Research

The Ethics of Analytics

By Bill Franks, Sep 12, 2019

Available to Research & Advisory Network Clients Only

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.

Read More »

Seven Steps to Implement DataOps

By Christopher Bergh, Sep 06, 2019

Available to Research & Advisory Network Clients Only

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.

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 »

Creating A Data Engineering Culture: What it is, why it’s important, and how, and how not, to build

By Jesse Anderson, Jul 31, 2019

Available to Research & Advisory Network Clients Only

Why do some analytics projects succeed while so many fail? According to Gartner analyst Nick Heudecker, as many as 85% of big data projects fail. However, the ROI from the other 15% that succeed is incredibly promising. With such a clearly high barrier to competency in executing big data strategies, there remains significant opportunity for first-mover advantage for enterprises that can crack the code to improving their outcomes.

So, what can organizations do to increase their chances of big data success? Part of the answer lies in creating a data engineering culture. This is the necessary foundation underpinning a big data analytics proficiency and enables companies to outperform the competition.

Read More »

Mastering the Art & Science of Storytelling

By Brent Dykes, Jul 26, 2019

Available to Research & Advisory Network Clients Only

Analytics experts love data. But just presenting raw data or even insights derived from data isn’t good enough. To create business value from data requires that analytics professionals develop skills at data storytelling. This entails telling persuasive stories, tailored to a specific audience, that combine data, narrative, and visuals effectively.

Why Storytelling?

Human beings love stories. In fact, author Philip Pullman has written, “After nourishment, shelter, and companionship, stories are the thing we need the most in the world.” And scriptwriting expert Robert McKee has said, “Storytelling is the most powerful way to put ideas into the world today.”

Read More »

Graph Analytics Use Cases

By Daniel Graham, Jul 10, 2019

Available to Research & Advisory Network Clients Only

Introduction In 1996, two computer science students — Larry and Sergei — were enthralled by the emerging internet. But finding anything on the undeveloped web was horribly difficult. Then came the “Aha!” discovery that academic web page citations (URLs) are a proxy for popularity. If many websites “like” the same web page, that page value is probably higher to researchers. So Larry and Sergei designed an algorithm called PageRank. It measured “link juice” — the strength between web pages. Google emerged from PageRank, web URLs and an advertising business model. This article explores the incredible value of “link juice.” Graph analysis turns the relational…

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 »

Building a Storytelling Culture Inside Data and Analytics

By Ruth Milligan, May 22, 2019

Available to Research & Advisory Network Clients Only

Let’s start with why storytelling matters in data and analytics. Brad Lemons, the SVP of Customer Insights and Analytics at Nationwide Insurance, is known to say to his team, “If you can’t sell your insights, they are worthless. Storytelling is not an option, it is a requirement.” Likewise, Scott Berinato argues that storytelling is one of six “musts” for a strong data science organization. But it persists nonetheless as an unresolved competence gap with only a few shining examples.

Storytelling reveals data insights and analytics science. After completing the rigorous problem-solving and data analysis for a business challenge, it is the best chance of synthesizing the insight to advance key business objectives.

Storytelling is an art, not a science. Analytics professionals tend to be scientists, not artists. The innate ability to understand how people hear and listen is not usually a fluency among the scientific set of analytics practitioners. It demands use of emotion, using the senses so that people can remember and repeat what was shared. It is no less rigorous than science, however, in that a strong story requires rounds of iteration and feedback to ensure it supports the key insights.

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 »

Refactoring Analytics for the Cloud

By David Macdonald, Robert Morison, Apr 16, 2019

More and more organizations are leveraging cloud computing in pursuit of tangible benefits of agility, scalability, and cost savings. Many analytics applications are natural candidates for migration to the cloud because they require very large amounts of data and computing power, but only temporarily while large-scale models run. The migration is on, the opportunities are great, and the landscape for analytics in the cloud continues to change. As organizations have gained collective experience moving analytics to the cloud, we have a clearer picture of migration benefits, options, and best practices.

Read More »