So goes the tale; Fourth Industrial Revolution (4IR), Big Data, Artificial Intelligence (AI), Machine Learning (ML). From the public to the private sector, these have formed part of the whirlwind that has indicated that a company or industry is clued-in on the future. Since the late 1700s when the first industrial revolution was documented, the generation of skills, language of the economy, and the influence of technology on our world has changed tremendously.

In the present day, computational power has connected all tools of production and  introduced automated ways of assessing their efficiency. Today more than ever, software and hardware systems are agile and applicable to all industries across various functions in an organisation.

This computational power has revolutionised the world of work and elevated traditional ways of performing tasks. Many, if not all, existing and emerging industries stand to gain from the increased computational power and innovative technology available on the market today. Today we know more about our customers because we have enough data to predict trends beyond our gut-feeling and as a result we are able to make better strategic and operational decisions.

When analysing data, it’s often taken for granted that we are gathering the right data, measuring the right metrics within said data, and that the sources from which we are getting the data are being integrated into our analytics tools in a comparable manner. Tasks such as data sanity checks, reviewing data cleaning processes, testing various hypotheses and analysing the nature and impact of ‘lost’ data are often neglected for the more glamorous parts of business insights such as data dashboards and reports. If we are to make data driven decisions, then we need a team that can ensure that we are analysing and predicting our future on data that is extracted with understanding, analysed with the right tools and techniques, and presented with the business needs in mind.

This is where the true power of a Data Science team comes in. Practitioners in the field who extract deeper and more actionable insights from data form an integral part of data storytelling. The team extracts data (responsibly), conducts data analysis and visualisation, applies machine learning model design and predictions, and has capabilities to automate the whole process. All this is done with the help of continuous and rigorous research. In practice, the team uses data that is being collected but currently not being analysed.

Ultimately, the mandate of any team is to use the best methods possible in order to offer satiable insights and under the leadership of digital marketing experts. But, an effective data team is able to tailor these techniques to fit marketing best practices and overall industry needs.

As with many future facing concepts, Artificial Intelligence and Machine Learning are often framed as solutions looking for questions. However, the case-use approach the Conversion Science data team has implemented clearly demarcates the use of Artificial Intelligence as a guiding technique and Machine Learning as a core tool. Although the projects we are working on are experimental in nature, we are applying theoretical research to real-world scenarios where actionable insights can be applied. With a growing amount of data there is no cap on the potential of this kind of analysis. In doing so we hope to offer greater value and continue to lead the digital marketing space.