International Institute for Analytics

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?

As we move rapidly toward an AI-first world, laws and regulations are not keeping pace. Laws were made to govern human interactions, and as such, the legal system is playing catch up to figure out how to oversee autonomous systems. However, even in the absence of clear laws upon which to manage responsible applications of AI and autonomous use of data, organizations must still take action against their considerable liability and risk.

Inquiry:

We are establishing an HR analytics function. Unfortunately, there’s no consistency with even the most basic headcount metrics. Before we can move on to cool analytics, we need to reconcile the headcount numbers across the enterprise. What’s the best way to work through this initial hurdle?

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?

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.

Inquiry:

As part of our strategy for BI and self-service, we’re planning an enterprise-wide Tableau rollout to 2500 users initially. What are best practices we should be aware of to ensure and measure success?

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.

Since the dawn of Wall Street, investors have sought an edge: an advantage they can leverage to beat the market. Gaining some type of edge is the only way to win in the brutally competitive, zero-sum game of capital markets.

Today, that edge can come from insights gleaned from information found in the exabytes of data created in the real world — on websites, from smartphones and sensors, through supply chains, and from countless other types of real-world data. Collectively, these sources of unstructured and large nontraditional data are called “alternative data.”