Every business has massive amounts of data flowing into and out of the organisation at all times. In hospitality, for example, data science empowers the hotel to differentiate itself from the competition through real-time business intelligence. In healthcare, data can be moved from department silos and make it accessible, in real time, across the enterprise. In the public sector, data science leads to improved planning and reporting, while maximising revenue and capital investments.

ITPro confirms that digital transformation is “a process of using technology to radically change your business”. The Global Center for Digital Business Transformation says that “organizational change is the foundation of digital business transformation”. That’s because changing the nature of an organisation means changing the way people work, challenging their mindsets and the daily work processes and strategies that they rely upon.

Data Science and Digital Transformation

A recent Infor Executive Brief confirms that data science has incredible potential for businesses of all types. It allows businesses to create models that find patterns in this data and use them as the basis for transformative software. The brief states that; “Data science needs to be a fundamental component of any digital transformation effort.”

Heilet Scholtz, Executive at EOH Infor Services, Infor’s Master Partner in Africa, advises the four key opportunities that data science offers in the digital transformation journey:

  1. Machine learning, which moves away from static software, and towards a progressive approach;
  2. Omni-channel architecture, as decisions made in one class or channel have an impact on products in other classes and channels;
  3. Cloud, offering flexibility, agility, and affordability; and
  4. Network visibility; offering a fully integrated picture of business performance.

Three Principles to Use Data Science Effectively

Infor’s Executive Brief outlines three ways to use the data available to make better decisions and improve operations:

  1. Make data consumable – Data science models, and the data they produce, must be easily consumable by the average user. Having access to easily consumable, real-time insights and visualisations of complex sets of data can unlock new opportunities and revenue streams—and help improve customer relationships and the bottom line.
  2. Make data adaptable – Models should be self-learning and highly automated, so users can get the most from them. The models must learn and evolve, to ensure the data is relevant to users, both today and in the future. The models and data also need to be accessible through existing enterprise platforms, making access easy and universal.
  3. Make data transparent – “Black box” solutions that hide their functions from users or that can’t be re-used across varying technology ecosystem are no longer acceptable. When users can’t justify or explain why they accepted a solution’s recommendation, they stop using it. Users must be able to drill down to understand where the data behind the recommendation originated. When users understand the recommendation as well as the reasons for that recommendation, their experience is more meaningful.


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