In today’s business landscape, the volume and availability of different data types have become enormous, creating businesses’ need to expand their data-management infrastructures swiftly. In this context, data lakes come into the picture. It is a new type of data-management system in today’s digital age. It bears a strong potential to keep data safe.
The steep growth in computer processing power increased cloud storage capacity, and advanced network connectivity has transformed the data into a stream of information about various customer aspects.
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With the information, businesses can know about their customers’ tastes and preferences, buying habits, and feedback. The information also carries sales and e-commerce data, apart from email and web analytics.
Many sources contribute to the information acquisition process. The information sources include Internet-of-Things (IoT) devices, social media sites, sales, and CRM systems.
With increasing data sources, the demand for strong analytics is surfacing across industries. Businesses cannot leverage the entire benefits of data just through analytics alone in the highly competitive environment. They should also transform their businesses into insights-powered enterprises.
What is the Relevance of Data Lakes?
You can regard a data lake as a repository of data that includes several types of data, such as structured (relational), semi-structured (XML, JSON), and unstructured (images, audio, and video).
Data lake acts as a storehouse for all raw data in a single place at a low cost. Often, distributed file systems, like Hadoop File System (HDFS), serve the purpose of a data lake.
Hadoop acted as the first version of the data lake platform. The latest version of the data lake is cloud-based. It is gaining significant popularity in the market because of its advantages of storage flexibility, resource management, and data protection.
Many large businesses will experience more data flow from data lakes to data warehouses in the years to come. Industry experts anticipate that data lakes will be critical components of the majority of businesses in the future.
The anticipation also indicates the typical mindset of business owners regarding data warehouses as the standard for data management will change in the days to come.
However, owning a data lake will not solve all problems. With a wide proliferation of data lakes, businesses are likely to face bigger challenges in maintaining huge data lakes.
Bear in mind that if your data lake is not well-curated, it might take the form of a data swamp to flood your business with information that is difficult to locate and understand.
Ways to Keep Data Lakes Relevant
The moot concern is that you need to maintain the relevance of your data lake. When you embark upon a digital transformation for your business, the process is not so simple.
It involves identifying authentic and accurate data sources in your business to capitalize on increasing volumes of data. The data authenticity is also necessary to drive actionable insights to help your managers take prudent decisions, which are likely to push your business’s growth.
You can keep your data lake relevant through the following ways:
Identify Your Data Goal
The first step to keeping your data lake clean, dynamic, and viable is identifying your data goal. Thereafter, set clear definitions for the type of information you collect and intend to do what with the data. It is one of the most important steps to avoid data swamps in your business.
But, it would help if you did not focus on only collecting a huge volume of data. It would be best if you also had clarity on what you want to get from the data.
Implement a clear data strategy to avoid data silos, and incorporate a data-driven culture. It will help you remain customer-centric and cater to the needs of the modern-day data environment.
Formulate Data Governance, Privacy, and Security Policy
In a data lake strategy, data governance and metadata management play a prominent role. They make the strategy effective and efficient.
A prudent data lake strategy leads to clean, easily accessible, trusted, and secure data. As such, data becomes high quality, and users will find it easy to understand. At the same time, you should put in place staff to take responsibility for data.
Leverage Automation and AI Capabilities
Artificial Intelligence (AI) and automation are now the key imperatives in data lake strategy. It has become essential to automate data and transformation processes due to multiple data types and high velocity while data moves to the data lake.
You can leverage next-generation data integration and enterprise data warehousing (EDW) tools to manage your data lake.
AI and machine learning can help businesses classify and analyze data at high speed and learn from the data better.
DevOps processes go hand in hand while building and maintaining a healthy data lake. Establish clear guidelines about where and how to collect data to prevent data wildness.
Ensure that your staff follows the standards when it comes to your data lake. Spend some time evaluating the data sources, and allow only the trusted sources to stay. Discard the untrustworthy data sources as they will bring in problems.
Actuate preventive measures to ensure that the trusted sources remain trustworthy. A little effort on the front end will be highly valuable while putting data to use.
Incorporate Modern Data Architecture
Today’s data-driven businesses need modern data architecture as the old models do not satisfy the needs.
Retaining and re-using the huge amount of unstructured data have posed severe challenges to businesses.
Data profiling is a way to deal with the challenges by enabling businesses to understand the current data assets and conditions.
It also allows you to scan, classify, and index structured and unstructured data residing in different sources.
In this way, you can have an information database to manage better to elicit maximum value for your business.
It is possible that the data, one of your business departments collects, can also provide value to other departments. But, users might not have an understanding of its entire uses in your business.
You can make a data catalog for your business, which can help manage and maintain the existing datasets and KPI glossary to enable easy access to all the available data within your business to get the maximum benefit out of it.
You can protect your active and inactive data on the cloud with a proper data backup facility. With a data archive solution, you can process continuous data for longer periods and at a minimal cost. Cloud vendors have tools and technologies to archive data robustly for businesses.
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