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.

The Great Marketing Data Revolution

I think it’s safe to say that “Big Data” is enjoying its 15 minutes of fame. It’s a topic we’ve covered in this blog, as well. In case you missed it, I briefly touched on this topic in a post titled “Deciphering Big Data Is Key to Understanding Buyer’s Journey,” which I published a few weeks back. For those of you who don’t know what it is, Big Data refers to the massive quantities of information, much of it marketing-related, that firms are currently collecting as they do business.

I think it’s safe to say that “Big Data” is enjoying its 15 minutes of fame. It’s a topic we’ve covered in this blog, as well. In case you missed it, I briefly touched on this topic in a post titled “Deciphering Big Data Is Key to Understanding Buyer’s Journey,” which I published a few weeks back.

For those of you who don’t know what it is, Big Data refers to the massive quantities of information, much of it marketing-related, that firms are currently collecting as they do business. Since the data are being stored in different places and many varying formats, for the most part the state they’re in is what we refer to as “unstructured.” Additionally, because Big Data is also stored in different silos within the organization, it’s generally managed by various teams or divisions. With the recent advent of Web 2.0, the volume of data firms are confronted with has quite literally exploded, and many are struggling to store, manage and make sense out of it.

The breadth of data is simply staggering. In fact, according to Teradata, more data have been created in the last three years than in all past 40,000 years of human history combined! And the pace of data is only predicted to continue growing. You see, proliferating channels are providing us with an unprecedented amount of information—too much even to store! In a marketing sense, the term Big Data essentially refers to the collection of unstructured data from across different segments, and the drive to make sense of it all. And it’s not an easy task.

Think about it. How do you compare email opens, clicks and unsubscribes to Facebook “Likes” or Twitter followers, tweets or mentions? How does traffic your main website is receiving relate to the data stored in your CRM? How can you possibly compare the valuable business intelligence you’re tracking in your marketing automation platform you’re using for demand generation, against the detailed customer records you’re storing in your ERP you use for billing and customer service? Now throw in call center data, point of sale (POS) stats … information provided by Value Added Retailers (VARs), distributors and third-party data providers. More importantly, how do they ALL compare and relate together? You get the picture.

Now this begs the next question; which is, namely, what does this mean to marketers and marketing departments. This is where it gets very interesting. You see, unbeknownst to many, there’s an amazing transformation that is just now taking place within many firms as they deal with the endless volumes of unstructured data they are tracking and storing every day across their organization.

What’s happening is firms are rethinking the way they store, manage data and channel data throughout their entire companies. I call it the Great Marketing Data Revolution. It’s essentially a complete repurposing and reprocessing of the tools they use and how they’re used. This wholesale repurposing aims not only to make sense out of this trove of data, but also to break down the walls separating the various silos where the information is stored. As we speak, pioneering companies are just now leading the charge … and will be the first to reap the immense benefits down the road when the revolution is complete.

Ultimately, success in this crucial endeavor demands a holistic approach. This is the case because this drive essentially requires hammering out a better way of doing business by reprocessing across these four major steps: Process Workflow, Human Capital, Technology, and Supply Chain Management. In other words, doing this right way may require a complete rethinking of the direction that data flows within an organization, who manages it, where the information is stored, and what third-party suppliers need to be engaged with to assist in the process. We’re talking a completely new way of looking at marketing process management.

With so many moving parts, not surprisingly there are many obstacles in the way. Those obstacles include legacy IT infrastructure, disparate marketing structure scattered across various departments, limited IT budgets and, of course, sheer inertia. But out of all the obstacles companies face, the most important may be the dearth of data-savvy staff and marketing talent that firms have on staff.

Firms are having a difficult time staffing up in this area because this transformation is actually a hybrid marketing and IT process. Think about it. The data are being created by the firm’s marketing department. As such, only marketing truly understands not only how the data are being generated, but more importantly why they’re important and how this information can be put to actionable use in the future. At the same time, the data are stored within IT’s domain, sitting on servers or stacks, or else stored in the cloud. And because the process involves a complete rethinking and reprocessing, it really needs a new type of talent—basically a hybrid marketer/technologist—to make it happen.

Many are deeming this new role that of a Data Scientist. Not surprisingly, because this is a new role, employees with these skills aren’t exactly a dime a dozen. You can read about that here in this article that appeared on AOL Jobs titled “Data Scientist: The Hottest Job You Haven’t Heard Of.” The article reports that, because they’re in such high demand, Data Scientists can expect to earn decent salaries—ranging from $60,000 to $115,000.

Know any Data Scientists? Are you involved in a similar reprocessing transformation for your firm? If so, I’d love to know in your comments.