Data analysis is the backbone of any industry. Data analytics certification courses have enabled big data engineers and AI analysts to look at emerging problems in algorithm designing and deployment with a refreshingly new objective and approach. Data analysis and other emerging techniques may have countless applications, but there are problems too, and certified data analysts from reputed data analytics certification institutes are turning no stone unturned in balancing the pros and cons of working in this domain.
Let’s explore further.
Data analysis’s emergence was never planned
A very famous AI leader had once said that if you have to succeed with machine learning applications, it has to be based on data analysis. In simple words, data analysis was promoted as the “best shot” at progressing with AI as it could solve complex issues and challenges using big data analysis and business intelligence. However, the idea faltered due to poor execution and a distorted focus on big data management.
We can blame this on the way machine learning algorithms are being developed and their pace of deployment in real-life scenarios.
The family of machine learning algorithms is expanding at an unbelievable pace.
A lot of effort is being invested in developing machine learning algorithms using data science and big data mining. One of the fastest-growing segments of machine learning techniques is deep structured learning (DSL). DSL applications are making existing technologies more and more powerful by integrating different aspects of augmented intelligence, intelligent automation (IA), computer vision, RNN, and CNN. These applications are used in diverse industries such as machine learning translation, bioinformatics, drug engineering, recommendation engines, image processing, and speech recognition.
While all this sounds and looks amazing and happening at a rapid pace, there are problems galore that are latent to machine learning development.
Challenges are more or less related to the operationalization of big data assets that can be linked to DSL. It is important to analyze the challenges in big data as associated with DSL development.
Too much data — that too, too much of bad data
No data team likes to work with too much of noisy and unclean data, and if problems related to misfit data continue, it would be impossible to augment big data techniques to generate accurate outcomes. These challenges are attributed to a lack of training data and noises that accompany the transformation of data for data analysis purposes.
Big data analysis works largely on supervised test data and then progressively augments its learning using labeled data that happens in a supervised manner.
However, the problem is not just with the volume of big data but also with the quality of data that is being used to train the machine learning algorithms to produce adequate results. The complexity of the neural networks and training data make the data analysis create another kind of problem called “overfitting” which results in faulty analysis from predictive models.
Step down, poor data can be eliminated at the training set itself if analysts are able to identify factors that cause distortions in the data set. These are part of AI optimization and auto-machine learning operations. These are evolving very quickly even as data scientists are looking to further refine the training processes of machine learning algorithms using emerging techniques in regression analysis, backward integration, and outlier/ anomaly detection.
Tip: When data grows, anomaly detection can improve machine learning models to deliver accurate results.
How far are we from 100% accurate data analysis models?
Now that we understand data analysis is facing some data crises with respect to integration and hygiene, it’s important to decode the gaps. In a study conducted by an independent AI firm, it was found out 97 percent of the global organizations agree that big data is the key to driving their AI objectives, yet 70% of these are also aware that their AI efforts are going to fall short due to lack of knowledge sharing and abnormal dependence on rudimentary analytics tools for data analysis.
If we monitor closely, data analysis models require a lot of training effort. And, human intelligence is still at the helm of this training effort. As long as this remains to be organized at the human level of engineering and training, neural networks with data analysis frameworks would continue to fail and remain ineffective. In its current context, we are still 5-6 years away from achieving context friendly AI models.