International Institute for Analytics

More business applications, including both everyday and advanced analytics, are moving to cloud-based platforms. They seek the advantages of agility, innovation, and cost-effective high performance. But many cloud migrations fail to gain all these potential benefits. To maximize the performance and value of analytics in the cloud, weigh the options, and choose the right approach to migration.

While the role of Chief Data Officer (CDO) is becoming commonplace in large corporations, the responsibilities of the role vary greatly. In fact, the variation in job expectation is substantial enough to delineate several distinct CDO personas into CDxOs.

Last week, IIA attended the O’Reilly Strata Data Conference (@StrataData) in New York City. The conference tagline was “Make Data Work” and it provided a comprehensive overview of continued innovation in the big data and analytics ecosystems. In this blog, VP David Alles covers the top seven highlights from the conference.

Artificial Intelligence (AI) is one of the hottest topics in the analytics space today. Within a two- to three-year span, AI went from relative obscurity to an extreme level of industry attention and media coverage. As a result, organizations that barely knew how to spell artificial intelligence a few years ago are now charging full steam ahead in pursuit of AI initiatives. In many ways, this is a good thing. After all, AI is quite powerful and has the ability to drive tremendous value if applied appropriately. However, this attention also has some negative consequences. Most notably, the topic of AI is so full of hype today that many organizations are struggling to separate what is real and achievable from what is pure hype and wishful thinking.

Everyone who has lived within the world of analytics has seen cases where different parts of a business have made use of slightly differing definitions of core business metrics. Sometimes these differences lead to only minor and non-material disagreement. At other times, the differences in definition can cause massive divergence of reported results and related actions taken. Organizations must ensure that where differences exist in definitions those differences are either reconciled or clearly labeled and articulated to provide the proper context. When is the last time your organization took the time to really examine the strengths and weaknesses of the metric definitions being used on a daily basis?

The purpose of this research brief is to summarize the key elements of DELTA Plus and Five Stages of Analytics Maturity, and discuss how these two frameworks can be used to understand analytical maturity in your organization. Two new components were added to the DELTA model, creating the DELTA Plus model. The DELTA Plus Model Framework encompasses the five foundational elements of a successful analytics program (Data, Enterprise, Leadership, Targets, and Analysts) and introduces two new elements (Technology and Analytical Techniques) required for high performance.

This fall IIA is bringing together some of the top analytics professionals from around the country to speak at the 2018 Analytics Symposium – Atlanta.

Analytics advances are accelerating at an unprecedented pace, and perhaps nowhere more quickly than in marketing. Advances in computing hardware, software applications and delivery channels enable marketers to deliver timely and relevant messages to customers in ways that were unimaginable five or 10 years ago. New data sources are becoming available, and innovative ways to use data to shape the customer experience continue to push the envelope of possibilities. However, keeping track of the advances and being able to decipher which are relevant to your reality has a direct inverse relationship to the rate of change.

Inquiry:

Our data and analytics group is six months old, and we’re challenged by many things: creating an intake process, developing the community, managing data ownership. We’re a heavily top-down organization—with the executives dictating our strategic initiatives for analytics—so we wonder if an analytics pipeline would fit into our culture.

Everyone seems to agree that analytics, AI and Big Data are changing the world and that cloud computing is accelerating the adoption of these technologies. Combined, they are disrupting traditional business models and creating new market opportunities. If you are an analytics leader, data scientist, ML engineer, software engineer or other related role you see first-hand the power of analytics to unlocked hidden value in existing businesses or to enable completely new businesses. Are you using your first-hand knowledge of analytics to shape your investment strategy?