International Institute for Analytics

There is a tendency to jump into something called a “data strategy” without understanding what it is at its core. In IIA’s view, at the center of a data strategy is a schematic of an entity’s information economy1 with in-depth awareness of the constituent needs on the demand side of the economy — the data consumers. Because ultimately the primary purpose of a data strategy is to solve questions about how to improve the availability, timeliness and quality of data, in that order of priority, for the constituencies demanding it.

My Immediate Response:

1. ALL HANDS ON DECK FOR OPERATIONAL REPORTING
   There will be new reporting needs and the frequency of information will need to speed up. Look for options and data that will be impacted by the current crisis. You will need to figure out how to quickly deliver information even if not perfect. Do the best you can, provide any glaring risks. Executives are starving for information. Make sure to provide the information to the most senior people first. Analytics teams can assist operations teams, data science people should even jump in as they have some of the best skills even if they are not using their modeling skills. Work with IT to pull information more quickly. Accept that you will be reporting off of sandboxes that are not production quality, get the exceptions from Risk and IT. You may need to time shift your team to earlier in the day as the executives will want things first thing when they come in if possible. Blend new data sets together to provide insights. Make sure to grab the customer feedback as even simple things like a word cloud may help.

As we move rapidly toward an AI-first world, laws and regulations are not keeping pace. Laws were made to govern human interactions, and as such, the legal system is playing catch up to figure out how to oversee autonomous systems. However, even in the absence of clear laws upon which to manage responsible applications of AI and autonomous use of data, organizations must still take action against their considerable liability and risk.

Most analytics organizations at large companies do not own the entire lifecycle of their analytics applications. Instead, often-distributed analytical applications teams have to work with their partners in IT/IS organizations, as well as seek requirements, advice and consent from legal, compliance and governance functions within their organization. The essentially distributed nature of these work streams frequently results in inefficiencies or even full-on breakdowns in progressing an analytics application from proof of concept (PoC) to production. Success in distributed environments requires an overt, agreed-upon, stepwise plan along with the emotional intelligence to navigate the necessary conversations with your colleagues to build that plan.

Organizations today face an increasingly challenging business environment. Across industries, new companies and nimble competitors are taking advantage of analytics and leveraging the full potential of the internet, disrupting traditional business models and markets.

Cloud computing and open source have caused fundamental changes in analytics infrastructure, enabling the introduction of new technologies such as artificial intelligence and machine learning. The most nimble, innovative companies have quickly taken advantage of these new technologies — and the analytics they enable — to gain a competitive advantage.

Traditional companies are struggling to deal with this complexity and effectively compete on analytics. Though many top executives realize that high-quality data, analytics and AI are critical to the future success of their companies, up to 70 percent of analytics initiatives and projects fail to meet their objectives.

Since the dawn of Wall Street, investors have sought an edge: an advantage they can leverage to beat the market. Gaining some type of edge is the only way to win in the brutally competitive, zero-sum game of capital markets.

Today, that edge can come from insights gleaned from information found in the exabytes of data created in the real world — on websites, from smartphones and sensors, through supply chains, and from countless other types of real-world data. Collectively, these sources of unstructured and large nontraditional data are called “alternative data.”

The life expectancy of large companies is declining more quickly today than ever. The leading reason for this diminished lifespan is in their persistence to combat challenges that do not alter the company’s success or failure — they are tilting at windmills in a way not too dissimilar to Don Quixote fighting imaginary giants. An incredible amount of resource can be wasted fighting the wrong problem; instead, I propose that large companies methodically chase innovation and move to new business models to survive and succeed.

Each year, the International Institute for Analytics ends the year with a look at the latest analytics trends and the most pressing analytics challenges currently facing organizations. Our predictions are based upon our day-to-day work supporting and advising analytics leaders and organizations. We take advantage of the breadth of expertise and cross-industry perspectives we encounter every day from our clients, partners, and members of the IIA expert network. This is our 10th annual look toward the upcoming year, and our annual Predictions and Priority research brief and the associated webinar have become among IIA’s most popular content of the year. This year, we’ve stuck with our approach of augmenting each of our predictions with a specific priority for leaders to focus on as they attempt to address that prediction. As a result, each priority provides specific guidance as to how to best prepare for, and adapt to, its corresponding prediction.

Open-source software (OSS) is inevitable. For most corporations it’s not a question of “will we use OSS?” The right question is “how much OSS do we already have in production?” More than 90% of IT organizations have OSS in their mission-critical systems. Many commercial software products — from vendors such as IBM and Microsoft — also contain open source software.
Herein we explore these major OSS subjects:

1. OSS history and concepts
2. Quick tour of OSS analytics
3. What does OSS really cost?
4. Positives and negatives of OSS analytics
5. Guidance

To set the stage, the analytics organizational structure I’m presenting below pertains to an analytics organization between 60 and 120 people; this is the size that seems to be a sweet spot for an effective and efficient team (large enough to have specialized skill sets, but small enough to effectively demonstrate the benefits of the team). Moreover, I’m presenting such an organizational design in consideration of an analytics effort at an established, traditional corporation, not a digital native. Digital natives will break down differently with more need for data science and data management. With 60 to 120 people, I prefer a centralized organization with P&L Analytics/Ad Hoc Analysis dotted-lined to their business partners.