It was just in 2013 that the term “big data” was added to the pages of the Oxford English dictionary, and in such a short time, the big data landscape has exploded with the teraflop scale of data getting generated every day through sensors, social media streams, and countless other devices.
Globally, businesses of all types are starting to see their data as an important asset that can help make their operations more effective and profitable. By 2025, it has been estimated, there could roughly be 463 exabytes of data produced each day. The problem however begins here.
For businesses with large datasets to be able to effectively leverage it, the data must be periodically organized. Sometimes, the enormous volume and variety of data make it hard for organizations to structure the format, classifications, type quality, etc.
Without structure, large repositories of data result in the data getting piled up to an extent where it becomes difficult to categorize mainly due to the volume and data format. Usually, data engineers understand their datasets and are well equipped to structure it. But issues arise when some amount of data classification and analysis happens in an external environment, and context and structure are missing.
The concept of the data lake was first coined by James Dixon in 2014. He said that data lakes are a large body of raw data in a more natural state where different users come to examine it, delve into it, or extract samples from it.
A data lake is an information repository that can hold massive amounts of data that can be either structured, semi-structured, and unstructured. There are no real fixed limits or restrictions on the size of this data. This form of data storage is mainly used because of its simplicity and cost-efficiency.
If you look at it, data lakes primarily offer storage space for those who use transformative data from a variety of sources such as sensor data, social media feeds mobile, cloud applications, and so on. Hence unlike other formats of data storage, data lakes capture the data at its raw form. Usually, the data structure and its demands are not defined until the data is needed. Simply put a data lake equips companies to retrieve and use their data effectively.
Data swamp is nothing but an outcome of undefined data sets from multiple sources. Poor data management and governance When a data becomes oversaturated with a range of data, with no structure or meta tags it results in a data swamp. While data lakes have defined accessibility and structure, a data swamp has almost zero organization or no system altogether.
When enterprises are unable to structure data, they could find that what was once a well-organized data lake is now a data swamp flooded with the information they may never need or information that they would never be able to break down and make sense of. Corporates and media-related business which rely on huge datasets often get into the problem of data swamps as they are unable to process the mere volume.
Some Data Swamps can be cleaned by using Data Curation and Data Governance to organize data sets. However, organizations are beginning to realize that if not done right all the time and effort spent building massive data lakes can be a fruitless exercise if data governance and management are not given importance.
What is data governance?
Data governance supports and facilitating seamless collaboration between IT and business end-users. Through the mapping of metadata to business requirements scalability of an ever-changing and expanding data is ensured; mapping various data formats that are tailored to different personas accelerates data lake deployment.
Data governance is just one component of a comprehensive suite of solution stack that manages, optimizes, and leverages data across formats and sources. Adopting a “governance by design” approach ensures that a data lake delivers ROI and business benefits. Another aspect to keep in mind in the newly emerging era of GDPR and CCPA remaining compliant with the emerging privacy policies is only possible when data governance is ensured.
It is crucial that all organizations handing a huge repository of data, make it mandatory to have structured data governance in place before they start collecting data. Prioritization via data governance should be taken up in the very beginning to ensure that the data stored be of value to the organization and fits into the needs.
To conclude, a perfect world for data processing does not exist. To evaluate the data’s true potential, just collection and storage is never enough as it leads to saturated data. A Data Lake needs Data Governance for organizations to be able to prioritize data and to create the best business context to help them in exploring this data further. There needs to be a data strategy in place to ensure that there is no oversaturation at any level of data collection. Flexibility in data analysis and insights in today's world cannot be ignored and data governance will be a transformative tool in empowering how databases are handled in the future.