The following article is a guest post.
Data lakes sound simple in concept: an organization simply pools data or information in their original format right into a new data system. This data system—the data lake—should have enough processing speed and have large enough storage capabilities so that the data can be accessed at any time, changed as needed, and then stored on the same system.
However, moving data from their original source onto an enterprise data lake is not without challenges.
For one, the initial on-boarding of huge volumes of data from multiple sources is not only a time-consuming task due to latency issues, it also presents a challenge in data management since raw data takes up more space.
Real-time data replication is also an important element in enterprise data lakes, especially in fields like finance, retail, communications, and logistics.
With all these factors in mind, here are some tips in building a successful data lake.
1. Determine Your Use Case.
Before building a data lake, an organization must first determine their use cases.
Sit down with your entire team and identify all the key users and support users—both human and automated systems, internal and external. Determine their roles and the requirements they need to fulfill these roles. Afterwards, set goals or define problems that the data will help achieve or solve.
The use case document will serve as a modeling technique to define the features needed in your data lake. A use case also makes it easier for all the stakeholders to appreciate the transition, as it can better illustrate the benefits of building a data lake for your business.
2. Data Governance.
Once you know what your priorities are, you now need to determine the architecture of the data lake and how its contents will be governed.
For example, there should be a framework on what kind of data should go into the lake, how they are going to be transferred into the lake, and the protection protocols that should be in place depending on the kind of data.
Another important aspect is metadata capture and management, especially if you don’t want your data lake to turn into a data swamp.
A data lake accepts any data, by default. But without a mechanism to maintain it, your data lake might become bogged down by little details with little value to your organization.
3. User Training and Engagement.
In order for a data lake to be effective, all of its users must have the technical skills to access, interpret, manipulate, transport, manage, and share the information contained within.
Specific skills—such as data management and data governance—may be limited to certain players. Otherwise, all stakeholders should be able to master the basics of using the data lake. It’s also critical to highlight not the data lake itself but the role it plays in business success, such as process optimization or data analytics.
If your organization already has an existing data management system, you would be well advised to integrate the data lake with the current environment instead of completely replacing it. Not only does integration prevent “damage” to the prevailing system, it may also help manage the learning curve and develop a better understanding of the data lake’s purpose.
4. Data Security Strategy.
Developing a security strategy is especially important if your data lake will be a shared platform among business units, or even between internal and external stakeholders. Data privacy and security are critical in things like sensitive personal information, business intelligence, and proprietary information.
You may have to develop specific sets of rules in data access and sharing, depending on the party’s security level clearance and key roles (defined in the use case). For example, some users may be able to access a data pool but not share it with others. This is important when you have multi-tenancy within the organization or if you serve multiple external audiences.
5. Disaster Recovery and Long-Term Plans.
Given how fluid data can be, it’s wise to have a disaster recovery plan should something go wrong. Depending on the different service level agreements (SLA) in place, you may need multiple recovery plans to support each SLA.
Organizations should also accept the fact that data lakes will continuously evolve. From becoming hybrids of data stores to being able to support real-time data processing, even the possibility of building private clouds.
Companies should have a plan on how to capture, store, manage, organize, analyze, and secure data as technology continuously changes.
Constructing a data lake is more than just pouring all the data you have into it. However, once you have a clearer picture of the issues involved and how to address them, it will be easier to build a successful data lake.