The art of capturing information in data lakes: An outlook of newer realms of research in this domain

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Introduction

In the present times, the value of a data lake to businesses is increasing because of its unprecedented ability to derive insights from huge data sets. The capabilities of a data lake that enable a business to process, analyze, and visualize voluminous amounts of data add great value to business management.

A shift from the traditional data warehouse

It is important to discuss how a data lake differs from traditional data warehouses. The traditional data warehouses lack the feature of scalability and flexibility that a data lake provides. A data lake is also a much more cost-effective option as compared to legacy warehouses. With its high performance and suitability to structured data sets, a data lake has the potential to process various streams of enterprise data with a lot of ease. Designed to support functions of ETL, that is, extract, transform, and load, a data lake provides one of the most effective analytics platforms to work with.

How do data lakes capture information?

Data lakes capture new information from various kinds of business operations. Let’s take a look at some of these in deeper detail. When it comes to sales and marketing, the amount of data generated is extremely diverse. For better customer experience, customer interaction, and support, it is important to get deep insights into this data. This insight can lead to long-term customer engagement and customer retention. One of the most potential functions of a data lake is that it integrates the marketing platform with the customer management platform. This not only enables us to target customers with better recommendations but also helps us to position a brand effectively among the digital audience.

A data lake also enables us to carry out predictive analytics and graph analytics with surgical precision. With the help of time series analysis, a dynamic model can be conceived which may be used to construct long-term relationships with clients and predict their future interests in various products. This also acts as a precursor for effective digital and social marketing. With the help of graph analytics, a clear picture of customer choices is presented and this information enables a business to frame a future roadmap for its business strategies.

Newer realms of research

The newer realms of research in this domain are leading to better development of various products and services. The various types of improvements and innovations with the help of machine learning techniques are leading to valuable analysis and significant insights. We are also exploring how the cloud environs could lead to extensive modeling and design of data products. The transformation to cloud may be the next big thing for analytics in general and data discovery in particular. It is important to mention some of the emerging areas of research in this domain. The extensive modeling of various biochemical processes is being carried out which is leading to pharmaceutical drug inventions. At a micro level, biochemical processes involve very large data sets for capturing information related to DNA proteins. This is especially important for finding new ways and means to treat a particular genre of diseases.

Clearing the air around data lakes

There has been a lot of criticism related to data lakes and the critics have gone to the extent of calling modern data lakes as “data swamps”. This is especially true for those business organizations that have failed to fetch the variable returns after significant investment in data lakes. However, this does not mean that the ability of data lakes to collect, store and analyze data can be doubted. Given the dynamic and competitive nature of the business market, organizations are very susceptible to losses. It has been noticed that such organizations put the burden of their failure on data lakes rather than other critical factors of the business ecosystem.

The reliability, performance, scalability, and agility of data lakes are a testimony to the kind of processing capabilities that they provide. With enhanced security features, a data lake can successfully cater to even the sensitive information of an organization. When it comes to disaster recovery options, the platform of a data lake is the most trustworthy option.

The road ahead

The effective integration of the cloud environments with data lakes is what is needed in the future. Not only would it improve the performance and scalability but would also lead to improved agility and analytics. The processing capabilities of the data lakes would also be boosted in the long run. So, the future of data lakes is quite secure with cloud transformation.

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