Data Discovery & Governance
Data Discovery & Governance
Organisations have zero visibility into what lies within their unstructured data and dark data making correlation between file data and a specific requirement practically impossible (e.g., privacy, business, security policies, geographic regulations).
Organisations suffer from an inability to manage the huge data volume without the tools to identify risk within that data and to prioritize the handling of the risk.
- Data mapping for dark and unstructured data
- Automated risk quantification of personal information (PI) and sensitive business information
Provides automated visual mapping so that file data can be analysed easily for multiple dimensions. For example, multi-state organisations need to map data according to geographic, security, privacy regulations and business policy interests. Automated continuous assignment of a risk score per file by analysing the variety and quantity of Personal Information (PI) entities and sensitive business information contained in the file.
Leverages Artificial Intelligence (AI) and Machine Learning (ML) to scale down the big data challenge and groups information about file data in a variety of dimensions (e.g., meta-data, content, risk, location, permissions). Puts a risk score to every cluster or classification for clear-cut prioritization.
- Provides multi-dimensional mapping within seconds no matter the size of the data.
- Automates applying risk scores in a unified view across file types and data sources giving end users the flexibility to customize.
Cloud Data Optimization
Adopting cloud infrastructure can be extremely costly if an organisation’s data is not scanned and cleaned of redundant, obsolete, and trivial (ROT) files. In a hybrid environment of multiple cloud use, organisations experience data sprawl that makes the application of data retention policies exceptionally challenging and at times impossible.
- Smart cloud migration
- Data retention
Automated and continual analysis and categorisation of data that identifies ROT and redundant file data that should not be moved to the cloud. Normalizes the data via visual analysis, across the hybrid cloud environment. Continuously supports and monitors the implementation of data retention policies that significantly reduces cloud costs.
De-duplicates and identifies near duplication using visual correlation of file data. Leverages cloud APIs to continuously analyse the data on a granular level and how its categorized for optimal data retention.
- Efficient automation on top of big data analyses a variety of formats and platforms finding both the actual duplication and the “near duplication” data minimizing migration costs from 30-50%.
- Correlating multiple dimensional analysis that enables granular dissection of data, thereby enabling implementing customised data retention policies.
- Automated, fast identification of duplicate files in unstructured data including attachments, teams messaging and graphic objects, OCR/ Images, scanned PDFs, Office, text/csv and binary data.
Data Protection & Secure Collaboration
The massive increase of cloud platforms in a hybrid environment has resulted in unprecedented file sharing of business critical and sensitive data across all internal business units and as well as externally. The many mistakes and false positives in legacy file labeling and policy enforcement tools can cause organisations to either abandon the process completely or to misclassify files. In both cases, organisations are vulnerable, and their shared files will eventually be mishandled.
- Granular classification and policy enforcement of shared files
- Data protection policy modeling with virtual labeling & integration with Microsoft365 and encryption
Automated identification and labeling of business critical and sensitive data to enable secure and compliant cloud collaboration, access control, rights management, and encryption across a hybrid environment.
Enables policy simulation and fine tuning of the desired result before invoking the policy action. This optimizes the accuracy and reduces false positives, improves the protection and reduces the overhead of security teams. Enables true policy enforcement with protected file sharing.
Automates classification using multi-dimensional machine learning analysis that enables virtual labels. Centralizes, continuously indexes, and models the data, allowing for virtual policy simulation, particularly valuable when policies of security, privacy and business operations may be in conflict.
The importance of data management cannot be overemphasized, our solution helps minimize potential errors by establishing processes and policies for usage and building trust in the data being used to make decisions across your organisation. With reliable, up-to-date data, organisations can respond more efficiently to market changes and customer needs.
As SAS partner, we have expertise in using SAS® data management solution for your organisation data management to unleash its full potential.
Event Stream Processing
Analyse big data while it’s in motion. filter, cleanse, and correct fast-moving data before it’s stored. And get instant, tangible results so you can respond to opportunities and problems in real time, all from a single interface. A truly disparate and diverse data sources integration technology that is seamless, reliable, and enormously scalable. Can provide an easy and quick connection to the data needed, irrespective of the location – on-premises, or in data lakes, in the cloud, on mainframe systems. Our solution connects to all kinds of data – structured, unstructured, text, documents, and images.
Data Integration & Access
Ability to access the data quickly and easily when you need, regardless of its location knowing that your data is primed and prepared for the next step with auditing tools that monitor processing and source data lineage. A central web-based dashboard makes it easy to graphically administer, monitor and maintain connections and data caches. It can incorporate new wave of advanced data sources (MongoDB, Cassandra, etc.) and computing frameworks (Spark, MapReduce, SAS Viya), supporting your data fabric with a single solution.
Our solution is a purpose-built data management solution developed using ground-to-up approach and not like most other data management and governance solution that have been cobbled together with bits and pieces.
Data Management Partner Ecosystem
SAS partners with other leading-edge companies to enable transformative data management solutions that drive real business value. Some of SAS Data Management Ecosystem partners include cdata, precog, Progress, SIMBA, SingleStore, SQREAM, XTREMEDATA and Yellowbrick. It provides a single point of control for all existing data sources and access the data you need by supporting multiple data processing run times (batch, real time, streaming, in database, etc.), therefore increasing productivity and getting more value from your data.