The digital universe is expanding exponentially making data the most sought after fuel of the economy. All kinds of data – structured, unstructured or semi-structured – are being produced and need a place to be stored for quicker processing.
For anybody that has in some way or other worked or learned about big data, ‘data lake’ won’t be a foreign term. For those that aren’t involved in big data, the term data lake would hint at some kind of data storage. Big amounts of data require different and higher capacities of storage and data lake is one of them.
What is a data lake?
When not processed for bottled water, a lake is a natural body of water that allows all kinds of water into it.
A data lake is a vault where you can store all your data whether structured or unstructured at any scale. The data can be stored in its rawest form without having to be structured, processed or analysed. Along with this, data can be accepted and retained from all data sources in a data lake since it supports all types of data.
How is it different from a data warehouse?
A data warehouse would be a more refined version of a data lake. All data is stored in an organised manner with everything in order and archived correctly. When the data comes in, it is analysed and checked to know what its purpose to business processes would be. Only when there is a purpose the data is solving, it is added to the warehouse. The structure and scheme of the data are established so it can ready to use for analysis and operations. Another major distinction is that a data warehouse stores data that comes from transactional systems and business applications.
Here is where data lake particularly stands out. It stores data that comes from non-relational data such as mobile apps, IoT devices, and social media as well. This data can be tried with different types of analytics such as SQL queries, full-text search, real-time analytics, machine learning, etc which could give fruitful insights.
What are its benefits?
A Data Lake will empower faster decision making since there are all kinds of data available that can be analyzed un multiple ways which can yield better results. A data lake brings these advantages to an organisation:
Better customer interactions
Since a data lake accepts data from all kinds of sources, the CRM platform could be fed with customer data that has become from social media analytics and marketing platforms with buying history. This would mean that better insights on customers would make promotions and rewards better and hence result in increased customer loyalty.
A good source for R&D innovation
Since the data is not structured or has a hierarchy, it would be easier for the R&D department to test their theories on this data and ascertain results. Since they are able to test their theories, the results will help reach a successful result that would benefit business processes.
More efficiency in operations
As the IoT revolution calls for more data collection, a data lake makes it possible to store data and run analytics on that data easily. This will bring more clarity and insight into operational costs and quality.
What are the challenges?
The one thing that a data lake can lead to is a data swamp. Since the data is raw and there is not any restriction on what content is stored, it can be difficult to find the right data. Without the right data, it will be tougher to draw up insights and solutions. Hence, a data lake needs some kind of regulation so the assortment of data is easier and not completely off track.
Summing Up…
We are beyond the stage of deciding whether a data lake is needed by an organisation. The question is how do you plan to use it? With advantages for both analytics and operational reporting, your use of a data lake determines how efficient are your business processes. If, however, you are in the deciding stage, just determine your goal and see if a data lake achieves that!