Modern Data Processing
Transform Data into Actionable Intelligence
Real Time Data Processing to change business at all levels
Businesses have become data-dependent, and their dependency continues to grow every day. It’s used at every level to make crucial decisions.
Having the ability to obtain data from customers, day-to-day operations, or research is no longer an issue in most companies. Currently the greatest challenge is how to transform this data into intelligence that can really improve the business, and do it as quickly as possible to make the right decision at the right time, even better if it is in real time.
In order to make strategic business decisions, real-time data is essential. It is a proven asset and can give your business a competitive edge.
Nowadays, being agile and competitive depends on having a clear and effective data strategy since it can mean a great competitive advantage to outperform competitors. The future of big data processing and analytics promises to change the way businesses operate in all the industries.
What is for sure is that companies should prepare for the future, which means: creating a culture for using machine learning models and their output, standardise and digitise processes, experimenting with a cloud infrastructure solution, have an agile approach to data projects, and creating dedicated data units. Being able to execute on some of these points will increase the likelihood of succeeding in a highly digitalized world.
The 3 tiers in Analytics
Descriptive analytics acts as an initial catalyst to clear and concise data analysis. It is the “what we know” (current user data, real-time data, previous engagement data, and big data).
Predictive analytics applies mathematical models to the current data to inform (predict) future behavior. It is the “what could happen”.
Prescriptive analytics utilises similar modeling structures to predict outcomes and then utilises a combination of machine learning, business rules, artificial intelligence, and algorithms to simulate various approaches to these numerous outcomes. It then suggests the best possible actions to optimize business practices. It is the “what should happen”.
Prescriptive Analytics, the future of Big Data
Prescriptive analytics use optimization technology to solve complex decisions with millions of decision variables, constraints and tradeoffs, and provide recommendations around optimal actions to achieve the objectives. In essence, prescriptive analytics takes the “what we know” (data), comprehensively understands that data to predict what could happen, and suggests the best steps forward based on informed simulations.
It is the natural progression from descriptive and predictive analytics procedures.
Knowledge Graphs (KGs) are one of the key trends among the next wave of technologies.
A graph database expresses the information in a single, simple relationship structure: two “nodes” (some identifiable things) are connected by an “edge” (the relationship between them). KGs adds a knowledge layer onto this to better understand and explore this interconnection.
The focus, then, is on the relationships among the elements with the purpose of identifying, classifying behaviour and even predicting future behaviour.
Specialised Data Processing: Knowledge Graph
Benefits of Data Processing in the Cloud
The future of data processing is in the cloud, accelerating its speed and effectiveness.
The benefits of cloud data processing are in no way limited to large corporations. In fact, small companies can reap major benefits of their own: the pay-as-you-go system also applies to data processing and data storage, and offer the flexibility to grow and expand capabilities as the company grows.