How to be successful in your own data transformation

Data transformation at scale requires a new way of thinking. This talk explores the latest trends that would lead to being successful in your data transformation journey. Towards the end it - there is bonus content on how to approach this on the cloud successfully.

Unlock business value through Data Fabric

In today’s rapidly changing digital marketplace, companies formed within the last two decades hold an inherent advantage over companies with legacy IT systems. This is because these digital natives are built on a digital core that relies on distributed technologies, which give them the ability to capture, track and analyze customer interactions in real-time. As a result, these companies get smarter with each transaction, increasing their efficiency and lowering costs so they can respond even faster to emerging trends and threats as they go to market. In this way, data fabric plays a pivotal role in helping organizations achieve resiliency and sustainable growth. Data fabric represents a paradigm shift in how companies leverage their data. By accessing data through one common platform, the data fabric solution provides a single view of data that was previously housed in separate data lakes and warehouses. With the creation of a semantic layer, companies can apply Artificial Intelligence (AI) and other analysis tools to understand new trends and revamp go-to-market strategies in days instead of weeks. EY’s data fabric unlocks value for organizations. Companies engaged in an IT transformation can often reduce at one-seventh the cost per transaction, enabling the implementation of a plan to progressively decommission the legacy systems. The data fabric solution also helps companies without a digital core to launch new ventures that turn them into digital natives at the outset, setting up operations that are significantly more efficient.

Data Insights using AI and ML for Business Benefit

Businesses are flooded with a large volume of data that is growing with each passing second. This data could be structured or unstructured; it could have different formats such as numeric, text, image, speech, sensor data etc.; and it could be live streamed or gathered otherwise. The important question is how to best utilize the data for the benefit of business and its customers. This talk would focus on utilizing analytics, AI/ML, and cloud technologies for exploring hidden insights in large volumes of data. These insights could be utilized for real-time, or as required, data-driven decisions by deploying end-to-end business solutions. This talk would cover deployment architecture of AI/ML systems and 360-degree view of industry across market segments.

Next Generation Data Lake

"A data lake is a very fundamental solution component in the digital transformation roadmap for most companies. But, ability to get data ready and available for generating insights in a governed manner is still one of the most complex, costly and time-consuming process. While data lakes have been in practice for many years now, newer tools and technologies are evolving and a new set of capabilities are added to data lakes to make them more cost effective and improve their adoption. This presentation is to cover some of the interesting trends that we see in the industry- * Self-service based Data Ingestion/Processing & Delivery, Analytics Workbench for business teams that are fully configurable * Automated Data Classification & PII Discovery * Low/No Code approach for data modernization & migration programs * Reduce time to market with virtual Lakehouse technologies * Next generation Data Observability solution * Platform Observability to monitor data platforms in centralized manner & respond."

Building Trust in the data & All Ops

The key focus for any data-driven business is to ensure that the underlying data can be trusted. Additionally, with an increasing number of ML and AI-driven applications Ops has become a critical component in stabilising pipelines. We at MathCo. believe in providing the most trusted data to our end customers and our pipeline building philosophy revolves around Ops first approach.

Building a resilient, scalable modern data platform

Evolution of modern data architecture and application.This session will cover in detail options available on different platforms. We will further dig into data buzzwords and how these are shaping up.

Panel Discussion: Powering business growth with an intelligent data strategy

With data being the key part of businesses, having an intelligent data and analytics strategy will help businesses stand out from their competitors. An intelligent data & analytics strategy enables businesses to break down data silos and manage data with the utmost security and governance controls. This discussion will revolve around how businesses can create an effective data strategy and the associated challenges.

BI Modernization

Business Intelligence has evolved through a long way, leveraging analytics to derive insights for better decision making. A lot of companies are burdened by legacy BI platforms and are not able to deliver the right data at the right time to their users to run their business. Modernizing your BI solution enables you to position your data as a competitive differentiator and value generator. This session provides you various BI solutions that enterprises can use to build modern, future-proof analytics solutions-

Training Data for effective AI deployment

As AI products and services are increasingly being deployed into the real world, ML Data OPs have had to rapidly iterate to meet the challenges of handling data for model training and continuous testing. At iMerit, we have handled these challenges by working with a broad spectrum of AI companies in domains ranging from self-driving cars to medical imaging and conversational AI. We would like to share some practical insights around handling training data for complex AI systems operating at scale.

Data Engineering and Analytics in Business

Intelligence and Unified Data Governance in the multi-cloud age

In today’s multi-cloud world, it is imperative for organizations to adapt to an increasingly data-driven world and adopt analytic agility. But given the varied sources of information that organizations handle and complex data handling mechanisms, including data movement, data discovery, cleansing and preparing trusted data for Analytics, and at the same managing data security, this becomes a challenge. This challenge is magnified two-fold when you are unsure of where your data is coming from and what it means. Kirthi shares insights and key learnings and best practices around intelligent management of metadata, security and governance in a diverse and largely distributed data environment. As an additional bonus she would also cover Google Cloud’s point of view and solutions to address this problem area.

Analytics 3.0 - The world of data enriched offerings

Back in 2013 Harvard Business Review published an article about Analytics 3.0. The article was authored by Thomas Davenport and in this article he talked about how the world of Data & Analytics has evolved from the era of Business Intelligence to the era of big data and then finally to the era of data-enriched offerings. When you think about this evolution what is clear to us is to get into the world of Analytics 3.0 we needed foundational blocks to make things happen. Those foundational blocks are Business Intelligence (BI), Artificial Intelligence (AI), data platforms and finally the cloud. In this tech talk we explore this evolution and what organizations and practitioners need to do to be prepared to ride the wave over the next decade.