Data

Adapting your business amid increasing digital disruption isn’t about creating a list of digitization priorities but instead figuring how you are vulnerable to attack and where you can create value.

Digital strategy: Understanding the economics of disruption


In a podcast interview with Joe Pasqua EVP of Products at MarkLogic, discusses the latest innovations and how MarkLogic aims to differentiate against its NoSQL database peers.


What's new in MarkLogic 9 NoSQL?

In this podcast episode, I talk multi-model databases with Damon Feldman, solutions director at MarkLogic. If this conversation gets you interested in multi-model databases, be sure to check out his live webcast on December 1, 2016.

Damon Feldman on Multi-Model Databases

There’s greater potential in big data. What’s ahead as the field matures?

How to win in the age of analytics

What exactly is a Data Management Platform, and why is it so important? Miles Pritchard, Global director of marketer and agency solutions at Lotame, will explain what a DMP is and how they are being used by marketers and agencies. We'll also look ahead at some of the challenges that advertisers are facing, and how being able to collate data from multiple different sources can help deliver more targeted and effective advertising. DMPs are becoming a critical part of every marketer's technology stack, so tune in and listen to Miles explain the industry best practice and how to get started.

15. What is a Data Management Platform? Miles Pritchard from Lotame

In this podcast episode, Ken Krupa, enterprise CTO at MarkLogic, walks me through the challenge of data integration and outlines an approach for dealing with it known as the “operational data hub.”

Integrating Across Silos With Operational Data Hubs

In this podcast episode, O’Reilly’s Shannon Cutt talks with Damon Feldman, solutions director at MarkLogic, a company that has developed an operational and transactional NoSQL database that integrates data silos to give customers a single view of their data. They discuss data lakes, data hubs, integrating data in a lake format, data governance related to security, and more.

Damon Feldman on Combining a Data Hub and Data Lake

The McKinsey Podcast

Facing up to digital disruption: Reinventing the core with bold business strategy

Our solution experts share how the CDO at the Bank of Ireland enabled cross-functional collaboration and helped his team embrace a “data culture.”

Episode 1: One CDO’s Journey Beyond Data Governance

This week's episodes is for data scientists, sure, but also for data science managers and executives at companies with data science teams. These folks all think very differently about the same question: what should a data science team be working on? And how should that decision be made? That's the subject of a talk that I (Katie) gave at Strata Data in early March, about how my co-department head and I select projects for our team to work on.

We have several goals in data science project selection at Civis Analytics (where I work), which can be summarized under "balance the best attributes of bottom-up and top-down decision-making." We achieve this balance, or at least get pretty close, using a process we've come to call the Idea Factory (after a great book about Bell Labs). This talk is about that process, how it works in the real world of a data science company and how we see it working in the data science programs of other companies.

Relevant links: https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/63905

How to pick projects for a professional data science team

Decentralised Data Infrastructure for Science:

Sönke Bartling "Governance and Business Models", British Library, 21 April 2018

slides: https://drive.google.com/drive/folders/1QQR2f_pFnHSdpdRa5Tg2W8WoRsTl-51i

TASCAM DR-40

Sönke Bartling "Governance and Business Models"

Decentralised Data Infrastructure for Science:

James Littlejohn, Living Knowledge Network -- "Building on DaMaHub to create a Living Knowledge Network", British Library, 21 April 2018

slides: https://drive.google.com/drive/folders/1QQR2f_pFnHSdpdRa5Tg2W8WoRsTl-51i

James Littlejohn "Building on DaMaHub to create a Living Knowledge Network"

Hear our solution experts talk about how organizations are reimagining their data governance programs, and how we can empower you to operationalize your program.

Architecting a Data Governance Program

If you're a data scientist at a firm that does a lot of software building, chances are good that you've seen or heard engineers sometimes talking about "agile software development." If you don't work at a software firm, agile practices might be newer to you. In either case, we wanted to go through a great series of blog posts about some of the practices from agile that are relevant for how data scientists work, in hopes of inspiring some transfer learning from software development to data science.

Relevant links:
https://www.locallyoptimistic.com/post/agile-analytics-p1/
https://www.locallyoptimistic.com/post/agile-analytics-p2/
https://www.locallyoptimistic.com/post/agile-analytics-p3/

Agile Development for Data Scientists, Part 1: The Good

There's just too much interesting stuff at the intersection of agile software development and data science for us to be able to cover it all in one episode, so this week we're picking up where we left off last time. We'll give a quick overview of agile for those who missed last week or still have some questions, and then cover some of the aspects of agile that don't work well out-of-the-box when applied to data analytics. Fortunately, though, there are some straightforward modifications to agile that make it work really nicely for data analytics!

Relevant links:
https://www.agilealliance.org/agile101/12-principles-behind-the-agile-manifesto/
https://www.locallyoptimistic.com/post/agile-analytics-p1/
https://www.locallyoptimistic.com/post/agile-analytics-p2/
https://www.locallyoptimistic.com/post/agile-analytics-p3/

Agile Development for Data Scientists, Part 2: Where Modifications Help

If you've been in data science for more than a year or two, chances are you've noticed changes in the field as it's grown and matured. And if you're newer to the field, you may feel like there's a disconnect between lots of different stories about what data scientists should know, or do, or expect from their job. This week, we cover two thought pieces, one that arose from interviews with 35(!) data scientists speaking about what their jobs actually are (and aren't), and one from the head of data science at AirBnb organizing core data science work into three main specialties.

Relevant links:
https://hbr.org/2018/08/what-data-scientists-really-do-according-to-35-data-scientists
https://www.linkedin.com/pulse/one-data-science-job-doesnt-fit-all-elena-grewal

Data

Jeremy Martineau