Power of Analytics
Understanding customer preferences, attitudes
and predicting emerging needs
Supply Chain and Delivery Management
Health Care Delivery
Churn Management in Telecom sector
Underwriting insurance policies to reflect individual
customer’s risk ratings
How can prepayment or payment default be
predicted ahead of time and timely preventive
measures initiated?
Where is Big Data Analytics being used today?
19. Power of Analytics
While using data from Social Media, tread
with caution
How representative of the population is
the sample data? (This has to be
established before projection)
How robust and reliable is the model
that extracts beliefs from the Word.
{Can it be validated?}
Social media lets us analyze the spoken/written word to peer into the human intent and
validate action based intent deduced earlier.
Can be the most powerful means to tailor a product or promotion methods
to suit the needs of an individual.
20. Power of Analytics
Even a strong correlation is no proof for cause-
effect relationship
Neither simulation nor optimization offers validation
for cause effect relationship
No model can be used to predict outcomes outside
the range of the data set used to build the model
Every time before a model is used, we have to
ascertain the data range validity
Words of wisdom
21. Power of Analytics
Models need periodic validation too.
No model can be used to guide decisions at a
level of granularity greater than the data
used to build the model.
Models form the base of Analytic enquiry.
Knowledge resides at places where
uncertainty exists. Uncertainty is the result
of diversity. Diversity is the rule of nature.
Analytics is the right vehicle in which such a
journey can be undertaken
Words of wisdom
22. Thanks and Best Wishes
Parasuram Balasubramanian Ph.D,
Founder & CEO,Theme Work Analytics
Bangalore, India 560041
balasubp@gmail.com
ORSI International conference &
47th Annual Convention
S.V.University, Tirupati, India 1-3 December 2014
Business Analytics: Processes and
Pitfalls in Value Discovery