One of the greatest Big Data challenges ahead of us is the scarcity in the available talent. The right Big Data scientist is a multi-skilled person that understands the world of IT and business and has the right creativity to develop difficult, technical, solutions that really help a data-driven, information-centric organisation. However, this mix of talent at the intersection of business, information technology, data sciences and operations can be difficult to identify and develop. For most of the companies it is a true challenge to find the right Big Data talent. Therefore organisations should start paying attention to the available talent in house and retrain them to match the requirements of today’s fact-changing environment.
Creating the right training programs to have them learn the right methodologies and gain the right skills takes time, but when you start today, you are ready when your companies will likely need it most. Data, and especially Big Data, is here to stay and will only require more skilled personnel. So the earlier you start developing the right talent, the better you will be of in the data-driven future. On the other hand, fortunately, more and more universities around the world are developing Big Data programs to create this multi-skilled talent.
In this video, developed by PWC, they address this major challenge and explain how companies could deal with it. Apart from starting to train the talent, they should also organize their talent in a different, more innovative, way to create the right results. Creating a team that consists of the different skills, added with outside experts to match the missing skills, will help organisations create a work-around the expensive and scarce Big Data scientist. Many companies. already have different talents within their organisation that could easily work together on solving Big Data challenges if the context is correct.
Big Data is a challenge for almost all companies, but with the right talent and the right mindset, organisations can achieve great results.