Great Marketing Analytics Can’t Drive Managerial Courage

Great marketing analytics can’t drive managerial courage, but the reverse is true. Recently, I decided to have coffee with an old acquaintance of mine. He has been in almost every company imaginable and has such a specialized role that he is in constant demand.

Great marketing analytics can’t drive managerial courage, but the reverse is true.

Recently, I decided to have coffee with an old acquaintance of mine. He has been in almost every company imaginable and has such a specialized role that he is in constant demand. Every few years, there is an explosion on innovative management books designed to put him out of business — yet he remains in high demand.

Nobody was already at the café when I arrived. He was sitting in the middle of the café wearing a shiny grey suit, black shirt and sunglasses perched on his slicked-back salt and pepper hair, purposefully baiting my awe and contempt. He flashed a big toothy grin as I approached.

“Hi, ‘Nobody.’ I hope I did not keep you waiting,” I said, trying to hide my disdain.

“Nah, it’s all good,” he replied. “I was just people watching.”

“So what have you been up to?” I asked.

“Same old, same old … consulting business is as good as ever.” To punctuate his point, he grinned and leaned back with hands behind his head, as if he were ready to fall back into a hammock.

“Yeah, tell me what you do, again?” I asked.

“My consultancy focuses on accountability. It is really a simple model. When something breaks down in the workplace, or there is a failure to perform, I am called in to take accountability. Usually, when I show up, people will be stressed out. The guilty parties think someone else is responsible or are looking to share the blame, leadership does not want to create a toxic environment, and everyone wants to just move on. As a result, I come in. Everyone points to me, and they agree that it is Nobody’s fault.”

“Wow! And what do you charge for this service?”

“Depends, but it is usually a large percentage of gross revenue or net profit, depending on the size and type of failure I assume responsibility for. Business is great!”

My memory of our last conversation is suddenly jarred.

“That’s right; last we talked, we discussed how the wave of data-driven management was going to put you out of business. Wasn’t there some concern that measurement and analytics were the new wave of human capital management and that through measurement, greater accountability would come about?”

“Nobody” brightened up and leaned forward. His eyes opened up and his jaw slackened in awe of his luck.

“Yeah, that was what I was afraid of,” he said, “but it turns out, this big data threat has turned out to be a big hoax. You see, I was not called in because accountability was difficult; I was called in because accountability was icky. No amount of data and measurement will help my clients generate a healthy approach toward accountability if they don’t have the vision of what good accountability looks like. “

I had always disliked Nobody. While he feared the disinfecting power of data, I spent a good part of my career preaching the gospel of insightful data. I had always seen him as a Luddite; someone unaware and clinging to old ways. However, after this insightful confession, I found a sudden rush of respect for him. He knew things about business that I was now just learning for myself.

“Nobody, you are right,” I said. “I don’t deal with accountability directly, but I am often asked to help clients with data-driven customer strategy and marketing effectiveness. I have found that the analytics part is easy. However, it is often lack of clarity, purpose, and vision that prevents data and analytics from being effective.”

He smugly flashes that familiar, self-satisfied, toothy grin and instinctively my resentment reappears. However this time, it’s a different resentment. This time, my disdain is seeded with a healthy and well-deserved sense of respect and fear.

“You know your business model is still destined for obsolescence,” I insist. “It is just a matter of time. Wait till artificial intelligence shows up.” I am embarrassed as soon as the words part my lips. I feel small and helpless, like a kid fighting off a bully by threatening to call in an older sibling.

“Nobody” senses the change in our dynamic. He leans in closer than at any time in our conversation. Like a Bond villain, secure in his advantage, unafraid to share a horrifying truth.

“YOU-DON’T-GET-IT.” He pauses after each word, maximizing the dramatic effect, entirely playing out the Bond villain cliché.

“Data, AI, analytics — none of this matters, unless you have the courage and vision to use it in transformative ways. In fact, in this data-driven age, managers are so enamored by what they CAN do, it is hard to think about what they SHOULD do. As a result, my friend, managerial courage and vision are harder than ever. ”

Damn, he’s good.

How Big Is Your Vision?

Way back in the Internet dark ages of January 1996, Bill Gates wrote about and coined the phrase “Content Is King.” He was talking of course, about Web content and the need for people and organizations hoping to monetize the Internet to consistently produce fresh and relevant topics in order to gain the interest and loyalty of viewers, just as television had been doing, radio before that and print media the longest of all. His assertion that “over time, someone will figure out how to get revenue” from Internet advertising is frighteningly similar to today’s gurus predicting much the same in regard to social media marketing. Just as back then—when companies and marketers struggled with deciding whether a Web presence was needed—today there are still major corporations only testing the social media waters, even if only half-heartedly, to keep pace with competitors.

Way back in the Internet dark ages of January 1996, Bill Gates wrote about and coined the phrase “Content Is King.” He was talking of course, about Web content and the need for people and organizations hoping to monetize the Internet to consistently produce fresh and relevant topics in order to gain the interest and loyalty of viewers, just as television had been doing, radio before that and print media the longest of all. His assertion that “over time, someone will figure out how to get revenue” from Internet advertising is frighteningly similar to today’s gurus predicting much the same in regard to social media marketing. Just as back then—when companies and marketers struggled with deciding whether a Web presence was needed—today there are still major corporations only testing the social media waters, even if only half-heartedly, to keep pace with competitors.

For me, however, two lines in the Gates vision statement take on a slightly different connotation than his thoughts on content: “The definition of ‘content’ becomes very wide” and “Over time, the breadth of information on the Internet will be enormous, which will make it compelling.”

I read those two lines and what immediately strikes me is the overwhelming amount of data being generated during these last 17 years and how it is being captured, nurtured and put to work in areas such as Lead Generation, Brand, Affinity, Cross-Channel and Retention marketing. If at all.

IBM has an infographic regarding the flood of Big Data they use in demonstrating how their Netezza device handles integration for several major marketing organizations. This shows how, with connectivity, speed and bandwidth issues having become nearly eradicated during just the last two to three years, the amount of collectible, actionable data has exploded.

Unfortunately, the amount of irrelevant and useless data being collected is even greater than the actionable data, and being able to simply store that much data, let alone begin to organize and digest it all, is a major concern for most organizations. Before even thinking about the incorporation of Big Data initiatives, there should be an organizational review of quality for the existing information held in the collective datamarts that feed the central repository used for decision-making. Long before Big Data, the issue of Bad Data must be addressed.

Whether you are a B-to-B or B-to-C marketing entity, the creep of inaccurate data is constant across every customer and prospect contact you currently maintain. Experian-QAS has a stark reality “Cost of Bad Data” infographic showing the millions of dollars lost each year as a direct result of inaccurate and incomplete contact information. Complacency and budgetary shortcuts speed the process even more. Whether it is via an in-house effort or using third-party tools and vendors to perform ongoing hygiene, the vitality of your contact strategy is not sustainable without regular maintenance.

Once secure in the clarity and accuracy of your core data, you can move on to the integration plan for all of the additional goodies sprouting up from the Big Data seeds being sewn across every outbound and inbound marketing channel being utilized. But again, more planning and decision-making is critical before just jumping in and trying to grab every nugget. Perhaps the Fortune 50- to 500-level corporations might have the resources to take this on in one massive project, but I doubt that many small, mid or even larger brands can just dump everything into a pot and begin using the information gleaned into a successful series of campaigns. In a SAS/Harvard Business Review whitepaper I read recently; “What Executives Don’t Understand About Big Data,” this quote stood out to me:

“What works best is not a C-suite commitment to ‘bigger data,’ ambitious algorithms or sophisticated analytics. A commitment to a desired business outcome is the critical success factor. The reason my London executives evinced little enthusiasm for 100 times more customer data was that they couldn’t envision or align it with a desirable business outcome. Would offering 1,000 times or 10,000 times more data be more persuasive? Hardly.”

Having the foresight to develop phased approaches for data incorporation based on both short- and long-term ROI is the most realistic approach. Using results from the interim stages provides the ability to thoroughly test and analyze and measure value, keeping the project moving forward steadily while minimizing roadblocks to the longer-term goals.

My initial recommendation for the process would be along the lines of:

  1. C-Suite leadership establish the long-term goals for organizational success and with other Senior Management develop the phases to follow based on data, budget and resource availability to be assigned through each phase.
  2. Set the expectations and build the benefits case of the project across the entire company, communicating these goals in order to coordinate the gathering and availability of resources needed from whatever silo in which they reside.
  3. Design the KPIs that will be required in determining accuracy of marketing integration of the insights being introduced during each phase.
  4. Test and Measure every step of each phase for completeness and success before moving on to the next.
  5. Build simple and multivariate test panels into marketing campaign segmentation to analyze what new data elements truly provide sustainable lift in response.

I would love to hear your thoughts.

MDM: Big Data-Slayer

There’s quite a bit of talk about Big Data these days across the Web … it’s the meme that just won’t quit. The reasons why are pretty obvious. Besides a catchy name, Big Data is a real issue faced by virtually every firm in business today. But what’s frequently lost in the shuffle is the fact that Big Data is the problem, not the solution. Big Data is what marketers are facing—mountains of unstructured data accumulating on servers and in stacks, across various SaaS tools, in spreadsheets and everywhere else you look in the firm and on the cloud.

There’s quite a bit of talk about Big Data these days across the Web … it’s the meme that just won’t quit. The reasons why are pretty obvious. Besides a catchy name, Big Data is a real issue faced by virtually every firm in business today.

But what’s frequently lost in the shuffle is the fact that Big Data is the problem, not the solution. Big Data is what marketers are facing—mountains of unstructured data accumulating on servers and in stacks, across various SaaS tools, in spreadsheets and everywhere else you look in the firm and on the cloud. In fact, the actual definition of Big Data is simply a data set that has grown so large it becomes awkward or impossible to work with, or make sense out of, using standard database management tools and techniques.

The solution to the Big Data problem is to implement a system that collects and sifts through those mountains of unstructured data from different buckets across the organization, combines them together into one coherent framework, and shares this business intelligence with different business units, all of which have varying delivery needs, mandates, technologies and KPIs. Needless to say, it’s not an easy task.

The usual refrain most firms chirp about when it comes to tackling Big Data is a bold plan to hire a team of data scientists—essentially a bunch of database administrators or statisticians who have the technical skills to sift through the Xs and 0s and make sense out of them.

This approach is wrong, however, as it misses the forest for the trees. Sure, having the right technology team is essential to long-term success in the data game. But truth be told, if you’re going to go to battle against the Big Data hydra, you need a much more formidable weapon in your arsenal. Your organization needs a Master Data Management (MDM) strategy in order to succeed.

A concept still unknown to many marketers, MDM comprises a set of tools and processes that manage an organization’s information on a macro scale. Essentially, MDM’s objective is to provide processes for collecting, aggregating, matching, consolidating, quality-assuring and distributing data throughout the organization to ensure consistency and control in the ongoing maintenance and application use of this information. No, I didn’t make up that definition myself. Thanks, Wikipedia.

The reason why the let’s-bring-in-the-developers approach is wrong is that it gets it backwards. Having consulted in this space for quite some time, I can tell you that technology is one of the least important pieces in the puzzle when it comes to implementing a successful MDM strategy.

In fact, listing out priorities when it comes to MDM, I put technology far to the end of the decision-tree, after Vision, Scope, Data Governance, Workflow/Process, and definition of Business Unit Needs. As such, besides the CTO or CIO, IT staff should not be brought in until after many preliminary decisions have been made. To support this view, I suggest you read John Radcliffe’s groundbreaking ‘The Seven Building Blocks of MDM: A Framework for Success‘ published by Gartner in 2007. If you haven’t read it yet and you’re interested in MDM, I suggest taking a look. Look up for an excellent chart from it.

You see, Radcliffe places MDM Technology Infrastructure near the end of the process, following Vision, Strategy, Governance and Processes. The crux of the argument is that technology decisions cannot be made until the overall strategy has been mapped out.

The rationale is that at a high-level, MDM architecture can be structured in different ways depending on the underlying business it is supporting. Ultimately, this is what will drive the technology decisions. Once the important strategic decisions have been made, a firm can then bring in the development staff and pull the trigger on any one of a growing number of technology providers’ solutions.

At the end of 2011, Gartner put out an excellent report on the Magic Quadrant for Master Data Management of Customer Data Solutions. This detailed paper identified solutions by IBM, Oracle (Siebel) and Informatica as the clear-cut industry leaders, with SAP, Tibco, DataFlux and VisionWare receiving honorable mention. Though these solutions vary in capability, cost and other factors, I think it’s safe to say that they all present a safe and robust platform for any company that wishes to implement an MDM solution, as all boast strong technology, brand and financial resources, not to mention thousands of MDM customers already on board.

Interestingly, regarding technology there’s been an ongoing debate about whether MDM should be single-domain or multi-domain—a “domain” being a framework for data consolidation. This is important because MDM requires that records be merged or linked together, usually necessitating some kind of master data format as a reference. The diversity of the data sets that are being combined together, as well as the format (or formats) of data outputs required, both drive this decision-making methodology.

For companies selling products, a product-specific approach is usually called for that features a data framework built around product attributes, while on the other hand service businesses tend to gravitate toward a customer-specific architecture. Following that logic, an MDM for a supply chain database would contain records aligned to supplier attributes.

While it is most certainly true that MDM solutions are architected differently for different types of firms, I find the debate to be a red herring. On that note, a fantastic article by my colleague Steve Jones in the UK dispels the entire single-versus-multi domain debate altogether. I agree wholeheartedly with Jones in that, by definition, an MDM is by an MDM regardless of scope. The breadth of data covered is simply a decision that needs to be made by the governance team when the project is in the planning stages—well before a single dollar has been spent on IT equipment or resources. If anything, this reality serves to strengthen the hypothesis of this piece, which is that vision more technology drives the MDM implementation process.

Now, of course, an organization may discover that it’s simply not feasible (or desirable) to combine together customer, product and supplier information in one centralized place, and in one master format. But it’s important to keep in mind that the stated goal of any MDM solution is to make sense out of and standardize the organization’s data—and that’s it.

Of course there’s much more I can cover on this topic, but I realize this is a lot to chew on, so I I’ll end this post here.

Has your firm implemented, or are you in the process of implementing, an MDM solution? If so, what process did you follow, and what solution did you decide upon? I’d love to hear about it, so please let me know in your comments.