Just as embedded processors (and cheap memory) sparked a new generation of smart systems and consumer devices like the smartphone, embedded BI and analytics – enabled by advances in big and small data processing, open source projects like BIRT and Hadoop, and rich APIs – has the potential to change the face of many categories of applications (and devices).
Think hyper personalized and portable customer experiences, or smarter trading grids that anticipate disruptions or automatically seek out the best deal. Or new views into markets or business operations that reveal previously unseen relationships or potential innovations.
Meanwhile we are all looking to get closer to our customers, by gaining a true 360 degree view of what they want and how they are interacting with us and each other. This is where some of the new Analytics-as-a-Service (“AaaS”) offerings like Watson Analytics and the recently launched OpenText Big Data Analytics in the Cloud fits in. Combining advanced and predictive analytics, delivered as an easy to use managed cloud offering, AaaS aims to bring the power of big data to everyday business users, creating one view of their customer base, with a super-fast dedicated analytics data base and pre-built algorithms for handling the most common marketing and operational analyses.
The future of analytics is clearly about these types of tools that serve the growing population of “citizen data scientists.” It’s also about delivering insights from new data sources (think IoT) to users on their device of choice like smartphones, tablets or even smart watches. And building on a foundation of good information design (as detailed by Edward Tufte), blending the right unstructured and structured content, and applying the latest digital engagement models and approaches (like those from my friend and former colleague Esteban Kolsky).
Discover more in Vegas at Enterprise World
All of these scenarios will be front and center in the Analytics track at the upcoming Enterprise World 2015 event in Las Vegas in early November. I’ll be there with a cast of 1000’s delivering a couple keynote addresses as well as parts of several breakouts and demos looking at the role of big and small data, analytics and digital asset management, and helping to premiere our latest IoT demo (hint: it includes a network of Raspberry Pi-based sensors, an MQTT broker, and visuals powered by the OpenText iHub).
Learn more about the event and our sessions by clicking here.
The small data movement continues to gain followers. In fact, just in the past couple weeks we’ve seen articles in a wide range of publication including Ad Age, CITEWorld (smart data!), InformationWeek India, RetailWeek, and the SmartData Collective, as well as new opinion pieces on the problems with big data in the New York Times and FT magazine. Meanwhile one of the more popular sessions at Adobe’s marketing summit tackled the topic and featured my former colleague Scott Liewehr who reviewed findings from the study we did at DCG last summer (see nice recap of the session by Trueffect here).
As I’ve discussed over the past 2 years, many of the benefits of thinking small focus on making personalized insights more pervasive via new, smarter devices, tools, and apps. Bringing big data down to size helps both decision makers and “everyday” business users or consumers find (just the right) information/content they need, in a format that makes it super clear and easy to take action. This to me is the essence of what it means to be data-driven, and in many ways is powering the excitement around wearable technology like Pebble, Fitbit, and Google Glass.
So, for consumers, small data continues to be about connecting (with data and peers), convenience, and portability. Yet for businesses looking to tap the power of big data for everyday decision making, it’s also about speed. In fact, one could argue it’s mostly about speed and being faster to market and more responsive to customer needs. How so?
As brands strive to be more data driven in their product development, marketing campaigns, and experience design, thinking fast means getting actionable insights in minutes and hours vs. days and weeks as we’ve become used to in “traditional” BI and Big Data (thanks to having to wait for IT resources or our neighborhood data scientist to pay us a visit!).
In today’s business environment, it’s increasingly tough to justify these bottlenecks. Especially when many business users are already part of the way there with tools like Excel or Tableau, and others have seen the potential of a small data approach via their experiences with consumer apps and Websites.
The bottom line? When social and mobile are the rule, and data increasingly drives competitive advantage, speed, and being more responsive, is the new “big.” For business users, this starts with being ruthlessly focused.
Staying Focused to Move Fast
Moving fast, failing fast, even being an agile fast follower, are all proven business strategies. This is the case for organizations in growth industries, and especially for leaders who need to deal with external competitors as well as internal adversaries like budget cycles or organizational inertia. But to go fast, you need to have focus. And think small (and fast), when it comes to data access, discovery, and delivery.
In business, being focused is critical to bringing benefits of big and small data to the broadest set of users. And often requires that we reduce scope to just the essential questions/algorithms/visuals needed to help business analysts and their peers understand the essence of their data and optimize campaigns and experiences in near real-time.
Building on our small data design principles, this means striving for simplicity in our tools, being smart “enough” to handle role-specific tasks, being responsive in terms of portability AND delivering immediate value, and making it easy to compare notes and socialize findings.
Customer Analytics for the Rest of Us
Digging deeper, if we review our definition for small data, the last part (be accessible, understandable, and actionable, for everyday tasks) effectively becomes our checklist for designing and evaluating prospective customer analytics tools that are geared to the non-data scientist and everyday sales, marketing, and service tasks (like Actuate’s BIRT Analytics), as follows:
- Is it fast and simple to access all the data we need – from CRM, ERP, Web, campaign tools, social channels, and other sources – to create a true picture of customer and market needs (and easy to clean the data if needed)?
- Can non-technical users explore, enrich, and understand this multisource data in one place – creating an analytics “sand box” as Boris Evelson from Forrester has called it?
- It is easy for business analysts to create visualizations, share findings, and apply insights in new campaigns and experiences (via workflow and connectors) without needing expert assistance?
- Are common “everyday” tasks/use cases like segmentation, cross-sell campaigns, or social/Web targeting supported – and are there pre-build algorithms tied to these use cases?
Moving fast shortens the time to value, and allows more time for socializing insights and field-testing new campaigns and offers (as a type of “rapid prototyping” perhaps). Doing so requires simpler and smarter tools, and removing IT bottlenecks. But most importantly it means putting the power of advanced analytics in the hands of everyday business users.