Research

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.

Read More »

Mapping an Information Economy

By Doug Mirsky, Aug 16, 2019

Available to Research & Advisory Network Clients Only

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.

Read More »

Multi-Model Databases: A Primer

By Daniel Graham, Jun 05, 2019

Available to Research & Advisory Network Clients Only

Multi-model databases (MMDBMS) have been expanding the definition of database for several years. A multi-model database combines several data stores in one database. Those data storage services support distinct data models. Data models include relational, graph, documents, key-value, time-series, and object stores. But simply storing different kinds of data is insufficient to call it multi-model. Specialized programming services must exist for each data model. In the best MMDBMS, a single query can combine data from all data models.

Read More »

Artificial Intelligence – A Primer On Several Common Approaches

By Bill Franks, Apr 24, 2019

Available to Research & Advisory Network Clients Only

There is a lot of well-deserved hype for artificial intelligence algorithms and for deep learning in specific. Self-driving vehicles are already being tested and rolled out into our communities. So, the future is here. The way the cars are enabled is partly through using convolutional neural networks to do object detection. There are certainly many other algorithms that are part of the self-driving process, but a lot of the key algorithms that enabled us to get to where we are today are the convolutional neural networks that are explained in this research brief.

Read More »

Portland 2019 Analytics Symposium Video: Michael Hoffman

By Michael Hoffman, Apr 17, 2019

Available to Research & Advisory Network Clients Only

Mixed Reality and Analytics

Mixed reality (XR) technology is providing quantifiable business value through multiple features and benefits, which include shared 3D context, spatial mapping, data visualization, and much more. Companies are deploying XR across multiple uses cases—and many of these use cases require the use of analytics to analyze and gain insights from massive amounts of information.

Read More »

Portland 2019 Analytics Symposium Video: Melanie Mitchell

By Melanie Mitchell, Apr 17, 2019

Available to Research & Advisory Network Clients Only

AI Hits The Barrier of Meaning

Hype about AI is not new. In 1965 experts predicted that by 1985, AI would do anything humans could. Today, many are optimistic about AI while others want to put the brakes on. So how close are we to human-level AI?

Today, the most common form of AI is deep neural networks, which can do impressive things like object detection and tracking. Classification errors have gone down and are now only 3%, while detection has improved. Combining vision with language enables systems to identify a picture and generate a caption, often with impressive results. Speech recognition and translation have improved as has the ability of machines to answer questions. Machines have shown improved reading comprehension and the ability to play video games. This progress is why some feel AI is closer to human-level intelligence.

Read More »

Portland 2019 Analytics Symposium Video: Mark Madsen

By Mark Madsen, Apr 17, 2019

Available to Research & Advisory Network Clients Only

The Black Box: Interpretability, Reproducibility, and Responsibility

Historically, a model produced a result that was interpreted by a person who made a decision. In recent years, as the amount of data and number of decisions have grown, agency has been taken from humans and given to machines, which make decisions in a black box. Black boxes raise issues around explainability (or interpretability)—being able to explain how a decision was made—and reproducibility —being able to use the same data and model to make an identical decision.

The reality is that being able to explain complex decisions is extremely difficult, and may not be necessary. And, being able to reproduce decisions is also very challenging, as data, tools, software, models, and environments change. Any single change can have a ripple effect that changes everything. The real issues are trust, reliability, and repeatability, particularly in high-stakes decisions. Building trust starts with IT policies, governance, and infrastructure, to enable preserving history and allow for understanding and reproducing decisions. This is the key to gaining trust and scaling analytics.

Read More »

Portland 2019 Analytics Symposium Video: Marc Demarest

By Marc Demarest, Apr 17, 2019

Available to Research & Advisory Network Clients Only

Information Economy Mapping

Every organization has a naturally occurring information economy. The rules of other economies hold: there is supply and demand, supplier and buyer power, competitive alternatives, infrastructure, regulation, taxation, and more. Two important rules of thumb: demand always finds a way to get its needs met, and there are legitimate, necessary restrictions on freedom.

Roughly 80% of organizations have a Soviet-style, state-controlled information economy. In the other 20% it is a laissez-faire, demand-style information economy. In every organization it is important to know where you are and where you want to go. The right answer is always something other than a command economy or an unfettered laissez faire economy. It is analytics professionals’ job to figure out the optimal state by balancing those requirements and brokering solutions that are palatable to all.

Read More »

Building The Analytics Factory at Deere

As a 180-year-old company with 65,000 employees in 30 countries, Deere is the stark opposite of a digital native. Incorporating analytics into different parts of the company has required significant transformation on both the technical and people sides. But all changes have been grounded in the company’s foundational values.

Transformation has required partnerships between the analytics function and other stakeholders, including IT, manufacturing, sales, legal, and more. Partnerships and flexibility have been necessary in reworking traditional processes to become faster and more iterative, and in revising governance and decision making.

Read More »

Portland 2019 Analytics Symposium Video: Jana Eggers

By Jana Eggers, Apr 17, 2019

Available to Research & Advisory Network Clients Only

Lessons From The Underbelly of AI

The promise of AI is justified; long term, AI could cure cancer. But that is not where AI is today. One leading AI thinker believes AI already meets or exceeds human performance in perception and categorization, but is not yet equal to human performance in contextualization, prediction of cause and effect, and planning and decision making. Jana Eggers believes this overstates where AI is.

Read More »