Data transformation

New technologies have reduced the cost of storing massive volumes of data. While many organizations have embraced the “data lake” concept, the reasons for storing massive volumes of data are often unjustified and seldom clear.

All too often, the data stored is unfit for purpose, lacking standardization or duplicated. Sound familiar?

A data lake can easily turn into a data swamp that has significant overheads but serves little purpose.

Imagine a platform that offers a practical way to stream, analyze and archive data to bring order to chaos. In an ideal world, your organization would ingest data, analyze it in real-time, and store only what it needs to support the business.

With cloud and artificial intelligence, this is now possible. Data can be “strip-mined” to determine which data sets are of intrinsic importance and need to be stored. It’s likely that you need only a fraction of the data you currently store.

Adopting this innovative approach may require new technology. But it also requires a new, confident mindset and methods that may be at odds with current thinking. GFT can help you with both.
 

We have unique practical experience in delivering smart data management solutions that straddle big data and traditional enterprise data. Our approach is groundbreaking and based on our own technology and methods.
 

We can help you develop a strategic approach to data that will deliver a quantifiable and sustainable improvement to your business. Our aim is to deliver tools and methods that will enable you to become self-sufficient in the future.

Identification of patterns across transactions and recommendation of relevant products

Video intelligence surveillance, machine learning, (home) automation, robotics and space exploration (MARS)

Ingest normalized real-time data and deliver measures of predictions from ML algorithms

Streamlining of business processes

Image recognition for mortgages and insurance

Email categorization, entity recognition, sentiment analysis and routing

Analyze and assess data trends for selecting explanatory variables

Natural language processing (NLP) for document classification and entity extraction

Based on historic data, identify unusual movements

Analysis of behavioral models and dynamic customer segmentation for identifying core behaviors and patterns