Data plays a crucial role in an organization’s daily operations, which is why many workers are spending about 90% of their work week preparing and analyzing their data. Much of these 36 hours per week are being allocated to tasks related to the gathering and preparation of data rather than to data science or data driven application development. This makes data-related activities one of the more inefficient expenditures of time within an organization.
About 54 million data analysts and engineers around the world encounter challenges with the diverse nature and scale of their organization’s data. While roughly 80% of companies are leveraging their data to improve business outcomes, about 44% of data workers experience challenges due to lack of collaboration, inability to adjust to change, and knowledge gaps.
With that, about a third of workers have expressed that they spend the majority of their time preparing data and suffer from slow response times to their requests for additional data. On average, data workers are utilizing at least six data sources and producing about seven different outputs when generating business value from their data. With a growing number of sources, it becomes difficult to organize data and derive the maximum value that is crucial to your company’s growth and achievement of goals. A way to navigate these inefficient tendencies is by implementing a cloud native data lake within your business operations.
A data lake is a centralized repository for all structured and unstructured data. Data from real-time sources such as operational databases, social media platforms, CRM tools, and more are ingested into a data lake for efficient storage at unlimited scale. This allows for data from different sources to be easily joined in order to produce advanced analytical value. In addition, cloud native data lakes are central to a wide variety of services that expose opportunities to explore machine learning and business intelligence.
According to an Aberdeen survey, organizations that implemented a data lake outperformed similar companies by 9% in terms of organic revenue growth. These industry leaders were given the ability to perform different types of analytics that they weren’t previously exposed to such as machine learning with new sources such as log files, data from click-streams, and internet connected devices. This allows them to identify and respond to opportunities for business growth faster by attracting and retaining customers, boosting productivity, proactively maintaining devices, and making informed decisions.
Data lakes provide additional value beyond the improved efficiency of data organization. Other key benefits include:
1. Improved Customer Interactions
Data lakes blend customer data from a CRM platform and incident tickets to allow organizations to understand the most profitable customer cohort, the reason for customer churn, and the promotions that will increase loyalty.
2. Better R&D Innovation Choices
A data lake can help your research and development teams test their hypothesis, refine assumptions, and assess results—such as choosing the best materials in product design resulting in a better understanding of the willingness of customers to pay for different attributes.
3. Greater Operational Efficiencies
The Internet of Things (IoT) presents more ways to gather data on processes such as manufacturing using real-time data. A data lake makes it easier to store and run analytics on machine-generated IoT data to expose new ways to reduce operational costs while increasing quality.
Over time, the use of data lakes will exponentially grow and will continue to protect company data with the security offered by the cloud. According to Markets and Markets, the market for data lakes will be worth almost $9 billion by 2021, while Gartner believes that by 2021, organizations using a strategy of incorporating data lakes and warehouses will support 30% more business use cases than their competitors. This will allow them to quickly explore other business opportunities that may not have presented themselves in the past.
Interested in learning more about how you can implement a data lake in your company? Contact us to speak with a cloud architect or software engineer or fill out the Data Lakes Readiness Assessment down below!
Ryan’s experience includes a concentration in data lakes, AI/ML, and cloud architecture. He values delivering clients the proper tools and expertise to fit their needs. Ryan is an AWS Solutions Architect Associate and is certified with an AWS Machine Learning Specialty.
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