A Modern Data Strategy to help companies look at the past, present and future.

Today, organizations are becoming more data-driven by using their data to build new products, stay ahead of the competition and provide better customer experiences. As a result, data management and processing it for various stakeholders needs to be fast, automated and scalable.

Organisations currently collect and process the data in a manner that allows them to make decision making process simpler, helping them gaze the past, manage present and predict the future. Data warehouse supports analysis of operational system data for BI while data lakes support newer use cases that leverage data for machine learning, predictions, and real-time analysis. The question is: does your business need both and why?

Brian Flug, with decades of worldwide demonstrated pioneering as a Technologist, big data, analytical, distributed intelligent cloud computing, HPC, IoT wizard, HPC/ML/AI data storage, data intelligence, CAE/CAD/CAM/CFD Expertise in life sciences, medical, financial, entertainment, gaming, manufacturing, defense, DOE, DOJ, automotive and consumer goods. With success in computational solutions, from supercomputing, to cluster and grid computing, to pre & post cloud computing, research, business intelligence, scientific analytic s and engineering, simulations and animations, data sciences, distributed and parallelism computing, from Startups to IOP’s, patents, copyrights and investment funding successes, from consultant, technical sales, CTO, CDO, management, director, solution, data and enterprise architect.

We have created Next Best Offer recommendation for users. We have send the offer via App on Mobile phone.
There are various best offers for every client based on their past behavior. There are basically three best offers that every eligible user receive that are relevant for him.

20+years industry experience world-wide (Netherlands, USA, Serbia), from science& engineering (PhD in EE, UTwente NL) to marketing, business, analytics to Applied Data Science. In 2016 he started Data Science & Analytics team within marketing department of Telenor Serbia. He has been a keynote speaker and presenter to many Data Science conferences in Europe; promoting applied data science in Marketing & Business. Currently, he is as a freelance consultant on Business Convenient Analytics & Data Science.

The newest industry disruption is the Low-Code/No-Code (LCNC) development aims to empower business users to rapidly develop codeless applications to meet their needs. The distinction between tools that were thought to be simple enough for citizen developers and powerful enough for professional development teams are disappearing. In a true IT democratization process, citizen development is helping accelerate digitization of processes at an unprecedented rate. Through real-world examples, this talk aims to highlight the LCNC trend and how it is helping with Analytics and BI. Talk also provides constructive approaches which help IT / engineering teams embrace citizen developers while eliminating the historical points of friction from “shadow IT” work.

Garima V. Sharma is a senior engineering leader at Microsoft. She is a seasoned Tech professional, keynote speaker, STEM champion, technical blogger and proponent of diversity and inclusion. She runs a global team of data engineers and scientists focussed on building world-class analytics and BI solutions and next-gen AI applications. Garima has a passion for technology and has worked in progressive companies on Cloud Computing, Big Data & Artificial Intelligence solutions. She has helped shape science and technology for mission critical software, reliability in operations and re-design of architecture all geared towards advancements in medicine, security, cloud technologies and bottom line savings for the client businesses. An enthusiastic technology and STEM blogger, she brings together her passion for tech and diversity in her professional and personal endeavors.

Data is everywhere and lots of it. In this presentation, the author discusses the challenges of procuring data, cleansing it and derive segmentations for marketing campaigns or operational transactions to engage effectively with your customers.

Rama Ravindranathan is an innovator and technologist who solves complex healthcare business problems. Rama focuses on building the product engineering discipline to build the solutions. Rama leads technology strategy for UHG Corporate's Occupational Health and Safety solutions. Data Engineering is an essential pillar in Rama's portfolio and it plays an important role in gathering, feeding and processing it for use within transactional and business intelligence systems.

For a long time now, an evolution of technology has been occurring. The end result of that evolution is the data lakehouse. This presentation describes that evolution.

Bill Inmon, the father of the data warehouse, has written 61 books. One book has sold over 500,000 copies and has been translated into 9 languages. Bill was named by Computerworld as one of the ten most influential people in the history of computer science.

This evolution from centralized and golden record centric of IT mindset to business user-centric golden profiles, has resulted in the need for complementary contextual and analytical models

Siva Juluri, a Data Scientist, has over 20 years of IT experience predominantly in DATA serving across various industries. In his current role, Siva is Practice Lead for the "Data Management & Governance (DMG) for North America Clients" . He is responsible for practice vision and strategy, Customer Advisory, Thought leadership and nurturing of emerging trends and partner ecosystem management in the areas of DMG.

Data Science & Machine Learning | Artificial Intelligence & Big data | Introduction|History|Regulatory Aspect| Application of data science | Tools and evolving capabilities | Data | Uniqueness & challenges | Classification Algorithm

Case Study:

Predicting bank loan default | Data| Machine Learning Algorithms | Results & Discussion.

In this talk I will provide an overview of banks history, its regulatory aspects and application of machine learning in banking with real-time use case.

Deepti Gupta has done her MBA in Finance and PGPM in operation research. She has worked with KPMG and IBM and is currently working as a data scientist in the banking sector . Deepti has extensive experience in predictive modeling and machine learning with an expertise in SAS, R and Python. Deepti has authored a book on data science and machine learning and has developed data science courses, delivered data science training, and conducted workshops for both corporate and academic institutions. She has written

Definition Purpose of Strageic planning
Current AI Use in Marketing and Financial Management
Steps in SP
Data types in SP
Data sources in SP
Datawarehouse vs Data Lake
Problems to be addresses
Our Solutions
A video on Animated description on how we do

AI/Natural Language Processing technologies to analyze and improve voice-based interactions with customers by analyzing conversations at large scale

As Vice President of Engineering at Zillow Group, Andrei leads the teams responsible for the company’s search and discovery engines, including search science, development, infrastructure, and operations. As part of this role, he heads Zillow Group’s conversational AI efforts, which consist of initiatives around natural language processing platforms, speech analytics, call center AI, and conversational interfaces, with the goal of improving Zillow’s business and customer services using natural language processing and speech understanding.

Before joining Zillow in 2019, Andrei led search science teams within eBay and Walmart. Prior to Walmart he worked at Google and Apple, serving as a core contributor to products like Google web search, Apple Maps, Apple’s AppStore, and iTunes search engines. He previously led engineering efforts for Recruit Holdings’ AI Lab. He founded and sat on the advisory board of Ozlo, a Conversational AI startup acquired by Facebook in 2017.

Andrei earned a Doctor of Philosophy in Computer Science from The University of Manchester, United Kingdom and Master of Science Degree from the Moscow Institute of Physics and Technology.

“Data-Driven Marketing: Framework, Role of Machine Learning and Practice.”
What are the benefits of data driven marketing? Which ML techniques are relevant to specific stages of relating with the customer? How do you convert data to actionable insights? What are the biggest challenges to achieving success? In a hyper-competitive economy, data-driven marketing is crucial to the success of a company. Come learn the strategies and ML techniques necessary to thrive in a customer-centric field.

Dr. Savitha Namuduri, Ph.D. has over 20 years of experience in Data and Analytics space, spanning over several industries such as Telecom, Utility, Retail, Healthcare and CPG. Her analytical skill sets range from developing visualizations, to customer segmentation to building predictive models and democratizing data science techniques to solve day-to-day business problems. Savitha's career has been focused on marketing analytics for the past 10 years running Analytics teams for CRM, media marketing and Customer Journey. In addition to visualization and data analysis, Savitha also focuses on providing data-driven recommendations for strategy, setting up experimental designs, conducting primary and secondary research, and developing learning plans to inform marketing and sales strategies. She brings a unique blend of business acumen and technical skill set to consult with clients on the best marketing practices and their measurement.

This talk will introduce how deep learning has been used in several use cases within the pharmaceutical space. These use cases include:

Identification of abnormal patterns for drug safety
Characterization of phenotypes in cell-based imaging
Classifying protein crystallization results

Peter Henstock is the Machine Learning & AI Lead at Pfizer. His work has focused at the intersection of AI, visualization, statistics and software engineering applied mostly to drug discovery but more recently to clinical trials. Peter holds a PhD in Artificial Intelligence from Purdue University along with 6 Master’s degrees. He was recognized as being among the top 12 leaders in AI and Pharma globally by the Deep Knowledge Analytics group. He also currently teaches graduate AI, Software Engineering, and Computer Vision courses at Harvard.