Our Technology

Information Management

Event Engine: Components which process data in-flight to identify actionable events and then determine nextbest-action based on decision context and event profile data and persist in a durable storage system. Data Reservoir: Economical, scale-out storage and parallel processing for data which does not have stringent requirements for formalisation or modelling. Typically manifested as a Hadoop cluster or staging area in a relational database

Logical view

Data Sources. These represent all potential sources of raw data which are required by the business to address its information requirements. Sources include both internal and external system. Data Ingestion and Information Interpretation. These are the methods and processes required for ingestion and interpretation of information to and from each of the data layers in our architecture.

Speeding up response

Prevalent techniques tend to rely on analyzing historical data, while many applications of big data need predictive analytics to access current information, and to provide insights or modify models in (near) real time. This necessitates new PA techniques that are in some sense “self-correcting” or “self-learning”, such as Bayesian methods or machine learning algorithms.

Strategic perspective

Most of all, it is important to approach this type of analytics from a strategic perspective, rather than starting with the technology or the data. The strategy should be developed to suit the enterprise as a whole, to avoid conflict between, or duplication of effort by, sales, marketing, and so on.

Creating new analytics

New types of analytics are becoming possible as a direct result of the additional types of data now available. These analytics can help a company to sell more products and provide a better service and overall experience for customers.

What We Do

Improve accuracy

100% accuracy is not achievable, but there are ways to improve on SMM tools – for example with natural language processing. Semi-structured sources of social data are easier to analyze accurately. Numerical product ratings and “likes” can be aggregated to provide insight into how customers feel about products, and can be tied to a structured piece of data like a digital asset

Focus on action

Insights must generate actions. Empower your “air traffic controllers” to make decisions, and make sure those decisions are relayed to the parts of the organization that must act on them: customer service, product development, sales and so on.

Work out what is relevant

Volume To know which items in the sea of available data are relevant to your business, you need to integrate SMM with other data-related activities. If you are applying text analytics to social media data, then why not use it on other unstructured “verbatim” sources such as surveys and call center notes? Add structured data to the mix and you are positioned to identify.