The Influence of Data in Company Decisions
Data and AI are big buzz words that you hear everywhere. We all feel like it is the future. But how are companies using them right now? Data is well known for being one of the most important elements for corporate development. During the pandemic our digital presence was more than ever before and, search engines were fueled with a lot of new information. This situation created a big opportunity for companies to invest in new technology and find suitable professionals in data and artificial intelligence (AI).
Before we begin our deep dive into the influence of data in company´s decisions, let’s make sure we understand what data and AI actually are. Data is defined as units of information that express a qualitative or quantitative variable about people or objects. According to Built In, artificial intelligence is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence.
The union of data and AI created a big change in societies, companies, and the perspective of citizens. For some people, the use of data is seen as an optimization of budget and process and for other it means a big risk for human development. Jobs are increasing everyday by the huge demand of technology-focused professionals, the question is… who is hiring them?
Companies in The Netherlands that use data and AI
Development, technology and innovation are some of the characteristics of Dutch companies. Nowadays, we include data and AI as a necessity in different Dutch industries. According to Global Legal Insights, there are more than 65 companies that are working to incorporate the use of AI in The Netherlands.
Companies that are already using data and AI come from different markets and have completely distinct goals. However, we are going to talk about five companies that all Dutch people know of or at least, have heard of.
1. Postillion Hotel: A big hotel chain that processes over 5000 emails with request per month. Their key employee is Michiel, an AI who can do the administrative work of 17 people in one and can offer added advantages. The other employees can focus on providing the best service, while not having to worry about the boring administrative duties anymore.
He does not sleep, is never ill, works independently, and can multitask without blinking.
Michiel allowed the hotel to personalize the service and optimized the budget for the employees.
He is a state-of-the-art neural network that uses natural language processing techniques such as text classification.
He can name entity recognition and can perform deep learning to read an email and translate it into a quote.
Michiel is linked to reservation systems and can issue quotations 24 hours a day, seven days a week. And he can do it all within a few minutes.
2. Picnic: The online supermarket that deliver groceries for thousands of customers has found the road of success through data science. As the number of clients was increasing exponentially every day, they decided to create a strategy where Picnic would be able to deliver groceries within 20 minutes. A big goal requires a big system, so they started off with some exploratory data analysis and found information that would help them meet the delivery time. Factors such as customers, orders, regions and runners (couriers) were taken. And for the information that was not easy to get, they constructed proxy variables.
3. Booking.com: During the last few years, the world´s largest online travel agent has applied machine learning to improve the experience of customers and businesses. While multiple literatures of machine learning focus on algorithms and mathematical aspects, there is not many studies that talked about the impact of it in an industrial environment where commercial gains are fundamental. Based on their experience, they conducted an analysis of 150 successful customers facing applications of machine learning developed by multiple teams in the company.
According to booking.com, these cases were validated through rigorous randomized controlled trials. As they learned that machine learning can be used for many products in different contexts, they used the models as a tool for the teams to improve the products and to learn from their customers. Actions such as optimizing the size of a specific element of the client helped them to provide tailored recommendations for one specific moment in the sales funnel. Another of the achievements was the creation of the models that act as a meaningful semantic layer. This model helped the team to understand how flexible a user could be for a destination and for instance, give them the tool to be a part of the customer’s journey.
4. ING: One of the most famous banks in The Netherlands and the world has around 80 data scientists who are working on AI projects. This organization is well known for giving data and AI professionals the opportunity to grow and explore their potential. According to the Chief Analytics Officer Görkem Köseoğlu, the use of valuable information can convert ING into a “smart bank” that uses AI to predict customer’s needs. Data and AI can be used for multiple areas in the company, for example to predict anti-money laundering, terrorism financing and fraud.
Also, another example in ING is the use of data to predict a customer´s saving habits. Here they will identify the percentage of people´s savings with them and with other banks. Therefore, they could construct strategies to solve this problem that many companies are working on.
5. Shell: The British-Dutch oil and gas company is incorporating data to their management. Currently, they are looking to drive down the shipping carbon footprint. The use of data influences the decisions taken by the company and gives accuracy to the information that impacts business. Seeing data in real time allowed them to proactively intervene and solve problems as soon as they arose. For example, it gave them the opportunity to understand where their footprint was the biggest and reduce that by setting correct benchmarks and take actions for the future.
In conclusion, we must bear in mind that what unites the use of these companies is the correct acquirement of efficient professionals who focus on driven data. This data doesn´t mean anything without the correct analyses or use.
Companies require capable people who can resolve problems swiftly and can understand and organize the unimaginable quantities of data that are being produced every day.
Data and AI are fundamental for a company that will take decisions not just for the customers but for the environment. Firstly, it will help companies to predict the future and take actions that can reduce considerable loss of money. Secondly, as the example of ING shows, data is helping companies to become much safer in tracking the risk of cybercrime.