Do You Speak... EDA (Extreme Data Analytics)?
Marc Derquennes
Consultant in Strategy, Board Advisor, DeepTech innovator, Media Consulant and Editorial Advisor
EDA - EXTREME DATA ANALYTICS - OVERVIEW
Let’s try to keep it simple to make it understandable even by non super-skilled professionals. :-)
It is easy to be confused by the proximity between two expressions:
Despite this proximity, EDA is another level in data analysis compared to BDA, much more demanding but also much more rewarding in terms of impacts.
BIG DATA ANALYTICS (BDA)
Big Data Analytics is the process of analyzing large volumes of data to draw meaningful insights and generate added value. It involves the use of sophisticated tools and techniques to identify patterns and correlations in data sets that are too large to be handled by traditional analytics methods.
The key technical characteristics of Big Data Analytics are:
1. Volume: Big data refers to data sets that are too large or complex for traditional data processing applications.
2. Variety: Data can come in structured, semi-structured, and unstructured forms.
3. Velocity: The speed at which data is generated and processed.
4. Veracity: The accuracy and completeness of the data.
5. Visualization: The ability to create visual representations of data to gain insights.
6. Validation: The ability to validate data to ensure its accuracy and completeness.
7. Scalability: The ability to scale data processing applications to handle larger and more complex data sets.
EXTREME DATA ANALYTICS (EDA)
Extreme Data Analytics is the process of using complex algorithms and sophisticated tools to analyze extremely large datasets. It involves the use of advanced analytics techniques such as machine-learning, artificial intelligence, and natural language processing to uncover patterns and correlations in data sets that are too large, too complex, or too disparate to be analyzed by traditional methods.
The key technical characteristics of Extreme Data Analytics.
1. Big Data processing – Extreme Data Analytics platforms are designed to process large amounts of data quickly and efficiently, allowing for rapid analysis.
2. Scalability – Data Analytics platforms must be able to scale quickly and easily to accommodate changes in data and user needs.
3. Machine learning – Machine learning enables Extreme Data Analytics to process complex data sets and uncover new insights.
4. Predictive analytics – Predictive analytics allow for more accurate predictions of future trends and events.
5. Data visualization – Data visualizations provide interactive, visual representations of data, enabling users to quickly identify patterns and make decisions.
6. Security – Security is critical to ensure the safety of data and user privacy. Extreme Data Analytics platforms should offer secure, encrypted data storage and transmission.
EDA & BDA: key differences
The key characteristics of EDA differ in that they focus more on processing large amounts of data quickly, including with a very demanding near-real-time approach.
This requires specialized software, hardware, and architectures that are designed to handle massive data volumes.
Extreme Data Analytics also requires advanced algorithms and techniques to process data in real-time.
Additionally, Extreme Data Analytics often requires predictive analytics techniques to gain insights from the data.
Big Data and Extreme Data analytics are linked in the sense that the Extreme Data Analytics is the more capable, more powerful, and more sophisticated child of Big Data Analytics.
But they strongly differ by:
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TYPICAL USE CASE for EDA - Extreme Data Analytics
This a rapidly evolving subject, but we can already list 20 typical use cases.
This list is a first illustration of the potential impact on economy and society of Extreme Data Analytics. EDA is clearly a strategic set of technologies for the future of nations, countries, economies and even security and defence.
It is to be noted that depending on the geography, this list could differ significantly.
The leading countries are USA, China, Europe, but today with a clear leadership from USA and China, each of them with differences in the main field of application. Europe is lagging behind, but is currently investing very significant amounts (Funding from Horizon Europe Program) to catch-up and acquire a minimum level of sovereignty.
This will be discussed in another article.
Today’s 20 top uses cases (excluding Defence, Intelligence and Security):
1. Financial Risk Analysis
2. Supply Chain Optimization 3. Customer Analytics
4. Network Security
5. Marketing and Advertising
6. Social Media Analysis
7. Fraud Detection
8. Text Mining
9. Predictive Maintenance
10. Image Recognition 1
1. Natural Language Processing
12. Recommendation Engines
13. Revenue Management
14. Healthcare Analytics
15. Vehicle Telematics
16. Weather Forecasting
17. Network Traffic Analysis
18. Sports Analytics
19. Stock Market Analysis
20. Connected Device Analysis
There are a few factors that contribute to Europe's slower adoption of Extreme Data Analytics.
Firstly, Europe has historically been slower to adopt new technologies due to its complex regulatory environment. This is because many EU regulations have to be considered before any new technology can be implemented. This can make it more difficult and costly for companies to implement new technology. Secondly, there is also a shortage of skilled data scientists in Europe, which can make it difficult for companies to find the expertise they need to make full use of Extreme Data Analytics.
Finally, there is also a lack of investment in the technology from European companies. Many European companies are still hesitant to invest in Extreme Data Analytics due to the cost and complexity involved. This is in stark contrast to the US, where companies are much more willing to invest in the technology.
China is making significant strides in the field of Extreme Data Analytics. In recent years, Chinese companies have invested heavily in developing advanced AI technologies to analyze large volumes of data for decision making.
Chinese research institutes have made breakthroughs in areas such as deep learning, natural language processing, and computer vision. Companies like Baidu, Tencent, and Alibaba are using their platforms to leverage extreme data analytics to improve their businesses.
Additionally, the Chinese government is investing heavily in the development of extreme data analytics capabilities, with the aim of becoming a global leader in the field.
In following articles we will give an overview on:
IT-Services und IT-Consulting
1yVery complete view on EDA and the differences! Appreciate the work behind! My question is are these Horizon fundings the best possible way to bring European aims up to speed? There is a huge amount of funding behind but also many topics. What do you think?