International Institute for Analytics

Inquiry:

Headcount is currently calculated in a number of different ways, and before we can move on to more interesting HR analytics we need to reconcile the headcount numbers across the enterprise. What’s the best way to work through this initial hurdle?

Inquiry:

We’re growing our analytics team from 10 to 25 people in the next year. In addition, we’re transitioning to an Agile approach and aiming toward operationalizing RPA. The current team consists of two data engineers, one project manager, one designer, three BI/visualization analysts, one QA specialist, and two product owners. Do you have any advice that will help ensure the team’s success?

Inquiry:

We’re just getting started with HR analytics, and we’re struggling with building a baseline reporting capability. There is an HRIS team; should HRIS own the analytics? Where should we start with our initial HR analytics projects? We use Workday for human capital management; can we use it for analytics too?

Inquiry:

We’re planning an enterprise rollout of an automated machine learning (AutoML) tool like DataRobot. What are some of the caveats that we should watch out for?

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.

Inquiry:

We just formed a Center of Excellence (CoE) for our data science community. We’re finding ourselves challenged by the translator role. Should we up-skill current members, bring in external people from the business, or not have anyone at all?

Inquiry:

Our advanced analytics team has been tasked with looking at the state of our data program with an eye toward arriving at a new future-state framework. Currently, our biggest challenge is that our internal customers’ processes are not set up to consume what we provide. Do you have any advice?

Since the dawn of Wall Street, investors have sought an edge: a source of advantage they could leverage to beat the market. It’s the only way to win in the zero sum game of institutional investing. Today that edge comes from information found within the exabytes of data we all create every single day of our lives; something made possible by the technology boom of the 90s along with Moore’s Law of improvements in processing power and data storage. The prevailing belief is that the predictive signals buried in the “data economy” have the power to move markets. This is the age of alternative data, and investors are racing to get their hands on it. With an estimated market size of $7 billion by 2020, alternative data is transforming capital markets. Abraham walks you through how investors are finding, leveraging, and profiting from alternative data today.

The vast majority of people building analytics and data science processes have every intention of being good and ethical. As a result, most potentially unethical and evil processes arise in situations where that wasn’t the intention. The problem is typically that proper focus and governance is not in place to keep analytics and data science processes on the side of good. On top of that, what is good and what is evil isn’t nearly as clear cut as we’d wish it to be.

Information Economies in Organizations

The data warehouse revolution began in 1991 when Bill Inmon published Building the Data Warehouse. Inmon observed, early in that book, that every organization has a naturally occurring information economy, and that most naturally occurring information economies were inefficient, duplicative and prone to produce suboptimal decisions.

This observation of Inmon’s has not gotten anywhere near the credit, or attention, it deserves. A decade’s worth of collective practice in advanced analytics should tell us that everything we know about real-world economies applies to our information economies. There is demand for information by people and functions in an organization, and there is a supply of (some of) that information. There is (some amount) of technical and procedural infrastructure – some kind of market — to bring demand and supply together in an organized way. That “market” infrastructure is often partial, fragile and in some cases ineffective. There are competitive alternatives (like cloud service providers and SaaS vendors), over- and under-regulation (various data governance models), excessive demand-side taxation (cost allocation strategies), failure to invest in infrastructure, and all other elements of economies.

When organizations are planning strategy-driven large-scale advanced analytics programs, they should begin their planning by characterizing their as-is information economy.