It’s one thing to understand the principles of big data analytics, but it’s another to ensure projects are successful.
As an early adopter of advanced and predictive data analytics, Hotels.com has gained in-depth knowledge on providing consumers with a personalised user experience and custom-made recommendations.
Drawing on data from clickstreams, reviews, personal preferences, and hotel profiles, we are able to build algorithms behind the booking procedure that understand the customer journey beyond just one session.
Here are five key things that helped make big data analytics projects successful, which could help any business.
More than big data
Big data is closely linked to analytics, but think beyond just big data. One of the most important uses of data is to deliver a cross-device user experience for multiple screens and independent from where they are.
One part is analysing the user data in order to determine which devices are being used. The other part is logging data in order to be able to recognise the destinations each specific user has been searching for on the company’s booking platform, and to ensure that customers are recognised on any device so they can pick up where they left off in their search for a hotel.
This allows users to begin looking for accommodation, for example, on a tablet while travelling by train, before confirming the booking on a desktop at home without the need to start the booking journey again.
Choose the right platform
We all know that the right technology platform makes or breaks an IT project; the same can be applied to big data and analytics. So how do we make the right choice?
A technology decision should be based on a thorough assessment of business needs and deciding what could benefit from data analytics in the future.
CIOs also shouldn’t focus purely on investment costs when choosing the technology. The evaluation should also include performance, reliability, usability, data security and, most importantly, scalability.
Get the bosses on board
A big data and analytics project requires both investment and cross-company collaboration; silo thinking could be an obstacle to long-term success.
Data privacy comes first
Customer trust is a precious commodity and respect for data privacy is one important key to success. Therefore CIOs should ensure that their big data analytics strategy is carefully balanced with a commitment to protect customer data security.
The use of anonymisation is vital to protect every user’s privacy, especially when analysing large quantities of aggregated data.
Make data analytics business as usual
Regarding cost, time and organisational restraints, project leaders should always keep sight of long-term development to build a reliable, efficient and future proof platform.
Scalability is an important component in long-term planning to ensure that the technological platform is able to keep pace with the ever increasing flood of structured and unstructured data.
CIOs should start to integrate the analytics processes into everyday business once the platform has been established and first use cases have yielded results. Only when this has become business as usual should IT and data teams address new projects to advance the company’s business even further.
Chief Analytics Officer Europe
15% off with code 7WDCAO17
Chief Analytics Officer Spring 2017
15% off with code MP15
Big Data and Analytics for Healthcare Philadelphia
$200 off with code DATA200
10% off with code 7WDATASMX
Data Science Congress 2017
20% off with code 7wdata_DSC2017