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.

It seems every company is pursuing Data Culture and Data Literacy programs, but what exactly are they hoping to achieve? These topics get a lot of press, sound very inspirational, and a lot of really smart folks* are working on them. With all the other priorities that data professionals are chasing right now, how can they assess the real potential of these concepts and separate the hype from the value?

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?

If you have ever participated in a sports team, academic team or a club, or volunteered with a group of individuals, then you experienced the power of coming together to work on a common goal. A Community of Practice (CoP) is a lot like a team-based activity, and this paper discusses how to establish a CoP within your organization and why it’s an important activity to consider as an analytics leader.

When to Consider a Catalyst:

• Is your organization or team realizing your full potential from your analytics efforts?

• Are analytics models making it to production and adding quantifiable value?

• Is leadership trusting the results from the investment you’ve made in Data Scientists, Data Wranglers and Data Engineers?

• Are internal and external customers delighted in the solutions presented, or for that matter, is it even being measured?

Complying with the Long-Duration Targeted Improvements standard presents major challenges to the business processes and information systems of insurance companies. At the same time, it offers them the opportunity to make rapid progress in modernizing those processes and systems to deliver management insight of unprecedented value. To explore the two sides of the LDTI coin, IIA interviewed Naxine Chang, FSA, MAAA, North America Insurance Strategy Head at SAS Institute.

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.

Inquiry:

We’re building our own customer data platform and are considering a graph database to handle a 360-degree view of the customer. Would a graph database be beneficial and also scalable for us?

Inquiry:

We want to get proactive about predicting service health and correcting customer issues. We’re struggling with growing pains when it comes to model monitoring and effectiveness. How have you seen this done?

“Self-service is best characterized as a broader business effort to continue the maturation of your enterprise analytics. Core to that maturation is a focus on identifying more and better business challenges against which to apply predictive and prescriptive (advanced) analytics techniques, by reducing the resource expenditure on supporting your organization’s reliance on reporting and descriptive business intelligence”

Join Metric Insights and IIA as we discuss IIA’s framework for Self-Service BI and share some of the ways to think about and implement a self service BI strategy.

This webinar discusses how to:

• Clean up your current BI environment so users can find what they need
• Implement a BI governance layer to ensure trust in your BI
• Provide a mechanism for all users to publish their own BI and Analytics
• Ensure that you have a way to measure and manage your Self Service program
• Free up your analytics resources to focus on advanced analytics