Rediscovering the Benefits of Simple Design

Recently, I met a coworker from almost twenty years ago whose clearest memory of our time together was our discussions about design.   And how I got us all to make a field trip to the break room to take a look at the microwave oven there.

It had a dial and no buttons.  Pull the handle and it shut off the element automatically.   I loved the simplicity of this.   I loved how it made no demands of the user, and anybody could immediately put it to use.   There was no training, no documentation, no "insufficiently skilled users".

At the time we were rolling Microstrategy out to hundreds of users.    Microstrategy is a ROLAP (Relational On-Line Analytical Processing) tool that once you provided data in a relational database within a star-schema, defined that to Microstrategy in the form of metadata then any user could use it easily - they could quickly create new reports by dragging and dropping element names and it would generate the SQL for them.   It was a very powerful tool that in the right hands could achieve amazing results.   Prior to our roll-out of this tool the backlog on reports for our organization was ten months.   After we rolled it out I signed onto and delivered an 8-hour average SLA for the creation of new reports.

Analysis and the 'So What' Question

While at Strata I had an opportunity to participate in quite a few sessions that demonstrated how to take raw data and analyze it with various tools.  The output was usually a set of graphs, charts, etc, though sometimes just simple tables.   All of this was useful to get a sense of how the tools work, but what was missing was the final step in the analysis - a powerful insight or understanding that one could use to make an intelligent change to a process.   Generally, the presentation technique was fine, the tools were great, but the demonstrated impact of the tools was trivial.

One reason for this is that some of the presenters may have to hold back on their most significant discoveries until the right time - and this just wasn't that time, or this wasn't the right audience.  I can understand this - since most of my best analysis can't really be shown without getting NDA and other agreements in place first.  Another reason is that the presenters might have wanted to focus on the tool and not the data or business being studied which is just serving as a necessary example to work on.   But this is misguided, since delivering insights is the bottom line - not delivering pretty pictures.   The last reason I can imagine is that delivering powerful insights is hard, and while these presenters are working on it they may not yet have a suitable example.  And I think that this is the most likely answer.

My concern is that people spend a lot of time building gorgeous but empty-headed analytical solutions that just don't have much to say.    This is pretty similar to the chart junk problem that Edward Tufte complains about.   To make this a little more clear I've included a few examples below.

Breadth of Data vs Depth of Analysis

One of the things that I felt was missing from O'Reilly's Strata Conference was a nuanced sense of the trade-offs between complex analysis and vast volumes of data.  Because there is a trade-off and I've seen it play out consistently.  It works like this: where do you spend your investment?

• deep analysis - with unpredictable costs and benefits
• broad sets of data - with predictable (high) costs and benefits

Buy, Reuse or Build ETL Software?

While talking to someone today he mentioned a concern about my team's "homegrown" software: that it would nickle & dime us to death compared to "more robust commercial software". I respected this guy - he was very bright and had a lot of successes under his belt. But I also felt that he was both echoing a common corporate perception, and was quite wrong.

I've run into this notion so often that I now plan for it: in the minds of many commercial software has more credibility than open source software, which in turn has more credibility than custom-built software. And since these perceptions are often held by those that control my budget - perceptions matter.

Mashups vs Data Warehouses

Mashups have come from the application side of IT, warehouses from the data side. They overlap quite a bit - but there's not a lot of thought of how to leverage the best of both worlds.

What's Missing From Data Science? or A Partial Review of Data Analysis with Open Source Tools

I just got my copy of Data Analysis with Open Source Tools - by Phillip K. Janert, published this year by O'Reilly.

I'm very excited about this getting this book since it addresses my weak area in data science and business intelligence: the math. The structure of the book is great, the products that he covers are perfect - NumPy, matplotlib, R. gnuplot, etc. His writing style is fine. I'm looking forward to really digging into this more than any technical book I've come across in a few years.

As I slowly work through this book I'll analyze the content more thoroughly. But, in the meanwhile, I've done a spot-checking on his coverage of reporting, business intelligence and data. And the gaps that I've found between the state of these domains and what the book describes - is completely consistent with what I've generally found in the data science writing: a vast lack of understanding of BI.