Enterprise Data World Webinar: Make BIG DATA Work for You
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TARGIT CTO Dr. Morton Middelfart has professionally devoted himself to one thing: turning DECISIONS into ACTION faster, more efficiently, and more intelligently than ever. He continues to develop patented features that zero in on solving the challenges that companies face every day. One of those challenges is harnessing the massive amounts of external data relevant to your company. Big data is … well … big. And companies that don’t learn from it and adapt will be quickly left in the dust. Join Morton for his session “Make BIG DATA work for you” and discover how to get the most out of your big data through Business Intelligence. What you’ll learn: - Why external data is increasingly important to today’s businesses - Exciting new technologies developed to help you make sense of the growing amount of Big Data - How to measure and monitor consumers’ sentiments about your company - What your external data says about your internal data - What you need to take action to constantly stay ahead of the curve
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Joe Caserta, President at Caserta Concepts presented at the 3rd Annual Enterprise DATAVERSITY conference. The emphasis of this year's agenda is on the key strategies and architecture necessary to create a successful, modern data analytics organization.
Joe Caserta presented What Data Do You Have and Where is it?
For more information on the services offered by Caserta Concepts, visit out website at http://casertaconcepts.com/.
02 a holistic approach to big data
The document outlines an agenda for a presentation on big data. It discusses key topics like the state of big data adoption, a holistic approach to big data, five high value use cases, technical components, and the future of big data and cloud. The presentation aims to provide an overview of big data and how organizations can take a comprehensive approach to leveraging their data assets.
The Bigger They Are The Harder They Fall
This document discusses big data and the importance of data quality for big data initiatives. It defines big data as large, diverse digital data sets that require new techniques to enable capture, storage, analysis and visualization. The key challenges of big data include integrating diverse structured and unstructured data sources and ensuring high quality data. The document emphasizes that poor data quality can undermine big data analytics efforts and lead to wrong insights. It promotes establishing a data quality framework including profiling, standardization, matching and enrichment to enable valid big data analytics.
How Can Analytics Improve Business?
TechWise with Eric Kavanagh, Dr. Robin Bloor and Dr. Kirk Borne
Live Webcast on July 23, 2014
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=59d50a520542ee7ed00a0c38e8319b54
Analytical applications are everywhere these days, and for good reason. Organizations large and small are using analytics to better understand any aspect of their business: customers, processes, behaviors, even competitors. There are several critical success factors for using analytics effectively: 1) know which kind of apps make sense for your company; 2) figure out which data sets you can use, both internal and external; 3) determine optimal roles and responsibilities for your team; 4) identify where you need help, either by hiring new employees or using consultants 5) manage your program effectively over time.
Register for this episode of TechWise to learn from two of the most experienced analysts in the business: Dr. Robin Bloor, Chief Analyst of The Bloor Group, and Dr. Kirk Borne, Data Scientist, George Mason University. Each will provide their perspective on how companies can address each of the key success factors in building, refining and using analytics to improve their business. There will then be an extensive Q&A session in which attendees can ask detailed questions of our experts and get answers in real time. Registrants will also receive a consolidated deck of slides, not just from the main presenters, but also from a variety of software vendors who provide targeted solutions.
Visit InsideAnlaysis.com for more information.
Big data and Marketing by Edward Chenard
Big data and marketing is becoming an important tool for companies. The document discusses how big data can be used for personalization, listening to customers, and responding to better serve their needs. It outlines the key steps in the process from data collection and analysis to insights and actions. Various big data tools and techniques are mentioned to understand customer behavior and trends in order to tailor marketing and customer experiences. The challenges of translating data into insights and actions are also addressed.
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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.
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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.
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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.
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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.
Make Data Work for You
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.
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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.
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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.
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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.
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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.
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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.
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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:
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- 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.
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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.
Data Governance Trends - A Look Backwards and Forwards
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
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- The great Data Governance tool explosion
- The future of Data Governance
Data Governance Trends and Best Practices To Implement Today
1) The document discusses best practices for data protection on Google Cloud, including setting data policies, governing access, classifying sensitive data, controlling access, encryption, secure collaboration, and incident response.
2) It provides examples of how to limit access to data and sensitive information, gain visibility into where sensitive data resides, encrypt data with customer-controlled keys, harden workloads, run workloads confidentially, collaborate securely with untrusted parties, and address cloud security incidents.
3) The key recommendations are to protect data at rest and in use through classification, access controls, encryption, confidential computing; securely share data through techniques like secure multi-party computation; and have an incident response plan to quickly address threats.
2023 Trends in Enterprise Analytics
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.
Data Strategy Best Practices
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
Who Should Own Data Governance – IT or Business?
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
Data Management Best Practices
This document summarizes a research study that assessed the data management practices of 175 organizations between 2000-2006. The study had both descriptive and self-improvement goals, such as understanding the range of practices and determining areas for improvement. Researchers used a structured interview process to evaluate organizations across six data management processes based on a 5-level maturity model. The results provided insights into an organization's practices and a roadmap for enhancing data management.
MLOps – Applying DevOps to Competitive Advantage
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.
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Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Webinar: Designing a schema for a Data Warehouse
Are you new to data warehouses (DWH)? Do you need to check whether your data warehouse follows the best practices for a good design? In both cases, this webinar is for you.
A data warehouse is a central relational database that contains all measurements about a business or an organisation. This data comes from a variety of heterogeneous data sources, which includes databases of any type that back the applications used by the company, data files exported by some applications, or APIs provided by internal or external services.
But designing a data warehouse correctly is a hard task, which requires gathering information about the business processes that need to be analysed in the first place. These processes must be translated into so-called star schemas, which means, denormalised databases where each table represents a dimension or facts.
We will discuss these topics:
- How to gather information about a business;
- Understanding dictionaries and how to identify business entities;
- Dimensions and facts;
- Setting a table granularity;
- Types of facts;
- Types of dimensions;
- Snowflakes and how to avoid them;
- Expanding existing dimensions and facts.
Digital Marketing Trends in 2024 | Guide for Staying Ahead
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Generating privacy-protected synthetic data using Secludy and Milvus
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Enterprise Data World Webinar: Make BIG DATA Work for You
- 1. Make BIG DATA Work for You Presented by Dr. Morten Middelfart, TARGIT CTO targit.com/research morton@targit.com @dr_morton on Twitter
- 2. Flight
- 3. or Fight ?
- 6. Supporting the decision loop with intelligence Decision Based on Facts Use all available information Action Changes Communication Reaction/Action Orientation Analytics Simulation Observation Dashboards Reports Agents
- 7. The TARGIT Journey # Data Sources x Data Size Time
- 8. The TARGIT Journey Strategic Operational # Data Sources x Data Size Time
- 9. The TARGIT Journey Strategic Getting Started Using Accelerators -> Easy Analytics & Reports Operational # Data Sources x Data Size Time
- 10. The TARGIT Journey Strategic Integrated Action Loop Dashboards, Analytics, Reports & Agents -> Self-Service BI & Ad-Hoc Analytics Getting Started Using Accelerators -> Easy Analytics & Reports Operational # Data Sources x Data Size Time
- 11. The TARGIT Journey Strategic Data-Driven Culture Dynamic Integration/Rejection of Internal & External Data -> Competing on Analytics Integrated Action Loop Dashboards, Analytics, Reports & Agents -> Self-Service BI & Ad-Hoc Analytics Getting Started Using Accelerators -> Easy Analytics & Reports Operational # Data Sources x Data Size Time
- 12. The TARGIT Journey Strategic Data-Driven Culture Dynamic Integration/Rejection of Internal & External Data -> Competing on Analytics Integrated Action Loop Dashboards, Analytics, Reports & Agents -> Self-Service BI & Ad-Hoc Analytics Getting Started Using Accelerators -> Easy Analytics & Reports Operational # Data Sources x Data Size Time
- 13. Why?
- 14. Technology Trends – Usual Suspects Faster Smaller Cheaper -> More Capacity -> More Bandwidth
- 15. Human Behavior – the Unpredictable Social Data Open Data -> Valuable Insights -> High Volume of Data -> Real Time, Streaming -> Social vs. Search
- 16. Cycles – the Weird Stuff Browsers: three, one, five Servers: mainframe, pc, client server, cloud Files: programming db (flat files), relational db, multi-dimensional db, programming db in-memory, programming db (flat files) -> specialized servers physically optimized (e.g. SSD, ASIC)
- 17. #FactBeatFear when data speaks
- 19. #FactBeatFear when data speaks 1. Firms will realize that “big data” means all of their data. 2. The algorithm wars will begin. 3. Real-time architectures will swing to prominence. 4. Naysayers will fall silent.
- 20. There are 1.8 zettabytes of digital data in the world (2011), projected to grow to 7.9 zettabytes by 2015. zettabyte = 1021 or 1,000,000,000,000,000,000,000 bytes ~ 45% annual growth The Growth of Unstructured Data: What To Do with All Those Zettabytes? Dataversity, 2013
- 21. Big Data Data that exists in such large amounts or in such unstructured form that it is difficult to handle in the traditional data warehouse or any other type of database. Right?
- 23. From Control to No Control
- 24. What it means for organizations: Loss of Control Data amounts overwhelming Data Scientist rather than an Accountant -> Sampling rather than complete datasets!
- 25. What it means for organizations: Volume and Speed + Smaller and Faster Devices = more Analytics per Interaction + on the Device External Data exceed “Owned Data” -> organizationsmust compete on a larger share of external data, thus data they do not control
- 26. Inverted Data Warehouse (IDW) o o o Inspiration from CERN’s LHC “Shotgun Approach”; equal to formulating hypotheses; data scientist No single point of failure (parallel Query Nodes have also been tested)
- 27. Inverted Data Warehouse (IDW) Possible to Query 32.5B “rows” for “the Little Guy” Robust Operationally & Strategically Useful
- 28. What it means for TARGIT customers: Would it be interesting to know what a customer is looking for before entering your business?
- 29. What it means for TARGIT customers: Would it be interesting to know what customers say to friends after doing business with you?
- 30. Why?
- 32. The Future
- 37. The TARGIT Journey Strategic Data-Driven Culture Dynamic Integration/Rejection of Internal & External Data -> Competing on Analytics Integrated Action Loop Dashboards, Analytics, Reports & Agents -> Self-Service BI & Ad-Hoc Analytics Getting Started Using Accelerators -> Easy Analytics & Reports Operational # Data Sources x Data Size Time
- 38. The TARGIT Journey Strategic Data-Driven Culture Dynamic Integration/Rejection of Internal & External Data -> Competing on Analytics Integrated Action Loop Dashboards, Analytics, Reports & Agents -> Self-Service BI & Ad-Hoc Analytics Getting Started Using Accelerators -> Easy Analytics & Reports Operational # Data Sources x Data Size Time
- 39. Fight !