Getting a big data project in place is a tough challenge. But making it deliver results is even harder. 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 provide a substantial impact on your organization.
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 really be able to properly leverage such huge quantities of data.
The International Data Corporation (IDC) also predicted a need for 181,000 people with deep analytical skills, data management, and interpretation skills, this year. AI comes to 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 big 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 good for organizations that depend on it for growth as well.
In a big data architecture, data is collected from different data sources and then moves forward to other layers.
Using machine learning, this process of structuring data becomes easier, thereby, making it easier for the organizations to store and analyze their data.
Now, keep in mind that large amounts of data from various sources can sometimes make data analysis even harder. This is because we now have access to heterogeneous sources of data that add different dimensions and attributes to the data. This further slows down the entire process of collecting data.
To make things much quicker and more accurate, it’s important to consider only the most important dimensions. This process is what’s called data dimensionality reduction (DDR). With DDR, it is important to keep note of the fact that the model should always convey the same information without any loss of insight or intelligence.
Principal Component Analysis or PCA is another useful machine learning method that’s used for dimensionality reduction. PCA performs feature extraction, meaning it combines all the input variables from the data, then drops the “least important” variables while making sure to retain the most valuable parts of all of the variables. Also, each of the “new” variables after PCA are independent of each other.
Once data is collected from the data source, it then needs to 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’s large-scale data without any defined categories or groups. The K-means Clustering algorithm performs pre-clustering or classification of data into larger 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 great as it automates refining of the large-scale data. So, as the data keeps coming, the machine learning model will keep storing it depending on what category it fits.
After the data storage layer comes the data analysis part. There are numerous machine learning algorithms that help with effective and quick data analysis in big data architecture.
One such algorithm that can really step up the game when it comes to 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 great for performing data analysis are decision trees. Decision trees help you reach a particular decision by presenting all possible options and their probability of occurrence. They’re extremely easy to understand and interpret.
LASSO (least absolute shrinkage and selection operator) is another algorithm that will help with data analysis. LASSO is a regression analysis method. It is capable of performing both variable selection and regularization which enhances 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 data utilization part comes into play. Data helps to inform decision making at various levels and in different departments within an organization.
Heaps of data gets generated every day by organizations all across the globe. Given such huge amount of data, it can sometimes go beyond the reach of current technologies to get right insights and results out of this data.
Artificial intelligence takes the big data process to another level, making it easier to manage and analyze a complex array of data sources. 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 smartest and most 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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