Research

Inquiry Response: Feedback On A Job Description For A Data Engineer

By Eddie Satterly, Jul 05, 2021

Available to Research & Advisory Network Clients Only

Inquiry:

I’ve put together a list of responsibilities for the data engineer we’d like to find and hire, and I’d appreciate your advice. I lead analytics for enterprise operations—procurement, manufacturing, engineering—and we plan to migrate data and analytics to Azure.

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Inquiry Response: Improving Performance Measurements for Analytics Teams

By Todd Holloway, Apr 12, 2021

Available to Research & Advisory Network Clients Only

Inquiry:

We’re uniting our data engineering and analytics teams into a consolidated, Agile-based analytics organization. We want to improve how we measure team and individual performance.

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Inquiry Response: Scoping Analytics Roles For A New Talent Management System

Apr 05, 2021

Available to Research & Advisory Network Clients Only

Inquiry:

We recently launched a new talent management system and are working to define job descriptions for data analysts, data scientists, and data engineers. We’re struggling with mapping existing roles to new roles and ensuring we’re on the right track with necessary change management that goes with this. Could you help us think through this?

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Webinar: Modernizing The Roles In Your Analytics And Data Science Organization

By Bill Franks, Drew Smith, Mar 23, 2021

Available to Research & Advisory Network Clients Only

A recurring theme among analytics and data science leaders is the concern of not being able to keep up with all of the rapid change taking place – both individually and as a team. In years past, it was possible to stand up an organization largely made up of analytics generalists who would handle initiatives end to end. In today’s world, it is necessary to utilize a range of specialists focused on either specific methods or specific points in the lifecycle of an analytics and data science initiative. This talk will discuss the trends driving the need to evolve your organization’s talent model, new roles you need to consider implementing, and how they all fit together.

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Inquiry Response: Promoting Analysts To The Role Of Data Scientist

By Jennifer Prendki, Dec 21, 2020

Available to Research & Advisory Network Clients Only

Inquiry:

We’re building a team to focus on using machine learning to enhance our end user experience. Many of our analysts aspire to be data scientists, and we would like to fill data scientist roles with the talent we have where possible. How should we delineate roles and responsibilities of a data analyst vs. a data scientist?

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Inquiry Response: Uniting Analytics And Technology Partners

Nov 16, 2020

Available to Research & Advisory Network Clients Only

Inquiry:

The analytics team and IT partners team just merged and we’re running into a number of challenges. We have different definitions of what it means to put thing into production, and we’re not communicating cleanly with the business stakeholders. How can we make this work?

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Inquiry Response: When Data Scientists Are Expected To Do It All

Oct 26, 2020

Available to Research & Advisory Network Clients Only

Inquiry:

We’re attempting to upskill our data scientists to instill good coding behavior and the tenets for model testing and system functionality for application delivery. It’s been a challenge. Do you have any suggestions?

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Inquiry Response: Two Types Of Analytics Catalysts

Oct 05, 2020

Available to Research & Advisory Network Clients Only

Inquiry:

As a manufacturing company with a new data and analytics operating model. How does the role of analytics catalysts fit into our organization?

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Creating A Data Strategy: A Framework

By Doug Mirsky, Jun 15, 2020

Available to Research & Advisory Network Clients Only

There is a tendency to jump into something called a “data strategy” without understanding what it is at its core. In IIA’s view, at the center of a data strategy is a schematic of an entity’s information economy, with in-depth awareness of the constituent needs on the demand side of the economy — the data consumers. Because ultimately the primary purpose of a data strategy is to solve questions about how to improve the availability, timeliness and quality of data, in that order of priority, for the constituencies demanding it.

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The DataOps Transformation: 7 Steps To Prevent The Great War of Data Scientists

By Christopher Bergh, Mar 31, 2020

Available to Research & Advisory Network Clients Only

Join Christopher Bergh as he presents the seven steps to get these groups of people working together. These seven steps contain practical, doable steps that can help you achieve data agility through DataOps. This presentation will illustrate how to make changes to big data, models, and visualizations quickly, with high quality, using the tools teams love. We synthesize techniques from DevOps, Deming, and direct experience.

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