Getting a grip on your expanding data mountain
Hidden within every company's avalanche of data there lies an enormous potential for optimisation. The secret to realising this potential lies in building an ETL process (Extract, Transform, Load) driven data warehouse that automatically collects information and provides this in a bespoke form. Because by combining various data sources, important discoveries can be made.
Big Data commonly refers to large, rapidly incoming volumes of data from social media, machine data, logs, etc. These new data sources are characterised not only by their volume, but also by their heterogeneity. But traditional database and data warehouse systems in this environment quickly reach their limits. By using Big Data stores such as Hadoop, NoSQL, or MPP databases together with Data Lake, previously untapped data sources can now be leveraged.
But the only way to really strike gold here is by combining data sources with each other. This combination of data warehousing and Big Data is called data blending. Modern analysis platforms permit blending at the data integration level without having to cache the data. The problem, though, is that data integration often entails a lot of time and effort. This is, however, worth it because in the end you gain a holistic view of customers, processes and products while at the same time gaining valuable perspectives and keeping an eye on the bigger picture.
By obtaining more business information from a larger source of data, new business models and growth opportunities can be opened up for innovative companies.