International Institute for Analytics

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…

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.

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.

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.

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.

To help attract quality candidates in the data science industry, it’s important to understand what these individuals are typically looking for in a job, how you can best adjust your recruitment processes to communicate the benefits of your analytics organization, and methods of improving the experiences candidates have with your organization throughout the job evaluation process. In developing this report, we polled and interviewed 45 members of the data science community across the U.S. to help inform our recommended strategy.

This paper explores the processes and success factors for operationalizing customer analytics by drawing on the experiences of four varied financial services institutions: a large credit union, a full-service bank with a strong focus on retail customers, and two firms focused on small and growing business customers. We’ll profile each and then look across them for commonalities and lessons learned.

In November 2017, IIA published the multipart research brief “Improving Analytics Measurement.” Our objective was to take the pulse of analytics measurement practice, including finding patterns of metrics usage and identifying innovative and useful metrics. Consult the brief for a range of analysis and recommended actions on improving analytics measurement. For that research, 19 organizations participated in a predominantly qualitative survey of how they measure ¬the work of analytics in their enterprises (we did not ask respondents to exhaustively list metrics in use). But based on that research, we were able to develop and conduct a broader survey of 52 specific metrics in use. This companion research brief shares and provides commentary on the results.

All too often resource allocation against prioritized business problems is either “next person up” or “Jane should work on this because she delivered well on her last one.” And when selecting technology, buyers may fall into the trap of looking entirely at technology features rather than if anyone in the team will be able to integrate and/or use the technology. Clearly none of these approaches take full and efficient advantage of the distinct mix of relevant attributes within team members that impact an effort’s successful outcome: [a] truly proficient skills with relevant use case experiences.