00:00 – 01:15 Intro. My new favorite data quote by Zack Matere, a Kenyan farmer.

01:15 – 04:05 Part 1. The current flawed data org structure, its challenges, and the new optimal org structure to truly bring big action to big data.

04:05 – 06:20 Part 2. A framework, inspired by Donald Rumsfeld, for big data vendors to think about when creating solutions and the unique space in which big data analysts should actually play in (only the "unknown unknowns!").

06:20 – 10:25 Part 3A. My first, tactical, example: How to auto magically solve the problem of having millions of rows of data, and not knowing how to find the 15 valuable rows that could have a huge business impact. Leveraging interestingness!

10:25 – 15:00 Part 3B. My second, strategic, example: Leveraging predict, mine, correlate to shift away from data puking to, even more auto magically, find trends in the data that truly are the unknown unknowns and identify causal factors for those trends so that we can move from data to action at light speed.

Take 15% of your decision making budget and give it to one really, really smart person (Ninja! OK, Data Scientist) and give that person the freedom to experiment in the cloud with big data possibilities for your companies.

It is cheap. You can do dirty data warehousing pretty darn fast. You can find all the ugly warts and problems. You can be much smarter when you start to mainstream big data into your company, while preserving the data awesomeness that already exists in your company.

Structure your big data efforts, at least initially, to fail faster while failing forward. Don't build the biggest, baddest big data environment over 32 months, only to realize it was your biggest, baddest mistake.

I can't think of any other time in our lives where we could literally swim endlessly in an ocean of data, without having anything to show for it. Big data is that world. If you don't know where you are going, you will get there and you'll be miserable (if your company has not fired you already, in which case you'll be miserable and sad).

I've championed the need to leverage frameworks like the Digital Marketing & Measurement Model, in the web context, to ensure that the analysis we do is deeply and powerfully grounded in what's important to the business. You have to have that one page, even if it is roughly defined by your Sr. Management. Have something.

If your management refuses, or is not visionary enough to provide you with even basic starting points, then build one by yourself. All it takes is a little business analysis. Here's my post: Five Steps to Finding a Purpose for your Analysis.

When you have access to all this data, the answers you find will be surprising, the insights you deliver will be brilliant, and your impact on the business will be huge. But that can only happen if there is a model that defines the purpose of your sweet big data adventures.

For every $100 you have available to invest in making smart decisions, invest $10 in tools and vendor services, and invest $90 in big brains (aka people, aka analysis ninjas, aka you!).

I will admit that Oracle and IBM and SAS and solid state drives are very expensive. Nine times that to invest in big brains might seem egregious. Perhaps it is. Let the 10/90 rule be an inspiration to simply over-invest (way over-invest) in people, because without that investment big data will absolutely, positively, be a big disappointment for your company.

Computers and artificial intelligence are simply not there yet. Hence your BFF is natural intelligence. :)

Real time data is almost insane to shoot for because even for the smallest decisions, you'll have to do a lot of analysis first (5 hours), then present it to your superior (1 hour), who will add two bullet items and send it to a team of people (20 hours), who will in turn argue about priorities and how much the data is wrong (16 days), but ultimately come to an agreement because the deadline to make the decision passed 7 days ago (20 seconds), and send the data to the big boss who'll read just the first part of the executive summary (3 days), and decide that the data is telling her something counter to what she has always known works, and she'll make a decision based on her gut feel (5 seconds), and some action will be taken (14 days).

Total up those numbers. Was the real time data of any real value?

Ok so that is way over the top. But every company has a complex decision making structure that is time consuming and therefore unable to react in real time. If you can't react in real time, why do you need real time data?

Understand when is the right time for data in your organization. Shoot for systems and processes that match delivery of data (better still, insights ) to that time frame. You'll have less stress. You'll focus on big, important, strategic things (real time data is really good at driving the best companies to do tactical silly things). And you'll save a lot of money, because real time everything is really expensive!

Here's one way to check if you really need real time data: Does a human have to be involved from data receipt to taking action? If the answer is yes, then you don't need real time data, you need right time data. If the answer is no (say you have intelligence/rules driven automated systems), then you need real time data.

That is the title of my post from June 2006. And look how far we've come. :)

The core thrust of my post was that data on the web will never get to 95% clean and it will have big holes and it will be sparse in some areas. We should aim to collect, process and store data as cleanly as humanly possible, but after that we should move on to using the data, because we will still have more data about the web than what God's blessed any other channel with. Let's not become the type of people who continue to waste time on quality beyond the point of diminishing returns. Let's not become persistent javascript hackers and sprop variable tweakers at the cost of delivering value from data now.

Multiply all of that a million times when it comes to big data. We will have dirty data. We will have no idea what to do with videos or spoken text or (omg!) social media overload. We will be missing primary keys. We will suffer from a lack of clean meta data (or sometimes any meta data!). We will realize the shallow limits of sentiment analysis. We will cry from the pain of the painful business process fixes that usually result in good data.

And yet, we are standing on a mountain of gold.

Do the best you can in terms of collecting, processing, and storing data of the cleanest possible quality. Know when to shift to data analysis. Start making decisions. Make small ones at first. (Remember, even they will be revolutionary, as these datasets have never come together!) Make bigger ones over time, as you understand the limitations of what you are dealing with.

Here's the kiss of death: Big data implementation projects where the first touch of an Analyst will come 18 months after the project was first conceived. You see, the world would have changed so dramatically in 18 months that nothing you possibly spec'ed for is relevant any more.

Think smart. Move fast. Slowly become Godlike over time.

This might be a little controversial. But stay with me.

Thus far in the history data analysis the objective for our queries has been trying to find the signal amongst all the noise in the data. That has worked very well. We had clean business questions. The data size was smaller and the data set was more complete and we often knew what we were looking for. Known knowns and known unknowns. (See video above.)

With big data, it is so much more important to be magnificent at knowing what to ignore. You must know how to separate out all the noise in the disparate huge datasets to even have a fighting chance to start to look for the signal.

It is amazing but true. If you are not magnificent at knowing what to ignore, you'll never get a chance to pay attention to the stuff to which you should be paying attention.

Your business savvy. Your analytical gut instinct. Tuning your algorithms to first ignore and then hunt for insights. That is what will have a material impact.