Research

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 economy1 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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Big Data and Analytics in the COVID-19 Era

By Jesse Anderson, Mar 26, 2020

Available to Research & Advisory Network Clients Only

Big Data and analytics are going to change in this COVID-19 era. In this Webinar, Jesse Anderson, leading expert in big data and related technologies and techniques, shares what he’s been telling his clients who call and ask, ‘Jesse, with all that’s going on what should we do now?’ Jesse explores a number of topics ranging from the implications of working from home to the tweaks that may be necessary to your current organizational models in the current economic climate.

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CAO Perspectives: Analytics Team Actions in Response to COVID-19

By Doug Hague, Mar 25, 2020

Available to Research & Advisory Network Clients Only

My Immediate Response:

  1. ALL HANDS ON DECK FOR OPERATIONAL REPORTING
    There will be new reporting needs and the frequency of information will need to speed up. Look for options and data that will be impacted by the current crisis. You will need to figure out how to quickly deliver information even if not perfect. Do the best you can, provide any glaring risks. Executives are starving for information. Make sure to provide the information to the most senior people first. Analytics teams can assist operations teams, data science people should even jump in as they have some of the best skills even if they are not using their modeling skills. Work with IT to pull information more quickly. Accept that you will be reporting off of sandboxes that are not production quality, get the exceptions from Risk and IT. You may need to time shift your team to earlier in the day as the executives will want things first thing when they come in if possible. Blend new data sets together to provide insights. Make sure to grab the customer feedback as even simple things like a word cloud may help.

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Inquiry Response: Growing Our Analytics Team

By IIA Expert, Gary Cao, Mar 09, 2020

Available to Research & Advisory Network Clients Only

Inquiry:

We’re growing our analytics team from 10 to 25 people in the next year. In addition, we’re transitioning to an Agile approach and aiming toward operationalizing RPA. The current team consists of two data engineers, one project manager, one designer, three BI/visualization analysts, one QA specialist, and two product owners. Do you have any advice that will help ensure the team’s success?

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Analytics Applications Lifecycle Framework

By Doug Mirsky, Mar 02, 2020

Available to Research & Advisory Network Clients Only

Most analytics organizations at large companies do not own the entire lifecycle of their analytics applications. Instead, often-distributed analytical applications teams have to work with their partners in IT/IS organizations, as well as seek requirements, advice and consent from legal, compliance and governance functions within their organization. The essentially distributed nature of these work streams frequently results in inefficiencies or even full-on breakdowns in progressing an analytics application from proof of concept (PoC) to production. Success in distributed environments requires an overt, agreed-upon, stepwise plan along with the emotional intelligence to navigate the necessary conversations with your colleagues to build that plan.

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Inquiry Response: Making A Matrixed Analytics Org Model Work

By IIA Expert, Bernie Smith, Feb 17, 2020

Available to Research & Advisory Network Clients Only

Inquiry:

We recently implemented a matrixed analytics model as a way to achieve scale. We have six business-facing analytics directors on the matrix, and we divide our projects by types: BI and analytics. Do you have any tips for integrating this model effectively?

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Inquiry Response: Documentation Priorities For Your Analytics Team

By IIA Expert, Feb 03, 2020

Available to Research & Advisory Network Clients Only

Inquiry:

I’m rebuilding my analytics teams after layoffs. Going forward, I would like a documentation process in place to retain knowledge in case of future resource talent loss. What should my priorities be?

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Inquiry Response: Caveats When Deploying An Automated ML Tool

By IIA Expert, Jan 20, 2020

Available to Research & Advisory Network Clients Only

Inquiry:

We’re planning an enterprise rollout of an automated machine learning (AutoML) tool like DataRobot. What are some of the caveats that we should watch out for?

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Inquiry Response: Targeted Self-Serve Rollout

By IIA Expert, Jan 13, 2020

Available to Research & Advisory Network Clients Only

Inquiry:

We have a diverse analytics ecosystem across the enterprise with people using many different tools at various levels of competency. We want to provide more access to data in a self-service capacity, but in a targeted way. How do we decide who to target first?

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