How Will Your Enterprise Use Data Lakes?
Companies vary in their approach to data strategy. Some rely on relatively few sources where traditional data warehouse solutions work well. However, many enterprises are now discovering the value that lies in previously untapped sources, using new data and advanced analytics to unlock strategic value from their data assets.
To perform these processes, they need an effective place to store the raw content that will feed their tools and applications. This is where data lakes take center stage, holding massive stores of information in largely unsorted and unstructured forms. Between 2016 and 2021, the data lake market is set to rise from $2.53 billion to $8.81 billion.
The benefits of incorporating data lakes as part of a broader data strategy are numerous and are derived from their biggest advantage – flexibility. By collecting data in its raw, unstructured form, far greater streams of data can be available for analysis. This allows enterprises to derive real value from all their data – leveraging advanced and predictive analytics solutions that can drive direct revenue or cost saving impact to lines of business.
As volumes of data continue to grow exponentially, however, companies are increasingly realizing the need for a more agile and unstructured way to manage it. Anyone who has been looking at big data for any length of time is already likely to be overexposed to the “data lake vs. data swamp” analogy. The trick to making these resources deliver value is finding ways to ensure the integrity and veracity of the content, ensuring the truth doesn’t get lost in the constant input from multiple sources.
Data quality processes are becoming a central part of the data lake concept. Companies that segment their lakes into different zones and implement other sorting functions on incoming data may have an easier time detecting discrepancies and deriving valuable information right away.
In the years ahead, companies will implement top-down policies to get their new and larger data reserves under control. Implemented correctly, these operations can help organizations see increased value, while retaining the flexibility of a less controlled and less structured environment.
Maximizing the data lake experience takes three separate processes. First, companies must establish a shared vision that suits every department of users, ensuring no team goes rogue and undermines the system. Then, firms must develop new and different skills among employees to suit the unique data science landscape. Third, teams must ensure there is strong data governance, with a style that suits the catch-all nature of data lakes.
Learn more about these three developmental areas by downloading our 2018 Technology Vision report on data lakes.