Insight on Insight — Part 2 — All About Speed

A few weeks ago, I started my Insight on Insight series.  (This is a sup­ple­ment to the already infor­ma­tive Insight related blog that my col­league and friend, Michael Hal­brook, Insight Guru Extra­or­di­naire, is also writ­ing).

And then I kind of took a step back — because, well, I had to work.

But I have been want­ing to revisit this topic and so here we are, friends.

Today, I wanted to address one of the sub­jects I brought up in my last post on the subject.

What makes Insight a true dif­fer­en­tia­tor in the marketplace?

Again.  I am a ven­dor.  I know that you just might take what I am say­ing as “suspect.”

How­ever, I also want you to know — I am a ter­ri­ble liar.   I am very bad at the art of deception.

And I am ter­ri­ble at Poker.

But here goes.

1) Speed to process — Insight can process mas­sive amounts of data expo­nen­tially faster than tra­di­tional busi­ness intel­li­gence solu­tions.  Part of the speed that is gained is that the data schema is not cubed or tab­u­lar in nature, but rather, cir­cu­lar.  Omni­ture works closely with its clients to size solu­tions in a man­ner in which the entire dataset can be REBUILT if needed in the course of 1 — 2 days.

I have worked with clients who will rebuild datasets over a given week­end to add new dimen­sions or incor­po­rate new data feeds with 5+ ter­abytes of data.

Process starts on Fri­day night.  Ends on Sunday.

Some­times we hear that this is too slow.  But this is from clients who started their dataset with 5 months of data and have now seen it grow to 15 months.  Or have started with 50 cus­tom dimen­sions and have now expanded that to 200 dimensions.

I guess the best anal­ogy to that is — and this is just off the top of my head — you can buy a sports­car and expect great per­for­mance when you take care of it and fol­low the main­te­nance plan.

Or you can let it go, not bring it in for ser­vice and shove ten peo­ple in it and com­plain that the doors won’t close.

You wouldn’t do that.

Would you?

The dia­gram above shows a sim­ple view of the Dataset Schema within Insight.  Within the demo schema above — there are five lev­els — Vis­i­tor, Engage­ment, Visit, Hit and Com­merce.  This dataset is an exam­ple of a multi-channel dataset which is com­bin­ing cus­tomer inter­ac­tions along with web activity.

(Most of the dataset is not seen as the image would be too long.  Ping me if you are inter­ested.)

All new dimen­sions, or cat­e­gories, are asso­ci­ated to the indi­vid­ual level when they are added at the dataset.  The lack of a tab­u­lar con­struct makes it pos­si­ble to query across the dataset with greater flex­i­bil­ity than in a cubed environment.

2) Speed to change - One of the aspects of the soft­ware that make it so cov­eted by clients who know how to max­i­mize it, is its abil­ity to be flexible.

Like I men­tioned above — as long as the data is fed into the sys­tem and can be asso­ci­ated through a pri­mary key, it can be made into a new dimen­sion in a rel­a­tively straight­for­ward reprocess of the data.

In addi­tion, the abil­ity to save seg­ments as dimen­sions and cre­ate your own met­rics is an equally valu­able propo­si­tion for clients whose busi­ness analy­sis needs can change with enor­mous flu­id­ity based on the busi­ness demands of the day.

For more infor­ma­tion on that, check out Mike’s piece on Seg­ments in 5 Min­utes.

3) Speed to answers — Tra­di­tion­ally, when mak­ing a query from a data ware­hourse, you sub­mit your query.

And then you wait.

And then you get an answer.

733.

So what do you do with that?

Usu­ally, great analy­sis is iter­a­tive.  It does not stop when you have the answer, because the answer usu­ally leads to more ques­tions.  If you are an ana­lyst, know­ing that 733 is the answer to your ques­tion will cause you to ask more ques­tions like how, why, from where?

As soon as you start a query in Insight — the sys­tem starts return­ing an answer.  The imme­di­ate answer is an extrap­o­la­tion based off of a local sam­ple — but as the query reaches 100%, it becomes more and more exact.

What this allows you to do is start ask­ing ques­tions, and maybe some more ques­tions as you see the fall­out of trends.  The sam­pling is highly accurate.

So you don’t have to take 10 cof­fee breaks wait­ing for your sep­a­rate and very dis­parate ques­tions if you are still work­ing in the old order.

Which is a good thing, right?

I guess it is.  Unless you really, really like coffee.

4) Speed to action — It goes with­out say­ing that hav­ing the abil­ity to process so quickly and drive answers so effi­ciently enables your busi­ness to act more quickly and make rel­e­vant deci­sions to drive value.

I have worked with one large retail client who, prior to hav­ing Insight, had to scram­ble madly after the Black Fri­day rush to deliver find­ings and pro­vide eval­u­a­tion for C-level report­ing.  How­ever, in try­ing to pull data from dis­parate sys­tems and try­ing to merge reports — Black Fri­day analy­sis was not fully com­plete until …  well …

Jan­u­ary.

Which is great.

Except at this point — the infor­ma­tion was not really an impe­tus for change.  Instead — it was solely that — infor­ma­tion.  Some level of insight could be extrap­o­lated for future busi­ness deci­sions — and poten­tially to change behav­ior for NEXT Black Friday.

After imple­ment­ing Insight and join­ing data that pre­vi­ously needed to be manip­u­lated, mas­saged and inter­preted for weeks prior to deliv­ery, the com­pany was able to make some dra­matic changes.

A true par­a­digm shift.

What took months to ana­lyze could now be deliv­ered to the C-level team within days after Black Fri­day and with the abil­ity to make changes to store inven­tory, web­site spe­cials and hol­i­day cam­paigns to directly influ­ence the cur­rent hol­i­day season.

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So there you go.  Just some Insight on some core foun­da­tions of Insight that ele­vates it beyond tra­di­tional report­ing tools and makes it a true “game-changing” fac­tor for com­pa­nies who have embraced it.

More to come on my Insight on Insight series — Is Insight too Com­plex for Me?

You can fol­low Kiran on twit­ter @measureTHISgirl.