Research

On the Road: Data Down Under

By Jack Phillips, May 25, 2017

As the leader of a world-class service organization, I’m on the road a lot meeting with research clients and prospective customers alike. Each week I try to meet or speak with at least one team at IIA’s ever-growing group of high-performing research clients. I thought it was time to start sharing some field notes of what I’m seeing and hearing. Last week I finished a trip to New Zealand and Australia, visiting with 10 companies from four distinct sectors in three major cities.

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Corruption is essentially the abuse of entrusted power for private gain; it uses a company as a tool for personal gain which is contrary to the official or fiduciary duty of the organization. Companies serious about reducing fraud within their walls need to recognize that regardless of their size and type, corruption is one of the most pervasive and impactful fraud types. It can occur in any department/division making purchases or from the other side of the business transaction in the company sales cycle.

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Any new leader in any field will have to face several challenges in the first few months on the job if he or she is to succeed. On May 11, IIA hosted a webinar where co-founder Tom Davenport and I discussed some of the challenges analytics leaders face and what they can do to ensure success. While the action steps apply broadly, we focused on how they apply specifically within the realm of analytics. This blog explores the key themes at a high level.

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Balancing Analytics Agility and Stability

By Bill Franks, May 11, 2017

There have been many science fiction stories (as well as video games!) that revolve around the tradeoffs between powerful, strong, hard to harm combatants and those that are small, nimble, but easy to harm. Both have their merits and both can be useful in different situations. However, the same profile doesn’t work best in every situation.

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Understanding Power in the Digital Economy

By Geoffrey Moore, May 09, 2017

We are all stakeholders in the economic systems within which we live and work, and the better we can understand their dynamics, the more likely we are to navigate them successfully. For the most developed economies of today, this means understanding the transition from an industrial to a digital economy, and specifically, how economic power is migrating from familiar to unfamiliar sites.

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Move Your Analytics Operation from Artisanal to Autonomous

By Thomas H. Davenport, May 02, 2017

Many organizations today are wondering how to get into machine learning, and what it means for their existing analytics operation. There are many different types of machine learning, and a variety of definitions of the term. I view machine learning as any data-driven approach to explanations, classifications, and predictions that uses automation to construct a model. The computer constructing the model “learns” during the construction process what model best fits the data. Some machine learning models continue to improve their results over time, but most don’t. Machine learning, in other words, is a form of automating your analytics. And it has the potential to make human analysts wildly more productive.

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When I attend industry conferences or speak with Chief Data Officers (CDOs) and Chief Analytics Officers (CAOs) of large financial institutions, one popular question that arises is, “What do you hear about open source analytics in other large banks? Is it ready for production?”

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Less than 30% of companies have a process in place to measure the return on investment of their emerging technology projects according to a recent survey of 150 CIOs and CTOs. Too many companies still measure the performance and business value they get from IT based on the old work of IT rather than the new work of IT.

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As I have discussed in prior blogs, the focus of enterprise computing for most of the 20th century was on deploying Systems of Record, first on mainframes, then minicomputers, then client-server systems. These were and continue to be the transaction processing backbones that drive global commerce. In the first fifteen years of this century, however, we have seen a profound shift in spending emphasis away from Systems of Record, which are now in maintenance mode, and toward Systems of Engagement, the focus being on connecting with customers, partners, and employees in digitally effective ways leveraging the ubiquity of smart phones. That movement has been inside the tornado for some time now such that, while there will be a lot of money spent here over the next ten years, I think it is time to look ahead to the next wave.

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A New Chapter In The Analytics Journey

By Bill Franks, Apr 13, 2017

As I write this, I am finishing a major leg of my personal analytics journey. As many readers are likely aware, I will be leaving Teradata this month. I had a terrific 14-year run with Teradata where I made a lot of friends, worked with some amazing clients, and got to witness firsthand how the world’s largest organizations have dealt with the rise of big data and analytics. Teradata treated me well and I like to think that I, in turn, contributed a lot to the company. It wasn’t an easy decision to leave, but I came across a great opportunity and every good run has to end at some point.

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