Research

Inquiry Response: Starting a Knowledge Management Program

By Mark Madsen, Aug 28, 2017

Available to Research & Advisory Network Clients Only

Inquiry:

Our analytics department is starting a knowledge management program. What are some best practices of starting a program for our analytics department? We’re looking for lessons learned and anything that would help a knowledge management program be more successful for people doing analytics.

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Talent Analytics uses data gathered from our own proprietary talent assessments as an input variable to predict hiring success – pre-hire. We treat this dataset just like any other dataset in our predictive work. We are careful to analyze it for a strong (or weak) correlation to actual job performance. Our theory? If there is no correlation between data gathered via this method our clients should stop using it. Continuing without proof of success would be a little like a doctor “knowing” a certain medication doesn’t work for you, but continues to encourage their patients to keep using the medication. Malpractice at the very least.

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5 Things New Analytics Leaders Should Do to Succeed

By Bill Franks, Thomas H. Davenport, Jul 19, 2017

Available to Research & Advisory Network Clients Only

There is a fair amount of management research suggesting that the first 90 days or so are the most important time of a leader’s tenure. It’s when you establish your reputation and it determines what people start to think about you in your role. It’s often hard to change those first impressions. Therefore, IIA held a webinar to discuss this very important period for senior analytics leaders like a Chief Analytics Officer, Chief Data Officer, VP of analytics, or similar senior role. This paper captures the key elements of the discussion between Bill Franks and Tom Davenport, which focused on five essential things new analytics leaders should do to set themselves up for success.

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Inquiry Response: Suggestions for Getting Data Scientists to Embrace Agile Methods

By Mark Haseltine, Jul 17, 2017

Available to Research & Advisory Network Clients Only

Inquiry:

Our company has recently adopted a Scrum/Agile framework, which has caused some hiccups with our data scientists, who are used to managing their projects themselves. They tend toward perfectionism, which takes longer. Our goal is to build model minimum viable products (MVPs) faster, using two-week sprints for testing/incrementing the models. Part of the problem is that the data scientists don’t fully trust the process because of the loss of control to the Scrum master and also because of the continued perception that they have to produce perfect models the first time out. How can we get our data scientists to embrace the Agile process?

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Inquiry Response: Building a Data Science Team, Recruitment and Hiring

By Rumman Chowdhury, Jul 10, 2017

Available to Research & Advisory Network Clients Only

Inquiry:

We’re a major player within a massively complex industry with a three-year mandate to build a data science practice to help us drive competitive advantage. How do we assess the gaps in our current talent pool and what are the considerations for new hiring and recruitment?

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Sticky problems keep our data scientists engaged

By Sarmila Basu, Jul 06, 2017

When you bring together a wildly diverse group of geniuses, the hard part isn’t finding work for them to do; it’s finding something that’s hard for them to solve, something so challenging that they get a little bit mad and a lot fired up. If not, they’ll get bored and they might wander off. That’s why it has taken me seven years to build my team: an eclectic mix of statisticians, economists, mathematicians, electrical engineers, biophysicists, and telecommunications specialists who are helping shape the way Microsoft uses data.

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A Comprehensive Recruiting Strategy for Analytics Talent

By Joe DeCosmo, Bill Franks, Ralph Greco, Dan Magestro, Jun 30, 2017

Available to Research & Advisory Network Clients Only

While we recognize that the data-driven transformation of business leads to macro-level systemic challenges and potential shortages in the workforce, we believe that individual businesses actually hold a wealth of power and opportunity to meet their specific needs for analytical talent. In fact, to us the analytics talent gap is less about a broad shortfall of analytics talent, and more about the specific gaps between business needs and the analytical skills to meet those needs that present challenges in every company.

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Inquiry Response: Considerations for Rotational Training Programs

By IIA Faculty, Jun 12, 2017

Available to Research & Advisory Network Clients Only

Inquiry:

We would like to create a rotational training program for our in-house analytics professionals. New hires would rotate through various other business units to gain a broader view of the overall business, while bringing the data science perspective into those units.

Questions:

  • Would a rotational program like this appeal to recent analytics and data science graduates?
  • How does a rotational training program for analytics professionals benefit business?
  • What are some best practices for rolling out a successful rotational training program for analytics professionals?

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Inquiry Response: Tips for Assessing Talent Readiness

By Jenny Schmidt, May 30, 2017

Available to Research & Advisory Network Clients Only

Inquiry:

How do we assess the readiness of the talent in the organization for our future analytics capability needs?

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A Talent Playbook for Analytics 3.0

By Emilie Harrington, May 08, 2017

Available to Research & Advisory Network Clients Only

Organizations often face key investment decisions in the analytics area when trying to decide whether to invest a given dollar in talent or in an emerging analytics technology. Analytics capabilities run on an expensive combination of fiber, blade servers, and apps that require significant investments, but hardware and software without the right level of talent will result in limited capabilities at best. By investing in the right talent first and treating workforce investments as the foundation of analytics capabilities, an organization can maximize the return on its invested capital – both the human capital and the technology infrastructure. These organizations can begin to differentiate themselves from competitors in the market and ultimately achieve analytic competitive advantage.

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