Synerscope

SynerScope focuses on users in Financial Services, banking and insurance; Energy utilities and Oil&Gas; and Critical Infrastructure. These three sectors all share the challenge of having to work with complex combinations of data. Equally they are all subject to different regulations across their respective industry that demand demonstrating transparency about the data and information they use. In these verticals next to numbers large volumes of text and increasingly IoT and digital image data have to be combined to stay ahead of competition while staying compliant too. SynerScope operates both direct sales and channel sales strategies to stay close to its user base but also to be able to operate at scale.

SynerScope partners with some of the Top Ten Consultancy firms including one of the Big Four next to several boutique consultancy firms operating in the markets mentioned above. For implementation, deployment and support services SynerScope works with a network of certified SI partners (system integrators) including at least one partner with global presence.

SynerScope is open to OEM its technology and products for markets outside its current focus, first engagements in healthcare follow this track.

Synerscope itself – as opposed to its partners – defines its solutions by both vertical market and topic. In the case of the former these include financial services and critical infrastructure, while the latter includes cybersecurity and GDPR (general data protection regulation).

SynerScope seeks to eliminate bottlenecks in the processes from data to information. Acceleration of the information supply chain is achieved by first helping to streamline resource intensive data management and data preparations tasks of IT and data science staff. While the easy and broad access that SynerScope provides to unstructured data supports the spreading of analytic workloads across a large number of domain experts as users rather than other tooling that focuses on supporting data scientists only.

SynerScope is specifically developing products for users in financial accounting and controlling to help forge the link between financial data and actual business drivers the data of which resides very often in highly unstructured data sources.

Ixiwa is a data lake (Hadoop and Spark-based) management product that ingests data automatically, collects metadata about the ingested data (automatically) and classifies that data for you. However, it is how you explore the data that is most interesting, both with respect to structured data and image and text processing.

In the case of structured data, you can explore this visually – using a heat map-based approach – in any way that it is classified. Thus, for example, you could bring up a heat map based on the sensitivity of the data (the product integrates with technologies such as Apache Atlas and Ranger) or its quality, or simply because you want to look at all customer-related data. In the case of sensitive data, this might be based on PPI or PHI definitions or other standards that SynerScope supports, or you can create your own definitions. Other possible heat maps might be based around data size, the costs associated with data sets, and so on. Other types of data can also be clustered and explored in this way. For example, this technique can be used for exploring sensor data.

For unstructured data Ixiwa can classify individual elements within a single image using an object detection algorithm, and it also uses pixel sorting. This is more granular than auto-captioning, though you can use auto-captioning in conjunction with Ixiwa if you want to. Ixiwa does something very similar with text, for which it has a “text sorter”. For audio files it uses voice to text conversion first and you can use Ixiwa in conjunction with products that do feature extraction from text, if that is appropriate. The pixel and text sorters will extract similarities about, say, images, then infer clusters (same colour, same shape, same maker of car, facial recognition and so forth) and then tag those images appropriately. These sorters use TensorFlow to enable this sort of processing.

Under the covers, Ixiwa starts with the automated collection of metadata. There are three different types of such metadata that it collects, the first of which is “provided metadata”. That is, the metadata that accompanies the source system such as descriptions, field names and defined keys. The second type of metadata that Ixiwa supports is what the company refers to as “inferred metadata”. This would be metadata that is inferred from scanning the data itself. For this, Ixiwa has embedded machine/deep learning (based on TensorFlow) so that its inferences, and the tagging that it generates will improve over time. Finally, the third type of metadata that Ixiwa collects is what the company calls “attributed data”. This is where you can bring in external metadata about such things as data quality, cost and usage information. Full data and user level audit trails are captured, to support these sorts of metrics.

Synerscope offers conventional support and training capabilities. Services are also provided by the company’s business partners.

SynerScope offers business and data consulting services to drive quick results for proof of values and expansion of the user base across departments. The same services are available from certified consulting partners.

Whilst SynerScope certainly can directly provide deployment and implementation services either on-premise or in the cloud its preference is to have these services provided by a network of certified SI partners that serve a larger geographical footprint and have ample experience of integration work in enterprises.

SynerScope provides training and train the trainer services to customers and partners alike. Formal certification programs exist. User training is available in both conventional and online self-learning modules.