A large chunk of data that organizations possess today is in the raw or unstructured form. Apparently, 80% of the enterprise data is unstructured, and it is growing at the rate of 55% and 65% per year!
Manually processing this unstructured data to understand the hidden context from every piece of information is a Herculean task. Cognitive analytics does the work of understanding the meaning behind the available information through AI and machine learning. While it does not make any decisions on its own, it provides enough insights to assist in decision-making.
Organizations can utilize the internal and external data available in the form of social media posts, documents, pictures, emails, worksheets, customer details, vendor information, product data, etc. to understand their customers. It can help them improve the customer experience and create a competitive differentiator in the market. When used properly, cognitive analytics can also unveil solutions to real-time challenges.
Today, insurance companies are using cognitive analytics to offer better medical, home, and car insurance plans to their customers. Various applications like Alexa, Siri, Cortana, etc. have been introduced to provide better services to the customers. Even the auto giants tap into data through in-car services to improve their services and strategies.
Let’s look into how cognitive analytics can help automotive companies.
Why Cognitive Analytics?
Smartphone users spend hours on social media channels and millions of new users are getting connected through various apps. It generates billions of gigabytes of data each day, which automotive companies can leverage to create fascinating customer experiences.
For instance, people use weather and location-related services while driving. They also plug in smart devices that make their journeys convenient and happening. By using the data obtained from smart devices and services, auto companies can redefine their future product strategies and marketing campaigns.
Unfortunately, despite the humongous amount of data being created, traditional analytics cannot use it completely. It is due to the unstructured and complex nature of this data. To convert data into insights, they need newer and better technologies. These technologies can help them lead future innovations as well. This is where cognitive analytics comes into the picture.
Through cognitive analytics, machines can listen, understand, and react just like humans. Through natural language detection and identification of patterns, cognitive analytics makes machines smarter. It makes them capable of understanding human emotions and creates a strong relationship between humans and technology. They achieve this by using AI, deep learning, natural language detection, and PA (Predictive Analysis).
Auto companies have access to various data points such as data gathered through their products, services, customers, vendors, partners, and social media campaigns. This data, when used, can help them improve their business practices and develop better products and solutions. However, they don’t have the tools required to retrieve insights from the information-rich data. As a result, structuring data and getting insights from it is a huge challenge for them.
Cognitive analytics assists the data structuring process so that they can interpret what the consumers have to say to them. Automotive companies that are ready to invest in cognitive technologies can improve their productivity, enhance operational efficiency, and derive actionable insights through enhanced data-driven analytics.
Earlier, companies relied heavily on historical data to forecast changes in the market. However, they weren’t able to track real-time changes and evolving market trends. With cognitive analytics, automakers can understand the opinion of the market regarding real-time events such as fuel hikes, changes in the cost of materials like aluminum, glass, etc. Using this useful information, they can make more informed decisions.
Let’s take the scenario of care remodeling. Suppose an automotive company has undertaken the remodeling of its top-selling car. Traditionally, companies will make the changes based on their understanding of customer expectations, availability of raw materials, the overall design, etc. However, with access to only the historical data, the decisions are often made based on instincts. Therefore, situations like a shortage of inventory could derail the project (think of the semiconductor chip shortage during COVID which delayed the production plans of several companies.)
With cognitive analytics, predicting customer behavior, checking data related to individual parts, and analyzing market dynamics becomes simple. The data obtained from various data channels and sales history allows companies to optimize every stage of the remodeling process. Instead of depending on guesswork, cognitive analytics can make use of data sensing methods to manage inventory better. The logistics and other costs can be reduced by developing a leaner supply chain process.
Cognitive analytics opens a realm of opportunities in the auto sector. Giants such as BMW, Tesla, Toyota, and Ford are already benefitting from the use of data analytics and cognitive technologies. While there is a growing interest from auto companies on how they can transform their business using cognitive analytics, the first step is to identify why they need it. After understanding this, they can align the technological initiatives with their business priorities.
The best way is to build strategic partnerships with companies like Ascentt, which specializes in business intelligence, customer relationship management, and application integration. We offer technological solutions and can help you build and deploy cognitive apps for your specific needs.