Every day, a staggering 328.77 million terabytes of raw and unstructured data is generated globally. When analysed effectively, this data can help us gain valuable insights into our customers preferences, interactions, and online activities. These insights can help us make informed business decisions, boost operational efficiency, and enhance overall customer experience.
But here comes the challenge: how can we process and analyse this colossal amount of data produced from diverse sources such as Google search engines, social media platforms, e-commerce, and sensors and extract meaningful insights from it? New technologies give us new opportunities to extract actionable insights. One of these technologies that we are betting on is Artificial Intelligence (AI) .
Let's delve deeper to understand the correlation between AI and big data and their impact on decision making. Moving further, discover some use cases, limitations, and practical steps to employ these technologies in your businesses and drive success.
How AI and big data are interconnected?
AI and big data share a symbiotic relationship. While AI requires a massive volume of data to learn and enhance decisions, big data analytics (the process of uncovering trends, patterns, and correlations in large volume of raw data) leverages AI for better data analysis process. To unlock this profound connection in detail, let us consider their working methods.
Everyday millions of us use Google to search for things. We ask Google questions and Google comes back with the answers. The interesting trick with Google is that with every question we ask, it gets smarter, wiser, and more intuitive. Every search we make there creates new data. Google uses this data, learns from our constant searches, and gives us the result that we ask for. That way, Google – an AI-enabled machines – uses data to perform tasks with remarkable precision.
How can AI and big data impact decision-making process?
In the fast-paced business world, where customers’ expectations constantly evolve, making smart decisions is paramount. It helps organisations to become more customer-focused, adapt quickly to market changes, and retain valuable customers. AI-powered data analytics is the perfect solution here as it can enhance our decisions in various ways:
- Improving speed and efficiency: By harnessing the potential of AI algorithms, we can process substantial amounts of data in real time. As a result, we can turbocharge our decision-making process, respond quickly to changing market dynamics, and make well-informed choices.
- Unlocking hidden patterns: AI-powered tools have the ability to identify hidden patterns in complex data sets, which often elude human observation. These patterns can reveal market trends, correlations, and relationships that can significantly impact our business decisions.
- Accurate and fact-based decision making: One of AI's most remarkable attributes is its ability to approach decision making with a rational and unbiased mindset. By eliminating human biases and emotions, AI ensures that decisions are fact-based, which means error-free and accurate.
Use cases of AI and big data across different sectors
Here are three compelling examples that showcase how the employment of AI and big data can drive strategic decision making across different sectors.
1. Healthcare sector
AI-powered data analytic tools are game-changers in the healthcare sector. These applications investigate patients' medical records, genetic information, and real-time sensor data, supporting healthcare providers with vital insights. With these insights, they can create personalized treatment plans for patients, detect diseases early, and make highly accurate diagnoses.
2. Finance sector
AI and big data analytics play crucial role in detecting fraud and assessing risk. By continuously monitoring and studying large amount of data, these technologies can quickly identify suspicious activities and prevent fraud in real-time. Additionally, AI algorithms can enable financial institutions to evaluate creditworthiness and assess risks associated with lending, investment, and insurance. They can make well- informed decisions and improve their risk management process.
3. Retail sector
In the retail sector, AI and big data are essential for optimising inventory management and forecasting demands. By examining historical sales data, customer behaviour, and external factors such as weather patterns and social media trends, these technologies enable retailers to make data-driven decisions regarding procurement, pricing, and promotions. Moreover, it helps reducing stock shortage and excess inventory, ensuring that the right products are available at the right time to boost customer satisfaction and maximise profit.
What are the limitations of employing AI-powered big data analytics?
AI is an expanding technology with immense potential to transform businesses across several sectors, such as healthcare, finance, and retail. Data is the driving force behind this transformation. It serves as the lifeblood for training and testing AI models. Nonetheless, relying on AI-driven data present multiple challenges, which includes the following aspects.
- Data quality: Poor data quality can result in inaccurate or biased AI models, which in turn, compromise the integrity of our decision-making process. Hence, ensuring high data quality to train AI applications is pivotal.
- Data quantity: If we have insufficient or incomplete data, it can result into oversimplified and unreliable predictions. Therefore, we should ensure that we have enough data to make the right decision and prediction.
- Data diversity: Speaking of diversity, it’s more than a buzzword. Data diversity is critical to avoid bias. By training AI models on various perspectives, we can ensure a fair and rational decision.
- Data privacy: Last, but not least, data privacy is highly crucial when handling sensitive information for AI models. Hence, to ensure privacy, data must be protected and compliant with relevant regulations.
Embracing ethics in AI and big data utilisation
As we navigate the complexities and opportunities of AI and big data, it's important that we do so ethically. The increasing prevalence of these technologies in decision-making processes presents unique ethical questions. For example, how do we balance personalisation and privacy? How do we mitigate biases in machine learning models to ensure fair outcomes?
These are the questions that have to be central in the future conversation about AI and Big Data, whilst we start to leverage all of the exciting possibilities that these new technologies offer.
Let us prepare the future with AI and big data!
With the above arguments, it is evident that the role of big data and AI in decision-making is significant. By leveraging these technologies, we can make data-driven decisions, become more customer-centric and take a step closer to success. Although, challenges exist, with right strategies and frameworks we can overcome them and discover the true potential of these technologies. So, embrace the power of AI and big data to shape the future of decision-making today!
Want to discover how AI is disrupting different sectors? Read “AI in motion: how AI is disrupting the transport and distribution industry and enhance your know-how.