Demystifying Data Lakes For SMBs Leveraging Big Data
When most data scientists talk about challenges with rising data demands, you assume this only affects large enterprises. However, 95% of businesses need to manage unstructured data, but many of them don’t know how.
Small-to-midsize businesses (SMBs) are now taking advantage of business intelligence, analytics and big data, which is causing a rise in their need for data management. These businesses now turn to data lakes to provide better data availability and more efficiency.
Data lakes are storage and processing environments which allow organizations to store structured and unstructured data from a variety of sources. Data from internet of things (IoT) devices, social media analytics, and third-party demographics can all be stored in a single location, regardless of source or format.
Traditionally, data lakes were only used by enterprises due to their difficulty to deploy and manage, but new technology is presenting more scalable, cost-effective data lake options for the mid-market.
Rising SMB Data Demands
The use of predictive analytics, content analytics and business intelligence is rapidly rising. The worldwide big data market is estimated to be worth $77 billion by 2023. Startups and midsize businesses are finding new ways to use data to improve customer experience and boost bottom lines.
For example, Carvana uses big data to predict if cars purchased at auction were good values. With a staff of only 50, Carvana was able to leverage big data solutions to make smarter purchases, leverage analytics to discover regional customer preferences and leverage other tools to help them improve car purchases and reduce financing risk.
Their data scientists built a predictive analytics system that used car information, regional customer preferences and model availabilities to determine which cars at auction would be worth bidding on and which would not meet their quality standards. They also mined customer data beyond the usual credit check by scanning hundreds of variables across several databases to predict default loans and better tailor interest rates.
With the number of SaaS solutions increasing and SMBs leveraging analytics, business intelligence, IoT and other data sources, businesses of all sizes are now dealing with a rapid increase in data sources and a need to manage disparate data sets. As the data demands of startups and midsize businesses increase, these organizations need to find a way to build an infrastructure that simplifies data management while maintaining scalability and cost-effectiveness.