The storm of Big Data hype has settled, and now we “simply” have to face the tsunami of Big Data heading our way. No worries, right? Fortunately, with the right pieces in place, there should be nothing to fear from Big Data, and much to gain.
Over the past two decades, since the foundation of the World Wide Web, “information” technology (IT) has been a cornerstone of business, including digital communications, web content delivery, e-commerce and other online business activities. The focus was primarily on the technology of the Internet and of the new cyber-enabled business. Now, Big Data has put the “information” back into IT. This return has not diminished the focus on technology but has sharpened technology’s role as the enabler of analytics ROI from the massive information assets of the organization. We describe this brave new information world in the context of three areas (CxO’s leadership teams, 3 Ts of Big Data analytics and 3 D2Ds of Big Data in business) and three analytics aspects of each: these are the 3×3 keys to business success in the era of Big Data.
The CxO Leadership Team
We are witnessing a radical transformation in the role of the chief marketing officer (CMO) into that of a chief digital officer (or chief digital marketing officer), plus we are seeing the emergence of two new roles: the chief data officer (CDO) and the chief data scientist (CDS). These leaders are taking a prominent role in corporate executive leadership, alongside the CIO, whose contributions are still essential and critical, though perhaps more as the chief information security officer (CISO). First, the chief digital marketing officer oversees the digitalization of the marketing analytics, campaign design and customer engagement aspects of the business, including activation of digital business strategies and managing the organizational transformation to digital campaigns. Second, the chief data officer focuses on the data – acquisition, governance, management, quality and policies (including privacy and preservation) as well as acquisition and oversight of corporate data technologies (not IT in the traditional sense). Third, the chief data scientist focuses mostly on the analytics (data science) objectives, opportunities and obsessions that consume the modern data-intensive business, including value creation from data and establishing a data-driven corporate culture built around the business analytics objectives.
The 3 Ts (Tools, Techniques and Talent) of Big Data Analytics
As the Big Data avalanche threatens to overwhelm us, we seek more effective and more efficient tools, techniques and talent to extract the information, knowledge, insights and understanding from the data. First, increasingly more powerful Big Data tools come to us from many outstanding vendors in this space, including Pentaho, WebAction, MarkLogic and Treasure Data. The recent TechWise webinar hosted by The Bloor Group covered many highlights of the tools and technologies offered by those vendors. Second, Big Data techniques (specifically analytics and data science methods) are growing in their capacity and capabilities to extract information, knowledge and insight from Big Data’s bits and bytes. These data science methods are more efficient in the sense that they enable the discovery of the most informative data features and analytics models, thus avoiding lots of low-ROI queries and experimentation; and these techniques are more effective in the sense that novel discoveries (beyond known patterns and expected relationships) are enabled. Third, among the 3 Ts (tools, techniques and talent), the most elusive is Big Data analytics talent – those rare and hard-to-find data scientists, who will apply their aptitudes for curiosity, creativity, communication, collaboration and problem-solving to dig deep into data for the hidden nuggets of insight and knowledge.
The 3 D2Ds of Big Data in Business
We previously wrote an article on this topic (“Big Data – What Is It Good For?”) where we identified the three D2Ds of Big Data analytics: 1) data-to-discovery, 2) data-to-decisions and 3) data-to-dollars. The business objectives that these correspond to are knowledge (and insights) discovery, data-driven decision support and big ROI (return on innovation) from Big Data analytics. In all of these scenarios, the first step is to extract actionable features from the data, before we can derive actionable intelligence. These actionable features include: a) the data content as expressed through condensed representations of the data (e.g., patterns, correlations, associations, regression curves, trend lines, outliers and other novel aspects); b) the data context (e.g., the data’s source, channel, user, use case, reuses and all of the lovely metadata that tells you who, what, when, where, why and how); and c) third-party information (e.g., parameters and features from other data sources and databases for each entity or event that you are monitoring and measuring). These actionable features may be derived in at least three ways: 1) business analyst-generated, 2) machine-generated (though machine learning and business rules) and 3) crowdsourcing (either internally or externally, if you have the proper controls in place). After these actionable features are created, collected and curated, then the business of discovery, decision making and value creation through Big Data analytics can accelerate. The resulting synergy of these activities leads to improved training sets, more accurate predictive models, fewer false positives and negatives, and more efficient and effective human interactions (with your users, clients and customers).
In the face of ever-growing Big Data, business success is enabled, empowered and enlightened by unlocking the benefits of analytics using the keys described here. You will succeed! No worries.