To help you navigate the terrain of business data concepts, we’re going to give you a basic summary of what some of the most common terms refer to and how they relate to each other.
This is a very good article on definitions in the data space. So many times I hear executives talk about topics such as "big data", but they are really referring to analytics or data collection.
Always good to see what the researchers are predicting for the future. This is an interesting take on big data. It focuses on an outcome of big data and then from a business perspective, what will happen to big data.
No. 1: By 2020, information will be used to reinvent, digitalize or eliminate 80% of business processes and products from a decade earlier.
Very interesting. Most successful products take a human process and automates the process to increase efficiency. I'm sure this prediction is a slam dunk as businesses will use massive data to help enhance current products and processes.
No. 2: By 2017, more than 30% of enterprise access to broadly based big data will be via intermediary data broker services, serving context to business decisions.
This is another no brainer. As companies like Experian and Acxiom make it easier to access their data, more and more companies will begin utilizing this data to make better decisions about their customers. This is something that I believe in greatly. The more data to enhance marketing campaigns, the better equipped marketers are to change the behaviors of their most valuable, or better yet, their most potentially valuable customers.
No. 3 By 2017, more than 20% of customer-facing analytic deployments will provide product tracking information leveraging the IoT.
The Internet of Things will be very interesting when it comes to data. How companies use data about customers behaviors in their own house with the items they use will be a touchy topic in the coming years. If companies can prove they are using the data to make the customers lives better, it will be a smash hit. If they are becoming creepy with the data, then the IofT will never reach its full potential.
If you work with data regularly, chances are you trust it. You know how it's collected and stored. You know the caveats and the roadblocks you face when analyzing it. But, when you bring your findings to those further removed, you're asking them to take a leap of faith and trust in data they may know very little about.
Multiple times in my career I had to come into organizations and take teams that were not trusted in the organization and help build them into the trusted source of data accuracy and insights. This journey is never easy. It takes patience and requires a lot of persistence to change an organizations perception of the department. But these points are good advice on a roadmap to do this.
Start Small
When trying to get people to believe in your team, it can be tempting to chase the biggest problems first. These problems often take a long time to answer, and can take several tries to get right. It's often better to first establish trust by picking early projects that you know you can win, and win quickly.Try starting with basic arithmetic to answer crucial business and product questions. For startups, some example questions might be:
What are the most engaging features of your product?
What is the company's core demographic? What do they like about the product?
Often, people don’t judge the answers to these questions on technical rigor, they judge them on business impact. Starting small can open doors to the big questions that you may have wanted to start with; if you've earned credibility along the way, you'll have more time, flexibility—and maybe resources—to tackle them.
I always find it helpful to start answering questions that are not currently being answered. As Derek Steer points out in the article, don't start by trying to solve the worlds problem. If your team tries to tackle tough problems, there will be a much more critical eye on the work and the data produce. Allow your team to get some wins under its belt. Remember, this is a journey, not a sprint. Trust comes with wins, not home runs.
Know your audience
Keep your audience in mind as you begin to craft the story from your data. Add in the appropriate amount of detail your audience needs to focus on decisions rather than methods. What context might they need? Spending a little time thinking about what your audience cares about most also helps you anticipate possible questions and prepare answers in advance. Few things can help establish credibility faster than fielding a question during a presentation and immediately flipping to a slide that answers it.
This point is critical to garner trust. When presenting data, make sure there is a story that is being told along with the data. Guide the audience to the answers that you have found, don't let them have to figure it our themselves. Be sure to explain to the audience what they are seeing and why it matters. Make it simple, quick and insightful.
Don’t be a House
House, a brilliant albeit fictional doctor, routinely diagnosed rare diseases but had abysmal bedside manner. The thing was, House didn’t have to win his patients over—they were so desperate to survive that they would listen to his every word.
It's subtle, but consider your findings a conversation starter. Understand that the non-analysts have valid points too: they have experiences you don't have and they likely know something you don't. These discussions aren't about winning an argument, but making the right decision for the business.
Never use data for evil. This is fairly common in Finance departments, but it is important not to attack decisions, but rather try to initiate conversations to come to the best business decision. Once there is a tone of implication in the analysis, your team will lose the trust of the department that you are creating the analysis for. Those departments made decisions that didn't have data to drive their decisions, so treat them as a partner, not someone that needs help.
Be Transparent
Analytics can feel like a black box to many people—making that leap of faith appear even larger. By showing even just the basics of your process, you can help others believe in it. To increase transparency try:
Making your work simple and understandable. Monica Rogatti would urge you to try division before doing anything harder. As your audience becomes more comfortable, up the game to simple regression models—it's not usually difficult for folks to understand the direction and magnitude of coefficients.
Finding simple ways to convey advanced concepts. For example,confidence intervals and p-values can be confusing for many people, but charts with error bars make these concepts easy to understand.
Using stories. If you're presenting information about feature usage, or events with technical backend names, paint a picture of how a user would see these features, or put events in plain-English names.
Numbers are difficult to interpret at times, taking the complex and being able to tell a story with it is an art. Most analysts are great at finding data and creating insights if they have domain knowledge, however they can be terrible at communicating their findings. Always have available the methodology for coming up with the answers, even if you believe it is a waste of time. The haters in the organization will demand this, but it also humanizes the process for the non-analytical audience members that you want on your side.
The most important part of the journey is to persevere. The beginning of the journey is the hardest part. I remember in my last position, the department I took over was the laughing stock of the organization. It took them weeks to come up with an answer and no one believed in what they were saying because they were just being report monkeys, instead of providing any insights. By the time we got going, we were the defacto data source for the organizations. We created analysis for parts of the organization we didn't have anything to do with, but when the organization needed something done right, it came through our team. That was because we had many wins along a journey.
From Forbes.com:
Gartner recently predicted that “through 2017, 60% of big data projects will fail to go beyond piloting and experimentation and will be abandoned.” This reflects the difficulty of generating value from existing customer, operational and service data, let alone the reams of unstructured internal and external data generated from social media, mobile devices and online activity.
Yet some leading users of big data have managed to create data-driven business models that win in the marketplace. Auto insurer Progressive PGR -1.22%, for instance, uses plug-in devices to track driver behavior. Progressive mines the data to micro-target its customer base and determine pricing in real time. Capital One, the financial services company, relies heavily on advanced analytics to shape its customer risk scoring and loyalty and offer optimization initiatives. It exploits multiple types of customer data, including advanced text and voice analytics.
I believe what most people miss when they hear these success stories is the amount of human capital that gets thrown at these problems. Hundreds of data scientists create thousands of models, of which very few are actually incorporated into final production. The reason the Gartner stats ring true is most companies don't have the kind of resources to throw at the problem and most companies won't realize an ROI even if they could throw these types of resources at a problem.
Promise 1: The technology will identify business opportunities all by itself.
This is the direction the technology is moving towards, but it is not there yet. The technology enables a group of data scientists to identify the opportunities, it's not magic.
Promise 2: Harvesting more data will automatically generate more value.
The temptation to acquire and mine new data sets has intensified, yet many large organizations are already drowning in data, much of it held in silos where it cannot easily be accessed, organized, linked or interrogated.
More data does not mean better ROI on your initiatives. In fact, most companies don't take advantage of the data they already have to generate the maximum ROI. I always use a rule of thumb when purchasing new technology. If as an organization you don't believe you are already using the technology you currently posses to its fullest, then its not time to move on to something better. Your current technology should be preventing you from innovating, if its not then you either have the wrong technology or the wrong people.
Promise 3: Good data scientists will find value for you.
To profit consistently from big data, you need an operating model that deploys advanced analytics in a repeatable manner. And that involves many more people than data scientists.
Remember, data + insight = action. Actionable data is a combination or art and science. data scientists provide the science, however you need the team with the business acumen to provide the insight, this is the art. Data scientists will create a lot of questions that you never thought to ask of your data, but they cannot provide a solution in and of themselves.
Remember to walk before you run when it comes to data initiatives. It's always good to have a goal of using "big data" to improve your business and create ROI from where it didn't previously exist, however the journey to "big data" is more important. These examples of success with "big data" did not happen over night. They happened because advanced companies were butting up against the limits of their current technology and they were ready to take the next step.
Steve Lohr writes for NYTimes.com:
Armies of the finest minds in computer science have dedicated themselves to improving the odds of making a sale. The Internet-era abundance of data and clever software has opened the door to tailored marketing, targeted advertising and personalized product recommendations.
Shake your head if you like, but that’s no small thing. Just look at the technology-driven shake-up in the advertising, media and retail industries.
This automated decision-making is designed to take the human out of the equation, but it is an all-too-human impulse to want someone looking over the result spewed out of the computer. Many data quants see marketing as a low-risk — and, yes, lucrative — petri dish in which to hone the tools of an emerging science. “What happens if my algorithm is wrong? Someone sees the wrong ad,” said Claudia Perlich, a data scientist who works for an ad-targeting start-up. “What’s the harm? It’s not a false positive for breast cancer.”
I have written here many times of analytics being a combination of "art" and "science". Having data and insight leads to the most action, yet some data scientists want to remove the "art" part of the equation. The belief is that computers and algorithms can see more about the data and the behavior than a human ever could. Also, once there is so much data about an individuals behavior, there is no "art" left, all the data points are accounted for so the "science" is indisputable.
However, I have a hard time believing that "art", or the human insight, will ever be replaceable. There are so many variables still left unknown and a computer can't know all of them. The "science" portion will always get better at explaining the "what" happened, but they don't understand the business operations and strategy that goes behind the decisions that were made. I am a true believer in the "big data" coming of age. I believe it is fundamentally changing the way companies have to do business, but never forget about the human side, the "art" of understanding "why" the data is telling you "what" is happening.
These questions are spurring a branch of academic study known as algorithmic accountability. Public interest and civil rights organizations are scrutinizing the implications of data science, both the pitfalls and the potential. In the foreword to a report last September, “Civil Rights, Big Data and Our Algorithmic Future,” Wade Henderson, president of The Leadership Conference on Civil and Human Rights, wrote, “Big data can and should bring greater safety, economic opportunity and convenience to all people.”
Take consumer lending, a market with several big data start-ups. Its methods amount to a digital-age twist on the most basic tenet of banking: Know your customer. By harvesting data sources like social network connections, or even by looking at how an applicant fills out online forms, the new data lenders say they can know borrowers as never before, and more accurately predict whether they will repay than they could have by simply looking at a person’s credit history.
The promise is more efficient loan underwriting and pricing, saving millions of people billions of dollars. But big data lending depends on software algorithms poring through mountains of data, learning as they go. It is a highly complex, automated system — and even enthusiasts have qualms.
“A decision is made about you, and you have no idea why it was done,” said Rajeev Date, an investor in data-science lenders and a former deputy director of Consumer Financial Protection Bureau. “That is disquieting.”
Blackbox algorithms have always been troubling for the majority of individuals, even for the smartest of executives when trying to understand their business. Humans need to see why. There is a reason why Decision Trees are the most popular of the data models, even though they inherently have less predictive prowess than their counterparts like Neural Networks.
Decision Trees output a result that a human can interpret. It is a road map to the reason why the prediction was made. This makes us humans feel comfortable. We can tell story around the data that explains what is happening. With a blackbox algorithm, we have to trust that what is going on inside is correct. We do have the results to measure against, but as these algorithms become more commonplace, it will be imperative that humans can trust the algorithms. In the above bank loan example, when making decisions regarding bank loans, a human needs to understand why they are being denied and what actions they can take to secure the loan in the future.
This ties into creating superior customer experiences. Companies that will be able to harness "big data" and blackbox algorithms and create simple narratives for customers to understand will have a significant competitive advantage. Creating algorithms to maximize profits is a very businesslike approach, but what gets left out is the customer experience. What will happen over time is the customer will dislike the lack of knowledge and communication and they will not become future customers. A bank may say, this is good, they would have defaulted anyway. But what happens in the future when too many people have bad customer experiences? I don't believe that is a good longterm strategy.
In a sense, a math model is the equivalent of a metaphor, a descriptive simplification. It usefully distills, but it also somewhat distorts. So at times, a human helper can provide that dose of nuanced data that escapes the algorithmic automaton. “Often, the two can be way better than the algorithm alone,” Mr. King said.
Businesses need to also focus on the human side. When we forget there is also an "art" to enhance all of these great algorithms, businesses will be too focused on transaction efficiency instead of customer experiences which in turn will lead to lower sales.
Originally posted on IBM’s Smarter Commerce blog:
Loyalty marketing is more and more prominent in today’s retail landscape. It is becoming common knowledge that customer acquisition costs are increasingly rising, and data-driven customer retention is a key area filled with untapped growth potential. But loyalty marketing is evolving and is more intricate than just offering discounts to existing customers. As many marketers realize, there are three common problems that they run into when trying to implement an effective loyalty program:
They often feel stuck offering dollars-off discounts and are losing their margins without sustainably changing their customer behavior.
Personalization is not going further than using much more than a first and last name, and is not connecting to the customer and building customer relationships.
Their loyalty members are not actively participating and being engaged, and consequently not influencing long term results.
It is a buyers market as they would say in the real-estate business. Customers have the ability to buy from a multitude of companies with fairly frictionless transactions. Years ago, a customer would be limited to their location to buy many of the items they can now purchase online, which makes loyalty marketing a much harder task today. If the customer does not like an experience they have with your company, the friction to switch providers is much easier than in the past.
This has led to a race to the bottom with most companies. Instead of competing on differentiation, companies rely on sales and discounting to compete in this new world. Relying on discounts is not differentiated at all. Any competitor can match a price or beat the price if they are willing to decrease their margins for the business. As I wrote in Busy is Not a Strategy, many of your competitors will look at metrics like volume as their key metric which will force them to decrease margins and hurt your business.
Increasing Self-Identification
Loyalty incentivizes customers to provide more information about themselves and engage across channels, which leads to a richer understanding of your customers and how they interact with your brand. You may be surprised how many of them are open to providing information about themselves in order to receive more relevant communications and offers.
Spending most of my time in the casino industry has shown me that consumers willingly give away information in return for a richer experience. In the case of the casinos this comes in the form of comps, but in other industries this does not have to be a giveaway. This could be access to sales, in the case of grocery stores. Find out what your version of the comp is to increase customer self-identification. It may start off as a giveaway, but don't let it drive the future customer experiences with that customer.
Taking Personalization to the Next Level
In addition to increasing customer self-identification, you should track and analyze metrics such as order frequency, average order value, and from which channels customers are purchasing. Modern loyalty programs gather this customer data and provide a centralized hub which is used to personalize meaningful incentives and rewards for higher customer redemption and satisfaction, and also to send personalized messages. These messages can be targeted towards specific actions and customer segments, and are used to maintain relevance and build upon customer-brand relationships by making customers feel like you are paying attention to what they want.
If you aren't tracking the purchases of your customers then you aren't going to be successful in loyalty marketing. Creating meaningful customer experiences relies on gaining insight to the behavior of the customer. By getting the customer to opt-in, it allows the business to create the true value from the loyalty program as I wrote here. Targeted content will create meaningful customer experiences and this rich data is at the core.
Cohesive Omni-Channel Capabilities
With today’s consumer having the ability to interact with your brand across all channels, it is essential to have cohesive communication, connectivity of data, and customer access to your program and rewards at all touch points. Different consumers like to interact with brands through different channels – whether in-store, social media, or email – and your program should be available in their preferred channel.
Providing the same experience for the customer, no matter what channel they are using, is the key to creating meaningful customer experiences. This is the hard part of the new customer experience paradigm. Keeping the content and messaging across channels in an online and offline world can be complex, but is very rewarding. Customers don't care that different divisions in the company have different responsibilities and the online team doesn't communicate effectively with the operations team. Customers expect their experience to be seamless across channels and it is imperative that businesses adjust to create this seamless customer experience.
Ted Cuzzillo writes for Information-Technology:
We give business people everything. They’ve got data, and often it’s clean. They’ve got tools, and many are easy to use. They’ve got visualizations, and many of them speed things up. They’ve got domain knowledge, at least most do. Tell me: Why hasn’t business intelligence penetrated more than about 20 percent of business users?
This is a great question. So many organizations have executive leadership that says they want information, dashboards and realtime information, yet when provided to them, it goes unread. How does this happen? The answer is what most executives want is a story. They want someone to interpret the analytics and let them know what they should be looking at. The dashboards act as content for speaking points. Executives want the most important numbers at their fingertips so they can spit them out at a moments notice.
What executives want is the rest of the data to be fed to them in a story with a narrative. Here is the data, here is what we believe it says and here is what we are going to do about it. It coincides with my article Data + Insight = Action.
What executives need is all of these parts (data, insight and action) in one analysis. They need to see the data, using visualizations to make the data easier to read. They need the insight of the business experts in the form of a commentary, succinct and to the point. Then they need what action is the business going to take with this newfound knowledge. With all of this information to arm the executive, they can understand and make a decision on what to do.
To reach "The Other 80 Percent," let’s turn away from the “data scientist” and to the acting coach. “A lot has to do with intangible skills,” said Farmer. A lot also has to do with traditional story structure, which appeals to “a deep grammar that’s very persuasive and memorable.”
Storytelling isn’t a feature, it’s a practice. One practicing storyteller, with the title “transmedia storyteller,” is Bree Baich, on the team of Summit regular Jill Dyché, SAS vice president, best practices. While others talk about stories, she said, most people seem to start and end with data and leave out the storytelling art. They fail to connect data with any underlying passion. “What we need are translators, people who understand data but can tell the human story from which it arose.”
There is always an assumption that is made from an analyst that a visualization or a table of data is plain and understandable. A good rule of thumb is to assume the audience of an analysis doesn't see what the analyst is seeing. If analysts start with this assumption, they can then tell a story of why this data is fascinating. An analysis without text that explains why the data is interesting is going to fall on deaf ears. Once the analysis gets to a higher level, the executives will not have time to create the "insight" portion of the data and they will either send the analysis back, or ignore it completely. Always remember to include the data, with the insight as a story and what action is going to be taken. With this formula analysts will become more than report generators.
From Martin McDonald:
We’ve all been there, sat in a meeting with your boss, or client, and they’ve said something like: “Our competitors have got 40,000 Facebook likes and 20,000 followers on twitter more than we do, we need to double down on our Social Media!”.
Let’s be perfectly clear, tracking social media based on likes, or follower numbers, is a pointless metric. For a start, both can be easily gamed, but increasingly platform are moving towards more sophisticated content targeting which for many companies means their chances of getting an ROI out of social media is significantly reduced.
I couldn't agree more. I remember when we were first launching our social media sites for our brands at a casino/hotel company I was working. We were so obsessed with gaining followers, yet no one was really engaging with the content we were providing. Gaining followers was important, but if we weren't producing relevant content, then the followers would not lead to any brand equity.
The analytics that Facebook and Twitter are putting out are a good start:
Social media should never be considered a “broadcast medium” , its no longer suitable as a one to many distribution – it should be considered a discussion medium, where you can engage your audiences with your message, your brand and your personality.
Moving away from messaging and towards discussion and interaction reveals the true metrics you should be concerned with: Engagement rates!
Measuring Social Media Effectively
Thankfully, both Twitter and Facebook provide lots of metrics, and have robust, free, analytics platforms.
Twitter recently revamped their entire analytics platform and its accessible to everyone with an account just by going to http://analytics.twitter.com and it provides in depth statistics on a per tweet basis.
Being able to manage engagement has always been something I have been very interested in. Content is king and just broadcasting what you're selling or information that doesn't appeal to the many of your followers will result in ignoring your messages. This is very similar to email marketing.
Since baseball season is around the corner, I wanted to look at the best players by OPS the past 2 years and whether they were increasing or decreasing. Here's a little viz that shows that along with being able to take a look at their career and what makes up their OPS.
One of my favorite people once taught me the mantra of "Busy is not a Strategy". So many businesses use the wrong metrics or KPI’s when measuring success of the business. For brick and mortar companies, their eyeballs tend to deceive them and they use that as their main metric (we were so busy). For other industries it is market share. How many widgets can we sell. The problem can be using the wrong KPI’s along with having the wrong culture can lead to an unprofitable business.
I have implemented the “Busy is not a Strategy” with resounding success before. We had a casino/hotel in a declining market that had 1,800 rooms. They were moderately successful considering their location, but they were using the wrong metrics. Their KPI’s were hotel occupancy and casino revenues. Now anyone who knows the casino/hotel business is going to ask, what is wrong with those metrics? They had good casino revenues for the market and an occupancy of 87%. Most anyone would love these numbers. Plus, they were really busy.
When we took over the business strategy of the property we saw to get these impressive numbers, there were a lot of giveaways and very low hotel room rates. To drive the wrong metrics, they were servicing a large number of unprofitable guests. The belief was if the hotel is full, more profits would eventually flow to the bottomline. There was just one problem, the other centers of business were not large profit centers and the customers coming in at very low hotel rates did not gamble, because they didn’t have a lot of money.
To increase profits, we decided we were not going to busy, we would focus our attention on the best customers and try to drive more frequency from these guests while sacrificing the low-end of the business. This resulted in decreased occupancy and decreased casino revenues. Uh oh. Hotel occupancy went down to 44% and casino revenues were down 10%. The operators were crying “the business is being ruined”. Even competitors were coming over and asking the operators “are you going to be able to remain open until the end of the year”. There was pure panic. That was until the financials came out. EBITDA was up 100% for the quarter.
By focusing on the best and most profitable customers, this property saw increases where it mattered most, the bottomline. How did this happen? The expenses to drive the KPI’s that were important to this property were astronomical. They were essentially competing for market share instead of profit. What happened through time, is the best customers started to come more often as that was the new focus of the property. Casino revenues started to increase through time to levels much higher than before the strategy change, however occupancy remained at 44%. They did this by focusing on:
This is very similar to what I see is happening in the phone industry. There are many manufacturers and most of them are focusing on “Busy” as a strategy. Now the metrics for busy in this industry are phones sold and market share. Android accounts for approximately 80% of the worldwide market share for phones sold. Yet when it comes to profit, that metric is almost reversed. In fact it is a lot less than 20% in the last quarter. So how can this be?
The phone manufacturers are selling basically the same thing. They run Android software that they manipulate in small ways, but all the apps are compatible with their competitors. This creates an experience that cannot be differentiated in any way but price. This is the same thing that happened in the PC industry. All manufacturers ran the same operating system, Windows, and they had to compete on price which forced them to make deals of adding bloatware onto their machines that destroyed the customer experience. This is where the phone industry is heading. When price is the main differentiator, businesses eventually will go out of business unless they can outlast the competition.
So these OEM’s sell many millions of phones to increase market share which leads to… To what? I don’t know. From what I have read these manufacturers have a decent amount of customers that are buying new phones, but they are buying them for the price. So the manufacturer sold an unprofitable phone so they can gain a customer who will buy another unprofitable phone. That doesn't sound like a sustainable strategy. There is nothing that differentiates the experience of the customer enough to make that return customer more profitable. It is a vicious cycle.
The only company that is running a different strategy is Apple. Apple is making almost all of the profit in the phone industry by having a differentiated product that is customer focused. Apple is doing the same thing in the PC industry, their Macs account for about 10% of the market, but more than 50% of the profits. Apple has been able to run the “Busy is not a Strategy” strategy to ultimate success. Sure Apple sells a lot of phones and they would like to sell more, but these sales are the outcome of their strategy, not the focus. Apple has a culture that is design focused which leads to a product that has a better customer experience.
Apple is dominating the phone industry because they do not bow down to the marketshare gods. They focus on the customer first through their design culture. They make profitable, differentiated products which bring in the majority of the profits in the industry, which then allows them to spend more money on R&D to create more products and services to keep their customers in the ecosystem. These customers buy new phones at a nice profit which creates a beautiful cycle. All because Apple is NOT implementing “Busy as a Strategy”
Anum Basir writes for Analytics Weekly:
“Big data” has arrived, but big insights have not. The challenge now is to solve new problems and gain new answers – without making the same old statistical mistakes on a grander scale than ever.
This is an article every executive should read about "big data". I believe it fits right in to my narrative about event with data, companies need art along with the science to have true insight as I wrote here. The article is a long read, but it details the promises of "big data" along with the pitfalls that come in only trusting the results without having the proper insights and testing.
As with so many buzzwords, “big data” is a vague term, often thrown around by people with something to sell.
I believe in more data, not a term of "big data" When people are trying to sell "big data" to corporations, are they really helping?
But the “big data” that interests many companies is what we might call “found data”, the digital exhaust of web searches, credit card payments and mobiles pinging the nearest phone mast.
In some circumstances they might be, but what Basir discusses in this article is the idea of "found data". This is data that already exists inside the company, or data that is just not being tracked and analyzed. As I wrote here, companies are sitting on a treasure trove of data that they aren't using optimally already. Adding "big data" may send the corporation down a path they are not ready for. Always search for the next best data that will solve the answers to the questions that need answering.
Cheerleaders for big data have made four exciting claims, each one reflected in the success of Google Flu Trends: that data analysis produces uncannily accurate results; that every single data point can be captured, making old statistical sampling techniques obsolete; that it is passé to fret about what causes what, because statistical correlation tells us what we need to know; and that scientific or statistical models aren’t needed because, to quote “The End of Theory”, a provocative essay published in Wired in 2008, “with enough data, the numbers speak for themselves”.
Unfortunately, these four articles of faith are at best optimistic oversimplifications. At worst, according to David Spiegelhalter, Winton Professor of the Public Understanding of Risk at Cambridge university, they can be “complete bollocks. Absolute nonsense.”
Basic goes on in the article to punch holes in these four claims. "Big Data" is very promising, but it is a destination for most companies. When something is a destination, there is a path that needs to be taken to get there. The path may change and there are detours on the way, but most companies can't just jump all in on "big data" or "found data". Companies must build an analytics culture, live in their data and use that data to make decisions with the business acumen they have built up for many years.
As Basir points out in the article, the problems with data do not go away with more of it, they just get bigger.
Four years after the original Nature paper was published, Nature News had sad tidings to convey: the latest flu outbreak had claimed an unexpected victim: Google Flu Trends. After reliably providing a swift and accurate account of flu outbreaks for several winters, the theory-free, data-rich model had lost its nose for where flu was going. Google’s model pointed to a severe outbreak but when the slow-and-steady data from the CDC arrived, they showed that Google’s estimates of the spread of flu-like illnesses were overstated by almost a factor of two.
The problem was that Google did not know – could not begin to know – what linked the search terms with the spread of flu. Google’s engineers weren’t trying to figure out what caused what. They were merely finding statistical patterns in the data. They cared about correlation rather than causation. This is common in big data analysis. Figuring out what causes what is hard (impossible, some say). Figuring out what is correlated with what is much cheaper and easier. That is why, according to Viktor Mayer-Schönberger and Kenneth Cukier’s book, Big Data, “causality won’t be discarded, but it is being knocked off its pedestal as the primary fountain of meaning”.
But a theory-free analysis of mere correlations is inevitably fragile. If you have no idea what is behind a correlation, you have no idea what might cause that correlation to break down.
Correlation without causation arguments do not go away with "big data". Having insights to enhance the results is key to successful analytics. We are all familiar with the story of ice cream sales and shark bites are strongly correlated, so selling more ice cream causes shark bites? Well thats just silly and obvious, we all know because sales of ice cream and swimming in the ocean increase in the summertime.
But the example brings up a crucial point, do not trust the output of the data without using your vast knowledge on the subject as a barometer. Anyone can see the shark bite, ice cream example has nothing to do with each other, but findings of big data can be a lot more tricky to detect. What may look to be a relatively reasonable explanation of data from a model a data scientist created may actually ruin a business because the data scientist had no knowledge of the subject matter. When just solely relying on data, all the great human knowledge about the business are thrown away. This is art and science. Treat it as such. Get the artists into the room with the scientists and find the best answer, not the cheapest and easiest one. Actionable analytics is hard, don't underestimate the complexity of the problem.
Analytics can “open up many doors for healthcare organizations” of all types, including life sciences companies aiming to get new medications to patients faster or to “provide regulatory bodies with evidence of drug safety.” Or, for payers, analytics can “answer questions about future growth, profitability and sustainability,” or help them to detect and prevent fraud.”
It still amazes me that big organizations in so many industries are still talking about what analytics can do for them. Of course, this headline is a little deceiving as there really is no "big data" to be found. You get a lot of clicks when you have "big data" in the headline though.
Manji Matharu writes:
CMOs are now at a crossroads between data quality and data results. It’s no longer enough to dabble in analytics and come out with the richness required for informed decision-making. The business needs integrated systems across IT infrastructure, and marketers — not IT pros — must champion the call for improved data controls and governance as their cause.
Data quality is the first step in all marketing processes. Ensuring this is boring and hard, but it is a necessity. This is the first step when I come into an organization, determine the quality of the data and work to fix that. Once there is a trusted version of the truth, marketing analytics come to life.
Adobe Summit brought with it a great nirvana of a near-future where marketers are able to deliver relevant content to customers creating great experiences. The words that permeated throughout the conference were those, content, experiences and data. The big stars of the show were content and customer experiences, which I believe are extremely important as I have written before.
However, there is no right content delivered at the perfect moment to create wonderful customer experiences without data. Data is the key to making this all work, and not just any data. No, I'm not talking about "big data", I'm talking about actionable data.
Most companies are sitting on a treasure trove of data already. Without purchasing third-party data, understanding every click, customers have data that can transform their business. The issue is in interpreting the data, making it actionable. Actionable data isn't a product, it's a culture.
Actionable data is the combination of art and science. The path to actionable data isn't necessarily going out and hiring a bunch of talented data scientists, though it doesn't hurt to have these people on your team. The path to actionable data is marrying the data with the business acumen. It's not enough to have data telling you something happened, there has to be an understanding of the business as to why it happened.
Once there is an understanding of what happened (science) and why it happened (art), you have actionable data. Now you can create optimal tactics to deliver relevant content to create targeted experiences in the digital age. The great thing about this process is it's circular. Once a company creates great targeted experiences for their customers, customer behaviors will change and the entire process starts all over again. There are always puzzles to solve and amazing content and experiences to create.
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!!!
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.
A very interesting post about data visualization. I believe the correct answer is somewhere between 2 and 3. Excel days were just awful!
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.
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.
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.