Big Data Solutions development for startups and SME businesses
We offer end-to-end software development services and build big data platforms that enable business owners to gain best of breed user experience for platform clients.
“Information is the oil of the 21st century and analytics is the combustion engine” Peter Sondergaard, Executive Vice President, Gartner
What is Big Data
- Large data sets: collection & storage of thousands or millions of GB’s of data – sensors, user behavior or transactional data.
- Fast processing: provide near real-time reports, analytics & processing over relevant segments of the data set.
- Easy to scale: the infrastructure and deployment can be scaled depending on ever-changing tradeoffs between business goals and costs.
- Ease of access: large data sets can be used easily by different parts of the organisation or external parties.
- Secure: access to data is controlled via a flexible and data-oriented security model.
How it works
Big data is an essential component in the software products of the new digital economy: the sharing economy, personalisation of user experience, fraud detection, recommendation systems, Internet of Things etc.
The solution always involves the collection and storage of large streams of primary data. These streams contain information with a wide variety of domain-specific properties:
- Sensor data (ie. temperature, acceleration, light)
- Mobile / web user behaviour (ie. clicks, page visits)
- Public data (ie. weather, traffic, geo)
- Transactional data (ie. purchase orders, payments, user registration)
It’s common that different types of streams are provided by different sources (geographically distributed sensors, web portals, mobile apps, SaaS platforms, external partners etc.) and the solution will require a robust integration layer.
Big data processing is about joining, filtering and applying transformations on the primary data streams. The processing can be made so fast as to be considered almost real time. In practice, a business will chose a tradeoff between processing speed and infrastructure costs. The product team will decide what is the “right speed” for each individual use case.
The output of processing consists in derived (ie. secondary, tertiary etc) data streams. These resulting data streams power automatic or user feedback loops and various types of data reporting.
Common use cases include things like:
- Recommendations based on usage / purchase history
- Personalisation of the user interface
- Social / peer matching
- Aggregate or trends analysis
- Business analysis dashboards
Many companies use the power of big data to engage with their users in a deeper way, increase conversions and leverage the networking effects inside their products.