WEBINAR: How Enel Group built a data mesh architecture with Dremio and Agile Lab - 9 June 2022

 

How Enel Group built a data mesh architecture with Dremio and Agile Lab       Register Now             Thursday, June 09, 2022   11 AM CET         Speakers         Jeremiah Morrow Partner Solution Marketing Director Dremio Nicolò Bidotti Big Data Architect Agile Lab         Achille Barbieri Senior Project Manager ENEL         Data teams in large enterprise organizations are facing greater demand for data to satisfy a wide range of analytic use cases, yet they are challenged with providing access to all of their data across business units, regions, and cloud environments. In this webinar, learn how Enel Group worked with Agile Lab to implement Dremio as a data mesh solution for providing broad access to a unified view of their data, and how they use that architecture to enable a multitude of use cases. In this session, you will learn: How the silos development led to challenges with data growth, data quality, data sharing, and data governance. An example of datamesh paradigm adoption. How Agile Lab and Enel Group used Dremio to connect their disparate organizations across geographies and business units Leveraging Dremio for data governance and multi-cloud with Arrow Flight.

WEBINAR: Dremio & Tableau Build Best-in-Class Enterprise analytics - 21 April 2022

 

Dremio & Tableau Build Best-in-Class Enterprise analytics       REGISTER             Thursday, April 21, 2022   10 AM PT | 1 PM ET         Speakers         Anthony Roach Director of Product Management Tableau Software Jeremiah Morrow Partner Solution Marketing Director Dremio         Hi there, Data teams today are being asked to do the impossible: deliver enterprise-grade analytic insights to more data consumers inside and outside of their organizations faster than ever, even as data grows exponentially in volume, variety, and velocity, and as data pipelines become more complex and difficult to manage and maintain. In this webinar, learn how Tableau and Dremio work together to simplify data management and data governance and broaden access to timely insights based on an organization-wide view of your data. In this webinar we’ll share how the tight integration between Tableau and Dremio can accelerate your journey to becoming a data-driven enterprise.

WEBINAR: Industrialized ML for Governed, Responsible and Explainable AI - 20 October 2020

 

Sponsored News from Data Science Central   October 20         10am PDT Hi, Despite the $42 billion in funding for ML applications and platforms, scaling and operating ML applications remains challenging.  Join the October 20 webinar to learn how Accenture’s industry-based, reusable production ML workflows fit seamlessly with Databricks’ Unified Data Analytics Platform to enable data teams to operationalize machine learning at scale.  Using this model, hear how Navy Federal Credit Union leveraged industrialized ML, scaling its capabilities to deliver next generation member service. You’ll also leave understanding how to bake model quality, compliance, responsible AI and explainable AI into all your production models. SAVE MY SEAT

The Rise of DataOps (from the ashes of Data Governance) by @ryano144 via @TDataScience

This makes a comparison between software engineering and data analysis, which shows that that source control management is the fundamental transition that allows the practice to go from hobby to profession. Source control management provides reproducibility, which is the core fundamental requirement of any engineering discipline. 

Definitely worth a read and an in-depth think about how you can implement the various techniques mentioned in this article.

I Have a Data Warehouse, Do I Need a Data Lake Too? by Troy Hiltbrand via @TDWI Transforming

When building your data and analytics program, you must decide whether you need a data warehouse, a data lake, or both. Understanding the difference is the first step.

Important to understand the difference and this is very useful to help you understand it.

WEBINAR: Democratizing Analytics from the Ground Up 28 April 2020

Data Science Central Webinar Series Event

Democratizing Analytics from the Ground Up Join us for the latest DSC Webinar on April 28th, 2020 How can you transition your business users from a manual, slow, and prone to error data transformation process in Excel into a scalable and governed solution leveraging a centralized data lake and change the way your employees access and use data? In this latest Data Science Central webinar you will learn how to: Empower self-service data analysis for all of your employees to use Data Prep and Data Studio Transition from a process that heavily relies on siloed Excel work to a Bring Your Own Data (BYOD) approach leveraging a centralized data lake Improve data governance and inspire business and IT collaboration with a single consistent approach to data preparation Accelerate analytics project delivery from months down to weeks Featured Speakers: Christopher Dean, Head of Data, Business Intelligence & Analytics -- Travis Perkins Bertrand Cariou, Sr. Director Product -- Trifacta Hosted by: Rafael Knuth, Contributing Editor -- Data Science Central   Title:  Democratizing Analytics from the Ground Up Date:  Tuesday, April 28th, 2020 Time:  9 AM - 10 AM PDT   Space is limited so please register early: Reserve your Webinar seat now

Why is a data governance business case hard to get approved? by Nicola Askham via @infomgmt

It can be a real struggle to get your data governance initiative approved in the first place. So I wanted to have a look at the reasons why this might be the case so that you can both plan for and mitigate them.

I agree - it is actually very important BUT it is almost like a last resort if, and only if, there is time or there is enough of a benefit that can be clearly shown.

3 tips on how to stop misusing or under-utilising corporate data by Alex Toews via @Infomgmt

Few organizations have assessed how their data can be put to work in the most productive way. This leaves them vulnerable to inefficiencies and can prevent important information from making its way 'to the top.'

A data model is a great place to start as you can begin to understand how the data relates to each other. The data dictionary is also useful as you can see which fields are repeated which is crucial if you want to understand how you can join data together from different sources. Just pay attention to formats and if any conversion needs to be done.

As FTC cracks down, data ethics is now a strategic business weapon by Daniel Wu via @TechCrunch

Five billion dollars may not be much for a behemoth like Facebook, but it’s still the largest amount the Federal Trade Commission has ever levied for violating data privacy. With the FTC cracking down, many companies are taking a new look at their privacy protections.

This is a well thought out article that makes some very good points. I certainly think very hard about sharing any of my own personal data and don't trust Facebook as far as I could throw them (nowhere).

How to succeed in a career in data governance by Kayla Matthews via @infomgmt

If you want to transition to a data governance career, it's possible to do so and embark on a path that leads to lasting satisfaction and success. Here are some practical steps you should take.

Great list of pointers from Kayla that are a good starting point. I would add that looking at any job adverts, job specs or any other relevant information would be helpful and should be able to give you a good starting point.

4 best practices for improving governance strategies by Larry Alton via @infomgmt

A failure to articulate the correct approach to IT governance could result in costly mistakes that prevent the organization from being successful.

Larry is absolutely right - I would suggest you use these 4 points as a way of checking your own strategies to make sure that you are following best practice or if you need to go and make a change. It might seem like a waste of time to go back and double check but the cost of not doing it is going to compound over time.

Data quality issues - Who is responsible for resolving them? by Nicola Askham via @infomgmt

Your governance team will develop knowledge and expertise about the data your organization creates and uses, but they are not responsible for any cleansing that may be required.

Some great advice from Nicola which could form the centre of your own organisation's data quality and data stewardship strategy.

Human or Machine? Two Paths for Deploying Analytics by Bill Franks via @Datafloq

While organizations pursue analytics that informs both human- and machine-based decisions, the differences in the requirements for each must also be understood.

Bill definitely has a point - both sets of audiences have different needs and requirements. Yes, I agree there are commonalities but there are also some differences that he explores in this article.

6 best practices for using data to set yearly targets by Kayla Matthews via @infomgmt

Raw, unfiltered data can be a goldmine for businesses looking to expand their knowledge of the average consumer. However, the data has to be legible first, and this practice takes work.

I agree with most of the points in this article. I would like to point out that making sure that the data you use is as accurate as possible is a complete MUST. You should only make business decisions on data that is accurate and can be relied upon.

I would implore you to think outside of the box. You might be surprised at the uses of some data and what it can tell you. Just make sure that you use good test data when you try these things out so you can really make sure you know what is happening.

WEBINAR: Optimize The Data Supply Chain - 31st January 2019

Optimize The Data Supply Chain Join us for the latest DSC Webinar on January 31st, 2019 Every organization is aiming to produce more comprehensive understanding of their customers, their business operations and their risks, through data. Most organizations are still learning best practices that allow them to leverage in-house data science resources more effectively. A big piece of the puzzle is enabling better collaboration between data science teams and the lines of business. A team-driven approach is necessary to help. In this latest Data Science Central webinar, led by Mike Ferguson of Intelligent Business Strategies Limited, an independent analyst and consultant who specializes in BI, analytics, data management and big data, you’ll learn: What it means for an organization to be ‘data intelligent’ – and what it takes to get there Optimization of the data supply chain that can help create insight and foresight How a team-driven approach reduces two of the most costly enterprise resources: data and effort Best practices for data science groups to easily find and understand curated and trusted data sets to feed and influence their predictive models A brief overview and demo of Datawatch’s integrated platform that combines best-in-class self-service data preparation, a centralized data marketplace, predictive analytics and data governance Speakers: Mike Ferguson, Managing Director -- Intelligent Business Strategies Michael Rowley, Director Product Marketing -- Datawatch Angoss Ellen Wilson, Product Marketing Manager -- Datawatch Angoss Hosted by: Bill Vorhies, Editorial Director -- Data Science Central   Title:  Optimize The Data Supply Chain Date:  Thursday, January 31st, 2019 Time:  9 AM - 10 AM PST

Register here

Understanding the new ePrivacy Regulation and how it differs from GDPR by Christian Auty via @infomgmt

The ePR is expected to address electronic communications, including text messages, email, chat applications and IoT devices. Think of the ePR as the traffic cop for data as it travels between controllers and processors governed by GDPR.

This is an insightful article by Christian that I think is a good high level analysis of the differences between the two.

IoT analytics guide: What to expect from Internet of Things data by Bob Violino via @NetworkWorld

Data capture, data governance, and availability of services are among the biggest challenges IT will face in creating an IoT analytics environment.

Interesting article that definitely highlights so of the challenges that are involved in IOT data and reporting off it. This is definitely a new data source with it's own challenges and will need you to rethink the kind of validation needed in order to make important decisions based upon it.

Master data management is not the answer to GDPR compliance by Aaron Zornes by @infomgmt

By themselves, neither data governance nor MDM offer sufficient capabilities to meet GDPR requirements. Together, we are much more empowered as an organisation.

This article contains some really interesting points that I had not realised. Worth reading and thinking about - maybe they are true in your own organisation and you are not aware and may need to make some changes.

o succeed at digital transformation, do a better job of data governance by Darren Cooper via @infomgmt

To set the stage for initiatives like AI and machine learning, companies need a rock-solid governance framework.

Great suggestions by Darren in this article.

Only one in three AI projects reported to succeed by Elliot M. Kass via @infomgmt

IT execs point to inconsistent data, incompatible technologies and organisational silos as major impediments

From my own perspective I have observed the following common issues (even if they shouldn't be common).

• Inconsistent formats used for the same data element in different systems.
• Inconsistent definitions of the same data in different systems.
• Inconsistent values of the same data in different systems.

This is why many organisations have data warehouses and why people like me map between the different systems and the data warehouse so that some ETL code can be used in order to bring it into a common data format, data type and data values so it can be joined easily and you can compare like with like.