Machine Learning: The Difference Between Leaders And Followers

Andreas Kral

With the evolution of technology comes a whole new range of possibilities. Telephones no longer merely enable phone calls; they serve as your wallet, encyclopedia, map, social media source, Internet connection, and more—and are small enough to fit in your pocket. TVs stream videos on demand in any language from any country while keeping track of what you have watched in the past and which new programs are likely to interest you.

Your social media accounts suggest people you may want to connect with, content you may want to watch, and products you are likely to purchase. My mobile phone surprised me recently when I got into my car after work and it informed me that it would take 47 minutes to get home and asked if I wanted it to suggest a route. How did the phone know I was just about to drive home?

The answer lies in the evolution of technology.

More data can be captured through small sensors that are imperceptibly embedded in devices such as mobile phones, Fit Bits, and watches, which collect tiny bits of information: your location and movements, whether you are walking, running, driving, or taking transport, along with health parameters such as pulse, temperature, and much more.

Individually, these bits of information are not worth much, and it takes a huge amount of capacity and power to store and process this data. With virtually unlimited data resources available through cloud technology and cheap hardware, this sensory data can be stored and processed for pattern recognition and forecasting.

This allows analytical software to go beyond mere reporting, summarising, analysing and visualising data sets, which used to be the core value proposition of traditional business intelligence (BI) products. The algorithms that identify patterns and produce meaningful forecasts need significant processing power, which in the past has required expensive hardware and plenty of time.

However, today’s easy access to data storage and processing power makes predictive capabilities available to a wide range of consumers. As a result, analysis of historical data has become commoditised, and the difference between leaders and followers lies in forward-looking (predictive) BI, also known as advanced analytics.

The true value of this information is realised when the sensory data from the Internet of Things (IoT) is combined with transactional data from ERP systems and embedded in the user experience, as shown below:

The process of this embedding starts with reading and preparing the data to make it accessible to the predictive engine. Machine learning means the continuous cycle of training the model, applying the findings in the consumer context, and capturing feedback, followed by reading new data and retraining the model, resulting in the continuous improvement of the predictions (“intelligent machine”).

From a consumer perspective, it creates a “smart” user experience in which the software not only responds to user input but proactively suggests appropriate actions (products to buy, options to select, etc.). Secondly, the continuous improvement of predictions based on received new data and user feedback takes the user experience to an entirely new level.

Conclusion

Machine learning allows companies in all industries to take interactions and relationships with their customers to a new level. In the age of digital transformation, this user experience is expected by consumers and differentiates leaders from followers in every market.

To learn more, join me at the Leading Insights conference and refer to my previous articles Digital Transformation: Fad or Future, Do Companies and Individuals Have a Choice? and Digital Transformation with SAP S/4HANA and BusinessObjects Cloud.

Read more here about SAP Business Objects Cloud.


Andreas Kral

About Andreas Kral

Andreas Kral is the Solution Manager for Analytics, BI and Predictive, for SAP Australia and New Zealand. With a background in Mathematics, he joined Business Objects in Europe in 2005 as Presales, looking after mainly Financial Services customers in Switzerland. Since 2008, Andreas has been living in Melbourne and working for SAP Australia, helping customers across the country understand and realise the value of BI and Predictive solutions. Having completed his MBA at the University of Melbourne, Andreas has joined the Centre of Excellence team at SAP Australia in 2016, looking after the BI and Predictive solution portfolio. Andreas has a passion for analysing numbers and big data, turning hidden patterns and trends into meaningful pictures that serve Science and Businesses run better and achieve better outcomes.