Where Is the Data Movement Going?
The important lesson is that no matter how much improvement we will gain in computing and analytics in the future, no one should skip steps that are laid out in this example. However, I am concerned that too many new data players act as if some analytical silver bullet will make all the marketers’ dreams come true, when tool sets are, in reality, developed mostly for one major function at a time.
Let’s just say that the data refinement step (Step No. 2, in the example) is skipped over, as it happens so often even in organizations that currently want to adapt advanced analytics. Do you think that some analytical software will magically take care of categorization and data hygiene steps, too? One day it may. And even if such a day comes, it will take a different type of machine learning for that specific task, and the initial parameters will have to be set by humans with clear goals. Without a proper training process, the machine will not even understand the target categories when faced with gigabytes of raw data.
Too many developers, regardless of the company banner under which they work, are completely ignoring old-school disciplines. Many start-ups are acting like they are reinventing data mining all over again. Too many are attempting to develop a super machine that can just take any size of unrefined, unstructured, and uncategorized data and spit out answers, without funnel-like data reduction steps. Sometime in the future, things may just happen that way. In fact, I cheer for anyone who will have that kind of breakthrough.
However, at the risk of sounding like an old school geezer, I would say even such a machine will have to take steps laid out by our predecessors. Data mining started a long time ago with much slower computers, and the old-timers had to think about the steps even more carefully, as they had no time to waste precious machine time. And each necessary step definitely calls for a separate set of goals and different expertise. Data collection, mass storage, rapid retrieval, tagging and categorization, individual identification, data transformation and summary, modeling, scoring, customized message creation, and delivery of the final message to the right person at the right time through the right channel – they all call for different modules that must work together seamlessly. Just like the lunar module and its mother ship in the orbit, built by separate teams.
Stephen H. Yu is a world-class database marketer. He has a proven track record in comprehensive strategic planning and tactical execution, effectively bridging the gap between the marketing and technology world with a balanced view obtained from more than 30 years of experience in best practices of database marketing. Currently, Yu is president and chief consultant at Willow Data Strategy. Previously, he was the head of analytics and insights at eClerx, and VP, Data Strategy & Analytics at Infogroup. Prior to that, Yu was the founding CTO of I-Behavior Inc., which pioneered the use of SKU-level behavioral data. “As a long-time data player with plenty of battle experiences, I would like to share my thoughts and knowledge that I obtained from being a bridge person between the marketing world and the technology world. In the end, data and analytics are just tools for decision-makers; let’s think about what we should be (or shouldn’t be) doing with them first. And the tools must be wielded properly to meet the goals, so let me share some useful tricks in database design, data refinement process and analytics.” Reach him at firstname.lastname@example.org.