With the exponential increase in the amount of data collected by enterprises today, managing and analyzing these huge data sets has now become a basic skill that all enterprises must master.
If the data is new oil, then all companies must use this resource. Having a big data analysis strategy for your company is essential to ensure that the true value-often hidden in the collected data-is revealed and then utilized.
Due to the introduction of the Internet of Things and 5G networks, as the amount of data will increase again, when each device is connected to the network, the amount of available data will surge. If your business is struggling with data analysis methods today, it will bring more data analysis challenges in the near future.
It is also necessary to develop a detailed data analysis strategy. Companies that can connect their datasets to BI will be able to use the insights they gain to accelerate product and service development.
Big data analysis involves all market sectors: from banking to healthcare, the use of data to influence results and innovation has now become the core competitiveness of all companies' rapid development. Applying machine learning technology, BI delivered from a huge assembly data set is a resource that companies cannot ignore.
The power of big data analysis lies in the insights it provides. These insights can improve customer personalization and accurate predictive marketing. These techniques can take multiple data points and assign them to individuals or groups. Here, data becomes a flexible tool; all businesses can afford to use it because the analytical methods can be extended.
Predictive analysis is a high-value use case that we are beginning to see in the market, especially as IoT solutions mature. For example, operators of train lines will install sensors on trains that can predict when specific parts will break and thus Repairs can be carried out before the fact happens, without waiting for the break and subsequent service interruption.
To do this, you need a powerful analysis engine to view historical performance data and near real-time data (such as the temperature of a specific part). After successfully deploying predictive analytics, operating costs will be greatly reduced, and the customer experience that reduces travel interruptions will gain obvious benefits.
Big data analysis
The five components of big data analysis: quantity, speed, diversity, accuracy and value. Balancing these components of corporate data analysis methods will ensure that practical actions can be taken from the information extracted from these huge data sets.
Large cloud service providers have ensured that their services have built-in high-level analytics. As Azure Analytics for Power BI saves time through its better tools and automation, many IT professionals including database administrators, data scientists, and infrastructure support have become more efficient. Overall, the average time saved is 1.73 hours per week. Business users include advanced users such as business analysts and consumers of business intelligence. Save 1.75 hours on average per week. After three years of risk adjustment, a total of 4.9 million USD was saved.
Understanding the tools needed to extract insights from the large data sets that companies are currently processing is also critical. The huge amount of data in the Internet of Things will make AI and machine learning necessary. For example, we expect a wave of new applications that will combine open map data from Earth observation satellites with IoT data from autonomous vehicles and smart speakers.
Despite putting environmental applications first, the use of this open data will bring many business opportunities-from allowing companies to price rivals’ assets to predicting GDP and global conflict.
See the pattern
Since much of the information contained in the business management data will be highly personalized, security is of the utmost importance. When analyzing data (usually using automated AI systems), the deviation must be minimized from the results given.
One of the biggest challenges of data analysis is how to provide unified privacy and data security on all platforms while maintaining open access without destroying the analytical value of data. The necessary conditions to gather valuable insights that drive modern business. This is where data-centric security technologies such as tokenization shine-allowing organizations to protect their data while maintaining referential integrity across multiple platforms.
The key is not to rely on any platform, but to protect the data of the entire enterprise so that the data is protected when it is ingested, and cross-platform analysis is performed on the protected data, and the shared data is actually the protected data. This not only ensures the privacy of all systems at all times, but also enables analysis of large data sets, which are usually severely restricted or restricted due to data toxicity. The end result is that organizations can monetize their data and gain fruitful insights while complying with privacy regulations and meeting internal risks and security.
For CIOs and CTOs, big data analysis has become a reality. Artificial intelligence and machine learning are unlocking the true potential of big data analysis. Without intelligence, big data usually cannot prove the practical value of storage costs. In every organization we have spoken to in the past 12 months, the use of artificial intelligence is dominating the CIO's big data agenda. External investigations have confirmed this. PWC analysis shows that 77% of executives believe that AI and big data are interrelated.
When big data and advanced algorithms are applied to business problems to produce better solutions than before, analytics can create value. By identifying, sizing, prioritizing, and gradually adjusting all applicable use cases, companies can create analytical strategies that can generate value.
The top management is pushing for big data analysis. If companies have robust and meaningful analysis, they will increasingly see the true value of the data they own. In the future, the analysis will continue to mature and expand its scope. Machine learning is also developing rapidly in parallel, because automation is essential to make these expanded data sets meaningful.
How has big data analysis evolved in the past few years?
The origin of big data-led organizations is to develop best practice strategies to better store and aggregate data using technologies such as data lakes, business intelligence platforms, and master data management (MDM). In the past few years, big data solutions have evolved from data resident status to data optimization and retrieval. Going forward, the integration of artificial intelligence and big data will enable organizations to manipulate their big data sets to drive actionable insights.
What is the trend of big data analysis? For example, predictive analysis?
The three main trends analyzed depend on large data sets. These are artificial intelligence; streaming IoT and cloud computing. These trends are gradually developing business intelligence (BI) platforms to help organizations change their behavior and understanding of business.
Artificial Intelligence Platform
The combination of artificial intelligence and data governance aims to help organizations clean up data to obtain a unified view of their data sources. In this application, AI is positioned as a trusted advisor to provide guidance and advice to detect outliers and suggest data corrections.
Stream IoT data
Many organizations strategically incorporate sensory and real-time data into their business processes. Although IoT data is useful information, value-added components combine IoT and AI to provide real-time and more accurate responses to data. The generated data response emphasizes the importance of the BI platform, which can appropriately communicate results or alerts with personnel to obtain feasible results.
cloud computing
Although some organizations may want to keep their internal data, storing and maintaining large amounts of data coming in from multiple sources becomes expensive. Therefore, cloud and hybrid cloud solutions provide a quick and easy way to access big data, thereby greatly reducing the overall cost of the organization.
How do artificial intelligence and machine learning affect big data analysis?
The overlap of artificial intelligence and big data is being cultivated into a collaborative relationship, where these disciplines often work together because artificial intelligence is worthless without meaningful data, and big data now relies on artificial intelligence-driven analysis. Here are some examples showing where AI relies on big data and uses big data sets:
1. Retrieval and reasoning
2. Automated learning and dynamic planning
3. IoT streaming data
4. Natural language processing
5. Computer vision (image or video data
Natural language processing (NLP) is one of the fields in AI that requires a lot of data. For example, without a large amount of human speech, written records and recordings, NLP technology will not be possible. In order to obtain a general model for NLP, AI algorithms need to capture a large number of, varying and multiple language data points to produce high accuracy. In short, big data is continuing to grow, and artificial intelligence will be used in conjunction with big data to help end users through automated tasks.
When the Internet of Things becomes more and more popular, is a new data analysis method needed?
As the organization's internal IoT participation strategies increase, the combination of IoT streaming data and AI will enable companies to collect and convert data into usable and valuable information, which will be the most important.
Common interdisciplinary use cases include predictive maintenance, chatbots, KPIs that are automatically customized to enhance the user experience, dynamic thresholds, modern network security, and anomaly detection. Essentially, IoT improves business models by providing accurate real-time information on the system; since then, artificial intelligence has absorbed the dynamic characteristics of IoT data to provide actionable insights.
How to support privacy and security in the entire analysis system for finding value in large data sets?
There are undoubtedly major challenges in protecting big data, including: protecting data and transaction logs, input verification, access control, and real-time privacy protection. Although encryption in multiple stages can ensure data confidentiality, integrity and availability; companies are actively working to promote innovative big data best practices without sacrificing data privacy.
These practices include: by clearly defining the responsibilities of cloud service providers and cloud service users, and effectively using big data to enable them to have strong capabilities in purchasing and managing cloud services; by cleaning, pruning, matching and merging in the initial stage Data, merge the data into a true source, thereby disinfecting the data to avoid the above privacy issues.
Retrieved from: https://www.aaa-cg.com.cn/data/2076.html