Big Data

Demystifying Data Lakes For SMBs Leveraging Big Data

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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.

Data Lakes For Midsize Businesses

Data lakes have been in use by enterprises for years as a flexible way to allow data storage, sharing and management. Recently, SMBs lacking the budget or technical expertise to set up an effective data lake have found great success in deploying cloud-based data lakes.

A recent Aberdeen report found the main benefits of data lakes were increased operational efficiency (43%), data available from departmental silos, mainframe and legacy systems (32%), lower transactional costs (27%), and the ability to offload capacity from mainframe/data warehouse (26%).

Below are some ways data lakes can benefit your company:

• Increased Analytic Capabilities: Data lakes allow organizations to build custom schema to create specific queries and reports. Since data is unstructured, businesses have the ability to draw on all data available to the organization from various sources and departments to create much richer data sets and make new connections.

• Wider Data Availability and Diversity: Data lakes provide the ability to store a variety of data that isn’t siloed. This creates more data availability across departments, as the entire organization has access to a unified data storage solution. This also allows each department to leverage more diverse sets of data from various sources they would traditionally struggle to access.

• More Efficient and Agile Data: By receiving and storing data in its native form, data lakes help reduce the processing required to move data through solutions. This creates more efficient, agile data management.

Making Data Lakes Work For SMBs

The main reason more SMBs aren’t taking advantage of the above benefits is that building a data lake on their own requires a lot of knowledge, expertise and time. However, SMBs willing to rethink the way they manage data can find ways to reduce costs and create a more flexible big data architecture while also scaling up with the organization’s data demands.

Here are a few steps SMBs can take to improve their use of data lakes:

• Build Data Lakes With Clear Use Cases in Mind: Data preparation and ingestion can be a costly and time-intensive part of any new data initiative. Finding a way to limit the strain on data engineers can have a major effect on the bottom-line of any data lake project. By approaching your data lake initiative with clear schema and analytics in mind, you ensure the upfront work done by your data engineers is valuable to your company goals and will be immediately put to use by your departments.

• Organize Data Lake Into Departmental Silos: The ability to access all unstructured company data from one source can be valuable, but it can also make it difficult for each department to find the data most relevant to their needs. The use of data pools — multicloud, interconnected mini-data lakes — allow each department to maintain access to their most relevant data while still benefiting from the unstructured data available in the data lake.

• Increase Agility Through IaaS: According to Ovum, 45% of companies run some big data workloads in the cloud. The next logical step is to run a cloud-based data lake that can more easily leverage artificial intelligence, machine learning and analytics to simplify how SMBs process and utilize big data. Setting up a cloud-based data lake with flexible options for deployment, hosting and architecture can create a more agile and simplified data lake.

Simplifying data lake setup and management is crucial for anyone looking to leverage the benefits of data lakes. While data lakes may have been initially designed for large enterprises with major data demands, SMBs now have the opportunity to take advantage of their rising data usage to gain a real competitive advantage.

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