eLearning Roadtrip

When I Think of Student Success

The tendency to link software with student success has accelerated since 2012, as the educational technology sub-sector known as "integrated planning and advising for student success" has gained prominence through a series of grants funded by the Bill & Melinda Gates Foundation to colleges and the tech partners they have selected to help the colleges improve student performance. These days, more than 100 companies offer integrated planning and advising technology platforms and tools for student success includes that serve a wide variety of functions, from deciding where to go to college, to facilities and course scheduling, from helping students select select majors to staying on track with program completion. Student success is a huge part of the current ed tech market scene. (Disclosure: In my current day job, I work for a company with a large part of its business dedicated to Student Success.)

EDUCAUSE has led efforts to help institutions explore both the promise and the risks of Integrated Planning and Advising for Student Success (iPASS). Per their website:  "iPASS is an integrative approach to student success that promotes shared ownership for educational progress among students, faculty, and staff. It encompasses services that help students formulate and advance toward educational goals, including advising, counseling, progress tracking, and academic early alerts. iPASS technologies can contribute by documenting and tracking students’ educational plans, improving data analysis, offering self-service resources that reduce advisor workloads, and triggering interventions based on student behavior or faculty input."

There have been two Gates Foundation-funded rounds of grant-making by EDUCAUSE in support of student success. The first was the Integrated Planning and Advising System project, designed to get schools and their tech partners to collaborate on getting platforms and practices to work together better. I was involved in the first IPAS round when the Predictive Analytics Reporting (PAR) Framework was contracted by the Gates Foundation to evaluate the 19 "student success + software initiatives" that were funded in that first go-round. We visited the 19 IPAS campuses that were working with specific tech company partners to further the implementation of a tech platform that could help campuses with student planing and advising platforms. PAR had used the PAR Student Success Matrix we had developed to help our own PAR members make the connection between predictions of  student risk with selection of interventions. We were able to determine and demonstrate measured effectiveness with particular groups of students under particular circumstances - sort of like getting the most effective prescription for dealing with a particular medical condition. We had been excited about our own results; the fact that we were going to be able to evaluate student success impact with our matrix of time-in-program and actionable predictors with a broader set of institutions was thrilling.

By the time the second round of grants were funded, the name of the initiative had become "integrated Planning and Advising for Student Success: iPASS". The connection between tech and advising (e.g. watchlists, early alerts), tech and planning (e.g. curriculum pathway planners, strategic scheduling), program fit and course navigation (e.g. obstacle course GPS), integrating predictive analytics with real-time student activity stream data.) As it happens, the iPASS initiative helped spark PAR Framework's acquisition by Hobsons in January, 2016, when two state systems of education proposed integrating PAR's predictives and intervention measurement with Starfish's real time student activity data stream used to build student watchlist/advisement capabilities as part of their iPASS grant proposal.

5 years, two major initiatives and several rounds of small planning and implementation grants later, EDUCAUSE has produced a library of student success resources for educational practitioners, on everything from "Things you should know about IPAS" (yes, that acronym is different than in the previous paragraph; more on that in a bit) to implementation handbooks, data tables and evaluation reports. and  from https://library.educause.edu/resources/2013/8/integrated-planning-and-advising-services-ipas-research.

It has been great to be a participant in this wave of innovation in education, where data is being used as currency for more effective decision-making in schools and colleges. No doubt, when we look back on how decisions were made in 10 years from now, we are going to wonder how we managed to live without platforms that help visualize patterns, red-flag student risks before they become problems, provide a 3D map of an academic transcript that can cross-walk among formal and informal learning experiences. 

Even so, I need to say something.

When I think of student success I never, ever think of software first. 

Software can make things easier to do, of course. It offers far better ways to keep track of searchable information requiring any manipulation. I'd rather build formulae in a spreadsheet, and have huge collections of data fueling explorations that report results in approachable visual reports and dashboards. Learning management and content management and curricular tracking, assessments and performance support are all more available, more accessible and more targeted at specific points in the educational experience than ever before.

Just as data associated with online and digital transactions help inform marketeers of our retail intentions even before we know for certain, ourselves, patterns of students behaviors can raise red flags before students are even aware that they may be drifting into dangerous territory for "students like them".

Technological inventions, applied to performance and learning problems, can help solve those problems. In doing so, the inventions become innovations, human ingenuity applied to solve performance problems in novel and compelling ways.

Innovative educational entrepreneurs  - "edu-preneurs', if you will - see an opportunity for tech in schools and are compelled to create products that promise to disrupt education as we know it. They point to deep data science and predictive analytics as if these analysis techniques are beyond the norm.

It's seems important to remind ourselves that in education, the most most important innovator in this value chain are not necessarily the ed tech entrepreneurs. Rather, the ones on the front lines of teaching and learning, supporting student achievement putting new tech tools to the test in the service of student success are todays true innovators. 

It's important to have shared articulated expectations of "the norm", so that one can identify those not yet achieving at that level, and how one inspire those working "above average" to continue to improve. Common expectations of meaning and outcomes are essential elements of student success that gives all students a shot at achieving their dreams. We need to include ALL students in our considerations of success, not just those attending college for the first time, going to school full time, or living on campus. We need to keep the student at the center.

With thanks to Kalah Burks and Rebecca Wheeler, University of South Alabama, for the great conversation that shook this post out of my head.

The #OEB14 Debate: Is Data Corrupting Education?

Online Educa Berlin is a major international gathering of online/elearning/distance learning/mobilized/datified learning innovators. This year I was a participant in the Online Educa Debate, an annual featured event of the conference. The house motion for 2014 was "Data is Corrupting Education". Inge deWaard and I were asked to speak in favor of the house motion; Dr. Viktor Mayer-Schönberger and Dr. George Siemens spoke in opposition. I have provided the full motion below, in italics, for your reading pleasure.

MOTION: The House believes that data is corrupting education.

• Is the invasive extensive profiling and rigorous achievement test, backed by for-profit organizations, skewing the means of gathering information and determining relevance?

• Will a focus on data corrupt the processes of learning, training and development or will it reveal new unimagined insights that will make way for streamlines, personalized and effective learning

• Is big data destined to be in the hand of a few, or will openness prevail for the benefit of all?

At first, I was completely nonplussed by this challenge. But the more I got my head wrapped around ways to support it, the more I stretched my thinking, which of course is the whole reason one does these things. Collaborating with Inge was delightful; having the opportunity to present with Viktor and George was a real pleasure. I am very pleased to have participated.

The video of the Debate will be available soon, and I will share it when available. It was lively discussion, among we debaters and with participants from the audience. For a while I even thought the crowd was with Inge and me,  but I have to admit, even I would have had a tough time voting in favor of the motion as written.

While we lost the vote, I think we helped remind people that we all need to be mindful about the implications for unexpected consequences that innovative work may bring to practice. In the sprit of sharing have posted my notes of my opening argument, below:

Higher education has typically been organized around authority-driven models of decision-making; that is, the more senior among us are generally afforded positions of authority based on experience and expertise. The emergence of analytics tools and platforms are making it very clear that data can provide an even more complete, and certainly a more nuanced picture than expertise alone. 

Educators are just beginning to understand the IMPLICATIONS of data analytics, where mining data for insight to support decision-making is the focus of exploration. While it is clear that there are great opportunities for gleaning heretofore unimagined insights, the fascination with data makes many uncomfortable. We have come to realize that the patterns of our data generation, our online engagements, transactions and interactions, tell people things about us or things we are doing that that we never could have anticipated or expected.  It means that intelligence about us can be gleaned by those who have access to our various data streams. Are you aware of all the stories that your data are telling other people about you?

Our current fascination seems to be about data as a meme – an idea that spreads from person to person within a culture. Data-as-meme, especially BIG data, has come to stand for something bigger that the data themselves. It stands all of our excitement about the possibilities that they offer for risk mitigation and personalization. However it also stands for the doubts and fears about how all these data are going to affect how we do our work, how we go to school, how we live our lives, how we engage as citizens. 

Data without context has marginal value. However, data WITH context is information. Information is power.  And as the British historian Lord Acton reminded us, Power corrupts. Absolute power corrupts absolutely.  http://www.acton.org/pub/religion-liberty/volume-2-number-6/power-corrupts

Simply stated, corruption is the self-serving deviation from an ideal. If one accepts this simple description, then one CAN make a case that data may very well be used to less-than-ideal ends. It might mean that we actively exclude some learners at the expense of others. We may push people of varying predicted skill levels into programs of study based upon test results, rather than on personal passions and interest. Will data have an effect the way that we approach our pedagogies? Absolutely. It this going to change how educational professional approach our work? Absolutely. How do YOU feel about instructional design as a data driven algorithm? How about licensing digital content where the price point is determined in part to how well students do in the class where the content is used? How do faculty feel about the prospect of expectations that we will be lurking in the lives of our students to see what and how they are doing at every single moment? How do YOU feel knowing that students aren’t going to be the only ones who are going to be tracked and measured in these ways? 

I don’t think that data, by themselves, have corrupted, are corrupting or will corrupt education.  I do worry about uninformed, naïve or even nefarious uses of data. I worry about data being used to punish, to limit access, constrain or restrict participation.

While I am excited about the possibilities for personalized, customized learning experiences, I am also fairly certain that educational practitioners who use data badly, non-transparently, or naively are very likely going to give data a very bad name.

For my .EDU friends: Here's How Venture Capital Works

The education sector's current fascination with innovation, distruption and entrepreneurship is being fueled by the emergence of start-up commercial ventures that create new products and services as solutions for big hairy audacious problems plaguing today's schools and students. 

An April, 2014 post by CB Insights noted that investments in education technology hit a record high in Q1 2014 as investors poured more than $559 million across 103 deals in the first three months of this year.  Of note, Ed Tech funding in Q1 2014 represented nearly 45% of total funding to the sector in all of 2013. The record quarter comes after Ed Tech investments hit $1.25B across 378 deals in in 2013, the second-straight year of over $1B invested across the Ed Tech sector.  With everything from MOOCs to tutoring marketplaces to standardized APIs for K-12 schools getting funding, investors are clearly bullish on the education industry.

So what could possibly be wrong with getting rounds of other people's money to help you grow your big educational idea into a product that becomes the NEXT BIG THING?

Well, nothing...except you need to pay back all the money that everyone had given you. With interest and at a prenegotiated rate of return. That means that once you take someone else's money as an investment in your hot shot edu-preneurial company, EVERYTHING you do is going to be focused on paying your investors back. 

Aral Balkan recently blogged about his experience with a new social media platform called Ello. His post caught my eye because it included a pointed summary of how venture capital works. Having just spend the past several months at various educational technology meetings and trade shows where the buzz over acqusitions and investments is ever-present, I found myself sharing the following paragraphs with my friends from education who may not have had the experience of working with investors:

"Here’s how venture capital works: you go to an investor, before you’ve even built the thing you’re building and you tell them how you’re going to exit. It’s called an exit plan or exit strategy. You tell them, for example: “Hey, we’re going to get 100 million people using our new platform in two years time, how much will you give me for 100 million people?” And they go “Umm, we’ll give you this much for 100 million people because we’re pretty sure we can get that amount back several times over when we sell those 100 million people in an exit either to another company or in an IPO.”

"When you take venture capital, it is not a matter of if you’re going to sell your users, you already have. It’s called an exit plan. And no investor will give you venture capital without one. In the myopic and upside-down world of venture capital, exits precede the building of the actual thing itself. It would be a comedy if the repercussions of this toxic system were not so tragic.

"Let me put it bluntly: if a company has taken venture capital, you have already been sold. It’s not a matter of if, it’s simply a matter of when. (Unless the company goes under before it can exit, that is.)

"A venture-capital funded startup is a temporary company that has to convince enough people into using their platform so that they can make good on the exit they promised their investors at the very beginning. It is the opposite of a long-term, sustainable business." (Italics are mine).

You can read Aral Balkan's complete "Ello, Godbye" blogpost by clicking here.

I draw your attention to this article because every time I hear someone confide that their platform provider is a rising VC funded firm that is going for its next round of money, I wonder if these education customers understand the degree to which EVERYTHING their vendor provider must now do is focused on meeting their investors' expectations.

Driving the #LearnerAnalytics Bus

The message emerging from this week's University of Maryland University College (UMUC) Learner Analytics Summit (#innovatelearning) was explicit and to the point: Forward thinking instititutions are stepping up to opportunities for improving post-secondary experiences for students using data to inform proactive, evidence-based decision-making. 

No doubt, we all hope that the answers to questions we can't sometimes even articulate yet are going to emerge from dashboards and apps, fully formed, as if from the Magic 8-Ball. Sadly this is not the way things work. We need to build capacity for decision-making in a data-informed world just as much as we need to figure out the exo-structure required for systems to interoperate and for intelligence to be shared.

Matt Pistilli (Purdue University) tweeted one of the great tweets of the event when he noted that "the data do not speak for themselves. I have, in fact, been in the room with them, and they did not speak a word #innovatelearning".  It's a great reminder that if we are going to get anywhere quickly on this journey, we are going to need to be the ones driving the bus on learner anaytics adoption and creating organizational cultures of evidence-based decision-making.

Malcolm Gladwell has talked about tipping points, those moments when a new practice or innovation, idea or trend emerges into public consciousness. Learner analytics are at the point of tipping into public consiciousness in the war on student loss.  

Congratulations to Karen Vignare and her team for convening us, to Bill Moses and the Kresge Foundation for the means to do so, a high-five to Linda Baer for her guiding hand, and thanks to all the presenters and participants for helping us start figuring out how to drive the learner analytics bus, on the journey toward Evidence-Based Decision Land. (Cue unicorns, bluebirds and rainbows.)

Karen Swan and the Amazing Multi-institutional PAR Student Success Matrix

Back in June 2013 the PAR Framework openly published an intervention assessment framework called the Student Success Matrix to help PAR members figure out how to respond when confronted with predictions of which students were likely to drop out. "What should we do to respond to these predictions showing our students at risk?" our members asked.  At that time Dr. Karen Swan, Stukel Distinguished Professor of Educational Leadership at the University of Illinois Springfield, reminded us that there are already lots and lots and lots of things that academic advisors, faculty and other student success professional already do to help students succeed in school. Going back through the professional literature, she and Dr. Peter Shea, SUNY-Albany, helped us find models of student engagement and participation relevant to our work at hand.

Karen proposed a framework to map interventions using predictor behaviors of success and points in the academic completion cycle.  Over the past year Karen and Peter have worked closely with Sandy Daston, PAR Framework Director of Student Success and the entire PAR SSMx working group (Hae Okimoto, University of Hawaii System; Jennifer Freed, Dan Huston, and Fermin Ornelas, Rio Salado College; David Shulman, Broward College; Denise Nadasen, Jack Neill and Karen Vignare, UMUC; Patsy Moskal and DeLayne Priest, University of Central Florida; Michelle Wiley, Penn State World Campus; Mysti Reneau, Community Colleges of Spokane) to refine processes and tools for promoting intervention measurement. This group of experienced educational practitioners and decisionmaker has helped determine what can actually be done for student at risk - even down to knowing how to support those 37 students that are most likely to fail Math 101 this semester. 

PAR institutions have begun the process of inventorying, tagging, describing measuring and evaluating literally hundreds of interventions currenty in use on campuses to help student succeed. Thanks to the SSMx we have accelerated the process of connecting predictions of risk to the interventions that will mitigate risk. 

This post is to acknowledge Karen's leadership and contributions to PAR's thinking about using predictions as cataysts for taking action, and for linking interventions to predictions to underscore the importance of linking predictions to action.

 

When I realized how easy is was to get started with learning analytics, I laughed out loud

I've always wanted to write a headline like that.  

If you are like me, any link suggesting that learning analytics can be easy is worth a quick look. Even if only to sneer at the temerity of whoever would say that. So, thank you for being curious enough to click. Now that I have your attention...

Getting started with a learning analytics initiatve doesn't need to be hard. What's hard is sustaining a learning analytics initiative IF one is only doing something, anything, as a pet project, an exploration, or a trial run because someone gave you free software. 

To paraphrase the incomparable Tina Turner, we're gonna start this learning anaytics conversation nice and easy (don't worry, no dancing required). Here we go. Five quick easy steps to get started:

(1) Start with a real problem to solve. Scope your problem statement so that you maximize your probabiities of  success. Do you have market conditions to address, sneaking suspicions that there might be trouble in River City that you'd better find out about before someone else does? Maybe places where you are pretty sure you can do a better job? You might want to think of something that will help you test the waters of change without putting yourself at risk of total humiliation if it doesn't work.

(2) Figure out who needs to be involved in creating the business case to get this problem solved. How much of a priority is solving the problem you identified going to be for others in your organization? Who need to be engaged and on-board to get this done? 

(3) Determine what you need to answer questions related to solving this problem. What information do you need? Who owns it? Where does it live? How are you going to secure it? How will it be analyzed? Who can see it, work with it, publish it? How will results be used? How long will the results live, and who will have access?

(4) Determine the necessary project talent to make sure you can get the work done. It's not just about having access to people who understand how to work with data. It's about having the right team in place across the board who can execute to plan and deliver results, and making sure that you have planned for enough of their time to be effective. How will you find them? What will you need to attract their attention and keep them engaged?

(5) Develop a bottom-up budget to see if you can afford to do what you want to do. Odds are, you will need more money than you have on hand. (Well... odds are that you will end up needing more money than you think you do, too, but that's a conversation for a later time). So, where might you find the funding to do what you want to do? Who else might be interested in getting involved in your great adventure?

Obviously each one of these steps can take on a life of its own once you get going, but as a place to begin these are a great way to get your arms around your big idea and see if its possible to move ahead.

NEW NSF REPORT: New Technology-based Models for Postsecondary Learning

In January 2013 I  participated in a workshop sponsored by the National Science Foundation to help establish a 10 year vision to reconceptualize postsecondary learning. This past week our co-convenors, Chris Dede, Harvard University and Eric Grimson, MIT, released the 47 page report that captured the results of that meeting. Entitled New Technology-based Models for Postsecondary Learning: Conceptual Frameworks and Research Agendas, this is a comprehensive overview of promises, opportunities and challenges that will keep all of we post-secondary learning stakeholders on our toes for the next decade.

You can download the working draft of this paper here.

The basic premises addressed include the following:

• Advances in technology and in knowledge about expertise, learning, and assessment have the potential to reshape the many forms of education and training past matriculation from high school.
• In the next decade, higher education, military and workplace training, and professional development must all transform to exploit the opportunities of a new era, leveraging emerging technology-based models that can make learning more efficient and possibly improve student support, all at lower cost for a broader range of learners. 
• Potential risks must be managed, including those arising from the disruption of established delivery economics in our current learning institutions, the variable quality of learning outcomes these new models offer today, and the technical and conceptual challenges of better understanding how to design, develop, and implement innovative capabilities in ways that reliably deliver on their promise.

Workshop participants developed a framework for understanding this sea change and sketched steps towards a research agenda for realizing its benefits while avoiding pitfalls.

Contributors to this report include Daniel E. Atkins, W.K. Kellogg Professor of Community Information, and Professor of Information and Computer Science, University of Michigan - Ann Arbor. Lori Breslow, Director, Teaching and Learning Laboratory, MIT. John Cherniavsky, Senior Advisor, Division of Research on Learning, NSF. J.D. Fletcher, Senior Research Staff Member, Institute for Defense Analyses. Diana Oblinger, President and CEO, EDUCAUSE. Roy Pea, David Jacks Professor of Education and Learning Sciences, Stanford University. James W. Pellegrino, Distinguished Professor and Co-Director, Learning Sciences Research Institute, University of Illinois at Chicago. Bror Saxberg, Chief Learning Officer, Kaplan, Inc. James H. Shelton III, Assistant Deputy Secretary for Innovation and Improvement,U.S. Department of Education. Russell Shilling, CAPT, MSC, USN, Program Manager, DARPA. Greg Tobin, President, English, Math, and Student Success, Pearson Higher Education and yours truly, Ellen Wagner, Executive Director, Western Interstate Commission for Higher Education Cooperative for Educational Technologies. Arthur Josephson, Harvard Graduate School of Education, was our researcher.

The Reason THIS EDUdotcom May Not Be As Different From Last Time As You Think

If there was ever any doubt that we are in the midst of a new dot.com boom for education, that doubt was removed at this week's SXSWEDU. The event brought entrepreneurs and educators to Austin, Texas for four days of panels and a competition for education start-ups. I had the great fun of participating in a panel discussion on "Are Courses a Commodity?" with Myk Garn, Mickey Revenaugh and Michael Horn. Vanessa Dennen and I helped Curt Bonk rehearse his "cage match" answers while sharing beers and ribs at one of the Cengage social events. I enjoyed hearing Bill Gates speak to the crowd about his vision for a transformed education system. It was definitely an interesting mix of energies.

Gotta say, not all of it felt all that good. This morning's Chronicle of Higher Education led with a headline that explicitly named the tensions between entrepreneurs and educators. You can read the Chronicle report here.

While I was there, I kept hearing that THIS dot.com is different because it's about education, and because the tech is better and data will inform us and investors are smarter and the market is ready, and there are business plans and people aren't just throwing exuberant ideas at the wall and hoping that something sticks. 

Really? 

I have to say that the confident assurances that this educational technology boom is different just didn't make me feel any less skeptical. Because in the same way that there are some who really DO believes that MOOCs are the birth of online learning, it is clear that there is an entire generation of investors and entrepreneurs who really DO seem to believe that the ideas thrown against the wall of the 1990s are somehow less legitimate than some of the ideas that are currently being thrown against the wall today.

So let me tell you why this particular new EDU.dot.com isn't as different as visonaries and investors would have you believe.

Ready?

It's because no matter how new your tech is or how great the idea is, or how impressive the possibilities are, or the circumstances or the bandwidth or the platform or the operating system we won't figure out how to crack the code on transformation until we change the most important part of the equation. And that is the human factor. Eventually it comes down to people being ready to embrace the change. The ability to ride out the hype cycle and get oneself to a true plateau of productivty will all come down to the degree we can induce people to change their ways, completely rethink their practices, and help them figure out how to use new tools toys, apps and the like deliver on their promises.

The excitement around innovative technology futures for teaching and learning has energized educational researchers to think broadly and deeply about the possibilities they represent.  The venture capital communities’ recognition that education may be ready for its “Internet moment” has also generated massive interest in developing promising ideas for products with commercial consideration.

Between these two exciting arenas of exploration live equally important opportunities for transformation that come from practice-focused solutions contributing to demonstrable improvements in student engagement, faculty performance, and institutional accountability. 

People. We've got to get ready, too.

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Funsuckers

Marc Prensky once famously accused instructional designers of sucking the fun out of learning at an EDUCAUSE Learning Initiative Conference a number of years back. As you can imagine, instructional designers were not amused. 

I was reminded of Marc's quotable quote during a recent conversation regarding the value and role of structure and specifications - or the lack thereof - for designing learning experiences. This is a more nuanced conversation than differentiating between formal and informal learning, or whether one is online or on-the-ground. This also includes things that lead to immersion, augmentation, mobility, and personalization. It inevitably leads to thoughts of learning analytics. Life-long learning tracking even.

It's almost as if today's emergent designers want to dance on ADDIE's grave and declare that ID funsucking is a thing of the past. Which brings me to my point.

It is inspiring to know that there are exceedingly passionate designers out there who want to create learning experiences that engage and inspire, helping the world see new possibilities with fresh eyes. Good on ya. The world needs you. 

But designers also must bring order to chaos. Most of us work in settings where we are mostly responsible for the practical, pragmatic applications of design principles to achieve tangible measurable results for individuals AND enterprises.  It does not matter if we believe something to be true with total heart and soul if it cannot demonstrate tangible value of those beliefs to stakeholders. Not in the future. Now.

So here is a message for the "true believers". When someone tells you than an idea is a little early for their stakeholders....it doesn't mean he or she "don't get it", nor does it necesarily mean that she or he is a pin-headed, short-sighted idiot. It simply means that the case still needs to be made.

To be clear, if it weren't for people believing that they have a better way of doing something and being willing to social the ideas ad nauseum we probably *would* still be sniffing ditto copies in our obligatory F2F training sessions.

All *I'm* saying is that visionary awesomeness on its own probably isn't enough. Sometimes it might just take a few funsuckers to spin all that straw into gold.

 

 

Losing Our (Analytics) Religion

You may have noticed that I've been kinda quiet on the blogging front. It's not for lack of things to say, exactly. More that there has been so much sound and fury around the emerging topic of learning analytics that I've wanted to listen to what was going on before I added my .02.

But there have been a few things on my mind, especially now that we are well into the second year of our work on the PAR Framework. And we are coming up on fall conference season, where I expect we will all be hearing a lot about learning analytics solutions.

As you gird yourself for the coming onslaught of emerging thought leadership that will swirl around us regarding learning analytics, big data, and the like, just remember: Yes, it's hard to turn mashes of clicks into anything meaningful when you are dealing with something like learning, which, like it or not, is like a religion to many people. Anybody who tells you otherwise does not understand your work. 

Don't be shy about pushing back a little bit and asking for clarifications. The answers to learning questions - enterprise, individual or otherwise - are not likely to emerge fully formed from the mists of Apache Hadoop, Google Analytics or Adobe Omniture. All of which are very cool technologies. But seriously, they can't make up your mind for you.

It's not the data, the metadata or the paradata, nor is it necessarily the analytics we perform, that matters the most. What matters is what we DO with the information coming from these analyses, that will help us achieve results that matter to our stakeholders.