Data Analysis: 4 Trends to Watch

1. Data, data and more data!

Big Data and predictive and prescriptive analysis.

Data is omnipresent, and generally imposes significant demands in terms of infrastructure, networks and data centres. This, however, is changing. As Duncan Pauly, CTO of Edge Intelligence explains: “analytics will move to the cloud (or the edge). […] Edge and fog computing will coexist with the cloud and be used for different types of analytical processing”. 

In 2019, the volume of data will be even greater, and will keep growing. We are going to need powerful tools capable of adapting to these changes and we will also need to learn new ways of processing data such as using predictive analysis. Predictive analysis consists in analysing sets of historical data to predict future results and prepare for future actions.

Is Machine Learning (ML) the end of Data Analysis?

Data analysis will still have fine days ahead of it as Machine Learning still has a lot to learn. The immediate priority for ML is for it to learn and to carry out specific tasks. ML algorithms are best suited for specific predictive applications and situations. Traditional data analysis, however, will still be utilized for serial analyses, dashboards, etc. In fact, in 2019 will see the emergence of an ever-increasing number of vertical solutions which will integrate the latest advances in ML to meet specific business needs, such as fraud detection in real time, developing highly-personalized Customer Journeys to enhance satisfaction, etc.

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2. Artificial intelligence: adding value to customer experience.

AI: the “face” of the future

AI can and will eliminate many of the obstacles users are faced with today. Robotic Process Automation (RPA) will become more and more predominant, as will chatbots, virtual assistants and the IoT (Internet of Things). AI will soon be able to do hitherto unimaginable things and help deliver experiences which are 100% satisfactory. 

The risk we face today, or rather the limitation, is the huge gap that exists between the volume of data being created and the human capacity to process and leverage them. The real question, then, is how do you go from big data to smart data? 

There is also a gap between the analytical tools available today and their adoption within organisations. These gaps can – and should – be bridged and which will yield us more power over these technologies.

3. Data storytelling & conversational analysis.

A new language: explain, enlighten, engage.

Most people view data as boring. This is where machine-driven data storytelling can come into play, by using your data to tell a persuasive story through natural language generation. Conversational analysis will make this much more interactive, leading to easier decision-making.

Source: https://www.forbes.com/sites/gradsoflife/2018/12/20/how-to-shrink-the-s…

4. Customer intelligence.

Customers are changing and at a very great speed. Companies, too, must evolve to keep up with their customers, compile ever-increasing and -evolving customer data. This is when a CRM solution comes into play. Of course, we are not objective, but our software suites have been specifically designed to analyse your data securely and in full compliance with the requirements of the GDPR! 

The dynamics of Customer Relationship are changing. Enhancing customer experience should be your top priority. CRM solutions will need to be more precise, focus on the needs of customers, quantify and predict customer behaviours, analyse and segment customers, etc.  We will hear much about Customer Intelligence and how to convert data into value-added information. 

So, are you ready to take on this new year while being at the cutting edge of technology? Remember that it is not a matter of deploying every technology available for modernity’s sake. It is all about using these technologies wisely, and using them as a tool to reach your objectives and fulfil your business strategy.