International Institute for Analytics

Data management for analytics is an exciting field these days, to put it mildly. But the excitement often takes the form of confusion and anxiety because it takes daunting effort to navigate through what the multitude of commercial and open-source data management platforms do and where they should be deployed in order to promote better business operations, decisions, and outcomes. Organizations are increasingly feeling the pressure to more creatively democratize and monetize vast amounts of existing and new data in a variety of formats. Managing today’s data and putting it to work poses new challenges for the CIO, CDO and CAO.

Business leaders already have a tall stack of challenges on their desks in the realm of understanding and responding to customer needs. Adding to this challenge is the emergence of high-velocity, high-density business transaction data that describe how the firm’s customers conduct their business relationships and interactions. This rich base of transactional data represents a vast opportunity to profile the behavior of customers, to understand their unmet needs, and to predict their likely next actions. This data also presents a remarkable opportunity to respond by making data-driven decisions to allocate the firm’s finite resources to the most opportune interactions for the most ideal set of customers. With a continuous and evidence-driven feedback loop, a business can influence the customer’s decision-making in a way that simultaneously unlocks value for both the customer and the firm.

Last year around this time I made a lot of predictions about what 2014 would hold in store for supply chain… Let’s revisit some of these, and see what has happened (or what hasn’t!) Here are my predictions from 2014, and an update on what I think will happen in these areas.

When I think of the data economy, I usually focus on either online firms like Google and LinkedIn, or large, established companies like GE and Monsanto that have invested in data and analytics-based products and services for their customers. These companies use their own data to develop their own products and services. But there are certain advantages from having third parties do this sort of work for you. They can provide independence, scale, and processes that are solely focused on helping to monetize your data.

A successful analytics program should translate quickly into monetizing the data – where the data (and learnings from this data) helps the organization increase revenue, manage risks and pursue new product or service innovation. To accomplish this, what’s needed from a technology perspective? The key is to remove barriers and latencies associated with analytics lifecycle steps and remove the processing constraints caused by complex big data requirements. Today, the adoption in-memory analytics is growing in hopes that it can deliver speed, deeper insights and allow companies to do more with the data they have to solve a variety of business problems. As sophisticated data discovery and analytical approaches (descriptive analytics, predictive analytics, machine learning, text analytics, etc.) become commonplace, the efficiencies of “co-locating” both the data and analytical workloads are essential to handle the processing needs.

On December 10, I helped facilitate the International Institute For Analytics (IIA) webinar to announce our analytics predictions for 2015. I will provide additional commentary on several of the predictions that I am especially fond of in this New Year’s post.

As we close out 2014, and look back on how the analytics space has progressed over the past 12 months, it feels like we’ve seen a real inflection point in terms of the way companies view analytics. Organizations, now more than ever, are becoming intentional about building high-performing analytics programs.

Research conducted by the International Institute for Analytics (IIA) and sponsored by Dell, shows that advanced analytics, defined as predictive and prescriptive analytics rather than simple reporting, is increasingly being adopted by both midmarket organizations and large enterprises in an effort to gain a competitive advantage in their markets.

There is little doubt that the intelligence sector in the US - including the Central Intelligence Agency or CIA, National Security Agency or NSA, parts of the Federal Bureau of Investigation or FBI, Homeland Security, and many other agencies - and elsewhere is quite accomplished at several aspects of data management and analytics. It’s also clear that businesses can learn from these organizations in several respects. Below are a few lessons from which business leaders could draw.

Want to uncover interesting ways to drive value from data? Look for ways to provide value to external, third party stakeholders through analysis of the data that your organization initially collects just for itself.