Big Data analytics strategy and delivery
Move forward with confidence to harness the opportunities afforded by new, disruptive technologies alongside established systems and tools.
With emerging technologies and immature standards and practices the risk of Big Data projects failure are high.
It is also a big step for businesses to jump into a new technology with the investment in skills and infrastructure and the investment risks that such a step implies.
However, experience in other industries has also shown us that successful exploitation of Big Data technologies and approaches can produce immense business value.
At Kinaesis we focus on maximising the business value of Big Data. The start point for the engagement is not the technology, but the information needs of your organisation. From here we work with the customer to build a vision, and a roadmap for achieving the strategy.
Kinaesis are technology independent, and will therefore help you choose the best fit technology platform, at the best value. Whether it’s the traditional technology providers such as IBM, SAP, Microsoft and Oracle, or the raft of emerging technologies that have sprung up such as Hadoop, MongoDB, NoSql etc. Kinaesis have a strong track record and deep experience of delivery in traditional and Big Data technologies.
By focusing on the need, the long term strategy, the operating model and the roadmap, the Kinaesis functional and technical experts will help you deliver a successful project and derive intelligence and insight from your Big Data.
Kinaesis did an excellent job, especially during workshops where your expertise helped a lot in drawing architecture fundamentals.
BI Research and Development Manager
Our client needed to reduce the time to market for new analytical capabilities - traditional data warehouses were taking too long to market. They needed to analyse disparate sources of data to create lasting value from data through identification of patterns and knowledge.
They created a data lake using Big Data technology, but they needed to manage the data in a strongly governed and effective way. Their objective was to create a data cycle that enabled fast analytical investigation and fast, controlled production roll out to the broader user base.
Kinaesis worked with our client to design and implement a unified versioning and release framework covering data sourcing, data models and data content. This resulted in data sourcing code (ETL) always being released alongside versioned data structures and data onto a scalable hadoop infrastructure - ensuring alignment and consistency.
We implemented a data environment and embedded governance framework supporting the analytics development lifecycle from the data scientists' sandbox, to the production analytics and reporting system.
We worked with our client to design and implement the processing of diverse data sources, into structured production data targets, containing consistently described data for running production analysis and reporting. Analytics and reporting was presented through a restful web interface that provided advanced visualisation.
We created an environment where data scientists could develop models and analytics and then seamlessly release them into the production environment with consistent, versioned data schemas and data content.
The infrastructure enabled an effective, efficient end to end process from adhoc analytics to controlled production release. This delivered significant efficiencies in the analytics and product development lifecycle. The result was a dynamic development and production system fully aligned to the business process.