How we define data products
Data Products
Create opportunities for differentiation
Contribute to the key objectives
It is designed to be re-usable and to contribute to the key objectives of making money, reducing costs, and reducing risk. To achieve this, a data product requires high-quality, secure, and discoverable data.
Well-designed and effective data product
By providing a well-designed and effective data product, businesses can unlock valuable insights, make more informed decisions, and ultimately drive their bottom line.
Speed to insight
The quality of your data is hindering your ability to accurately and confidently make business decisions, resulting in lost revenue, wasted resources, and customer dissatisfaction.
Poor data quality
The quality of your data is hindering your ability to accurately and confidently make business decisions, resulting in lost revenue, wasted resources, and customer dissatisfaction.
Unoptimised data infrastructure
Your current data infrastructure is inefficient and inadequate, making it difficult to scale and support the growing needs of your business. This is leading to slow data processing times, increased IT costs, and decreased productivity.
Adaptive and responsive data management
You need a better way to manage our data that can adapt to changing business needs and respond quickly to new data sources and types. Your current data management practices are inflexible and time-consuming, causing delays and hindering innovation.
Monetisation
Data products generally present a great opportunity to open up new revenue streams and drive innovation. Indirect monetisation occurs with faster decision making, improved operational efficiency, increased customer engagement and a higher degree of competitive advantage.
Our delivery model & ways of working
Building shared objectives and understanding
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Data team
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Tech team
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Business stakeholders
Stage 01
Discovery
Find the data, do I have the right data, is the data good enough to use
Stage 02
MVP & build
Test hypothesis, start with the existing analytics, build technology solution
Stage 03
Iterate, Optimise, maintain
Data governance, compliance, reporting
Building a data culture
All data product engagements follow the esynergy
delivery principles
Focus on being the best we can:
by highlighting the art of the possible, we can challenge ourselves and our customers to go after the best.
Consistency of approach:
all esynergy projects are aiming for the same things. We believe that the principles provide a great scaffold from which we can build quality solutions which provide value to our customers in an accelerated timeframe.
Applicable to all contexts:
everyone's delivery landscape is different, we do not prescribe methods or tools which may not be appropriate for your context, the principles are here to guide but not dictate.
Freeing your creativity:
we encourage our network to bring experience and creativity to our engagements. We use the principles as a mandate to go after what's important for our clients in the most effective way.
Common
technology stacks
We have deep experience and partnerships across the most common data product tech stacks
Our key lead associates in
the data product space
Sunny Jaisinghani
Simon Massey
Kevin Fletcher
Jon Cooke
Prasad Prabhakaran
Dave Sheppard
Our Insights
The latest thinking from our industry experts.
Take your business to the next level!
Our work is measurable and directly ties back to our clients’ business objectives. This approach is at the heart of all our engagements with the objective of added value and insight for our clients.
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