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If you are instead trying to educate a broader spectrum of the population, including elite students, and you aren't using analytics, you won't know what's going on.

use of analytics is being driven by the objective of student success

at ASU, you've created a culture of innovation using analytics.

We've had distinguished professors in the hard sciences, such as physics, say they feel ashamed that for thirty years they didn't know why certain students were learning or weren't learning. They had no idea of the reasons

we have infinitely more information to allow us to help students be successful. Analytics are not the end. They are the means to the end: the successful world-class university graduate who has come to us from any family, any background, any income level.

Crow: For us, to be a public university means engaging the demographic complexity of our society as a whole. It means understanding that demographic complexity. It means designing the institution to deal with that demographic complexity. And it means accepting highly differentiated types of intelligence: analytical intelligence, emotional intelligence. Students are not of one type but are of many, many types.

At ASU, I could see that we would not be able to innovate fast enough without analytics. Without analytics, we can't understand what's going on, we can't understand the complexity of what we're trying to do, and we can't measure our progress. We needed tools to help us make better decisions—about everything. How should we design academic advising? How should we design individual courses? How should we design the overall pedagogical structure of the institution? Every facet of the institution requires robust analytics.

We wanted an academic advisor or a student affairs dean or an associate dean to be able to have a 360-degree analytical view of a student

FERPA is concerned with the university releasing information outside of the institution

And so, with the right training and the right controls and the right discipline, we were able to build this 360 analytical tool, which has been remarkably helpful for us in terms of creating a new way to advise students.

partly motivated by the Virginia Tech shooting incident,

At ASU, we are all responsible for the care, well-being, and success of our students.

It can't be just the English department or just the football team that is aware of unusual behavior, with no other campus department knowing.

We need to be graduating 90 percent, but we can't do that without enhanced analytics,

when the federal government calculates graduation rates, it calculates rates only for first-time, full-time freshmen. It does not calculate whether or not students actually graduate from somewhere else. It does not count transfer students who ultimately graduate. So we have a grossly underreported performance from some institutions, creating bad data that then goes into bad policymaking. We need more flexibility and more adaptability, and we need more recognition that students are moving around.

There are no more excuses. If you use these analytical tools, you will know where you are, you will know what you're doing, you will know if what you are doing is working or not, and therefore you will know whether or not you need to be doing new things customized to fit your particular school or your particular demographic to be successful. We are underutilizing these tools.

Crow: We're next headed away from hard, confined definitions of learning timeframes.

Minitab, Systat and JMP are all growing but at a much lower rate than either R or Stata.

R still dominates the discussions on the more statistically-oriented forums

Oliver likewise suggests that businesses work with data scientists. Data scientists and engineers provide the necessary expertise required to make the data lake a successful data and analytics tool.

Configurable Ingestion WorkflowsNew sources of external information will continuously be available. Make sure to have an easy, secure and trackable content ingestion workflow mechanism that can rapidly add these new information into the data lake.

Knowledgent states that "without a high-degree of automated and mandatory metadata management, a Data Lake will rapidly become a Data Swamp" and that "attributes like data lineage, data quality, and usage history are vital to usability".

Data lakes must be industry-specific to cater to the industry's unique needs.

How to Ensure Data Lakes Success