Top 4 Data Science trends­ that will have a serious impact on your business in 2018

foto van Alexander van Eerden
Alexander van Eerden
CEO & Founder

December 1st, 2017

Although data science has been a hot topic in numerous fields and markets in 2017, it will start to show its true potential in 2018, when focus will shift from experimenting to creating real business impact. These top 4 data science trends are a force to be reckoned with and will provide a competitive edge for businesses in the nearby future.

2017: Data science PROOF OF CONCEPTs

In 2017, companies of all sizes have started to experiment with data science and machine learning, with most proof of concepts happening in small scale experiments. In a 2017 study, 95% of the respondents said they are currently using or planning to use data mining and predictive analytics to improve their business. This confirms the growing interest in, and the potential of data science and machine learning.

Large corporations have the scale, resources and even the data to really benefit from the true power of these innovations. They might however, do not have the necessary skills (read our blog about the skills needed) to efficiently implement them.

This raises the question: how do we proceed? How will data science make impact in 2018?


In 2018, businesses will evolve from mere experimenting with data science and machine learning, to implementing it on a slightly larger scale and evaluating its business impact. To decide where to start, managers have to ask business driven questions like:

  1. In which of our products/services would data science and machine learning have the largest impact?
  2. Which processes could be more efficient, improving business results?
  3. In what areas can we improve our customer experience?
  4. In which business processes is an efficient implementation achievable?  (i.e. where is the right data available)

A second shift that we are seeing is the shift towards a customer-centric organization. Managers need to switch from thinking from business units and departments, to the perspective of the consumer in various stages of the customer journey. Because businesses care about their customers, and at the same time want to maximize their value, a customer-centric mindset is essential. Data driven customer journeys empower businesses to deliver a consistent experience and to personalize their offers and services to all consumers to optimize their customer lifetime value.

Take for example a consumer holiday service provider, that is active in e-mail marketing. Their goal is to increase conversion while improving customer satisfaction at the same time. Knowing what your consumers like and when they want it, makes it possible to send more relevant, personalized offers to individual subscribers of the e-mail marketing program at the right time. In term of results this will lead to a significant increase in click-through rates and conversion rates, while sending much less mails to individual subscribers. Which in turn leads to increased customer satisfaction because subscriber only receive relevant tailor-made offers.

This example shows evident impact on a specific business process. But e-mail and advertising are only two domains in which data science and machine learning can deliver value. The rest of this article will review the top 4 trends that will make an impact on various business processes in 2018.

#1 data science platforms will enhance ease of implementation

Organizations will discover that just hiring a data scientist isn’t the solution, since there are a lot of areas of detailed expertise within the data scientist's job. To really harvest the power of data science and machine learning, and tackle problems in different areas of expertise, assembling a highly skilled data science team would be necessary. Therefore, most organizations will be unable to perform all the required steps to successfully implement these data science solutions in their own organization. Since their is a huge scarcity of skilled data science on the job market.

External data science service providers, and data science platforms might offer the solution in the foreseeable future. A data science platform is a cloud based service for data science and machine learning. It offers a more efficient way for implementing, and staying up-to-date with, the newest data science and machine learning technologies to gain or maintain a competitive edge. Besides the expertise and ease of implementation external providers add, the real value of these platforms lies in scalability. Because, these can also be rolled out horizontally across departments. In this way these platforms provide added value for a wide array of specialisms within the organization, such as marketing, customer service or planning, and not only the data scientists in function. Because of this company wide application in the end this will result in less point solutions, and a more integral approach to data science and machine learning solutions.

Skills needed

#2 The Internet of Things will start to show its true potential

The gross amount of data collected by Internet of Things (all sorts of devices being connected to the internet) will empower businesses to better understand consumer’s needs. Because of the enormous number of touchpoints created by the Inter of Things trend, 90% of the world’s currently available data has been generated over the last two years, i.e. sensory data, data from smartphone apps, security camera’s, etc.

Internet of Things devices like Fitbit’s wearables or the ‘connected car’ (telematics) will help businesses to better understand their consumers' behavior in their natural environment. By analyzing Internet of Things data, results can be used to better serve the customer and enhance the consumer experience. It will be a lot more powerful than most of us can imagine at this moment. However, using consumer data has issued some privacy concerns. These concerns cannot be ignored, and therefore IoT data should only be used under high transparency and serve a mutual benefit for company and consumer.  

Example of IoT: Fitbit Wearables

Health apps or the connected car are an excellent example of how Internet of Things can creates new possibilities for insurance providers. They can create new services that weren’t possible in the past, like preventing insurance claims from happening, instead of just paying out when a claim is filed. This data can help prevent damage to the consumer’s health and, in this case, cars. Be sure to check out our vision on insurance claim prevention in our blog.

#3 Augmented reality: the future of omnichannel retail

Internet of Things is all about collecting and analyzing data, whilst augmented reality can be considered as the front-end of data science and machine learning, the part the consumer actually interacts with. Augmented reality will enhance the way businesses communicate with consumers in the offline world and create new digital touchpoints in the customer journey. This offers great possibilities in the retail industry.

In the nearby future augmented reality will be everywhere in our physical world, either with smart glasses (or eventually lenses) or other devices, such as smart mirrors. Augmented reality applications will be the future of retailing. For example, while shopping, consumers can make use of smart mirrors incorporated in the store and fitting rooms. Through image recognition and analysis of data from other consumers, past purchases and preferences, the smart mirror offers suggestions for accessories or other related products, and directly show them to the customer. To further enhance the customer experience, the smart mirror can show the consumer a map of the store, with the recommended products marked to enhance findability.

Example of an augmented reality application in retail: personalized promotion signs

#4 Artificial intelligence will help make informed business decisions

AI will help make better business decisions on the highest level. While AI has penetrated the consumer market quite a while ago (think of Siri, Cortana, etc.), there is more unharvest potential in business.

In the coming years, AI will climb the ranks and reach the highest level, where it will assist managers in making important decisions. Trust in AI on a business level is increasing, partly because of the proven results in consumer markets but also based on the evidence from proof of concepts in business. Besides, AI has become more sophisticated over the past years and will get more and more intelligent in the coming years.

AI supersedes human intelligence, and is able to process a vast amount of variables and discover patterns and relationships at a speed a human would not be capable of. This capability to make the best informed decisions possible on the highest level, gives businesses the opportunity to create a real competitive advantage, and communicate with their customers in surprising ways. Also, with AI it is possible to easily detect subjects and matters that deserve most attention and can assist your human employees to put their efforts and energy in fields with the highest impact.

Artificial intelligence in business

Do you want to know how your organization can benefit from data science and machine learning solutions and become more customer-centric? Feel free to contact us.