Slides: Migrate BI Dashboards to Run Directly on a Cloud Data Lake in Five Easy Steps

Too often I hear the question “Can you help me with our Data Strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component – the Data Strategy itself. A more useful request is this: “Can you help me apply data strategically?”Yes, at early maturity phases the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (must less perfect) Data Strategy on the first attempt is generally not productive –particularly given the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” Refocus on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. This approach can also contribute to three primary organizational data goals. In this webinar, you will learn how improving your organization’s data, the way your people use data, and the way your people use data to achieve your organizational strategy will help in ways never imagined. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs, as organizations identify prioritized areas where better assets, literacy, and support (Data Strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including: - A cohesive argument for why Data Strategy is necessary for effective Data Governance - An overview of prerequisites for effective strategic use of Data Strategy, as well as common pitfalls - A repeatable process for identifying and removing data constraints - The importance of balancing business operation and innovation

Data Strategy Best Practices

At Fifth Third Bank, about 450 people use data every day. They all start with Alation. But this wasn't always the case. In fact, getting hundreds of folks working in sync has been a monumental task. Just ask Greg Swygart, VP of enterprise data at Fifth Third Bank. Greg has led data consumption and interaction efforts since adopting Alation. Currently he’s scaling out data literacy for Fifth Third, replicating data capabilities to all roles across the company. Join Greg to learn how Fifth Third Bank moved from a command-and-control governance approach to non-invasive — and reaped the benefits. Greg will be followed by Bob Seiner, creator of Non-Invasive Data Governance, who will speak to data governance’s evolution, with an eye to what’s next. In this webinar, you'll learn: • About Fifth Third’s transition away from command-and-control governance • How Fifth Third leverages Alation as its data marketplace for curation & consumption • Why words matter when driving adoption • About the data catalog — and its role in human management

Data Management Meets Human Management - Why Words Matter

We’ll describe some use cases as examples of a broad range of modern use cases that need a platform. We will describe some popular valid technology stacks that enterprises use in accomplishing these modern use cases of customer churn, predictive analytics, fraud detection, and supply chain management. In many industries, to achieve top-line growth, it is imperative that companies get the most out of existing customer relationships. Customer churn use cases are about generating high levels of profitable customer satisfaction through the use of knowledge generated from corporate and external data to help drive a more positive customer experience (CX). Many organizations are turning to predictive analytics to increase their bottom line and efficiency and, therefore, competitive advantage. It can make the difference between business success or failure. Fraudulent activity detection is exponentially more effective when risk actions are taken immediately (i.e., stop the fraudulent transaction), instead of after the fact. Fast digestion of a wide network of risk exposures across the network is required in order to minimize adverse outcomes. Supply chain leaders are under constant pressure to reduce overall supply chain management (SCM) costs while maintaining a flexible and diverse supplier ecosystem. They will leverage IoT, sensors, cameras, and blockchain. Major investments in advanced analytics, warehouse relocation, and automation, both in distribution centers and stores, will be essential for survival.

Platforming the Major Analytic Use Cases for Modern Engineering

The Top 5 Factors to Consider When Choosing a Big Data Solution

Learn about the three advances in database technologies that eliminate the need for star schemas and the resulting maintenance nightmare. Relational databases in the 1980s were typically designed using the Codd-Date rules for data normalization. It was the most efficient way to store data used in operations. As BI and multi-dimensional analysis became popular, the relational databases began to have performance issues when multiple joins were requested. The development of the star schema was a clever way to get around performance issues and ensure that multi-dimensional queries could be resolved quickly. But this design came with its own set of problems. Unfortunately, the analytic process is never simple. Business users always think up unimaginable ways to query the data. And the data itself often changes in unpredictable ways. These result in the need for new dimensions, new and mostly redundant star schemas and their indexes, maintenance difficulties in handling slowly changing dimensions, and other problems causing the analytical environment to become overly complex, very difficult to maintain, long delays in new capabilities, resulting in an unsatisfactory environment for both the users and those maintaining it. There must be a better way! Watch this webinar to learn: - The three technological advances in data storage that eliminate star schemas - How these innovations benefit analytical environments - The steps you will need to take to reap the benefits of being star schema-free

The Death of the Star Schema

Data Quality is an elusive subject that can defy measurement and yet be critical enough to derail any project, strategic initiative, or even a company. The data layer of an organization is a critical component because it is so easy to ignore the quality of that data or to make overly optimistic assumptions about its efficacy. Having Data Quality as a focus is a business philosophy that aligns strategy, business culture, company information, and technology in order to manage data to the benefit of the enterprise. It is a competitive strategy.

Measuring Data Quality Return on Investment

Enterprises and organizations from every industry and scale are working to leverage data to achieve their strategic objectives — whether they are to be more profitable, effective, risk-tolerant, prepared, sustainable, and/or adaptable in an ever-changing world. Data has exploded in volume during the last decade as humans and machines alike produce data at an exponential pace. Also, exciting technologies have emerged around that data to improve our abilities and capabilities around what we can do with data. Behind this data revolution, there are forces at work, causing enterprises to shift the way they leverage data and accelerate the demand for leverageable data. Organizations (and the climates in which they operate) are becoming more and more complex. They are also becoming increasingly digital and, thus, dependent on how data informs, transforms, and automates their operations and decisions. With increased digitization comes an increased need for both scale and agility at scale. In this session, we have undertaken an ambitious goal of evaluating the current vendor landscape and assessing which platforms have made, or are in the process of making, the leap to this new generation of Data Management and integration capabilities.

The Shifting Landscape of Data Integration

Maturity frameworks have varying levels of Data Management maturity. Each level corresponds to not only increased data maturity but also increased organizational maturity and bottom-line ROI. There are recommended targets to achieve an effective information management program. The speaker’s maturity framework sequences the information management activities for your consideration. It is based on real client roadmaps. This webinar promises to offer a wealth of ideas for key quick wins to benefit the organization’s information management program. Attendees can self-assess their current information management capabilities as we go through Data Strategy, organization, architecture, and technology, yielding an overall view of the current level of information management maturity. This webinar provides a foundation for enhancing current data and analytic capabilities and updating the strategy and plans for the achievement of improved information management maturity, aligned with major initiatives.

What Is My Enterprise Data Maturity 2021

Peter Vennel PMP,SCEA,CBIP,CDMP

Aug 2017 damaga-peter-vennel

Digital Transformation is a top priority for many organizations, and a successful digital journey requires a strong data foundation. Creating this digital transformation requires a number of core data management capabilities such as MDM, With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.

Emerging Trends in Data Architecture – What’s the Next Big Thing

This is the age of analytics—information resulting from the systematic analysis of data. Insights gained from applying data and analytics to business allows large and small organizations across diverse industries—be it healthcare, retail, manufacturing, financial, or others—to identify new opportunities, improve core processes, enable continuous learning and differentiation, remain competitive, and thrive in an increasingly challenging business environment. The key to building a data-driven practice is a Data and Analytics Operating Model (D&AOM) which enables the organization to establish standards for data governance, controls for data flows (both within and outside the organization), and adoption of appropriate technological innovations. Success measures of a data initiative may include: • Creating a competitive advantage by fulfilling unmet needs, • Driving adoption and engagement of the digital experience platform (DXP), • Delivering industry standard data and metrics, and • Reducing the lift on service teams. This green paper lays out the framework for building and customizing an effective data and analytics operating model.

Building an Effective Data & Analytics Operating Model A Data Modernization G...

Mark Hewitt

Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how data architecture is a key component of an overall enterprise architecture for enhanced business value and success.

Slides: Enterprise Architecture vs. Data Architecture

Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a platform designed to address multi-faceted needs by offering multi-function Data Management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion. They need a worry-free experience with the architecture and its components.

Estimating the Total Costs of Your Cloud Analytics Platform

DAS Slides: Data Virtualization – Separating Myth from Reality

Syed Jahanzaib Bin Hassan - JBH Syed

Data Governance and Analytics

Today, data lakes are widely used and have become extremely affordable as data volumes have grown. However, they are only meant for storage and by themselves provide no direct value. With up to 80% of data stored in the data lake today, how do you unlock the value of the data lake? The value lies in the compute engine that runs on top of a data lake. Join us for this webinar where Ahana co-founder and Chief Product Officer Dipti Borkar will discuss how to unlock the value of your data lake with the emerging Open Data Lake analytics architecture. Dipti will cover: -Open Data Lake analytics - what it is and what use cases it supports -Why companies are moving to an open data lake analytics approach -Why the open source data lake query engine Presto is critical to this approach

Unlocking the Value of Your Data Lake

In order to succeed, organizations must realize what it means to utilize reference and MDM in support of business strategy. This presentation provides you with an understanding of the goals of reference and MDM, including the establishment and implementation of authoritative data sources, more effective means of delivering data to various business processes, as well as increasing the quality of information used in organizational analytical functions, e.g. BI. We also highlight the equal importance of incorporating data quality engineering into all efforts related to reference and master data management. Learning objectives include: What is Reference & MDM and why is it important? Reference & MDM Frameworks and building blocks Guiding principles & best practices Understanding foundational reference & MDM concepts based on the Data Management Body of Knowledge (DMBOK) Utilizing reference & MDM in support of business strategy

How to Create a Data Analytics Roadmap

CCG

Data-Ed Online: Unlock Business Value through Reference & MDM

Vanessa Pulgarín Auquilla

2. Getvisibility. Innovative data governance, control & oversight

Mais procurados (20)

Data Quality Best Practices

Data Strategy Best Practices

Data Management Meets Human Management - Why Words Matter

Platforming the Major Analytic Use Cases for Modern Engineering

The Top 5 Factors to Consider When Choosing a Big Data Solution

The Death of the Star Schema

Measuring Data Quality Return on Investment

The Shifting Landscape of Data Integration

What Is My Enterprise Data Maturity 2021

Aug 2017 damaga-peter-vennel

Emerging Trends in Data Architecture – What’s the Next Big Thing

Building an Effective Data & Analytics Operating Model A Data Modernization G...

Slides: Enterprise Architecture vs. Data Architecture

Estimating the Total Costs of Your Cloud Analytics Platform

DAS Slides: Data Virtualization – Separating Myth from Reality

Data Governance and Analytics

Unlocking the Value of Your Data Lake

How to Create a Data Analytics Roadmap

Data-Ed Online: Unlock Business Value through Reference & MDM

2. Getvisibility. Innovative data governance, control & oversight

Semelhante a Slides: Migrate BI Dashboards to Run Directly on a Cloud Data Lake in Five Easy Steps

Watch full webinar here: https://bit.ly/3AdAzkW Hybrid cloud has become the standard for businesses. A successful move will involve using an intelligent and scalable architecture and seeking the right help. At the same time, multi-cloud strategies are on the rise. More enterprise organizations than ever before are analyzing their current technology portfolio and defining a cloud strategy that encompasses multiple cloud platforms to suit specific app workloads, and move those workloads as they see fit. Learn the key challenges in multi-hybrid data management, and how you can accelerate your digital transformation journey in a multi-cloud environment with data virtualization.

Reinventing and Simplifying Data Management for a Successful Hybrid and Multi...

Watch full webinar here: https://bit.ly/3corOL4 More and more organizations are adopting multi-cloud strategies to provide greater flexibility, cost savings, and performance optimization. Even when organizations commit to a single cloud provider, they often have data and applications spread across different cloud regions to support different business units or geographies. The result of this is a highly distributed infrastructure that makes finding and accessing the data needed for reporting and analytics even more challenging. The Denodo Platform Multi-Location Architecture provides quick and easy managed access to data while still providing local control to the 'data owners' and complying with local privacy and data protection regulations (think GDPR and CCPA). In this on-demand webinar, you will learn about: - The challenges facing organizations as they adopt multi-cloud data strategies - How the Denodo Platform provides a managed data access layer across the organization - The different multi-location architectures that can maximize local control over data while still making it readily available - How organizations have benefited from using the Denodo Platform as a multi-cloud data access layer

Multi-Cloud Integration with Data Virtualization (ASEAN)

Modern data analytics platforms that fuel enterprise-wide data hubs are critical for decision making and information sharing. The problem? Integrating legacy data stores into these hubs is just plain hard, and there is no magic bullet. However, the best data hubs include ALL enterprise data. So how can you ensure that you are building the best modern data analytics platform possible? Join this webinar to learn more on: - Best practices for integrating legacy data sources, such as mainframe and IBM i, into modern data analytics platforms such as Cloudera, Databricks, and Snowflake - How Syncsort Connect customers are incorporating legacy data sources into enterprise data hubs to inform strategic use cases such as claims, banking, and shipping experiences

Making the Case for Legacy Data in Modern Data Analytics Platforms

Precisely

Watch this Fast Data Strategy session with speakers Clinton Cohagan, Chief Enterprise Data Architect, Lawrence Livermore National Lab & Nageswar Cherukupalli, Vice President & Group Manager, Infosys here: https://buff.ly/2k8f8M5 In its recent report “Predictions 2018: A year of reckoning”, Forrester predicts that 80% of firms affected by GDPR will not comply with the regulation by May 2018. Of those noncompliant firms, 50% will intentionally not comply. Compliance doesn’t have to be this difficult! What if you have an opportunity to facilitate compliance with a mature technology and significant cost reduction? Data virtualization is a mature, cost-effective technology that enables privacy by design to facilitate compliance. Attend this session to learn: • How data virtualization provides a compliance foundation with data catalog, auditing, and data security. • How you can enable single enterprise-wide data access layer with guardrails. • Why data virtualization is a must-have capability for compliance use cases. • How Denodo’s customers have facilitated compliance.

Govern and Protect Your End User Information

It is quite possible to use Agile techniques for creating and maintaining a data architecture. Doing so will dramatically reduce the risk of failed data warehouse projects. This webinar will give you a quick overview of the benefits and challenges of Agile Data Modeling, Evolutionary Database Design, Agile Modeling, Conformed Dimensions, Bus Matrix, Database Refactoring, and an Agile framework for Agile data projects

Agile Data Architecture

Cprime

Rushing to migrate data from legacy systems “as is” to cloud data warehouses means that you are bringing not only data, but also issues of the past, to the cloud. As a result, your business loses trust in the data they are using, and the gains you anticipated from the cloud fail to materialize. To maximize the benefits of your cloud data warehouse initiative, you need a data integration and migration plan. View this on-demand webinar to learn how to ensure migrations are prompt, costs stay within budget, and you don’t miss any data opportunities. During this webinar, you will learn: • How organizations are moving to the cloud and why having a migration plan helps ensure success • Best practices for your cloud data migration/integration strategy • How Precisely customer, Crowley Maritime, used data integration solutions to avoid common problems when integrating legacy data into its cloud data warehouse

Don’t Bring Old Problems to Your New Cloud Data Warehouse

Precisely

Data Mesh is a fairly new approach to help companies do more with data, faster. It requires both organizational and technical changes to enable autonomy and self-service, treat data as a product and encourage secure collaboration. In this session, we will discuss practical approaches you can implement today to help your company start benefiting from Data Mesh. We'll show you how to create autonomy by splitting responsibility between data producers and consumers, share datasets and make data discovery easy. We'll show a demo with producers building an ingestion pipeline that publishes datasets to consumer accounts (data mesh domains). SQL templates will be provided for members to follow along and build on their own. We'll present these use cases built with data mesh design patterns: 1. A multi-tenant data lake that allows data producers to share datasets with consumers outside of the organization (3rd parties). 2. A security data lake that allows different teams to publish curated logs to their local Elasticsearch clusters for analysis, and to a central data lake for retention, auditing and historical analysis. We'll also discuss managing data contracts/schemas between producers and consumers, to enable ownership and better data quality when sharing datasets. Meetup: https://www.meetup.com/boston-data-engineering/events/291383661/ Video: https://youtu.be/lIcmomYZ3mo

Boston Data Engineering: Designing and Implementing Data Mesh at Your Company...

Boston Data Engineering

Watch full webinar here: https://bit.ly/3rwWhyv The Data Mesh architectural design was first proposed in 2019 by Zhamak Dehghani, principal technology consultant at Thoughtworks, a technology company that is closely associated with the development of distributed agile methodology. A data mesh is a distributed, de-centralized data infrastructure in which multiple autonomous domains manage and expose their own data, called “data products,” to the rest of the organization. Organizations leverage data mesh architecture when they experience shortcomings in highly centralized architectures, such as the lack domain-specific expertise in data teams, the inflexibility of centralized data repositories in meeting the specific needs of different departments within large organizations, and the slow nature of centralized data infrastructures in provisioning data and responding to changes. In this session, Pablo Alvarez, Global Director of Product Management at Denodo, explains how data virtualization is your best bet for implementing an effective data mesh architecture. You will learn: - How data mesh architecture not only enables better performance and agility, but also self-service data access - The requirements for “data products” in the data mesh world, and how data virtualization supports them - How data virtualization enables domains in a data mesh to be truly autonomous - Why a data lake is not automatically a data mesh - How to implement a simple, functional data mesh architecture using data virtualization

Enabling a Data Mesh Architecture with Data Virtualization

Watch full webinar here: https://bit.ly/3mPLIlo A shift to the cloud is a common element of any current data strategy. However, a successful transition to the cloud is not easy and can take years. It comes with security challenges, changes in downstream and upstream applications, and new ways to operate and deploy software. An abstraction layer that decouples data access from storage and processing can be a key element to enable a smooth journey to the cloud. Attend this webinar to learn more about: - How to use Data Virtualization to gradually change data systems without impacting business operations - How Denodo integrates with the larger cloud ecosystems to enable security - How simple it is to create and manage a Denodo cloud deployment

A Successful Journey to the Cloud with Data Virtualization

In this webinar, we talk to industry analyst, author and practitioner David Linthicum who provides a state-of-the-technology explanation of big data integration. David also provides 5 critical and lesser known data integration requirements, how to understand today's requirements, and guidance for choosing the right approaches and technology to solve these problems. To learn more, visit: www.snaplogic.com/big-data

Webinar: The 5 Most Critical Things to Understand About Modern Data Integration

SnapLogic

Teams working on new business initiatives, whether for enhancing customer engagement, creating new value, or addressing compliance considerations, know that a successful strategy starts with the synchronization of operational and reporting data from across the organization into a centralized repository for use in advanced analytics and other projects. However, the range and complexity of data sources as well as the lack of specialized skills needed to extract data from critical legacy systems often causes inefficiencies and gaps in the data being used by the business. The first part of our webcast series on Foundation Strategies for Trust in Big Data provides insight into how Syncsort Connect with its design once, deploy anywhere approach supports a repeatable pattern for data integration by enabling enterprise architects and developers to ensure data from ALL enterprise data sources– from mainframe to cloud – is available in the downstream data lakes for use in these key business initiatives.

Foundational Strategies for Trust in Big Data Part 1: Getting Data to the Pla...

Precisely

Real Time Analytics

Mohsin Hakim

Learn the different ways Power BI lets you source and prep data for analysis. We examine four different scenarios and their impacts on scalability, security and performance. Scenarios include: directly querying operational systems, querying the data warehouse, dataflows and datasets. Senturus offers a full spectrum of services in business intelligence and training on Tableau, Power BI and Cognos. Our resource library has hundreds of free live and recorded webinars, blog posts, demos and unbiased product reviews available on our website at: http://www.senturus.com/senturus-resources/.

Power BI Modeling Use Cases: Desktop to Enterprise

Senturus

SoftServe BI/BigData Workshop in Utah

Serhiy (Serge) Haziyev

Things to Consider when Evaluating Options for Web Data Extraction

PromptCloud

Watch this webinar in full here: https://buff.ly/2MVTKqL Self-Service BI promises to remove the bottleneck that exists between IT and business users. The truth is, if data is handed over to a wide range of data consumers without proper guardrails in place, it can result in data anarchy. Attend this session to learn why data virtualization: • Is a must for implementing the right self-service BI • Makes self-service BI useful for every business user • Accelerates any self-service BI initiative

Self-Service Analytics with Guard Rails

Francisco González Jiménez

Six Reasons to Upgrade your Database

Luke Farrell

IBM 2016 - Six reasons to upgrade your database

How companies are managing growth, gaining insights and cutting costs in the ...

Virginia Fernandez

Six Reasons to Upgrade your Database

Luke Farrell

Semelhante a Slides: Migrate BI Dashboards to Run Directly on a Cloud Data Lake in Five Easy Steps (20)

Reinventing and Simplifying Data Management for a Successful Hybrid and Multi...

Multi-Cloud Integration with Data Virtualization (ASEAN)

Making the Case for Legacy Data in Modern Data Analytics Platforms

Govern and Protect Your End User Information

Agile Data Architecture

Don’t Bring Old Problems to Your New Cloud Data Warehouse

Boston Data Engineering: Designing and Implementing Data Mesh at Your Company...

Enabling a Data Mesh Architecture with Data Virtualization

A Successful Journey to the Cloud with Data Virtualization

Webinar: The 5 Most Critical Things to Understand About Modern Data Integration

Foundational Strategies for Trust in Big Data Part 1: Getting Data to the Pla...

Real Time Analytics

Power BI Modeling Use Cases: Desktop to Enterprise

SoftServe BI/BigData Workshop in Utah

Things to Consider when Evaluating Options for Web Data Extraction

Self-Service Analytics with Guard Rails

Six Reasons to Upgrade your Database

IBM 2016 - Six reasons to upgrade your database

How companies are managing growth, gaining insights and cutting costs in the ...

Six Reasons to Upgrade your Database

Mais de DATAVERSITY

Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a comprehensive platform designed to address multi-faceted needs by offering multi-function data management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion. In this research-based session, I’ll discuss what the components are in multiple modern enterprise analytics stacks (i.e., dedicated compute, storage, data integration, streaming, etc.) and focus on total cost of ownership. A complete machine learning infrastructure cost for the first modern use case at a midsize to large enterprise will be anywhere from $3 million to $22 million. Get this data point as you take the next steps on your journey into the highest spend and return item for most companies in the next several years.

Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...

Do you ever wonder how data-driven organizations fuel analytics, improve customer experience, and accelerate business productivity? They are successful by governing and mastering data effectively so they can get trusted data to those who need it faster. Efficient data discovery, mastering and democratization is critical for swiftly linking accurate data with business consumers. When business teams can quickly and easily locate, interpret, trust, and apply data assets to support sound business judgment, it takes less time to see value. Join data mastering and data governance experts from Informatica—plus a real-world organization empowering trusted data for analytics—for a lively panel discussion. You’ll hear more about how a single cloud-native approach can help global businesses in any economy create more value—faster, more reliably, and with more confidence—by making data management and governance easier to implement.

Data at the Speed of Business with Data Mastering and Governance

What is data literacy? Which organizations, and which workers in those organizations, need to be data-literate? There are seemingly hundreds of definitions of data literacy, along with almost as many opinions about how to achieve it. In a broader perspective, companies must consider whether data literacy is an isolated goal or one component of a broader learning strategy to address skill deficits. How does data literacy compare to other types of skills or “literacy” such as business acumen? This session will position data literacy in the context of other worker skills as a framework for understanding how and where it fits and how to advocate for its importance.

Exploring Levels of Data Literacy

Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace – from digital transformation, to marketing, to customer centricity, to population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.

Building a Data Strategy – Practical Steps for Aligning with Business Goals

Uncover how your business can save money and find new revenue streams. Driving profitability is a top priority for companies globally, especially in uncertain economic times. It's imperative that companies reimagine growth strategies and improve process efficiencies to help cut costs and drive revenue – but how? By leveraging data-driven strategies layered with artificial intelligence, companies can achieve untapped potential and help their businesses save money and drive profitability. In this webinar, you'll learn: - How your company can leverage data and AI to reduce spending and costs - Ways you can monetize data and AI and uncover new growth strategies - How different companies have implemented these strategies to achieve cost optimization benefits

Make Data Work for You

Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data. Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.

Data Catalogs Are the Answer – What is the Question?

Data Catalogs Are the Answer – What Is the Question?

Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords: “Big Data,” “NoSQL,” “Data Scientist,” and so on. Few realize that all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, data modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business. Since quality engineering/architecture work products do not happen accidentally, the more your organization depends on automation, the more important the data models driving the engineering and architecture activities of your organization. This webinar illustrates data modeling as a key activity upon which so much technology and business investment depends. Specific learning objectives include: - Understanding what types of challenges require data modeling to be part of the solution - How automation requires standardization on derivable via data modeling techniques - Why only a working partnership between data and the business can produce useful outcomes

Data Modeling Fundamentals

Analytics play a critical role in supporting strategic business initiatives. Despite the obvious value to analytic professionals of providing the analytics for these initiatives, many executives question the economic return of analytics as well as data lakes, machine learning, master data management, and the like. Technology professionals need to calculate and present business value in terms business executives can understand. Unfortunately, most IT professionals lack the knowledge required to develop comprehensive cost-benefit analyses and return on investment (ROI) measurements. This session provides a framework to help technology professionals research, measure, and present the economic value of a proposed or existing analytics initiative, no matter the form that the business benefit arises. The session will provide practical advice about how to calculate ROI and the formulas, and how to collect the necessary information.

Showing ROI for Your Analytic Project

Data Mesh is a trending approach to building a decentralized data architecture by leveraging a domain-oriented, self-service design. However, the pure definition of Data Mesh lacks a center of excellence or central data team and doesn’t address the need for a common approach for sharing data products across teams. The semantic layer is emerging as a key component to supporting a Hub and Spoke style of organizing data teams by introducing data model sharing, collaboration, and distributed ownership controls. This session will explain how data teams can define common models and definitions with a semantic layer to decentralize analytics product creation using a Hub and Spoke architecture. Attend this session to learn about: - The role of a Data Mesh in the modern cloud architecture. - How a semantic layer can serve as the binding agent to support decentralization. - How to drive self service with consistency and control.

How a Semantic Layer Makes Data Mesh Work at Scale

Enterprise data literacy. A worthy objective? Certainly! A realistic goal? That remains to be seen. As companies consider investing in data literacy education, questions arise about its value and purpose. While the destination – having a data-fluent workforce – is attractive, we wonder how (and if) we can get there. Kicking off this webinar series, we begin with a panel discussion to explore the landscape of literacy, including expert positions and results from focus groups: - why it matters, - what it means, - what gets in the way, - who needs it (and how much they need), - what companies believe it will accomplish. In this engaging discussion about literacy, we will set the stage for future webinars to answer specific questions and feature successful literacy efforts.

Is Enterprise Data Literacy Possible?

Change is hard, especially in response to negative stimuli or what is perceived as negative stimuli. So organizations need to reframe how they think about data privacy, security and governance, treating them as value centers to 1) ensure enterprise data can flow where it needs to, 2) prevent – not just react – to internal and external threats, and 3) comply with data privacy and security regulations. Working together, these roles can accelerate faster access to approved, relevant and higher quality data – and that means more successful use cases, faster speed to insights, and better business outcomes. However, both new information and tools are required to make the shift from defense to offense, reducing data drama while increasing its value. Join us for this panel discussion with experts in these fields as they discuss: - Recent research about where data privacy, security and governance stand - The most valuable enterprise data use cases - The common obstacles to data value creation - New approaches to data privacy, security and governance - Their advice on how to shift from a reactive to resilient mindset/culture/organization You’ll be educated, entertained and inspired by this panel and their expertise in using the data trifecta to innovate more often, operate more efficiently, and differentiate more strategically.

The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...

Emerging Trends in Data Architecture – What’s the Next Big Thing?

As DATAVERSITY’s RWDG series hurdles into our 12th year, this webinar takes a quick look behind us, evaluates the present, and predicts the future of Data Governance. Based on webinar numbers, hot Data Governance topics have evolved over the years from policies and best practices, roles and tools, data catalogs and frameworks, to supporting data mesh and fabric, artificial intelligence, virtualization, literacy, and metadata governance. Join Bob Seiner as he reflects on the past and what has and has not worked, while sharing examples of enterprise successes and struggles. In this webinar, Bob will challenge the audience to stay a step ahead by learning from the past and blazing a new trail into the future of Data Governance. In this webinar, Bob will focus on: - Data Governance’s past, present, and future - How trials and tribulations evolve to success - Leveraging lessons learned to improve productivity - The great Data Governance tool explosion - The future of Data Governance

Data Governance Trends - A Look Backwards and Forwards

Would you share your bank account information on social media? How about shouting your social security number on the New York City subway? We didn’t think so either – that’s why data governance is consistently top of mind. In this webinar, we’ll discuss the common Cloud data governance best practices – and how to apply them today. Join us to uncover Google Cloud’s investment in data governance and learn practical and doable methods around key management and confidential computing. Hear real customer experiences and leave with insights that you can share with your team. Let’s get solving. Topics that you will hear addressed in this webinar: - Understanding the basics of Cloud Incident Response (IR) and anticipated data governance trends - Best practices for key management and apply data governance to your day-to-day - The next wave of Confidential Computing and how to get started, including a demo

Data Governance Trends and Best Practices To Implement Today

It is a fascinating, explosive time for enterprise analytics. It is from the position of analytics leadership that the enterprise mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics. The coming years will be full of big changes in enterprise analytics and data architecture. William will kick off the fifth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.

2023 Trends in Enterprise Analytics

Too often I hear the question “Can you help me with our data strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component: the data strategy itself. A more useful request is: “Can you help me apply data strategically?” Yes, at early maturity phases the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (must less perfect) data strategy on the first attempt is generally not productive –particularly given the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” This program refocuses efforts on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. It also contributes to three primary organizational data goals. Learn how to improve the following: - Your organization’s data - The way your people use data - The way your people use data to achieve your organizational strategy This will help in ways never imagined. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs as organizations identify prioritized areas where better assets, literacy, and support (data strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including: - A cohesive argument for why data strategy is necessary for effective data governance - An overview of prerequisites for effective strategic use of data strategy, as well as common pitfalls - A repeatable process for identifying and removing data constraints - The importance of balancing business operation and innovation

Data Strategy Best Practices

The question is asked all the time: “What part of the organization should own your Data Governance program?” The typical answers are “the business” and “IT (information technology).” Another answer to that question is “Yes.” The program must be owned and reside somewhere in the organization. You may ask yourself if there is a correct answer to the question. Join this new RWDG webinar with Bob Seiner where Bob will answer the question that is the title of this webinar. Determining ownership of Data Governance is a vital first step. Figuring out the appropriate part of the organization to manage the program is an important second step. This webinar will help you address these questions and more. In this session Bob will share: - What is meant by “the business” when it comes to owning Data Governance - Why some people say that Data Governance in IT is destined to fail - Examples of IT positioned Data Governance success - Considerations for answering the question in your organization - The final answer to the question of who should own Data Governance

Who Should Own Data Governance – IT or Business?

It is clear that Data Management best practices exist and so does a useful process for improving existing Data Management practices. The question arises: Since we understand the goal, how does one design a process for Data Management goal achievement? This program describes what must be done at the programmatic level to achieve better data use and a way to implement this as part of your data program. The approach combines DMBoK content and CMMI/DMM processes – permitting organizations with the opportunity to benefit from the best of both. It also permits organizations to understand: - Their current Data Management practices - Strengths that should be leveraged - Remediation opportunities

Data Management Best Practices

MLOps is a practice for collaboration between Data Science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of “machine learning” and “operations,” MLOps applies DevOps principles to ML delivery, enabling the delivery of ML-based innovation at scale to result in: Faster time to market of ML-based solutions More rapid rate of experimentation, driving innovation Assurance of quality, trustworthiness, and ethical AI MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.

MLOps – Applying DevOps to Competitive Advantage

This project aims to predict whether a loan application will be approved or denied based on various factors such as applicant's income, credit score, loan amount, etc. Using a dataset containing historical loan application data, we employed machine learning algorithms to build a predictive model. The model was trained on features such as applicant's income, credit history, loan amount, loan term, and others. After training the model, we evaluated its performance using metrics like accuracy, precision, recall, and F1 score. The insights from this project can help financial institutions streamline their loan approval process and make informed decisions. Visit for more information: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

Mais de DATAVERSITY (20)

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Data at the Speed of Business with Data Mastering and Governance

Exploring Levels of Data Literacy

Building a Data Strategy – Practical Steps for Aligning with Business Goals

Make Data Work for You

Data Catalogs Are the Answer – What is the Question?

Data Catalogs Are the Answer – What Is the Question?

Data Modeling Fundamentals

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How a Semantic Layer Makes Data Mesh Work at Scale

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Data Governance Trends - A Look Backwards and Forwards

Data Governance Trends and Best Practices To Implement Today

2023 Trends in Enterprise Analytics

Data Strategy Best Practices

Who Should Own Data Governance – IT or Business?

Data Management Best Practices

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