Topology of a Data Product Team by Eric Broda via @TDataScience

The success of your data mesh journey will be dictated by your data product team structure rather than your technology choices. Here is what you need to know to setup your data product teams.

This is a great article that is well worth thinking about.

“Semantic-free” is the future of Business Intelligence by Andrew Taft via @FlexItAnalytics

 Metrics and universal semantic layers enable semantic-free BI.

I found this very interesting and worth reading so you can think about the future.

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.

The Best Data Science Certification You’ve Never Heard Of by Nicole Janeway Bills via @kdnuggets

The CDMP is the best data strategy certification you’ve never heard of. (And honestly, when you consider the fact that you’re probably working a job that didn’t exist ten years ago, it’s not surprising that this certification isn’t widespread just yet.)

Certainly, a certification to investigate - you might surprise yourself at how close you are to gaining this qualification and yet have not known about it.

Demand for big data-as-a-service growing at 25% annually by Bob Violino via @infomgmt

With big data-as-a-service, tools such as analytics software and storage are delivered via the cloud by a service provider.

Definitely, a cheaper way to have big data.

5 trends to expect in the new big data protection revolution by Andrea Little Limbago via @infomgmt

Instead of regurgitating many of the dominant predictions around tech buzzwords such as quantum computing, 5G, IoT, the cloud, and artificial intelligence, let’s instead focus on the inherent duality of technology.

A great article to use to compare with your own plans and strategy.

New study looks at the top trends in data mining and analytics by Bob Violino via @infomgmt

Data mining can help enterprises identify anomalies, patterns, and correlations within large unstructured data sets to predict business outcomes.

I found this interesting and it gives you some pointers on things to think about including in your own plans.

WEBINAR: Weaponizing Data in the Fight Against White Collar Crimes - 21 January 2020

Data Science Central Webinar Series Event

Weaponizing Data in the Fight Against White Collar Crimes Join us for the latest DSC Webinar on January 21st, 2020 In this webinar analyst and advisor Alasdair Anderson will discuss how escalating global white collar crime activity is driving adoption of advanced analytics at a pace never before seen in mature enterprises. Organizations are having to change the way they deal with data and change the types of data that they utilize. The old data management adage of "garbage in, garbage out" has been left behind as organizations realize that agile wrangling of all data is far more valuable that static historic curated data sets. In this latest Data Science Central webinar you will learn: How large organizations are using data to fight white collar crimes The crucial role cloud technology plays in advance analytics Why data wrangling is a key functionality in dealing with "garbage" data Featured Speakers: Alasdair Anderson -- Independent Consultant Matt Derda, Customer Marketing Manager -- Trifacta Hosted by: Stephanie Glen, Editorial Director -- Data Science Central   Title:  Weaponizing Data in the Fight Against White Collar Crimes Date:  Tuesday, January 21st, 2020 Time:  2:30 PM - 3:30 PM GMT   Space is limited so please register early: Reserve your Webinar seat now   After registering you will receive a confirmation email containing information about joining the Webinar.

'Big data' and 'analytics' - Two of the top buzzwords everyone secretly hates by ohn-David McKee via @infomgmt

Buzzwords are frequently abused as an attempted credibility builder. A way of showing others that you're in the know.

I agree - they are often used out of context and that just tells me that the user doesn't actually understand the word properly and what it entails to be actually delivered properly. I think Artificial Intelligence is used too often and that it is used too much as the fall guy by people who don't understand it.

WEBINAR: 20 Predictions for 2020 from AI to Data Management - 21 November 2019

Data Science Central Webinar Series Event

20 Predictions for 2020 from AI to Data Management Join us for the latest DSC Webinar on November 21st, 2019 AI, machine learning, cloud, self-service, data governance, etc...there is no shortage of buzzwords in data today. Every organization is seeking to outpace its competition by leveraging data to drive differentiation for their business. To win this race, companies are building up data science teams, investing in faster/more scalable cloud data platforms and utilizing the growing variety of publicly available datasets and algorithms. How do you stay ahead of what’s next and help drive the successful adoption of new technology and processes within your organization? This latest Data Science Central webinar will be interactive and will review where we think data management, analytics and ML/AI are headed next. The session will also focus on how to use the predictions and data we share in the session to drive modernization efforts at your company. In this webinar you can expect to learn: Will cloud-native services & kubernetes fundamentally change our approach to data infrastructure & application integration? Will the buzz around machine learning continue or will the first ML initiatives stumble out of the gates? How will the nature of self-service change with an increased focus on data governance & security? Featured Speakers: Will Davis, Head of Marketing -- Trifacta Eric Kavanagh, CEO -- The Bloor Group Evren Cakir, Senior Analyst -- The Bloor Group Hosted by: Stephanie Glen, Editorial Director -- Data Science Central Title:  20 Predictions for 2020 from AI to Data Management Date:  Thursday, November 21st, 2019 Time:  9 AM - 10 AM PST Space is limited so please register early: Reserve your Webinar seat now

When it comes to data, why the 'garbage in, garbage out' doctrine is all wrong by Michael Kanellos via @infomgmt

The problem is that there’s way too much of it and it’s not organized in a way that makes it easy to understand. It doesn’t form beautiful crystalline patterns like salt: it’s more like a huge pile of gravel.

It's clear to me that you can check the quality of your data, but you shouldn't throw away anything that doesn't match your vision or correctness. Flag it as not being "right" but don't lose it - it could still give useful insights.  Think of it this way - financial data must equal what is going into the financial ledgers. If you include the bad data it probably will. just make sure you mark r it in some way.

Four people your data team needs to win the model deployment relay by Sarah Gates via @infomgmt

To be effective at model management you need a strong team. The good news is that you don’t need a lot of people to accomplish this. Just like a relay race, the right four people can manage the complete model lifecycle.

This is a great read if you have no idea how to do this and want to know how many people are needed to do all of that.  Very useful article.

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.

9 key rules for transitioning a data and analytics strategy to the cloud by Pandian Muneeswara via @Infomgmt

Following are the nine key rules to be considered in getting an on-premises data and analytics environment successfully transformed into the cloud.

I really liked rule 6 and the comment that you should incrementally grow the cloud roadmap which I think is the right way to go - start small and grow it over time.

WEBINAR: Master Data Management at Scale - 29 October 2019

Data Science Central Webinar Series Event Master Data Management at Scale Join us for the latest DSC Webinar on October 29th, 2019 Master data management (MDM) software turned 15 years old this year. Originally launched in 2004 by SAP, master data management systems aimed to help resolve the data unification problem by creating a central source of standardized references to customers, products, employees, suppliers, physical assets and other data across their many IT systems. MDM is valuable, but it’s also slow, labor-intensive, and costly. As the scale of MDM projects increases to millions of entities and hundreds or thousands of data sources, the traditional methods often fail. Mike Stonebraker will share his view on how MDM technology and MDM organizations must change to fulfill the promise of MDM at scale. In this latest Data Science Central webinar, we will review: Why large enterprises need data management solutions that solve data mastering challenges at scale Why traditional, rule-based, data mastering options are struggling to keep up How Machine Learning can be used to address large-scale data mastering challenges Featured Speaker: Mike Stonebraker, CTO & Co-Founder -- Tamr, Inc. Hosted by: Stephanie Glen, Editorial Director -- Data Science Central Title:  Master Data Management at Scale Date:  Tuesday, October 29th, 2019 Time:  9 AM - 10 AM PDT Space is limited so please register early: Reserve your Webinar seat now

The good, the bad, and the ugly of becoming data driven by Cindi Howson via @infomgmt

It seems for all the investments that organizations make in gathering and generating data, few are actually able to turn that data into actionable insights.

Can they connect all of these different datasets, in many different formats, and can they then make sense of it - this is not simple nor is it quick.

Overcoming the five most common data analytics challenges by Christopher Roberts via @Infomgmt

To optimize your business, you must accumulate and analyze the data and feedback you’ve been getting from all aspects of your business. Here are the solutions to top challenges.

A very useful article that I think deserves a read and bookmark.

Why big data analytics is crucial to how the IoT works and grows by Savaram Ravindra via @informgmt

Big data is the fuel of IoT and artificial intelligence that drives the connected things is its brain.

good article that is worth reading and thinking about it a while if you want to implement anything with IoT you need to pay attention to analytics to get the full value from the investment.

Lack of digital standards making data management increasingly complicated by Bob Violino via @infomgmt

With no international alignment on how to regulate the digital environment, organizations are managing an increasingly complicated set of conflicting rules in key markets.

This is definitely going to get worse as I spent much of my working life creating standard data models and mapping data from systems to the data model. Not always easy to achieve data available and easy to understand.