The ability to turn data into meaningful insights for better business decisions is the essence of enterprises’ digital transformation. It is made possible by the nexus between big data and artificial intelligence.
Artificial intelligence services providers use statistical data models to find underlying meaning in big data. Big data is a pool of structured, unstructured, and semi-structured data. To put it in a simpler way, Big data provides the necessary raw material for AI systems to draw meaningful data insights. Meanwhile, big data analytics services providers use powerful machine learning algorithms to enhance their analytic tools to learn big data better and drive better decision-making.
This article will explore how the intersection of big data and AI is helping businesses reap the true value of digital transformation.
Comprehensive Study of Customers
Consumers are leveraging more than two digital devices to execute their daily activities. For instance, they use their smartphones as digital wallets to do all sorts of financial transactions. They use fitness trackers to monitor their health conditions.
These devices generate massive amounts of data. Enterprises capitalize on this data to determine the characteristics of every individual. Earlier, they had to move data in and out of their data warehouses. They created static data reports, which consumed a lot of time.
Enterprises leverage artificial intelligence services to create automated, distributed, and intelligent predictive analytics tools driven by powerful AI algorithms. These tools segment consumers based on buying frequency or liking towards any particular brand. It helps enterprises to conclude a particular customer’s preferences.
Prominent Big data analytics services leverage AI to convert consumers’ speech to text or the reverse. It is used as annotated transcripts while analyzing customer behaviour.
Improved Sales Forecasting
Enterprises tend to predict the current year’s sales based on their previous year’s sales performance. However, this is not a fail-proof method because natural calamities, socio-political unrest, and other factors can impact sales forecasts.
Big data allows enterprises to identify any underlying data pattern that hints at trends. These trends can have a huge impact on future sales performance. An experienced AI consulting company would create an AI-based data forecasting solution based on advanced machine learning algorithms. These solutions enhance data forecasting. These solutions are programmed to intuitively select a data model suited for specific business metrics such as sales. The solutions manage the entire forecasting pipeline independently, starting from data model training to activating hyperparameters. It helps enterprises gain fast and granular data insights.
Better Scopes of Predictive Maintenance
The combination of big data and artificial intelligence has enhanced the scope of predictive maintenance in digital twin technology.
A digital twin is a virtual replica of a product. This replica is fed by data generated from the sensors fitted into the actual product. AI-based predictive data models analyze this data to inform about the product and each component’s functionality in real-time.
It allows actionable insights into what is working in a product and what is not. Enterprises analyze these insights to identify any defect in the product and schedule maintenance accordingly. It improves production efficiency and curbs maintenance expenses.
Better Fraud Prevention
An experienced big data analytics company uses different machine learning models for fraud prevention.
Supervised machine learning models parse labeled data from past datasets. These datasets are based on any suspicious or genuine transactions done in the past. Unsupervised machine learning models parse unlabeled data. This data is based on unknown events, but it can still be analyzed to uncover any hidden pattern. Semi-supervised machine learning models use labeled and unlabeled data to detect any underlying pattern.
The quantity of data is responsible for augmenting AI tools’ capability to detect frauds. Therefore, big data analytics services providers train their AI models with multiple data on valid and fraudulent transactions. It allows models to self-learn and estimates fraud risk for individuals.
Conclusion
The benefits of AI and big data are imponderable. However, before partnering with a big data analytics company or an AI consulting company, enterprises must make the following considerations: Determine if AI and Big Data are the best solution for your current issue in hand and ensure that your analytic models are not biased. It can be caused by a biased data training set or the creator with a biased mindset. Partnering with an experienced big data company can solve core issues like these and more.