The business optimisation phase is where organisations use prescriptive analytics to make actionable recommendations to employees, customers or devices.
The specific recommended actions can be taken to improve business performance and optimise the targeted business initiative.
In the Business Optimisation phase, we support our customers in using embedded analytics to automatically optimise parts of their business activities.
Streaming analytics platforms can ingest, analyse, and act on real-time streaming data coming from various sources so you can take immediate action while the events are still happening.
The difference between streaming analytics and traditional analytics lies in the moment when data gets analysed. Traditional analytics first stores the data and then analyse sit for deriving insights. Traditional analytics is also mainly applied to data at rest.
In streaming analytics, we analyse the data first, while the events are still happening, and then store the relevant data for batch analysis. This allows streaming analytics platforms to handle the scale and constant flow of information and deliver continuous insights to users across the organisation
Machine learning is a subset of artificial intelligence (AI) focused on providing systems the ability to automatically learn from data, to predict and improve from experience over time without being programmed to do so.
In data science, an algorithm is defined as a sequence of statistical processing steps. In machine learning, these algorithms are 'trained' to find patterns and features in massive amounts of data in order to make decisions and predictions based on new data. The better the algorithm, the more accurate the decisions and predictions will become as it processes more data.
Today, examples of machine learning are all around us. Digital assistants search the web and play music in response to our voice commands. Websites recommend products and movies and songs based on what we bought, watched, or listened to before. Robots vacuum our floors while we do . . . something better with our time.
Machine learning engineers and data scientists work together closely to create usable products for clients. While there’s some overlap, data scientists focus on analysing data, providing business insights, and prototyping models, while machine learning engineers focus productising them by coding and deploying complex, large-scale machine learning products.
API stands for Application Programming Interface. In basic terms, APIs are a set of functions and procedures that allow for the creation of applications that access data and features of other applications, services, or operating systems.
Good APIs make it easier to develop a computer program by providing all the building blocks, which are then put together by the programmer.
Similar to how standard APIs help developers create applications, machine learning APIs make it easy for developers to apply machine learning to a dataset so as to add predictive features to their applications.
Today’s organisations have rapidly increasing volumes of data deployed on-premises and in multiple cloud environments. The types of data include data in relational databases, flat files, data lakes, data stores, and more. Pulling it all together is no easy task.
A data factory is a platform that consolidates data management into one environment, managing disparate data sources and technologies in both on-premises and cloud environments. It allows organisations to extract value from data assets by discovering, refining, governing and orchestrating any type, variety and volume of data across a distributed landscape.