Entries filed under “Business of Big Data”

Big Data gets mixed reviews in a recent study conducted by the CMO Council.

Lisa Arthur, writing in Forbes, says the study indicates that CMOs and CIOs both agree that Big Data will be a key competitive differentiator and play a major role in implementing a more customer-centric business culture.

However, the study also shows they view Big Data as a problem. “In fact, 52 percent of marketers and 45 percent of IT professionals believe functional silos block aggregation of data from across the organization, making it difficult to truly achieve customer centricity,” writes Arthur.

But according to the CMO Council press release announcing the study, the two functions are more aligned than at odds with each other.

The study also reveals that the two functions are more aligned than many may think,” the Council states. “Some 41 percent of marketers and 39 percent of IT executives say they are aligned with one another, but both admit there are still challenges to executing priority projects. Regardless of any setbacks, both overwhelmingly agree that the relationship is critical, according to 85 percent of marketers and 85 percent of IT executives. Multiple areas of opportunity emerge where further alignment and collaboration will improve the organization’s ability to execute customer-centric programs. Marketing is looking for a strategic partner that will come to the table with initiatives to advance customer centricity (26 percent). Marketing also believes the greatest value in the relationship can manifest in the ability of the organization to better gather data from across the enterprise (63 percent). For their part, IT executives see marketing as their partner in advancing analytics and data-driven decision making throughout the organization (62 percent). However, IT would like marketing to approach them earlier in the process to collaborate more on strategy (62 percent) and not just platform selection and deployment.”

In her article, Arthur asks CMOs, “Isn’t it time for those silos to come down? Aren’t you ready to develop a strategic partnership with the CIO?” She then goes on to list a number of steps CMOs and CIOs can take to more fully realize the advantages of Big Data.

In today’s hectic business world, it’s nice to find her advising CMOs that because integrating marketing and data is a “BIG” job, they need to pace themselves. Counsels Arthur, “Blending data, customer interactions and processes across organizations is not a project . . . It’s a journey.”

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You remember Zeno’s famous Dichotomy paradox. Roughly paraphrased it says: if you want to walk, for example, from your chair to the door, first you have to walk halfway there. And before you walk halfway there, you have to walk a quarter of the way, and so on ad infinitum. You can never reach your destination. Of course, after all that you simply stand up and walk to the door.

Could it be that the act of merely contemplating implementing Big Data in an organization can brings on an attack of Zeno’s conundrum?

Writing in Forbes, Matt Ariker, McKinsey & Company COO of the company’s Consumer Marketing Analytics Center notes that “Many who make the Big Data journey are overly fixated on making it to the ‘Promised Land.’ In far too many cases I see people who plan to build out a complete system and architecture before using a single insight or building even one predictive model to accelerate revenue growth.”

He recommends first clearly defining your end game – knowing where you are going before you start the journey. Then the trick is to avoid the fallacy of believing that you can’t gain any insights until the entire Big Data infrastructure is in place. Says Ariker, “Think of it more like a tool factory making all sorts of specific tools for specific jobs. Every time one tool is complete you can begin using it; you don’t have to wait for all tools to be complete.”

The consultant then offers four helpful pieces of advice that he uses to counsel his clients. These include: assigning the creation of an Insights Roadmap to a highly skilled team with the relevant skills; creating a detailed business case that focuses on P&L impact; and starting small by cherry picking already available data sets. Here’s the fourth and final step.

Build a roadmap. I know this step sounds obvious but I’m constantly surprised by how little effective planning takes place,” comments Ariker. “As counterintuitive as it may sound, it takes planning to work fast. Make sure you’re focusing on the models and necessary infrastructure that deliver the most value, and sequencing them all to minimize delays. Put in place clear data delivery deadlines and test timelines for each model to ensure timely delivery… One high tech company followed this “Insights Roadmap” route. By following the four steps I laid out above, the company started generating insights within two months of the project starting, long before the two-year Big Data warehouse build project was completed. Within three months, the company was able to book $5m in additional revenue. By end of year one, incremental revenue exceeded $75m. Your journey to reach Big Data’s promised land will take time. But realizing value doesn’t have to.”

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Over at Forbes, Edd Dumbill writes that as a flurry of Startups and incumbents scramble for a piece of the Big Data pie, the key to success is understanding that Big Data is a business problem.

Big data is ultimately about the smart use of data to drive a business. There are two kinds of data: data about your business, and data external to your business that you can create value from. It’s easy to see who might get success from the latter, external data. We can expect that massive data owners such as Google, Facebook, Thomson Reuters, Bloomberg will experience ongoing success for as long as they are able to create product from their data. Who owns your data, though? The obvious answer, you, isn’t the only answer. In fact, your data is locked up inside the platform choices you make, at both the hardware and software level. If your systems are based on Oracle, Microsoft, you are very unlikely to move in a hurry. Data likes to stay where it is, and tends to attract more data as you build systems around it. Production systems are expensive to replace.

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