Next best action is the proving ground for advanced analytics and big data; it’s also the infrastructure that provides analytics- and rule-driven guidance across one or more customer-facing touchpoints. You can find next best action at the heart of multichannel customer relationship management (CRM) initiatives everywhere. It’s even present in a growing range of back-office business processes such as order fulfillment and supply chain management.

Next best action will continue to develop as an overarching business technology initiative for many companies in the coming year. The market is emerging and is becoming aware of itself as a substantial new niche, in much the same way that the Hadoop market flowered in the past year.

Here are some of the highlights that Forrester anticipates in the next best action arena in 2012:

• The next-best-action market will continue to coalesce around core solution capabilities. Traditionally, next best action has been a capability embedded in your customer service, marketing, and other CRM applications. That remains the heart of the next-best-action solution market. However, the past several years have seen the development of a niche for next-best-action standalone infrastructure that you may deploy in conjunction with various CRM and back-office applications. In 2012, we will see more vendors converge on the next-best-action arena from various backgrounds, including predictive analytics, business process management (BPM), business rules management (BRM), complex event processing (CEP), decision automation, recommendation engine, and social graph analysis. Many established vendors will repackage and reposition their offerings in these segments under the banner of next best action in order to address hot new solution areas, including multichannel offer targeting, marketing campaign automation, and customer experience optimization.

Big data was inescapable in 2011. Without a doubt, it was the paramount banner story in data management, advanced analytics, and business intelligence (BI). The hype has been relentless, but it’s been driven by substantial innovations on many fronts.

The big data mania will intensify even further in the coming year. Here are some of the highlights that Forrester foresees in this exciting space in 2012:

• Enterprise Hadoop deployments will expand at a rapid clip. Many enterprises have spent the past year or two kicking the tires of Hadoop, the emerging open source approach for scaling data analytics into the stratosphere of petabyte volumes, real-time velocities, and polystructured varieties. The market for enterprise-grade Hadoop solutions has grown by leaps and bounds and now includes several dozen vendors. Users all over the world and in most industries have invested aggressively in the technology and stand poised to bring their Hadoop clusters on line in the coming year. The size of the in-deployment clusters will almost certainly grow at least tenfold in 2012 as companies roll new data sources, new analytic challenges, and new business applications into their Hadoop initiatives.

Data science has historically had to content itself with mere samples. Few data scientists have had the luxury of being able amass petabytes of data on every relevant variable of every entity in the population under study.

The big data revolution is making that constraint a thing of the past. Think of this new paradigm as “whole-population analytics,” rather than simply the ability to pivot, drill, and crunch into larger data sets. Over time, as the world evolves toward massively parallel approaches such as Hadoop, we will be able to do true 360-degree analysis. For example, as more of the world’s population takes to social networking and conducts more of its lives in public online forums, we will all have comprehensive, current, and detailed market intelligence on every demographic available as if it were a public resource. As the price of storage, processing, and bandwidth continue their inexorable decline, data scientists will be able to keep the entire population of all relevant polystructured information under their algorithmic microscopes, rather than have to rely on minimal samples, subsets, or other slivers.

Clearly, the big data revolution is fostering a powerful new type of data science. Having more comprehensive data sets at our disposal will enable more fine-grained long-tail analysis, microsegmentation, next best action, customer experience optimization, and digital marketing applications. It is speeding answers to any business question that requires detailed, interactive, multidimensional statistical analysis; aggregation, correlation, and analysis of historical and current data; modeling and simulation, what-if analysis, and forecasting of alternative future states; and semantic exploration of unstructured data, streaming information, and multimedia.

Data scientists are a curious breed. The term encompasses a wide range of specialties, all of which rely on statistical algorithms and interactive exploration tools to uncover nonobvious patterns in observational data.

Who belongs in this category? Clearly, the “quants” are fundamental. Anybody who builds multivariate statistical models, regardless of the tool they use, might call themselves a data scientist. Likewise, data mining specialists who look for hidden patterns in historical data sets — structured, unstructured, or some blend of diverse data types — may certainly use the term. Furthermore, a predictive modeler or any analyst who builds fact-based what-if simulations is a data scientist par excellence. We should also include anybody who specializes in constraint-based optimization, natural language processing, behavioral analytics, operations research, semantic analysis, sentiment analysis, and social network analysis.

But these jobs are only one-half of the data-science equation. The “suits” are also fundamental. Any business domain specialist who works with any of the tools and approaches listed above may consider him- or herself a data scientist. In fact, if one and the same person is a black belt in SAS, SPSS, R, or other statistical tools, and also an expert in marketing, customer service, finance, supply chain, or other business specialties, they are a data scientist par excellence.

Both of these skill sets are fundamental to high-quality data science. Lacking statistical expertise, you can’t understand which are the most appropriate algorithms and approaches to make the foundation of your statistical models. Lacking business domain expertise, you can’t identify the most valid variables and appropriate data sets to build into your models around.

Customer experience is becoming the new currency of business success. If you make quality of experience the centerpiece of your customer relationship management (CRM) strategy, you will be creating a sustainable business asset of substantial value.

Customer experience has qualitative and quantitative returns, as I will discuss next month at Forrester’s Business Process (BP) Forum. For a detailed discussion of customer experience optimization, also take a look at this recent Forrester report that I authored. You can measure the qualitative business return on customer experience in, dare I say it, love. Hopefully, your customers love the multichannel experience you provide, and, as a consequence, seek to deepen and extend the relationship. The concomitant of that is the quantitative return, summed up by a single word: money. If you’re making customers happy, hopefully that translates into sales, profits, renewals, referrals, and other bottom-line boosts.

That’s all well and good, but how can you directly translate love — i.e., quality of experience — into money, measure the impact, and calculate the return on your investment in experience-boosting technologies?

CRM next best action platforms are the key to realizing this promise. CRM next best action environments shape experience through embedded analytics that guide all interactions and offers across all customer-facing channels, processes, and roles. In addition to predictive analytics and business rules management systems, enterprises often incorporate into their next best action initiatives such experience-boosting investments in decision automation, sentiment analysis, conversation management, dynamic case management, knowledge management, and social networking.