Chase McMichael from InfiniGraph gave his talk on how marketing intelligence can find use case data in social media. Check out his slides on SlideShare. He observed that data harvested from outside your own organization isn't "clean." Dirty data contains noise from inaccurate or irrelevant sources that IT managers must sort through a labor-intensive data cleansing process. Chase thinks large enterprises lack integrated plans for aggregating and sorting Big Data. His ideal business intelligence plan proceeds from reporting to analysis to monitoring with predictive analytics as the top layer.
Let's pause to reflect on Big Data integration. My use of Google Analytics and Webmaster Tools pales in comparison to the challenges larger enterprises face. IMHO DevOps people must increasingly design apps with data collection goals and analytics in mind. If this emphasis isn't embedded early in the software development cycle, the finished app won't produce clean Big Data that feeds an ERP. Big Data demands a redesign of CRM/ERP integration. CRM will generate data marketers can use to adjust Customer Development, and that CustDev must drive the predictive analytics that will become ERP resource use forecasts.
I'm getting ahead of myself here, so let's get back to Chase's wisdom from his seminar. He noted that Apple's app strategy gave its iPhone the edge over BlackBerry because the app ecosystem generated demand for the device's adoption. I kept thinking about how companies track these ecosystems to look for demand trends. Chase thinks that content marketing depends on trend discovery, a Big Data problem. I now think that scoring an ecosystem's engagement with your enterprise's content is a key part of a Big Data strategy. The real-time tracking aspect gets hard for companies that aren't savvy in social media. User interactions in social media leave data fingerprints and DevOps people should build algorithms that track them as use cases.
Chase argues that Big Data analytics mixes anthropology, social science, and statistics. His preferred "real-time marketing" reacts with relevant messaging to data derived from demand. Now I see his earlier point about the ineffectiveness of labor-intensive IT methods. Machine intelligence creates scalable data that does not require intensive human labor in its production. The automation of Big Data's demand-driven response is what makes it scalable.
I grok this concept of automated data-driven marketing responses but I also heard similar things back in the late 1990s when I was on active duty in the US Army. Military technology developers were excited about fielding the "Force XXI" suite of systems that were going to automate away all battlefield confusion. These systems are now mature but the fog of war is still present on the battlefield. Even the RAND corporation was skeptical about Force XXI's assumptions while the program was at its highest visibility. Uncertainty in a competitive environment never disappears because executing a strategy requires human judgment to ensure the OODA Loop has the correct orientation and that progress tracks the correct milestones.
Big Data has a role to play in automating the marketing data collection feed into analytics, just as it can play on the battlefield in automating intelligence data collection. IMHO, Big Data poses a knowledge management taxonomy challenge to large organizations once they've sorted the data streams. My point is that humans cannot ever be fully removed from the OODA Loop, especially those C-suite executives who are responsible for ensuring the Orientation correctly reflects the enterprise's strategic posture. Humans designing these systems can't just turn everything over to Hadoop and hope for the best. The end result in a private enterprise would be like the US's McNamara Line in Vietnam where dirty data corrupted automated decision-making. The human beings working on corporate CRM / Big Data / ERP integration task forces need clear guidance from the C-suite on a KM taxonomy that will prioritize the types of data that get automated. The strategic guidance should also name the program managers or geographic region managers who will own parts of the automated decision-making cycle.
Chase finished with some hints on Googling ".xls" and other terms to see just how much Big Data that corporations have released is already in the public domain. He thinks RSS feeds are a universal standard for content publishing (with visual content being especially powerful), and we should use them to collect content for scoring. He also thinks we should check out InfiniGraph's SMO portal. I think we should all check out IIA's Analytics 3.0 while C-suite folks should attend the Chief Analytics Officer Summit. Those resources will given enterprises a start on developing guidance for DevOps and KM integrators as they start automating data-driven marketing responses.
Well done, Chase. You got me thinking. BTW, these Meetups make it cool to be a technology geek. I saw several very attractive women at this Meetup, including one hot blonde Russian chick named Olga. Hey Olga, send me your phone number and let's talk about Big Data at your place.