From Traditional BI to Modern Analytics

A Viewpoint from Audrey Limery

Data is an extremely valuable and powerful commodity, so much that it has been referenced to by many as “the new oil”. Businesses across the globe generate data daily, but not as many leverage data as an asset to lead their organisation towards success. Those who do are also the ones leading the game and staying ahead of the competition as they take advantage of the power of information.

Data is my passion, and as part of my work supporting the Procorre100 campaign – promoting women in consultancy – I wanted to outline some top tips from my chosen area of specialism to help businesses who are looking to reap the rewards of achieving a higher level of data maturity.

It’s time to make a change

We are currently at the verge of the 4th Industrial Revolution, where Artificial Intelligence (AI) and Machine Learning (ML) technologies are emerging at a very alarming pace. Some companies from various industries and sectors are starting to adopt these technologies to get competitive advantages and drive efficiency. But on the other hand, it seems that a large number of businesses are still stuck with traditional Business Intelligence (BI) systems that are holding them back, and tools that will rapidly become obsolete in a world where “the robots are already rising”.

Traditional BI technologies will soon no longer be able to cope with the near real-time insight generation demanded by today’s businesses and their customers. Organisations will have no choice but to adopt more modern Information Technologies such as Data Science, Artificial Intelligence and Machine Learning.

 But where to start?

The first step to making the transition from traditional BI is to define and implement a robust and well-thought out data strategy. A company without a data strategy nowadays is like a boat navigating deep waters without a compass: it is both risky and dangerous. The data strategy should be the roadmap that guides an organisation towards success while generating value. To define a data strategy, an organisation should foremost identify their own business needs and challenges, understand where they are going and what their goals are. It is essential for a business strategy to be implemented prior to the definition of a data strategy, as the latter should be intrinsically linked to the former. According to Bernard Marr, a strategic expert recognised in the data industry, a data strategy should be defined with the three following goals in mind: making better business decisions, improving business operations and turning data into business assets. The defined data strategy should be the key to unlocking powerful and valuable insights for an organisation and yield benefits such as enabling smarter decision-making, improving performance, increasing efficiency and boosting revenue.

Know where you are, to know where to go

Before one can know where to go, they need to know where they are. Accordingly, another critical step is to perform a data maturity assessment to help the company determine their current level of maturity, know how data-driven and data-ready they are and identify what they are lacking towards achieving the level of data maturity they desire.

It is very common for organisations to add to the complexity of their IT infrastructure over the years, by embedding components that will only answer short-term needs or partially solve some issues (in fact temporarily fixing the symptoms instead of focusing on the root causes), and thus without a data strategy in place or looking at the big picture. This often results in a very complex IT architecture difficult to decipher, systems operating in silos and data incoherence throughout the organisation. Consequently, my recommended third element would be data architecture modelling. This important activity should give an organisation much needed visibility over their current IT architectural state. This can then be used as a baseline to determine which legacy systems or tools should be retired, which new ones should be adopted, and ultimately help design the new IT architecture they would like to implement in order to increase their level of data maturity.

I believe modern analytics and data strategy to be one of the top priorities for any organisation looking to adapt and thrive in this new era.