Cogniflare works closely with Business and Technology stakeholders to translate their vision to reality. We use lean methodology to iteratively deliver features based on Minimum Viable Product.
Adaptable – Implement adaptive layers where possible to minimise change and disruption on existing source systems as part of integration
Scalable – Building scalable data processing frameworks to support your growing data footprint
Secure – Identify and enforce data security throughout the journey of data with a simplistic approach
Innovate – Bring in innovation as part of modernisation but still not compromising the basics
Accelerate – For large transformation programmes to be successful it’s important to have enablers and accelerators that should help expedite these programmes and Cogniflare has proven experience in building them
Optimise – When organisations adopt cloud adopting a lift and shift approach would be significantly expensive and we excel not only to transform the transition to be cloud native, but also support optimise the operational and management costs.
Operating enterprise scale big data analytics on-prem at scale becomes a significant challenge over a period of time with a number of factors that includes contention with resource usage across tenants, aging hardware, increased lead times, performance issues, management/ operational overheads and more.
It’s a natural move for organisations to consider moving into cloud inorder to address these challenges and benefit from reducing the total cost of ownership from cloud providers like Google Cloud with their managed service stack offering. However this brings in yet another set of challenges transitioning into cloud on a lift and shift model is not sustainable and may potentially incur higher cost than operating on-prem. So its important to take the advantage of cloud native capabilities in order to see the full benefits and sustainability.
Further as part of the migration and transformation initiative there are a number of factors which need to be taken into consideration especially on large multi-tenant platforms with peta-byte scale data. They include data migration/ validation, porting and transforming workloads to be cloud native, performance optimisation, cost optimisation and more.
With our enterprise scale experience in this space we have built a play book with accelerators that should enable enterprises to migrate and adopt cloud at pace.
Please contact us to find out more about how we can help you with Big Data Migrations and transformation into the cloud.
Migrating data platforms into the cloud is great and enables leveraging the power of the cloud and helps organisations to be more agile. However it’s equally important to ensure that there is a sustainable bidirectional flow of data as batch or streams from on-prem into cloud.
Majority of the large enterprises still have their key source systems (both business and operational) hosted on-prem and it’s very common to have a portfolio of tools across the organization (which might have been acquired organically) potentially duplicating sourcing of data and moving it one or more endpoints which considerably increases the operational costs with significant lead times.
Establishing a data express highway as a unified data sourcing and movement platform enables seamless sourcing and movement of data both as batch and stream. This should further enable organisations to source and move data at pace to one of more endpoints and thus reducing the lead times and also enable to reduce the operational costs significantly.
The data sourcing and data movement layers of our Data Bridge framework enables enterprises in establishing a unified data sourcing and data movement platform. Please reach out to us to know more about our Data Bridge framework.
When organizations move from on-premise to cloud a lift and shift model could be more expensive to run in cloud and generally not a recommended model. Further this also does not provide the ability to leverage the native power of cloud.
We enable enterprise customers to modernize their existing data transformation workloads leveraging the power of cloud and further integrating with the data operations ecosystem. The modernisation also includes identifying workloads that can be run leveraging the right technologies, so it is more cost optimal and efficient which is key as part of cloud adoption.
Large enterprises have significant challenges in managing their ever growing data estate. This gets further challenging when organisations adopt cloud at scale or decide to go multi-cloud. The power of the cloud with its agility could even more complicate this challenge if the right strategies are not set. Further there are also other challenges that include data residency, data silos, data security, data governance and more.
We have proven experience in designing and building the right data strategies for large organizations that are spread across multiple regions. Our strategies enabled all entities within the organization to be autonomous in terms of owning the data but still operate on a federated framework, providing the agility to make quicker decisions based with compliance to regional legislations. One of our key success stories includes the design and implementation of the Data Ocean strategy for Vodafone as part of Neuron Programme.
BI and Advanced analytics play a key role in all enterprises and certain organisations prefer to operate this as a unified service and some prefer to keep them separate. Irrespective of how this is managed the data sourced for these platforms are largely common and there are also overlaps on the workloads that are between these platforms.
We enable organisations by providing a harmonised data layer that can serve both BI and Advanced analytics workloads. Further we also support in establishing a framework where both can co-exist as a unified platform that will enable organisations to reduce their operational and management costs significantly.
For an organisation to be effective on decision making and be successful, it’s important to understand and manage its data estate. Data cataloguing enables organisations to define and set taxonomy across the organisation. However a proactive integration of cataloguing and continuous governance across the data processing platform is important to reflect the actual state of the data estate. Similarly establishing the quality of the data that can be subscribed to is equally important.
Our enterprise Data Bridge ecosystem provides a seamless continued integration of data catalogue, data quality and governance as part of data sourcing, movement and transformation frameworks.
Security and Compliance plays a key role as part of the data lifecycle which includes identifying sensitive data elements, transit level encryptions, persistence based encryptions, anonymisation or pseudo-anonymisation of fields, data retention and more.
This is again a space where we have proven experience on implementing data secure solutions at scale as part of our data bridge ecosystem.