Data Analytics

Making sense of Big Data is the domain of Data Analytics. There are various tools and techniques which are deployed in order to collect, transform, cleanse, classify and convert that data into easily understandable data visualization and reporting formats.

Data Analytics refers to the set of quantitative and qualitative approach in order to derive valuable insights from data. It involves many processes that include extracting data, categorizing it in order to analyze the various patterns, relations, connections and other such valuable insights from it. Today almost every organization has morphed itself into a data-driven organization and this means they are deploying an approach in order to collect more data that is related to the customers, markets and business processes. This data is then categorized, stored and analyzed in order to make sense of it and derive valuable insights out of it.

Data analytics opens the door to an amazing view of your customers and their behaviours, leading to a universe of insights and digital marketing benefits.

Improve personalisation and targeting in real time.

Live traffic visualisations show you the customer journey instantly, improving your ability to act with agility to personalise and target campaigns, content and experiences at the right time, through the right device and with the right message and offer.

Get a complete customer view across channels.

Through tightly integrated data sources, you can build a 360-degree customer profile that will drive better campaign and experience personalisation, targeting and delivery decisions.

Create rewarding future experiences.

You can anticipate customer and audience behaviour and proactively create engaging experiences by infusing data analytics with predictive intelligence capabilities.

Do more with your audiences.

With data fuelling audience segmentation, you can clearly identify common behaviours and traits that help shape high-value and look-alike audiences.


Data analytics (DA) is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions and by scientists and researchers to verify or disprove scientific models, theories and hypotheses.

As a term, data analytics predominantly refers to an assortment of applications, from basic business intelligence (BI), reporting and online analytical processing (OLAP) to various forms of advanced analytics. In that sense, it's similar in nature to business analytics, another umbrella term for approaches to analyzing data -- with the difference that the latter is oriented to business uses, while data analytics has a broader focus. The expansive view of the term isn't universal, though: In some cases, people use data analytics specifically to mean advanced analytics, treating BI as a separate category.

Data analytics initiatives can help businesses increase revenues, improve operational efficiency, optimize marketing campaigns and customer service efforts, respond more quickly to emerging market trends and gain a competitive edge over rivals -- all with the ultimate goal of boosting business performance. Depending on the particular application, the data that's analyzed can consist of either historical records or new information that has been processed for real-time analytics uses. In addition, it can come from a mix of internal systems and external data sources.