We all know that AI and Machine Learning has huge potential to unlock Big Data’s hidden values. In many instances, AI is an immediate solution for the volumes and velocities for which big data is known for.
DataIntelligence the combination of Artificial Intelligence and Machine Learning is emblematic of so much of big data’s promise. When combining the speed and size of these technologies alongside alternative cloud paradigms and the evolution of mobile applications eventually leads to much better outcomes.
Today’s customer experiences with the traditional 360-degree view is no longer sufficient. Only a 720-degree view can enable service companies to crack the code on what the 21st century customer expects to be more data-driven.
Additionally, there are related fields of Artificial Intelligence that study intelligent methods that also learn from data and their environment. Examples include computational intelligence and mateheuristics.
Data science will be one of the primary expressions of big data in the subsequent decade, as its emergence should become much more apparent in the coming 12 months largely due to the maturing influence of AI and the data intelligence techniques itself. The year 2018 will see additional organisations experimenting with ways those capabilities render big data less daunting and perhaps even more enjoyable.
In coming times data intelligence services will be most eminent application that would be provisioning prototypes for security measures to truly fortify the DIaaS.
There are certain market dynamics which determine the growth of the data and its related analytics. Thats where Data Intelligence’s adaptive dynamics comes into play to assess the factors driving organisation to adapt their existing, profitable lines of business. This help them to stay relevant in the future of the rapidly evolving world and enormous helper for Blue Ocean shift strategy.
Artificial Intelligence needs a strong data foundation which help to transform data into insights and make complete solution engagement capability includes Intelligent BI strategists, Intelligent BI analysts, data intelligence warehouse architects, data scientists, implementation and development experts.
“DataIntelligence can be used to explore the external business environments like industry trends, markets and other competitive factors”
Transformation of data in to Intelligence (DataIntelligence) is the highest stage that many service providing organizations ever reach in the data pyramid. Data is collected from many sources, cleansed, and finally turned into valuable reports that are used internally to measure performance in addition it also incorporate social and other external data sources to enrich and enhance information-based management.
As a outcome it establish data architecture, suitable data models and data intelligence warehouses by extending diagnostic analytics to different domains. Expansion beyond the traditional business intelligence applications and scientific application based on descriptive analytics.
Developing new predictive and prescriptive analytics based on advanced analytic techniques create new dimension for data intelligence where prediction based on scenario development rather than just probabilities. and prescription get build based on advanced simulation, DataIntelligence and visualization capabilities.
- Descriptive: A set of techniques for reviewing and examining the data set(s) to understand the data and analyze business performance.
- Diagnostic: A set of techniques for determine what has happened and why
- Predictive: A set of techniques that analyze current and historical data to determine what is most likely to (not) happen
- Prescriptive: A set of techniques for computationally developing and analyzing alternatives that can become courses of action – either tactical or strategic – that may discover the unexpected
- Decisive: A set of techniques for visualizing information and recommending courses of action to facilitate human decision-making when presented with a set of alternatives.
Driving factors include high demand for automations of analytics services, demand for data-driven decision marketing, and an increase in productivity and revenue. DataIntelligence is used to explore the external business environment (i.e. industries, markets and other competitive factors).
Applicable to any business, data intelligence, if carried out correctly, can yield a significant return on investment either through increased revenues or by simply avoiding a bad investment decision. Analytics models should be supporting enough to prescriptive and predictive outcomes to avoid chaotic situations as mentioned earlier.
Opportunities include high demand for predictive analytics across verticals. Factors such as lack of awareness among industry professionals and so called data scientist (no I am not sarcastic here). Have You Put Your Data to Work? Through the actionable analytics of data intelligence, you can discover the smartest business solutions for your data enterprise strategy.
This should not be confused with market research, which typically focuses on customers and customer preferences only. Today, many large budgets are aimed at efforts around collecting and harnessing the rapidly expanding volumes, rapid velocity, varied forms, and uncertainty of historical data.
On the other hand robust security management framework determines policies, procedures and guidelines for how we work with sensitive business information. Unlike in smart contracts which help you to exchange money, property, shares, or anything of value in a transparent, conflict-free way while avoiding the services of a middleman purely based on Data Intelligence based on blockchain technologies.
Conclusion – Dynamic demands, changing environments, disorganised and non-procedural situations influence businesses on almost daily basis. The crux of DataIntelligence follow is “Better data cleansed and synched in real-time across channel and networks help in plan meticulously, implement efficiently, launch flawlessly and triggers to accelerate actionable opportunities. Some organizations build long-term strategies out of historical views. This majestic error directs business actions in the wrong direction for years.
Categories: Data Science