International Institute for Analytics

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.

Inquiry:

We have a project management office (PMO) for our data and analytics team. This is a new role and aligning with the businesses in a more formal way is a new concept for us. Do you have any best practices for working with stakeholders?

Inquiry:

We’re finally at a point where we have data scientists within the sales organization who can work on advanced analytics. However, we’re struggling with how to get the business knowledge to the data scientists and move forward. How have you solved this dilemma?

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.

Inquiry:

I’m charged with driving analytic strategy, starting with formalizing an enterprise analytics function. We have scattered pockets of analytic capability. How do I ease these teams into the new paradigm?

Inquiry:

We have a customer loyalty program, and we want to improve our customer lifetime value (CLV) and retention, and also move more customers toward using loyalty cards. What are some interesting techniques and frameworks that could aid our efforts?

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.

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.

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.

Inquiry:

With our Agile transformation, there’s friction between IT and Analytics regarding project management responsibilities, and with the data scientists who don’t like Agile. How can we implement our Agile transformation to limit friction?