1. Big Data Will Become So Big, We’ll Stop Talking About It
The same way we don’t talk about the World Wide Web anymore, but rather just ‘the internet’ (which technically refers to the underlying connectivity supporting the WWW) we’re going to stop talking about Big Data because it will be the fundamental assumption behind everything we do. Big Data innovation is no longer about finding the next ‘Hadoop,’ but rather about embedding Big Data as an underlying platform in EVERY innovation going forward. The term Big Data will fade into the background as it becomes a part of every technology approach to create automated intelligence for improved operations. For example, CRM will have a function that looks across multiple data sources automatically and determine insight about customers as a function of CRM (e.g. because XYZ contact hasn’t received an email in 1 month since their support incident and they live in Iowa and they spend $5,000 a month on our service, they’re at risk for leaving). Additionally, the concept of Operational Intelligence will rise (using Big Data to understand the patterns and templates of behavior that will drive efficiency and results for curtailing user-behavior). The rise of the Data-Driven methodology is here and will guide all innovation moving forward – informing decisions and workflow based upon actual data trends, rather than human expertise.
2. The Information Security Paradigm Shift Should Be Everyone’s #1 Concern
The concept of an attack or data breach is no longer about “if” but about “when” and “how.” Advanced Persistent Threats and Complex Malware is likely already in your environment (some studies indicate it’s probably been there for 6 months or more). This comes from the #1 exploitable resource that can never be patched: humans. Social engineering is still the biggest security hole of any firm and technology can only mitigate risk, not eliminate it. At this point in the ‘security cold war,’ organizations can only hope to minimize dwell time of threats, rather than prevent them completely. The perimeter has dissolved (because of an “always-on” distributed workforce who wants to use any device, anywhere) while the number of endpoints have quadrupled (desktop -> laptop + tablet(s) + phone(s), and more). This nightmare for data and intellectual property leakage will necessitate CIOs to think of every device as inherently “untrustable” and shift the bottleneck to identity and authentication as the next battleground. Big Data will play a huge role here too by driving the effectiveness of SIEM (which is still an ineffective and ad-hoc repository of stale information). “Rear-view mirror” methodologies will give way “continuous response” and “adaptive frameworks” that allow for immediate risk mitigation based upon correlative and abnormal patterns. The days of “active defense” (because mounting an offense is truly the best defense) will not be far behind, leveraging much of the same techniques used by today’s attackers (ex. “Hacking the hackers,” widespread honey-nets, polymorphic AV executables, etc). Make no mistake: this is an arms race and attackers are paying attention to Big Data too. It won’t be long before social engineering is an automated process that is completely data-driven – all it takes is a malicious entrepreneur with some experience and willpower. The stakes have never been higher either – Target (who admits to being a “cautious” enterprise compared to risk-takers like Wal-Mart) had a simple breach that called for the removal of their major executives – even if the majority of customers will continue shopping there regardless. Layer on top of that the more stringent regulations around “data security practices” (more than just “policies”) and the fact that the FTC can sue companies for loss of customer data (per Wyndham-Worldwide), and the landscape is starting to get very bloody.
3. The Impact of Technology on Advertising and Media
The state of the current media supply chain is truly baffling. Creative agencies look at loose demographic data and use artistic methodologies to engage consumers across a variety of mediums. This work is then tested and targeted and mass produced for slightly different geographies and customers to improve efficiency slightly using rather indirect data gathered in highly inefficient formats. This creative work is often “given away for free” because of the way that the access to distribute this content is bought and sold by media executives over industry-sponsored dinners and backroom deals among a small private club of big media agencies using finger-in-the-air approaches combined with gin-inspired gut-decisions and mass purchases based upon multi-brand strategies. Mad Men may not drink scotch and smoke in the office before noon anymore, but the core of the middle-man business hasn’t changed much. Profit is made off of media transactions – not based upon effectiveness, but upon volume. It seems completely backwards – spend more money to get more results, because we’re going to cast as wide a net as possible. This will change. As data insight into the consumer experience increases (i.e. second screen activities, digital advertising and eyeball recognition, real time-bidding on high-value inventory), the sophistication of algorithmic-trading-esque programs for ad buying increases, and the ability to improve advertising effectiveness through hyper-targeted marketing and data-driven attribution will continue to increase. With that, the role of the Mad-Middle-Man will go the way of the Wall-Street-Floor-Trader yelling “buy!” and “sell!” as he takes orders via phone. CMOs will look more like data scientist and technology-fueled economists who have an aesthetic eye than business artists who can “connect with the people.” CIOs and CMOs will fight at first, but then ultimately become the same people at many organizations (the role of Chief Information & Marketing Officer will be popular for small and medium size firms, for sure). At the very least, they’re going to have to learn to get along – especially if Chief Digital Officers continue to secretly (or often publicly) try and make both positions obsolete.