Research

A Framework For Establishing A Self-Service Program

By Doug Mirsky, Drew Smith, Aug 31, 2020

Available to Research & Advisory Network Clients Only

As with many terms in the analytics space, “self-service” tends to have many meanings, depending on the vendor using the term. Self-service is used to describe both business intelligence and (advanced) analytics, and is frequently co-mingled with a number of other terms, including “data democratization,” “citizen data scientist” and, more recently, “data literacy.”

In general, this collection of terms points in the direction of a conscious strategy to have more employees in an organization, with broader and deeper access to data, use those data sets to make better, more timely data-driven decisions with little or no intervention from a centralized BI or analytics function, or IT professionals.

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Inquiry Response: Transitioning to a Data Lake

By IIA Expert, Mo Chaara, Aug 03, 2020

Available to Research & Advisory Network Clients Only

Inquiry:

We plan to transition to a data lake to generate future business value by enhancing analytics and reporting. Unfortunately, we’re struggling to see how to get this done and the end value.

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Inquiry Response: Tips For Successful Data Consolidation

By IIA Expert, Eddie Satterly, Jul 13, 2020

Available to Research & Advisory Network Clients Only

Inquiry:

We are working on a data consolidation project in which we’re replacing four old ERP systems with a new ERP system. We’re concerned with managing customer data from the different systems and prepping ourselves for advanced analytics in the future.

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Inquiry Response: Feedback on Data Platform Architecture

By IIA Expert, Jun 22, 2020

Available to Research & Advisory Network Clients Only

Inquiry:

We’re working on a new centralized data platform so that we can perform effective analytics with a quicker turnaround. We’d like to verify some aspects of a new architecture that we’re considering. Can you provide feedback?

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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 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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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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Competing on Analytics by Industry

By David Alles, Feb 17, 2020

Available to Research & Advisory Network Clients Only

Organizations today face an increasingly challenging business environment. Across industries, new companies and nimble competitors are taking advantage of analytics and leveraging the full potential of the internet, disrupting traditional business models and markets. Cloud computing and open source have caused fundamental changes in analytics infrastructure, enabling the introduction of new technologies such as artificial intelligence and machine learning. The most nimble, innovative companies have quickly taken advantage of these new technologies — and the analytics they enable — to gain a competitive advantage. Traditional companies are struggling to deal with this complexity and effectively compete on analytics. Though many top executives realize that high-quality data, analytics and AI are critical to the future success of their companies, up to 70 percent of analytics initiatives and projects fail to meet their objectives.

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