6/14/2023
Getting a big data project in place is a tough challenge. But making it deliver results is even more complicated. That's where artificial intelligence comes in. By integrating artificial intelligence into your big data architecture, you'll be able to better manage and analyze data in a way that substantially impacts your organization.
The Growing Importance of AI in Handling Big Data
With big data getting even bigger over the next couple of years, AI won't simply be an optional extra. It will be essential. According to IDC, the accumulated volume of big data will increase from 4.4 zettabytes to roughly 44 zettabytes, or 44 trillion GB, by 2020. Only by using Artificial Intelligence will you be able to properly leverage such vast quantities of data.
AI's Crucial Role in Big Data Architecture
The International Data Corporation (IDC) also predicted a need for 181,000 people with deep analytical skills, data management, and interpretation skills. AI comes to the rescue again. AI can ultimately compensate for the lack of analytical resources today with the power of machine learning that enables automation. Now that we know why Big data needs AI let's have a look at how AI helps big data. But, for that, you first need to understand the significant data architecture.
While it's clear that artificial intelligence is an important development in the context of big data, what are the specific ways it can support and augment your big data architecture?
It can, in fact, help you across every component in the architecture. That's good news for anyone working with big data and suitable for organizations that also depend on it for growth.
Machine Learning for Efficient Data Management in Big Data Architecture
In a big data architecture, data is collected from different sources and then moved to other layers.
Artificial intelligence in data sources
Using machine learning, this process of structuring data becomes more accessible, making it easier for organizations to store and analyze their data.
Now, remember that large amounts of data from various sources can sometimes make data analysis even harder. This is because we now have access to heterogeneous data sources that add different dimensions and attributes to the data. This further slows down the entire process of collecting data.
It's essential to consider only the most critical dimensions to make things much quicker and more accurate. This process is what's called data dimensionality reduction (DDR). With DDR, it is essential to note that the model should always convey the same information without any loss of insight or intelligence.
Principal Component Analysis, or PCA, is another helpful machine learning method for dimensionality reduction. PCA performs feature extraction, meaning it combines all the input variables from the data, then drops the "least important" variables while retaining the most valuable parts of all the variables. Also, each of the "new" variables after PCA is independent
Artificial intelligence in data storage
Once data is collected from the data source, it must be stored. AI can allow you to automate storage with machine learning. This also makes structuring the data easier.
Machine learning models automatically learn to recognize patterns, regularities, and interdependencies from unstructured data and then adapt, dynamically and independently, to new situations.
K-means clustering is one of the most popular unsupervised algorithms for data clustering, which is used when there are large-scale data without defined categories or groups. The K-means Clustering algorithm performs pre-clustering or classification of data into more significant categories.
Unstructured data gets stored as binary objects, annotations are stored in NoSQL databases, and raw data is ingested into data lakes. All this data act as input to machine learning models.
This approach is excellent as it automates the refining of large-scale data. So, as the data keeps coming, the machine learning model will keep storing it depending on what category it fits.
Leveraging AI for Advanced Data Analysis and Utilization
After the data storage layer comes the data analysis part, NNumerous machine learning algorithms help with practical and quick data analysis in significant data architecture.
One such algorithm that can step up the game regarding data analysis is Bayes Theorem. Bayes theorem uses stored data to 'predict' the future. This makes it a wonderful fit for big data. The more data you feed to a Bayes algorithm, the more accurate its predictive results become. Bayes Theorem determines the probability of an event based on prior knowledge of conditions that might be related to the event.
Another machine learning algorithm that is great for performing data analysis is decision trees. Decision trees help you reach a particular decision by presenting all possible options and their probability of occurrence. They're effortless to understand and interpret.
LASSO (most minor absolute shrinkage and selection operator) is another algorithm that will help with data analysis. LASSO is a regression analysis method. It can perform both variable selection and regularization, enhancing the prediction accuracy and interpretability of the outcome model. The lasso regression analysis can be used to determine which of your predictors are most important.
Once the analysis is done, the results are presented to other users or stakeholders. This is where the data utilization part comes into play. Data helps inform decision-making at various levels and departments within an organization.
Leveraging AI for Advanced Data Analysis and Utilization
Heaps of data get generated every day by organizations all across the globe. Given such a huge amount of data, it can sometimes go beyond the reach of current technologies to get the right insights and results.
Artificial intelligence takes the extensive data process to another level, making managing and analyzing a complex array of data sources easier. This doesn't mean that humans will instantly lose their jobs - it simply means we can put machines to work to do things that even the most innovative and hardworking humans would be incapable of.
There's a saying that goes, "Big data is for machines; small data is for people," and it couldn't be any truer.
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