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Transitioning from BI to AI

Benjamin Kuppe, Director of Business Intelligence, Monash Health

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Benjamin Kuppe, Director of Business Intelligence, Monash Health

In many industries advancements in technology and data processing capabilities have seen the emergence of artificial intelligence (“AI”). Whilst AI is a broad term which can mean many things it typically refers to the ability of machines and computers to perform human tasks or imitate human behaviour. Some examples might include an algorithm which predicts the likelihood of a patient contracting an infection, self-driving car or a piece of software which automates a set of routine, manual tasks.

Business Intelligence (BI) on the other hand has been around in some form for a number of decades. BI refers to the tools and processes used to turn large volumes of data into actionable insights. Most organisations today would have some type of BI platform and team which produce business critical items such as data warehouses, dashboards, scorecards and KPIs. These tools enable staff at all levels to understand performance and make informed decisions.

Advancements in technology have also blurred the lines between classic BI and other disciplines like data science, machine learning and big data. With so many buzz words it can be confusing to users and within an organisation BI can mean different things to different people.

The challenge many organisations are facing is how to transition from classic BI to AI and who in the organisation is responsible for that. Whilst defining responsibility is important the most important thing is to be clear on the purpose; that is what we are doing and why we are doing it.
In its simplest form, irrespective of how it is defined, organisations are trying to use data and technology to solve business problems and improve performance.

When we understand this the challenge becomes less about transitioning from one technology to another and more about building capability. In terms of capability the question becomes: does my team or organisation have the right people, technology platforms and processes to maximise the opportunities which data and technology provide?

• People – do I have existing people with the right skills or can I train them up? Do I need to buy in external expertise in the form of consultants or contractors?

• Technology – do we have the best technology platforms or do we need to upgrade? How quickly can we make use of new technologies when they become available?

When I am hiring I look for motivated, solutions focused and customer oriented people, not necessarily someone with the best technical skills. Whilst it is important they have solid technical and data skills the most important thing is to have a solutions focused attitude and a willingness to learn new technologies and ways of doing things.

With respect to technology it is important to have adaptable and flexible platforms. The cloud provides a great opportunity here as it enables server environments and new technologies to be provisioned quickly. All major vendors now have software as a service (SAAS) offerings which means there is no need to purchase and maintain physical hardware, this all happens seamlessly behind the scenes. A major benefit of SAAS is that you’re always using the latest version of a piece of software meaning you always have access to the latest features.

Even when an organisation has access to the latest technologies there is still no guarantee that staff will want to use them. Most people want their job to be as easy as possible, but they don’t really care what product or technology they use. If a 10 year old technology is doing everything that is required then there may be no need to reinvent the wheel. Therefore, it is essential to get stakeholder buy in form the very beginning before beginning a new project.

Transitioning from traditional BI to AI can be challenging. However, when the focus remains on building a high performing team supported by flexible technology platforms then the challenge becomes less. And when each project starts by clearly defining the business problem and gaining stakeholder support early on, then introducing a new technology becomes far easier.

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