Big data has lived out its buzz status and become a mundane day-to-day reality for a lot of enterprises. We are all aware of the baffling investments made in data driven disruptive technologies over the last decade. We have seen the likes of Facebook and Amazon leverage advanced analytics to reach incredible heights in terms of customer engagement and marketing precision. The point here is that we have had enough time to wrap our heads around big data analytics. It is time we stepped into the data driven future for real. Achieving a unified view of data is an essential element in this pursuit.
The unified view
Silos are your worst enemies when it comes to data governance, analytics, and data driven decision making. It gives birth to inconsistent data quality, fragmented data lineage, multiple ETLs that drag your endeavours down. A unified data view that allows you to govern and manage enterprise data from one platform and ensures seamless queries, consistency, and quality, can make all the difference.
Now, let us take a simple case in point. You run a flower business. You use two different spreadsheets for accounts and customer feedback. Both the sheets have customer details but there is no way to tell whether they are both accurate or at least mutually accurate. There may be terms used in different senses in two different sheets creating inconsistency of definition. Most importantly there can be an erroneous entry in the feedback sheet leading you to ignore the grievances of one of your most regular buyers. You may end up spending money on unfounded marketing efforts. A unified view aims to break these silos and establish effective consistency.
The challenges less talked about
Organizations have been storing data at different places in different formats and using diverse tools and methods for ages. While data storage has become cheap now, that was not the case in, say, 2009. Companies have spent serious amounts of money on data warehouses. They have thousands of workloads with hundreds of queries each, distributed across scores of nodes.
The problem with technology is that it does not let you prepare for a revolution, it just drops a radical change out of the blue. Data storage has become cheaper, there has been massive architectural development, long strides have been made in natural language processing, and computer vision, bringing a tonne of unstructured data into the purview of advanced analytics. You cannot possibly cope with this kind of change with siloed legacy systems that do not communicate with each other. Moving your data to a new architecture is a daunting task too.
The helping hand
Luckily there are tools available that help you migrate your data and the logic associated to it into a modern architecture with relative ease. Snowflake migration, for instance, allows you controlled yet easy access to analytical capabilities.
Time is the most valuable resource when it comes to implementing data management and analytics. The quicker your organization is to turn data into actionable insights, thicker are your chances to use them to make better decisions.