What Investors Ought to Know About Data Lakes: A Quick Guide

Picture of Antonio Banda Antonio Banda , 5 minute read , July 27 2022

If you’ve taken a basic computer course, you might have learned this famous phrase: Garbage in. Garbage out. It’s become so popular that people use it in other references, like diet and exercise and video or audio signal flow. But I digress.

What does the garbage in, garbage out phrase have to do with data lakes? Think of it this way, if you were to build an ideal lake for leisure, would you pump in any water? Probably not. My guess is that you’d want the cleanest, bluest, purest water you could find that would provide an ideal place for swimming, fishing, or whatever activity you like to do at a lake. So similar to the reason to pump good water into an actual lake for an ideal relaxing vacation spot, for example, we want to pump good data into a data lake because it yields ideal results.

Before we discuss SESAMm’s data lake, we’ll cover a few of these basics:

  • What is a data lake?
  • Why is a data lake needed?
  • How does a data lake work?

What is a data lake?

Data lakes are centralized repositories organizations use to store large amounts of unstructured, semi-structured, and structured data.

Data lake vs. data warehouse

The main differences between a data lake and a data warehouse are how they store your data and how the data is used. For example, data warehouses typically store hierarchically structured data in files or folders. In contrast, data lakes use flat architecture and object storage. Also, with a data lake, the data is raw with no specific purpose. But with a data warehouse, the information is structured, filtered, and processed for a particular purpose.

 

Why is a data lake needed?

Organizations like SESAMm employ a data lake for two main reasons: 

  1. Take advantage of advanced and sophisticated analytical techniques applied to complex and diverse data.
  2. Perform data access and retrieval activities more efficiently and easily.

More specifically, companies employ data lakes for simple data management, to store and catalog data securely, and to conduct data analytics. For instance, data lakes allow you to import any data amount from multiple sources in their original format.

They also allow various roles within your organization—business analysts, data developers, and data scientists—to access data sets. Moreover, they can use their preferred frameworks and tools, such as Apache Hadoop, Spark, and Presto, to name a few, without moving data to a separate analytics system.

Furthermore, data lakes allow companies to generate various insights, from reporting on historical data to forecasting likely outcomes through incorporating AI and machine learning models, practices that can prescribe suggested actions to achieve better results.

 

Benefits of a data lake

The biggest benefit of a data lake is that you can ingest your raw data in its native format. This raw unstructured format allows you to use the data in various applications and understand the data from multiple perspectives, running different types of analytics from dashboards and visualizations to big data processing and machine learning. However, if you have a specific intent for your data lake, including applying AI and machine learning, structured data input is ideal.

Another benefit to a data lake is because, according to AWS, “Organizations that successfully generate business value from their data will outperform their peers.” AWS further explains, “An Aberdeen survey saw organizations who implemented a data lake outperforming similar companies by 9% in organic revenue growth. These leaders were able to do new types of analytics like machine learning over new sources like log files, data from click-streams, social media, and internet-connected devices stored in the data lake. This [ability] helped them to identify and act upon opportunities for business growth faster by attracting and retaining customers, boosting productivity, proactively maintaining devices, and making informed decisions.”

 

How a data lake works (not technical)

As an investor, you probably won’t be building your own data lake because that’s what companies like SESAMm are for, but this section will give you a quick overview of how a data lake works. 

You only need a few elements to make a data lake work without getting too technical. First, you need to source data. Sources can include: 

  • Binary data (audio, images, and video)
  • Semi-structured data (CSV, JSON, logs, and XML)
  • Structured data from relational databases (columns and rows)
  • Unstructured data (documents, emails, and PDFs)

Second, you need reliable, secure, and fast data storage for your sourced data. Cloud storage providers could provide better scalability and affordability compared to on-premises solutions. Third, you need an analytics platform to access and analyze your sourced data. There are many open source and commercial platforms to choose from should creating a data lake be of interest to you, but we won’t get into the details here.

Last, you need to store the data in an open format like object storage. Object storage stores data with metadata tags, identifiers that make it easier to locate and retrieve data across regions. Overall, object storage and similar open formats enable many apps to take advantage of the data inexpensively while improving performance.

 

Four reasons SESAMm's data lake provides a unique foundation for data scientists' and investors' use cases

What makes SESAMm’s data lake unique and ideal for investment research and advanced analytics? SESAMm’s data lake is:

  1. Broad and large
  2. Includes more than 100 languages
  3. Tuned to key indicators
  4. Updated in near real-time

Including data since 2008, the data lake consists of more than four million data sources made up of more than 20 billion articles, forums, and messages, such as professional news sites, blogs, and social media, increasing by an average of six million per day. 

Moreover, the coverage is global, with 40% of the sources in English (the U.S. and international) and 60% in multiple languages. We select and curate these sources to maximize coverage of both public and private companies, focusing on quality, quantity, and frequency to ensure a consistently high input value. 

SESAMm’s developers also tune the machine learning algorithms for key indicators such as mention volume, sentiment and emotion, ESG, and SDG. Additionally, they optimize the structure and schema for optimized SQL queries. The data lake is also updated hourly to give investors near real-time insights into their investment interests.

To learn how you can generate alternative data from text using NLP algorithms on our industry-leading, ready-to-use data lake, request a demo today.