Between Data & Reality Falls the Shadow
Data is like electricity.
No modern organization can compete without data because it fuels algorithms and can illuminate decision making.
But, with the exception of a handful of companies (eg. technology platforms, large financial organizations) very few firms can differentiate by leveraging data, just like few companies differentiate today on how they leverage electricity.
And even companies with access to world class data often make terrible decisions.
Bad or Limited Data very rarely are behind bad decisions.
Bad decisions are primarily a result of individuals not understanding the limits of data.
Bad decisions occur when individuals do not how to limit the emphasis they place on the data.
Bad decisions are made when one does not take a holistic and longer term view versus optimizing for the present.
Math is not meaning.
A stanza from one of my favorite poets. T.S. Eliot frames the challenge:
“Between the idea
And the reality
Between the motion
And the act
Falls the Shadow”
Between the data and the reality falls the shadow.
What eradicates the shadow and truly leverages the power of data is not technology but people.
People who realize that it is human beings that we are trying to understand and humans are complex.
People who are pragmatic and know sooner or later they are going to have make a decision using the data because rarely will the data provide the answer.
People who realize that humans choose with their hearts and use numbers to justify the decisions they make. They recognize that data analysis is just an input to what the true goal is : data informed story telling.
Too much plumbing. Too little poetry.
In the world of business we are so fixated on the plumbing of finding the right person at the right place at the right time that we forget that the interaction we deliver will have to be absolutely right and brilliant not to piss of this superbly well located person at the exact right time. The better the “targeting”, the more important the tone, content and quality of the interaction.
Let’s think about the poetry (water) that will resonate versus just the plumbing that focusses only on relevance.
How the IP Method can turn data into IP.
Samuel Taylor Coleridge in his famous poem “The Rime of The Ancient Mariner” has a stanza describing what it is like to be stuck in a salty ocean under a withering sun:
Water, water, every where,
And all the boards did shrink;
Water, water, every where,
Nor any drop to drink.
Today we live in a data driven, data infested, data diarrhea world where we may plaintively wail:
Data, data every where
So much data that we will sink
Data, Data every where
Pray who will help us think?
It is clear, that data itself is being created in such piles that data itself is close to meaningless and information from it is often not too meaningful. What we really need is to be able to make this torrential flow yield a waterfall of actionable insights and maybe even wisdom.
This is unlikely to come from yelling “big data”. ” we need to own the data”, “data is critical”, and other data shibboleths that the most data challenged companies and individuals brandish like some magic sword.
If you wish to really make Data a part of your IP ( intellectual property) it might be worth considering the 6 I’s and the 6 P’s, which highlight the how to think about data from an infrastructure perspective and how to glean meaning from the math.
The Six Ps of Data: How to make sure you are approaching collection of data with the right mindset and infrastructure.
1. Perspective: What perspective do you expect to get from the data ? What connections are you hoping to see ? How do you plan to use this data? Asking the questions before you collect or cull through the data can be very helpful. There are times that the data itself may yield the answers but to do so you will need the next P which is people.
2. People: The shortage in data driven marketing is clearly not the data or the storage capacity or even the computing capacity but of this rare bird called the “data scientist”. John Rauser of Amazon in this fine talk explains how this species combines applied math and engineering with a layer of curiosity, skepticism and good writing skills
3. Punctuality: The half life of a tweet is probably 8 minutes and of any piece of data probably less. Collecting data is like building a museum to the past in a real time world. What is critical is to have data arrive where you need it, and when you need, both from some past archive and some just in time magic. As the world gets more mobile and place and time based relevance increases in importance so will the punctuality of data.
4. Privacy: As data scientists glean insights such as the likelihood of you being a valuable pet food buyer is if you celebrate/promote your pets birthday on Facebook , and combine it with the amazing technology of just in time, things may get all creepy and icky. And to ensure that this privacy issue will become a critical factor one can look to the Government. Not just the Europeans but of every country whose political structures are being disrupted by technology armed citizens. To make an example of things the Government will come after the big companies and so data policies and transparency will be key going forward to keep things all nice and elegant. Apple made privacy a key point in the launch of iPhone 12 and without a doubt is working on a privacy focussed search engine which will become the default search engine replacing Google. This will be inevitable to save billions of dollars of payments to Google, keep on the right side of the FTC and continue the long term differentiation strategy of Apple which is built on total vertical integration and privacy. This soup to nuts model is extending to chips in hardware ( watch the November 10 “One more thing” event on this topic) and bundled and integrated software and utilities on the other end from Apple Music, Apple Arcade and more.
5. Pooling: We are living in a connected world. The Internet is a connection engine. Data API’s and access to databases from all over will be critical to make data driven marketing a reality. It’s not just the data you have but the data you can access, share and pool ( with the right authorizations).
There is some data that is critical each business have which is how to have direct access to people via first party data. It is foolish to become highly dependent on one or two platforms as the principal way to your customer because it destroys strategic optionality ( If you cannot reduce spending on a platform without putting your business to risk you will soon have an existential crisis ).
6. Partnering: As large companies like Google, Amazon, Facebook, Epsilon, Acxiom and several others around the world build data stacks, warehouses and tools, the key will be to partner with these platforms that allow companies to process, pool and pull their own information. There are huge economies of scale that come with data collection and processing and therefore for all but the very largest companies it will be key to decide what platforms to partner with rather than build a complete vertical stack.
Strategically make sure that you do not become highly dependent on any one partner otherwise you could become nothing but a blind bidder turning over all your margin in a black box auction where your data might be used against you by the very platform that is supposed to be your competitive edge. Just see what Google did to the their largest advertisers in the travel sector ( In an investigation published in July, The Markup found that 41 percent of the first page of mobile search results is dedicated to Google content, including its vertical search products such as Google Flights and its jobs search.) or think how Dollar Shave Club used Gillette social media signals to identify and sample their key customers. Remember for David to bring down Goliath their slingshot is the targeting power of the platforms. Smart marketer recognize that while the platforms may be funded by advertising, they are really about new business and engagement models and should be discussed at every Board meeting as both strategic opportunities and threats and not a communication or advertising platforms.
They are amazing partners but also trillion dollar companies that need to find their next trillion of growth which will mean expanding into different businesses.
The 6 I’s of Data: How to Extract Meaning From Math
Over the years I have learned that the best way to gain insights and extract meaning from data is to follow what I call the 6 I Approach: Interpret, Involve, Interconnect, Imagine, Iterate, and Investigate.
INTERPRET THE DATA. Don’t just take all those facts and figures at face value. Sometimes, of course, they’re exactly what they seem. Other times, they can be misleading. For this reason, view ambiguous data (especially) from multiple perspectives. Develop hypotheses, search for patterns, look for outliers, create alternative scenarios to explain the information you’re receiving. Through interpretation you can enrich the data with meaning; you can identify the story it’s telling.
INVOLVE DIVERSE PEOPLE. As important as your analytics people are, expand the group that examines the data. When you involve people with various skills and perspectives, you’re likely to receive a richer interpretation. The analytics people may say, “The number of followers on our site increased 15 percent in the last month.” The marketing people may say, “That increase may be due to the incredibly successful brand licensing program that launched last month.” The human resources people might say, “Every time we have a significant increase in followers like this, we have a corresponding increase in job applicants.” The importance of diverse people is shown in debacles like the Gucci Instagram ad that resembled black face or the Pepsi ad with Kendall Jenner that misfired at every cultural level.
INTERCONNECT TO LARGER TRENDS AND EVENTS. What does the datamean relative to an emerging trend that’s having a profound effect on your industry? How does the information you’ve gleaned relate to a competitor’s new product introduction? Making these types of connections helps you take the data one step further, determining if it’s going to have a short-term or long- term impact, if it’s suggesting the end of a trend or the beginning of a new one.
IMAGINE AND INSPIRE SOLUTIONS. Too often we look at the data and allow it to set boundaries: “We can’t go into Market Z as planned because the num- bers indicate sales of our category is starting to fall off.” Rather than allowing the data to limit options and actions, explore the solutions it might inspire. If the numbers show that your product category isn’t doing as well as it once did in Market Z, is there an emerging opportunity because the market still has potential and competition will be reduced because of this data?
ITERATE. Data can spawn new and better data. Is there a test you might run based on the information you’ve gathered that can produce more insight- ful facts and figures? Can you think of fresh ways to generate feedback that might provide multiple perspectives and explain surprising, disturbing, and promising data?
INVESTIGATE PEOPLE’S EXPERIENCES. In a given organization, you have hundreds or thousands of people with data-relevant insights because in the past—whether while part of your organization or with a previous employer— they experienced something applicable to the current information. For instance, someone was part of a company that experienced a huge social media spike because they ran a Super Bowl commercial that went viral. As a result, this employee can relate their experiences to the current data on a similar topic. Tapping into this by seeking out relevant employees and asking about the data may provide ideas that would not otherwise be articulated.
Never forget that data tells a story beyond the facts and figures, but this story can only be told when you find ways to tease out the meaning.
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