Prescriptive analytics is about the path to a goal:
We know where we’d like to be. Which actions – which decisions – will take us there?
Think of descriptive and predictive analytics as contributing steps. Take your best-fit predictive model and evaluate what-if scenarios to find the set of controllable conditions that promises to land you closest to your goal.
Prescriptive analytics isn’t easy. Ability to execute fast, exhaustively, and accurately is key. Your modeling choices include machine learning and also traditional methods. The first excels at discerning emergent patterns in big data. Traditional methods, especially for the central classification challenge – where taxonomy, especially when constructed the variety of data domains, excels – provide reliability and high precision. Imagine the advantage that can be gained via a combined approach.
Given: Machine learning is this decade’s great technical advance.
The technology aims to identify, detect, classify, and predict interesting features in source data, both text and structured datasets. The underlying process involves modeling, evaluation, and feedback/reinforcement, the latter making the method “cognitive,” mimicking human learning. The hope is to improve on established methods, to achieve greater accuracy, robustness (model coverage and maintainability), and speed-to-production without sacrificing performance or ability to support the effect. (Availability and cost of data science talent is a significant concern.)
Some of the terminology is esoteric – words such as cognitive and reinforcement – but the concepts are relatively straightforward. In supervised machine learning, the software infers general decision rules – a predictive model – from training data. A human analyst annotates features of interest in a training set, choosing labels from a predefined set of type or categories. (Some organizations use crowd-sourcing for this labor-intensive task.) In unsupervised learning, by contrast, the machine makes a best guess as to the categories, grouping cases with similar characteristics. Feedback or other forms of reinforcement confirm or correct the machine’s choices.
Despite advances, results remain highly reliant on the choice of inputs and algorithms. Model validation to ensure accurate results and reliable performance is an essential step.
Concerns aside, the case for machine learning is clear. The prime motivator is ability to flexibly generate purpose-suited models from data. The ingredients for adoption – low-cost, on-demand computing resources and lots of data – are in place. Steps to put machine learning in production, however, can get quite complicated.
Cognitive is complex; don’t miss context
Consider IBM Watson, an example of a cognitive system. Watson feeds a knowledgebase by combining text-sourced data, extracted via text mining, with information from structured data sources. There’s a curation processing involved: Humans assess, select, and correct acquired knowledge. The system interprets natural-language queries and generates candidate responses. The machine weighs possibilities and offers the answer most likely to respond to the question/ query.
What we have is, in essence, contextualized machine intelligence: a system generated by machine learning and context-focused via classification. The results speak for themselves: In 2011, a Watson computing system that could beat human Jeopardy champions. In 2014, Watson was made available on-demand, via IBM’s Bluemix cloud, and more recently, specialized healthcare, for smarter cities, and for the spectrum of business challenges involving natural language.
Starting very recently, commodity machine learning from a variety of sources, often open source – from Google TensorFlow and Microsoft Azure Machine Learning to startups such as MonkeyLearn and MetaMind – has brought machine learning to the masses. Powerful tools in under-trained hands, however, will not produce best results. The contextualization we’ve discussed can be applied to improve outcomes, systematically, contributing at several stages to the accuracy, relevance, and usability of models and results, as we now discuss.
You seek to model diverse features, the features that matter for your business:
One other potential accuracy booster we’ll mention: Use of an ensemble approach. Combine outputs of multiple methods – perhaps machine-learned and traditional – to arrive at a best- consensus result.
These are some of the many ways to improve machine learning accuracy via classified context. Creative minds can surely come up with others. Where can these methods be applied?
Finally, we consider the question:
Who can make best use of contextual machine learning approaches?
We choose a few representative examples for purposes of illustration.
Brands, agencies & marketers study social status updates, consumer-generated reviews and forum postings, survey responses, e-mail and other customer contacts, and other, diverse insight sources with requirements that include:
- Audience and market profiling and segmentation.
- Identification of behavioral signals that predict commercial activity (e.g., ‘path to purchase’).
- Social listening, customer engagement, sentiment analysis, and customer experience management.
- Competitive intelligence.
Social media platforms & online publishers serve constituents who include visitors and subscribers – who both consume and produce content – advertisers, syndicators and aggregators, and their own editorial and business needs. Their analytics-dependent tasks include:
- Data monetization, applying classification for topic tagging and
- Whom-to-follow
- Ad matching, and content recommendation, based on the semantic signatures of the ad/content and of the visitor, based on content consumption.
Retail, manufacturing, and logistics deal with often-huge counts of product and service items and their categories, components, attributes, and specifications, as well as associated information describing usage scenarios, events, and sentiment. Analytics powers functions such as:
- Search keyword expansion to capture product categories and attributes.
- Self-service customer support, via semantic search that understands categories, components, and attributes.
- Product recommendation, matching product and visitor profiles.
But these are only examples. Really, the answer to our opening question, “Who can make best use of contextual machine learning approaches?” is:
Anyone with a lot of data – hence the applicability of machine learning – and with a real world problem where common-sense knowledge comes into play.
This paper was written for data scientists, software developers, marketing analysts, product managers, and the executives who work with them, crafting organizational data strategy. The assumption is that you and/or your colleagues have an aptitude for data wrangling and a degree of coding experience, whether for data analysis or product creation. That is, you have the facility necessary to work with the tools and techniques discussed in the paper. We assume that you’re currently applying machine learning to pressing analytical tasks or have an initiative in the works.
You’re looking to maximize model performance – precision and results relevance in particular.
The choice of machine-learning methods is out of scope for this paper – although we’ll offer the hints that a) recurrent neural networks offer best results for text and other sequence-dependent data and b) supervised methods, with models built from annotated training data remain quite popular, for good reason – so we’ll focus on implementation of the ML-classification hybrid we’ve been describing.
Proof via prototyping
programming interfaces – or by devising a processing pipeline where the output from one step is fed as input to the next. The focus should be on creating a repeatable process that will generate reproducible results with consistent performance. Given the plethora of cloud-deployed services and installable components available, and the possibility of scripting your own workflow for experimentation or for production deployment, there are few barriers to prototyping and development via an agile, iterative approach. Go for it!
Do prototype use of taxonomy. Focus on a detailed, holistic understanding of consumer conversations across categories to allow not only for right-level classification – by category, topic, brand, product, component, or attribute – but also providing indexation multiples – measures of in-category frequency expectations – that help you assess contextual significance.
Test on your own data, judging the correctness of results for yourself and evaluating the boost that contextualization, via classification tools, provides in test cases. You’ll wish to assess classified context at multiple process points, as described in Section IV of this paper. Use of an on-demand processing service with a subscription model will allow you to make efficient use of resources and manage costs. Do ensure that the system not only meets accuracy and performance needs, providing analytical lift, but also that it has the capacity to scale to meet production needs.
This paper has described classified context, a technical approach that boosts the accuracy of models built via machine learning. Classified context improves training-data relevancy. The approach provides for rich, expanded training-data annotation and supports model validation and reinforcement learning
The hybrid is contextual machine learning, analytical modeling for text-rich business applications drawing on social and other online media and a spectrum of enterprise information sources.
Contextual machine learning makes the most of analytical advances, the data economy, and human expertise, as captured in traditional classification methods, notably taxonomy.
Prototype with your own data, using best-fit machine-learning algorithms, and experience the advantage for yourself.