How to Create a Successful Data Lake
Data-driven decision making is transforming how businesses and IT operate. As organizations look to access all types of information, they have carved out a need for higher-level infrastructure experts who help unlock new value from their data. The modern-day DBA has an opportunity to be an in-house expert and operate as a strategic business partner for managing this data and ensuring it is available to those who need it. To do this, in addition to building up their cloud and DevOps skills, many DBAs are delivering on this opportunity by turning to data lakes, a large repository into which data—in its raw, natural form—flows from many sources. Users across an organization can then access and analyze the centralized data.
The true power of a data lake shines when you can maximize adoption across your enterprise so that big data informs as many business decisions as possible. To create your own data lake, you’ll need to decide on platforms and data sources, but, most importantly, you’ll need to determine how you can present the data lake to stakeholders to increase adoption across the organization.
What is a Data Lake & Do You Need One?
Enterprises in all industries and of all sizes are creating data lakes to address a fundamental need for making business decisions. However, the amount of data collected has continued to increase along with the demands for analysis from different business units.
Traditionally, IT departments and data scientists would have conducted this research and processing, but the demand now far outweighs the supply. IT departments often push back against requests for data analysis because they simply don’t have the resources, and that results in decisions being made without all the available information. The solution is to enable many people to access the data in its raw form and conduct their own specific analyses as and when they need.
The data lake was created to capture all raw data from an enterprise while allowing multiple users to tap into the repository and draw their own conclusions. Data lakes allow for self-service, and with analytic tools continuing to improve, a degree in data science is no longer needed to make sense of the raw data and gather the information business units require.
If your company is getting bogged down in data, and the requests for analysis are becoming too much for employees to handle, it’s time to consider the benefits of a data lake for your enterprise data management.
Get More From Your Big Data
To ensure that people adopt the data lake, you need to select an interface that speaks to differing levels of expertise. Include options to rank data based on quality, and allow users to choose the dataset they desire based on the available fields or data characteristics.
Without ensuring ease of use, you won’t have full adoption by your colleagues, which will turn your data lake into a data swamp with tons of untouched, uncharacterized, and unorganized data that users don’t know how to manipulate. Data swamps are typically a symptom of poor data governance and missing contextual metadata to help keep things curated.
In addition to the interface, you need to choose:
- the platform that fits your enterprise
- the data sources from silos in your company
Many cloud solutions are available to act as a platform for your data lake, and using a cloud service provider is advantageous in the scalability and the cost-effectiveness compared to housing all the data on-premises.
You are positioned to perfectly understand the obstacles to capturing the maximum number of data sources. Some business units may be prone to hoarding data, and so you need to ensure that all potential data sources are flowing into the data lake.
Use Proven Strategies to Optimize Your Enterprise Data Management
Rather than starting from scratch, get the entire roadmap, in full detail, for creating your own data lake to:
- maximize the number of decisions made based on data
- deliver hard savings of 30% to 50%
- reduce your daily management time by up to 60%
Download the O’Reilly digital book: Strategies for Building an Enterprise Data Lake.