DataIntelligence – We all know that AI and Machine Learning has huge potential to unlock Big Data’s hidden locked values. In many instances, AI’s specific techniques i.e. machine learning and its algorithms help us to uncover the unseen. Data science’s competencies and procedure get applied on data to perform the continuous explorations and investigation with many iterations. We need to be cognizant of the fact that ML and AI techniques on data may not produce an immediate solution for the volumes and velocities for which big data is known for.
DataIntelligence As A Service
DataIntelligence a strategy applied to big data by combining Artificial Intelligence and Machine Learning is delivering so much to businesses. When we combine the speed, emerging technologies alongside alternative cloud paradigms we get superior results at a low cost. The evolution of mobile applications on top of this will eventually lead to much better outcomes anyway. All this on the condition of having quality data, not just any data.
In coming times data intelligence services will be a most eminent application that would be provisioning prototypes for security measures to truly fortify the DIaaS.
The traditional 360-degree view of customer experience is no longer relevant, this needs more length. So 720-degree view is now enabling service companies to crack the code on what the 21st-century customer expects like behaviour analytics. Nothing can more exciting than telling a customer you bought a particular brand of cloth others have also bought these perfumes as well to make it complete and yes this has to be more data-driven.
The confluence of big data, big strategy, big computing powers and a big hunger for new businesses can lead artificial intelligence into a gold mine. Having a system that studies intelligent methods, learns from data science and data environment is best placed for this business. Examples include computational intelligence and metaheuristics.
Data Science of DataIntelligence
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.
There are certain market dynamics which determine the growth of the data and its related analytics. That’s where Data Intelligence’s adaptive dynamics comes into play to assess the factors driving the organisation to adapt their existing, profitable lines of business. This helps 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 helps 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 into Intelligence (DataIntelligence) is the highest stage that many services 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 incorporates social and other external data sources to enrich and enhance information-based management.
Data Transformation to DataIntelligence
Smart Data System – This will be an outcome of establishing data architecture, suitable data models and data intelligence warehouses by extending diagnostic analytics to different domains. It will many businesses to expand its thought process and the business view 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 a new dimension for data intelligence where prediction based on scenario development rather than just probabilities. and prescription gets 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 determining 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 automation 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).
Intelligence for Data
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 supportive 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.
Points to Note:
All credits if any remains on the original contributor only. We have covered all basics around data which is all about quality data, computing power and algorithms to look for information. In the upcoming post, we will also see how data gets manipulated for the wrong reasons. Generative Adversarial Networks. A family of artificial neural networks.
Books + Other readings Referred
- Research through open internet, news portals, white papers and imparted knowledge via live conferences & lectures.
- Lab and hands-on experience of @AILabPage (Self-taught learners group) members.
Feedback & Further Question
Do you have any questions about Supervised Learning or Machine Learning? Leave a comment or ask your question via email. Will try my best to answer it.
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