Great article by Nate Silver on why sports analytics is much better than other "real life" analytics.
I've always been such a baseball stat nerd. I remember playing stats only baseball games since I was a young kid, which might have been a precursor to my professional life.
Upon returning from Adobe Marketing Cloud Summit 2015 I've had some time to digest the experience fully. The Summit is a great weak of digital marketing discussions. Of course since this is an Adobe event, the discussions are around the products Adobe is selling with the marketing cloud. Fortunately for me, and Adobe, the overarching strategy Adobe is putting together with their products is extremely compelling.
Just five short years ago Adobe had $0 of revenue from digital marketing products. I believe in 2014 the amount of revenue was over $1.2 billion, but I didn't write the number down, it's not important. What is important is Adobe, through mostly acquisitions, has created the most compelling digital marketing hub/cloud in the industry. Adobe rates highest on the Gartner Magic Quadrant and it is in its infancy.
Having come from a software product background also, it is impressive they have been able to start to integrate most of these products together. What Adobe is setting out to accomplish is no small feat. Creating a singular platform from many disparate products is what marketers have to do on a daily basis with their own systems, but Adobe is attempting to make that life a whole lot easier.
Last year we were introduced to the marketing cloud strategy, a set of 6 products with 6 core services that support all the products. I was very bullish on what was being layed out by Adobe. The idea of taking a customer through their lifecycle with the company from anonymous to known, from new to dormant, all in one platform is very appealing to me as a marketer. Adobe is trying in essence, to let marketers control their own destiny.
Today marketers have to fight to get things done. Marketers destiny is in the hands of many other groups, from website developers, IT, database engineers and creative agencies. Sometimes it amazes me that we as marketers are able to get an email out the door, or target an individual on a website. The amount of effort sometimes makes a campaign not worth doing at all.
There are three main thoughts I came away from the Summit with this year.
Audiences
I have always firmly believed the customer is the center of all businesses, yet I never believed they were the center of the marketing universe. My belief is that everything starts with the customer and they are all different in their various ways. Advertising tended to lump all the customers into one bucket and make the product the center of the universe. Digital has come along and helped marketing become more targeted, but the hardest part of targeting is creating the single customer database.
Marketers have had to deal with a plethora of disparate databases of customers, which has made targeting especially difficult. The need for database engineers to create a data warehouse bringing all the different customer databases together with each individuals spend slows the process of driving behavior through targeted experiences down to a crawl.
Adobe audiences are referred to as a Core Service. What that basically means is that all of the applications of the marketing cloud can use this customer database. This makes audiences the key to allowing the marketing cloud to be the most targeted customer solution I have seen to date.
Adobe tracks a customer from their first anonymous visit, to authentication, through the entire customer lifecycle. The applications then allow marketers to target those customers in so many different ways. From purchasing of ads, to email marketing, to push notifications for mobile apps, through retargeting campaigns, audiences can be used in all of these ways. Same database. No need for database engineers. Hallelujah.
For example, a customer may come into the website, authenticate and reach a certain part of the purchasing funnel. Through Analytics, this group of customers can be identified and a custom audience can be created. Through AEM, email creative and a landing page can be created, by marketers, with approved assets from the brand team, to be used to create an email campaign for these guests. With Target, different messages can be tested to determine what is the most effective message and offer for a customer to create the conversion. Through Campaign, this audience can be used to send an email, measure the results, and a new audience can be created with all of the customers that didn't convert to create retargeting campaigns. That's pretty powerful stuff right there.
Adobe Messaging
One of the concerning parts of the conference was the introduction of 2 new products for the marketing cloud. The idea of having 6 products is already a little overwhelming. The constant comments I was hearing from other attendees was confusion on what products they need and why. This is a problem for Adobe. I believe they have 1 product, the Marketing Cloud, with many features inside the product. By keeping the multiple product structures, it is showing some infighting within Adobe. As I said earlier, these products were purchased by Adobe to then be integrated into the cloud. It seems those product owners are fighting for their power, which is making it confusing to develop the strategy with Adobe as a partner.
I also believe this product strategy makes the cloud more cost prohibitive. Because so many products have to be purchased, it becomes more expensive than turning on features. There also tends to be salespeople dedicated to the certain products, so there is a loss of 1 dedicated resource.
I'd like to see Adobe move away from products an into features. This will simplify the messaging and allow customers to purchase based on what they need, instead of what Adobe is trying to package. It will allow more customers to be locked into the ecosystem of Adobe, instead of keeping their current products that aren't as integrated. They should take some lessons from Apple. The ecosystem is the most important play for Adobe right now and they should have a longterm vision for this strategy. Once customers start crossing over into different products within the marketing could it will make it impossible to leave, because the customer database and all the processes are driven by Adobe.
AEM is the Center of the Cloud
The digital marketing platform all started with the purchase of Omniture which turned into Adobe Analytics. Analytics is the heart and soul of the Marketing Cloud and I believe has the largest user base by far. Analytics may be the soul, but I don't believe it should be the heart of the solution.
Adobe Experience Manager is the heart of this solution. It is the product that puts the marketers destiny into their own hands. The ability to manage assets, create approved templates, change website messaging, create emails and create landing pages with variable content is so powerful. It even can build mobile applications across platforms and manage all those apps in realtime.
This is the heart. Analytics allows the identification of opportunities to enhance conversion and make more money, but without AEM a marketer is stuck waiting for many other departments to help them take advantage of the opportunity. Campaign allows for great multi-channel marketing, but without email creative and dedicated landing pages, the marketer is in a waiting game. Target allows offers to be measured in real-time and a winner is chosen, but to get to that point, AEM has to manage all of the content and messaging.
Content is the key to delivering targeted experiences to customers in the digital age. Let me say that again, content is the key to delivering targeted experiences to customers in the digital age. The faster a marketer can deliver that content, through whichever channel, be it mobile app, website, email, social or ad, the bigger a competitive advantage that company will have over its competition. This is why AEM is the heart of the marketing cloud/hub. AEM will ultimately create the competitive advantage, because without it, the content will not be delivered at the speed in which customers will not only demand, but will also change their behavior.
Bravo to another great Summit from Adobe. Adobe is truly the leader in this nascent category and they are continuing to push the envelope with their vision. I am super bullish on Adobe and what the future holds for the Marketing Cloud Plus, having Imagine Dragons play at the bash was super awesome!!!
Not my favorite article, but some good points to live by as a database marketer. My main pet peeve is sending too many emails. This has slowly been getting better through the years, I don't find myself getting so many daily emails, which is a testament to analytics I would imagine.
There are a number of reasons, but there are several common ones. And there are a lot of reasons at different companies. But the main one that we've found is that customers receive too many emails. So, it's always important for an email marketer to understand [customers'] preferences—especially the mailing frequency that your customers prefer. It's always important not to overwhelm them with emails. When you send too many emails, you're causing them to tune out.... They'll look at them, and just ignore them.
Getting back a customer that has become inactive is very difficult, so have different strategies for customers that are trending toward inactivity. In my history I call these customers decliners and it is easier to save them before they become inactive.
Loyalty card programs are now a way of life. So many businesses in every vertical has a loyalty program based on dollars spent. The programs range from miles in airlines, to how many stamps does a customer have on their stamp card before they get a free yogurt. The belief is these programs will drive loyalty and incremental purchases because of the benefits offered for the spend. But do they really drive incremental spend? Or should the true purpose of the program not focus on the incremental spend, but something entirely different?
Airlines are the standard bearer for loyalty programs. Frequent travelers swear by the loyalty programs and can tell you how many miles they have in their account. With so many travelers being able to quote their miles, this must work correct? In reality very few of the people traveling through the air really care about the loyalty programs. Most will actually look for price or non-stops when making a decision on who to fly with. So who are these travelers who care about the program? They are the 2% that drive most of the revenue. Well that's a good thing right? The funny part about this model is most of these travelers are not actually paying for their flights. They are frequent business travelers who are not paying out of their own pocket, their work or customers are paying for it. The irony of these loyal customers is they would never spend that kind of money with the airline if it was their own. They are loyal to the program because they would like the free travel when they want to go somewhere on personal time, with the family.
So what happens with the remainder of the travelers? Is the program enough to drive loyalty? The answer is no. But that is ok. They shouldn't be designed to drive loyalty from these customers. If they actually did, they would more than likely be too rich of a program. So what happens to the 98%? Should companies just not push their loyalty cards on the rest of this market?
Loyalty program should only be rich enough for customers to want to be tracked. Now this means many different things for each industry. For airlines it might mean a free amenity if the customer is a member of the loyalty program. For a yogurt shop it could be a free topping for a member. For a casino it is the ability to receive a comp. Grocery stores are masters, you don't get the sale price unless you are a member. Of course I want to join for that $10 off of my grocery bill.
Loyalty programs are an opt-in for tracking behavior. For the majority of your customers, the loyalty rewards in your program will either be out of reach or not worth any incremental spend. But, what you are getting is behavioral data. How often is the customer engaging, how much is the customer spending, what are the customers patterns. Do they only come for sales? Do they come only when they have an incentive? Do they come a certain day of the week?
This is the gold that comes from the loyalty program. Mining that gold has unlimited opportunity. Loyalty programs have 3 major flaws.
Loyalty programs treat customers differently based on 1 metric, a total amount of something. Whether that's miles flown, purchases made or points that equate to dollars spent, the one metric is dollars spent. Well that is a good start for measuring a customer, but what if a Customer A spent $500 3 times and Customer B spent $10 150 times? They will both be in the same loyalty tier because they spent a total of $1,500, but they are entirely different customers. If the company can get Customer A to spend 1 more time, it is worth a lot more than if they can get Customer B to spend 1 more time. So the loyalty program doesn't incentivize customers equally.
Loyalty programs rely on customers to want to interact. They are reactive mechanisms, waiting for customers to spend enough to get whatever reward the customer may be wanting. Of course good database marketers can send out reminders that someone is close to a reward or they might move up a tier, but the reward has to be enough of a carrot for that customer to change their behavior.
All the great innovations a company can make in their program can be copied by anyone, because it is a documented program. If the strategy is to own loyalty by having the best program, any competitor could easily come over the top and have a richer program. This leads a race to the bottom mentality. The company could always come back over the top, but the programs start becoming too rich, remember only be rich enough to track behavior. If a competitor can negate your best selling points (loyalty program), then the program can never be a competitive advantage, nor do you want it to be.
This all leads to the true reason to have a loyalty program, tracking behavior. With targeted direct marketing, companies can inventive the behavior they are looking for. A company can give Customer A a much different communication and offer because they know that the customer will spend $500 the next time they can get the customer to engage. The direct marketing can be proactive. Direct marketing can take a customer from someone that rarely comes in, to someone that engages with the business on a regular basis. Last, but certainly not least, companies can innovate without being copied. Because direct marketing is not a published benefit, there can be many different tactics for a range of different customers and the competition is blind to the strategies.
There is so much more opportunity in direct marketing compared to the loyalty program. By keeping expenses as small as possible in the loyalty program, it leaves much more money for direct marketing to drive the business. When allowed to drive the business, direct marketing can target customers in many different ways, based on the customers individual behaviors, with incentives that will truly drive that particular customer. A loyalty program will never be able to do that as effectively.
It amazes me that in the year 2015 100% of the straw poll wouldn't be for decision making. In my humble opinion that is what analytics is all about.
It’s clear when you analyze analytic capabilities that there are three main reasons people use analytics:
A need to report on some aspect of the organization
A need to monitor the organization’s behavior or performance
A need for the organization to make data-driven decisions
As part of my recently completed research on the analytic capability landscape, we did an interesting straw poll. We asked those attending a webinar on the topic which of these was the business goal for their analytic efforts today and how did they see that changing in the next 12-24 months. The split is shown in an excerpt from the infographic at right. Today the split is pretty even with reporting and monitoring coming in at 37% each with deciding – making decisions – slightly under at 27%. This matches my experience – lots of companies are still focused on reporting, many have moved on to dashboards and performance monitoring as their focus while a growing number are explicitly focused on decision-making.
Very cool visualizations. My favorite one is the unemployment rate by county that iterates through time to show the growth. Very powerful.
In Geography of a Recession, Latoya Egwuekwe uses a short animated visualization to show the spread of the 2008 recession across the United States. By overlaying time, data, and geography, she is able to display both the rapid progression of unemployment and the regions hit hardest. Symbolically, the country visually turns darker as unemployment spreads. This effect of time-lapse on visualization is key to provoking insight from the viewers.
The road to Tableau was an eye opening experience for me. Noah really nailed it, there is nothing I couldn't really do before Tableau, but it just is so fast to do an amazing visual analysis that allows me to see opportunities, that I am so much more effective.
There’s absolutely nothing that Tableau can do that I couldn’t do before, but that’s exactly the point: it lets me do the exact same stuff much faster, cutting down on the parts of my job that aren’t the most exciting and leaving more time for more valuable work. So far, the things I use Tableau for take less than half as long as doing them with my more familiar toolset, and I end up with the same results.
A very interesting post about data visualization. I believe the correct answer is somewhere between 2 and 3. Excel days were just awful!
Today, he runs the marketing team like an independent agency within the organization complete with its own engineers — a strategy he highly recommends for small teams that need to get a lot done fast.
An interesting article to set up an in-house agency to support all of marketing. As a database marketer, I truly believe the team needs its own database and its own engineers to maintain this database. It has to be separate from the IT processes that slow down progress.
Why?
Why shouldn't marketing data be included in the rest of the organizations data?
The simple answer is time. Most data put into data warehouses are used for analytics. Sounds just as important right? Analytics is the driver of making money in the organization correct?
Sort of. This data can also include financial data that has different processes based on financial rules, especially for public companies. Some data might include credit card information, which need to be PII compliant. This data needs strict data governance and encryption of sensitive data. All of this takes time.
Time is the enemy of marketing. The amount of time it takes to get data into a marketing database relates to an amount of revenue that is being lost. Most data requested into a marketing database is used right away in segmentation for campaigns. These campaign changes either drive revenue or save on expense. Having engineers able to get data into the marketing database in an expedited process gives an organization a competitive advantage. The quicker new data equals the more efficient database marketers. All this leads to more money to the bottomline.
Who is the modern marketer?Regardless of the role in marketing, the expectations related to data and analytics need to be consistent. While there will always be more advanced analytical and technical positions, there is a new baseline for all marketers. The skill set includes a knowledge of data management principles and analytical strategies, and an understanding of the role of data quality, the importance of data governance, and the value of data in marketing disciplines. Today’s marketer needs to go well beyond reporting and metrics, and be more proficient in a full range of analytics, which may include optimization, text, sentiment, scoring, modeling, visualization, forecasting, and attribution.
Marketers need to have experience with the technology, tools, and design approaches that leverage data and analytics. Campaign design, multi-channel integration, content performance, personalization, and digital marketing can all be driven by fact-based decision-making, ideally with direct accountability to results and the ability to very quickly react and adjust to the demands of the customer and the market. The marketers I am referring to have a distinct blend of creativity and reasoning talents; they are inquisitive, inventive, and enthused by a culture that is advanced and agile.
Great article that really describes what marketers are becoming. I believe this change in what a marketer is has been happening for quite a few years now. A a marketer It is so important to understand the tools, data and how to analyze the data.
With so many data visualization tools out in the marketplace, it is a wonder why most organizations are still struggling to get these easy to build dashboards adopted throughout the organization.
It usually comes down to the data. The data still has to be accurate and up to date and reliable. So many organizations still struggle with this. I believe it comes down to structure within the organization. How does IT still control the data in many organizations? IT tends to create processes and documentation that takes data forever to get into the hands of the users.
It all depends on the size of the organization. When organizations are very large, this type of process and documentation is needed. Most organizations are not this size. The data is used by a handful of people within the organization and a more agile approach to data needs to be taken to always have the best data at the soonest possible time. Long lead times and processes that make moving things forward difficult lead groups within the organization to get their own data in many different ways and this leads to many different versions of the truth and lack of trust in the interactive dashboards.
Organizations need to move the data ownership into the hands of the data users to ensure one version of the truth.
I often ask people whether they know what Netflix, Harrah’s, Amazon and Wal-Mart have in common? The answer is pretty simple. They use data analytics to leave their competitors in the dust. Many other businesses are trying to do the same, spending millions of dollars on data software.
It takes more than a steep investment, however, to squeeze business value out of data. Companies have to establish an entire system to use data to drive competitive advantage.
Data as a competitive advantage needs a department that is responsible for the analytics and getting all the needed data. The data owners and the data users should reside in the same division to ensure the right data is always available and up to date. Also, the decisions on resources should be within that department, not within IT.
When IT is in charge of the data, they tend to not understand the business as well as needed to facilitate data. The operations does not understand databases and technology, however the analysts understand the business and the technology, so they should own the data and the facilitation of the data.
Clear presentation of data using graphics are critical to how fast people can understand the information and how comfortable they are in interpreting the information.
Great article using the Edward Tufte visualization of the data from the '86 Space Shuttle crash. The engineers were very concerned about the temperature on the day of the launch, which they felt heightened the risk of the O-rings being damaged.
The engineers presented all the data to the decision makers through multiple reports and with the data spread out on many different pages. Of course it was hard to put all the data together and understand the severity of the issue.
The actual fax of one page of the data to decision makers
As you cab see, not really something that shows the severity of the issue.
Edward Tufte's visualization of the severity for the launch
The graph shows 2 things.
Now most of us won't be presenting data that will save lives like the example above, however it really shows how a good piece of visualization shows outliers and gives the decision makers easy to understand data to make better decisions without having to go through pages of data.
n 2006 Netflix offered a $1 million prize for anyone who could improve its movie preference recommendations by 10%. Netflix, at the time, made most of its money sending DVDs in the mail to users’ homes
Mathematicians went wild. The competition was lauded by business pundits as an example of crowdsourcing genius. Because this was damned hard math, the project took years. And then in 2009, a team of mathematicians called “BellKor’s Pragmatic Chaos” actually cracked the code, achieved a 10% lift, and Netflix gave them the $1 million.
And then … Netflix never implemented the winning algorithm. Because personalization at that point no longer mattered.
Personalization has been such a buzzword for so many years. Netflix was one of the poster children for this. It's interesting to look at articles like this and understand they really don't utilize it like say an Amazon does.
In fact, this article is very critical of Amazon and I'd have to agree. Amazon has decent recommendations, but it seems to be a fairly basic market basket model that shows what others who bought similar items. That may be the best way to offer items to customers. Amazon has all the money in the world for R&D, in fact they flaunt how much money they put back into their business and if they are using this model, it must mean the personalization models of predicting other types of product must not bring in as much as the market basket.
When we entered the age of big data, many of us assumed we had left the age of big risk. We didn’t have to guess anymore. We didn’t have to go out on a limb. We’d follow the numbers, the “truths.”But time and time again we’re finding that it’s not that simple. No matter how good the research is, big data is nothing without big context.
The promise of big data is a complicated one. When I hear most non-statistical people talk about big data, they believe it will answer all the questions they have about their business. Big data is just making sense out of larger data sets that may not be historically the data everyone has focused on in the past.
Once you break down what big data is and what it isn't, the question then becomes how to use it. Context is extremely important. Not just in the form of survey or research, but in the form of humans that have been working in the business. The human intuition and psychology of consumers is just as important as ever. Just as before there was big data, organizations combined data with business acumen to make the best decisions. Nothing has changed with big data. There has to be business acumen to combine with the big data finding to build the best product and have the best marketing strategies.
This article looks at Microsoft and Windows 8 to put this into context.
Microsoft’s engineers discovered that people were doing less of the time-consuming writing and creating that had once been the norm. Increasingly, users were socializing for short bursts.
The research also showed that people loved having “touch” functionality and were avidly consuming small pieces of live information.
Consequently, Microsoft decided that Windows 8 should feature navigation that enabled multitasking and quick interactions, and that it should also have touch and live tiles.
People love touch. I love to touch on my iPad and iPhone all the time. However, those devices are more intimate than a computer. They don't have the bulk of a computer and they can sit on my lap or I can hold them up.
It turns out touching a screen on a computer is very hard over time. While little touches here and there will work, overall it is literally a pain to touch on a computer. After time your arm will become tired and a trackpad can solve many of the problems. While I believe at some point there will be a touch interface that makes sense in a computer, to have the whole interface built around touch does not make too much sense.
But what people say and what they do are two very different animals.
This is so important to understand when doing any research. It goes back to the famous Henry Ford quote of "If I would have asked my customers what they wanted, they would have asked for a faster horse."
Context is important, but so is psychology. Customers don't think beyond using the devises or tools they have when answering questions. They want a computer to solve a problem that another device should solve, but thats just because they don't know about the other device.
Also, doing research on what a customer would do if they were given a choice of the following is very deceptive. I never believe what a customer says they will do, I always rely on what they do in combination of what they say. When you combine the two, you get the truth which is always somewhere in the middle. Remember to never change an entire strategy based on customer surveys of "what they will do if" questions.
Organizations succeed with analytics only when good data and insightful models are put to regular and productive use by business people in their decisions and their work.
Actionable data is a buzzword that I have used and heard for many, many years. However, in practice it is much harder to produce actionable insight for business users. C-level executives are always trying to dissect old information which can be very useful, but only in cases where the answer will have some actionable insight. If the answers to question have interesting insight, but don't provide any action, it creates a time consuming chase in how to make something out of the data.
Organizations have to educate themselves of what is capable of their data. It's no use to ask questions that the data will not be able to turn into actionable insight. Organizations should spend time on questions that can be solved with the current capabilities, in other words the low hanging fruit. Once there is no longer any low hanging fruit, enhance the analytics with new tools and modeling capabilities to find the next round of low hanging fruit.
If you want to put analytics to work and build a more analytical organization, you need two cadres of employees:
Very true. An organization can hire all the analysts they can handle, but if the analysts have no business acumen and the business has no data acumen, there will always be a disconnect.
Organizations need to have both the business and analysts working together to find the best answers. Data Scientists can find very interesting items for them, however the business side may provide insight that shows the data scientists work is a known insight. The business may be working very hard to solve a problem that the data scientist can solve, taking intuition out of play and using data in the place of trial and error.
In my history I have always liked business and data analysts reporting to the same group. Many organizations don't like this structure because it can lead to a group "grading their own paper". However, the tight integration of the teams produces results more efficiently. Analysts are not wasting time chasing problems that don't exist and business people can bounce ideas off of analysts for quick insight before making decisions.
I’ve come to believe that less than 1% of the data is truly useful.
Exactly! Most businesses are very simple if you look for the key metrics. So many times people want to show their worth by over thinking the problem. If I can come up with some new innovative way to look at this problem, I'll be a superstar. But more times than not it isn't a complex problem. Humans are fairly simple to predict. Most humans will fall into patterns and want very straightforward things. New data doesn't need to be introduced until you have gotten everything out of the current data you have.
Big-data initiatives are proliferating, and the information is getting more complex all the time.
There’s a lot of potential benefit for both retailers and customers.
But only if the data is well managed and well understood. Statistics literacy isn’t very high in most businesses. A few educational institutions have realized this and are making a push to turn out business graduates who know their way around a regression analysis. But for the most part, businesspeople aren’t familiar enough with statistics to use them as the basis for good decisions. If you don’t understand the numbers, you can go a long way down a bad road very quickly. That’s why every team charged with making decisions about customers should include a trusted individual who understands statistics. If that understanding isn’t between your own two ears, make sure you bring a person with that skill set onto your team.
Being able to understand what the data is telling you is more important that having a degree in statistics. Interpreting data is really where the opportunities present themselves, not in figuring out the most optimal model. I suggest having someone who is proficient in building statistical models and ask a lot of questions from the output. Start to understand what the answers of models are telling you and simplify the results into something that can be used in the future. A model may tell you that people who buy a particular item are likely to be loyal, but is it the item that drives the loyalty or is this just a coincidence? The better you understand your data, the better decisions you will make and you don't have to be a data scientist to do that.
Laube, 43, Cardlytics’ president and COO, and Grimes, 51, its CEO, have since helped pioneer a data-driven advertising niche called merchant-funded rewards. It targets people based on what they buy, not who they are. “If you know where and how someone is spending money, you know lots of things about them without having to know their personally identifying information,” Laube says.
I have found that the transactions of customers is the most important predictor of future behavior in all data I have studied. While the demo, geo and psychographics of customers is very interesting data, to maximize the revenue from known customers is to really get to know their transactions.
While I don't know how good these algorithms are, the theory is solid. I happen to be a Bank Of America customer and the deals I receive don't seem to be any better than say my Rapid Rewards dining offers which don't seem to know my eating habits whatsoever. If these can be perfected, I think it's something that would get me to use my card more often instead of using my AMEX. I'll be watching because this is very intriguing.
I had the pleasure to sit through a keynote at the Tableau Conference in Washington DC and the speaker was none other than Nate Silver. It was a very good keynote and his book it a great read.
I find it very interesting what he says about education and working with data. I find the same thing. I don't have a background in Stats or Math, however I feel I have a good intuition with data. I have always loved numbers and working with them, but have never liked the mechanics of math or stats. I think in todays age of technology, it is more important to have the intuition than the mechanical knowledge.
Again, I think the applied experience is a lot more important than the academic experience. It probably can’t hurt to take a stats class in college.
But it really is something that requires a lot of different parts of your brain. I mean the thing that’s toughest to teach is the intuition for what are big questions to ask. That intellectual curiosity. That bullshit detector for lack of a better term, where you see a data set and you have at least a first approach on how much signal there is there. That can help to make you a lot more efficient.
That stuff is kind of hard to teach through book learning. So it’s by experience. I would be an advocate if you’re going to have an education, then have it be a pretty diverse education so you’re flexing lots of different muscles.
You can learn the technical skills later on, and you’ll be more motivated to learn more of the technical skills when you have some problem you’re trying to solve or some financial incentive to do so. So, I think not specializing too early is important.
When I look for new hires i tend to find people who are smart and try to figure out their critical thinking. The tools and the mechanical portion of data analysis and modeling can be taught, but it takes special people to have critical thoughts.
I always say that an analysts job is not to report on the data, but to find the money. The analyst that can take a dataset, find actionable insight and are able to articulate the findings are worth their weight in greenbacks.
Very cool article from a great thinker.
