International Institute for Analytics

Inquiry:

We’d like to free up our analysts for high-value projects by instituting more self-service, especially for BI reporting. One of our challenges is that we have software releases every week, and the end users need updates in real time.

The vast majority of people building analytics and data science processes have every intention of being good and ethical. As a result, most potentially unethical and evil processes arise in situations where that wasn’t the intention. The problem is typically that proper focus and governance is not in place to keep analytics and data science processes on the side of good. On top of that, what is good and what is evil isn’t nearly as clear cut as we’d wish it to be.

The ethics of analytics are receiving more and more attention today. Historically, the only aspect of ethics that received any substantive attention was the privacy of sensitive personal data. The broader aspects of ethics didn’t truly come to the forefront until late 2017 and early 2018.

What’s driving the sudden focus on ethics are the new, evolving artificial intelligence (AI) capabilities as well as the embedding and operationalizing of analytics as discussed in The Analytics Revolution. These two trends involve analytics making a huge number of automated decisions for us. Therefore, people want to understand what the algorithms are doing, how they’re doing it, and how we can know they are sufficiently ethical.

Inquiry:

We are a multinational that’s grown through M&As, so we have many legacy systems in place. We want to build community across the enterprise so we can execute on shared goals, but our data governance model—fragmented or non-existent—prevents us from making changes to support an analytics community. How can we be successful as we move forward?

The speed and flexibility achieved by Agile and DevOps, and the quality control attained by statistical process control (SPC), can be applied to data analytics. Leading edge proponents of this approach are calling it DataOps. DataOps, simply stated, is Agile development and DevOps with statistical process control, for data analytics. DataOps applies Agile methods, DevOps, and manufacturing quality principles, methodologies and tools, to the data-analytics pipeline. The result is a rapid-response, flexible and robust data-analytics capability, which is able to keep up with the creativity of internal stakeholders and users.

Inquiry:

We’ve built a robust set of dashboards in Tableau. Recently the enterprise has started to discuss choosing a single platform for dashboarding – they’re looking at power bi or Salesforce Einstein Analytics. If they decide to switch, how hard would it be to migrate out of Tableau?

In the context of a major corporate reorganization, the Chairman/President and leadership of Michelin North America recognized the need for an organization dedicated to analytics. Over the course of 2018, the company formed an analytics department with innovative and integrated structure, methods, and values. The department reports centrally, with members sharing identity, purpose, and key objectives. Most of the staff are embedded in the business in cross-functional, autonomous, long-lived “squads” aligned with major processes and business domains. Led by product owners and scrum masters, the squads develop and maintain analytics products rather than executing projects. Communities of practice foster experience sharing and learning across the department. The leadership team and management processes focus on enabling the squads to create business value.

Inquiry:

We have a new advanced informatics and analytics group. We’ve been asked to focus on informatics. Should informatics and data engineering be centralized within analytics, housed under IS/IT, or be its own group? How should we define ourselves?

Information Economies in Organizations

The data warehouse revolution began in 1991 when Bill Inmon published Building the Data Warehouse. Inmon observed, early in that book, that every organization has a naturally occurring information economy, and that most naturally occurring information economies were inefficient, duplicative and prone to produce suboptimal decisions.

This observation of Inmon’s has not gotten anywhere near the credit, or attention, it deserves. A decade’s worth of collective practice in advanced analytics should tell us that everything we know about real-world economies applies to our information economies. There is demand for information by people and functions in an organization, and there is a supply of (some of) that information. There is (some amount) of technical and procedural infrastructure – some kind of market — to bring demand and supply together in an organized way. That “market” infrastructure is often partial, fragile and in some cases ineffective. There are competitive alternatives (like cloud service providers and SaaS vendors), over- and under-regulation (various data governance models), excessive demand-side taxation (cost allocation strategies), failure to invest in infrastructure, and all other elements of economies.

When organizations are planning strategy-driven large-scale advanced analytics programs, they should begin their planning by characterizing their as-is information economy.

Inquiry:

We have a strong data structure and high analytics demand. Unfortunately, the business most often comes to us for help with hypothesis confirmation for decisions they’ve already made. How can we inspire them to improve their decision-making processes by engaging with the analytics capabilities we can provide?