Data and AI are in a symbiotic relationship in which AI is worthless without data, and understanding data is impossible without AI. Artificial Intelligence, and Machine Learning, relies significantly on big data for success. It is also assisting businesses in unlocking the potential in their data vaults in ways that were previously difficult or unattainable.
What role can AI and machine learning play in helping businesses gain better business insights from big data? Let’s have a sneak peek at their symbiotic relationship.
Feeding Data to Machine Learning Algorithms
AI is a data hog. ML algorithms depend upon large volumes of data to learn relationships and patterns. We can start to recognize and forecast future trends in commerce, technology, and everything in between by integrating the two disciplines.
To broaden and enrich the correlations made through the ML algorithm, you need data that is:
- From distinct sources
- In assorted formats, and
- About different processes.
A humongous amount of information is awaiting analysis to derive a concrete foundation for decision-making processes in a business. Businesses are using AI to collect:
- Social media activities.
- Buying patterns
- Loyalty and reward applications.
- Product reviews
- CRMs (Customer Relationship Management Systems).
But, there is a caution. Before feeding the machine learning and deep learning algorithms with data, sort out your data collection strategies and data structures properly.
Just like humans need good mental and physical nutrition throughout their growth to perform well, AI systems require proper, clean, and balanced data. For that, you will need to put “humans in the loop” (something that we will discuss later in this article).
How Is Artificial Intelligence Improving on Big Data Analytics?
At this point, we have gotten an idea about how data can nourish machine learning algorithms. Let us look at how AI is transforming data analytics.
Enterprises are using AI-based software solutions to identify and groom their data. The aim is to generate value through analytics models. But why did businesses feel a need for AI applications in data analytics?
Data is getting enormous day by day. Everything from home PCs to business equipment, mobile phones to IoT devices generates massive amounts of data that is too big and too diverse for human analysts to handle.
Experts need to create AI algorithms to tackle the massive challenge of obtaining insight from complex data.
AI-based solutions are great at sifting, parsing, and analyzing data holistically from various sources and in varied formats (voice, graphics, and texts). The holistic and comprehensive AI data analytics tends to improve decision-making processes.
The Perpetual Cycle of AI and Big Data
AI and big data can complement one another to get better results.
- First, humans feed data into the AI engine, which makes the AI smarter.
- Second, we reduce the human intervention for the AI to function.
- With every turn, you require fewer humans to run AI. And thus, we get close to realizing the full potential of Artificial Intelligence on a virtually perpetual AI-data cycle.
Such evolution will entail the participation of humans who got trained in data analytics and AI algorithm programming.
Massive quantities of data will be required for AI algorithms to grow. Natural language processing, for example, makes use of thousands of samples of human speech that have been captured and broken down into a format that AI systems can understand.
Statistical Data Models vs Machine Learning
You’ve accumulated all the data you needed. Now what? That’s the most pressing question that arises about the use of big data.
Deriving insights from big data has always required a great deal of human labour. Machine learning (ML), and AI, are making this task sleek and quick. What used to be statistical models has now been replaced by machine learning algorithms.
ML algorithms can learn from data without the need for rules. Statistical Modeling, on the other hand, is the formalization of connections between variables using mathematical equations. Enterprises can use AI technologies to reduce manual, labour-intensive burdens and increase speed.
Humans in the Loop: We Are Not Redundant Yet!
Enterprises need to fuse human intuition with machine intelligence to create the best models. The human factor is present in both the training and testing of the ML algorithm. It is also known as human-in-the-loop.
Humans annotate data; then feed it to the algorithm to learn and make predictions based on it. They provide a constant feedback loop that allows the algorithm to improve its performance over time.
Humans also put in place checks and processes to keep the model accurate. Because models are unable to feel, people must be the ones to challenge them and evaluate their functionality. Even if a model gives “correct” results, a person must be there to assess whether the findings are “accurate”.
Results from AI models that are fed on inadequate or inaccurate data can be disastrous. If your AI system is producing biased and inappropriate outputs, then that’s because of bias training data.
By resolving these biases, and accumulating data from silos, organizations can get more accurate insights and better efficiency with their AI models.
The “Dirty” Data Problem: A Case of AI and Data Interdependency
Dirty data refers to any erroneous, misleading, duplicate, non-integrated, incorrect, and non-compliant data that is not likely to give proper insights.
Dirty data can also be the information that is in computer memory but has not yet been put into a database. It is an outcome of poor data management and storage practices, in addition to erroneous data entering.
Interestingly, 80% of the time of data and IT experts are spent on cleaning and preparing the data for machine learning projects.
Even more interesting, machine learning and deep learning algorithms use, well, machine learning for cleansing the feed data. It is yet another example of the symbiotic relationship between AI and Big data. How?
- ML algorithms can detect missing and outlier values.
- ML algorithms can spot duplicate entries with slightly different terminology that describe the same thing.
- ML algorithms can normalize data to the standard format and vocabulary.
AI-Drive Analytics: A Journey from Historical Analysis to Future Predictions
In the past, future predictions were essentially the extrapolation of historical analyses.
Traditionally, analysts used to establish big data decisions based on historical and contemporary data points. It usually resulted in the linear extrapolation of trends. With AI, this has grown to epic and exponential proportions.
Prescriptive analytics, combined with artificial intelligence, can deliver company-wide, forward-looking strategic insights that can help your enterprise thrive.
To produce accurate and precise results, don’t let your system engulf more than it can digest.
Walk cautiously on the road of the AI-predictive model. The value to the business increases with each progression through the analytics maturity model:
- Start with process and data mapping.
- Then, move to descriptive analytics.
- After that, predictive analytics.
- Finally, to prescriptive analytics.
Final Thoughts
As per an article on Forbes, AI and Big data can automate:
- 80% of manual work.
- 70% of data processing.
- 64% of data collection/mining.
The combination of AI and big data has only begun to demonstrate a sliver of its potential. To begin, companies must transform their legacy systems to obtain clean, diverse data from all of their processes. That’s where they can start leveraging the potential of artificial intelligence in big data analytics.