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Enable Data Transparency and Company-Wide Efficiency With This One Technology

To be able to scale, optimize, and pioneer, businesses must have a strong relationship with their data. That’s the Data Imperative. While there are challenges along the way, any organization can achieve data transformation by following the five-step roadmap:

1. Strategize: Establish your future

2. Capture: Build a data lake

3. Equip: Enable your org with data

4. Integrate: Connect your data

5. Govern: Manage data, engage people

Beginning a digital business transformation requires creating a vision and strategy for the future of the org’s data. The next step is collecting all the vast, disparate data that flows into and around the organization in one place. Captured, centrally located data is data that can be used everywhere in a business instead of being siloed and segmented across departments. The ability to store it unparsed and draw exactly what is needed, when it’s needed, has brought about industry-shattering products like Uber and e-libraries. And that ability comes from data lakes.

There are a few options when it comes to lage data storage, and data lakes aren’t new to the digital economy. In fact, data lakes can be considered the 2.0 version of Hadoop (the predecessor of modern data storage architecture), and a counterpart to data warehouses.

For some orgs, a combination of data storage resources will work best. But every business should invest in a data lake. This article will tell you why.

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What is a data lake?

A data lake, per AWS, is a centralized repository that allows an organization to store data as is, without having to first structure the data, and run different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning to guide better decisions.

Data lakes can work with structured, semi-structured, or unstructured data from any source, at any time. When flowing into the lake, data is captured in its native format, without restrictions pertaining to on-premise, hybrid, or cloud computing systems. Data lakes also give orgs the power to analyze captured data using a multitude of processing languages, such as Python and SQL.

Consider the advantage of storing data as is, without needing to consider future use cases until, well, the future. That means images, audio, video, tweets, customer profiles, product specs, financial information—any form of data, for any use case, available exactly when anyone with access to the lake delves in.

What’s the difference between "data lake" and "data warehouse?"

Although similar, there are key differences between data lakes and data warehouses. While data lakes offer the freedom of handling any type of data, without structural limits, data warehouses are primarily used for large amounts of structured data. Data warehouses offer valuable insight into relational and repeatable metrics, such as monthly report tracking for web traffic or sales.

In terms of users and data accessibility, data lakes are easily accessible and well-positioned for data scientists, engineers, and those in need of in-depth data analysis.

How to store data in a data lake (4 steps)

Before we dive into the benefits of data lakes, let’s break down how data is filtered through data lakes:

Step 1: Ingestion. In this stage, raw data is collected and “ingested” from various external sources, including IoT, social media, and existing database systems.

Step 2: Landing. In this stage, the previously ingested data is stored with qualifying metatags and identifiers, to be later analyzed by data scientists and engineers.

Step 3: Processing. In this stage, data is analyzed and prepared for utilization.

Step 4: Refinement and Exploration. In this stage, data is segmented and refined by data scientists and engineers for consumption. At this stage, data is ready for insight extraction and advanced analytics.

Businesses can go through one or all of these steps, depending on the size and specifications of particular datasets. In addition to the steps of storing data in a data lake, organizations have options for the data lake’s location:

1. Cloud data lake: This option is great for businesses looking for a modern hands-off approach to data storage.

2. On-site data lake: This option is for businesses who want an on-prem option. This option can offer tighter control systems, but often leads to increased maintenance.

3. Hybrid data lake: This option is for businesses looking for a storage option both on-prem and in the cloud.

A solution we often recommend is Snowflake's data cloud, which offers a deep and broad data lake that is governed, secure, and fast. Its unique architecture separates storage and compute resources, which allows users to independently scale these components based on their specific needs. Snowflake supports a variety of data types and formats, making it versatile for different data processing tasks. Its flexibility extends to deployment options, offering choices between cloud, on-premises, or hybrid setups.

It is important to note that like any technology, data lakes come with risks. However, there are ways to mitigate the inherent risks of large data storage and ensure the organization embodies best practices.

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Benefits of a data lake

Becoming a catalyst for transformation while staying competitive is vital for digital-first companies. The following attributes of data lakes contribute to overall value and ROI for industries across the board.

1.  Simplified data management

Data lakes are both agile and adaptable. The option to store data as is, from any source, without the requirement for previous cleansing, encryption, or schema processing is advantageous for data teams. Quick ingestion, without limits on the type of data stored, allows data scientists and engineers to provide advanced analytics and empower business leaders with the tools to make informed decisions, identify potential organizational gaps, predict trends and new opportunities, and more.

2.  AI and ML incorporation

Strong data drives innovation. Data lake architecture enables scientists and engineers the tools to create intelligent processes and transform historic data as the foundation for machine learning (ML) and artificial intelligence (AI). Even in 2017, Aberdeen identified that organizations that implemented data lakes outperformed similar companies in organic revenue growth thanks to steps like using ML on log files and data from clickstreams, social media, IOT devices,and more—all of which were stored in the data lake. The results allowed these leaders to understand gaps and opportunities at an earlier stage and, again, make better and more proactive choices.

3.  Security and transparency through democratized data

Data governance, the final step in the roadmap to transformation, is crucial for successfully incorporating a data lake into an organization’s work. Governance in a data lake environment gives organizations a strong grasp on access control, data auditing, backup/recovery initiatives, regulation adherence, budget constraints, and democratized data.

Breaking down data constraints allows information to flow freely through an organization without the hindrance of silos. This strategy aids in stronger research and development (R&D), cross-channel functionality, and enhanced collaboration throughout the org.


Data lake storage is just one aspect of a complete data ecosystem. Developing an intelligent strategy to incorporate data pipelines, processing technology, data lakes, and enhanced cybersecurity is vital for digital-first companies seeking a competitive advantage and global scale.

No matter which stage a company, department, or team has achieved on the data transformation roadmap, it’s a challenge for any organization to reconcile their demands from data with the demands of data. To reset, evaluate current resources, and craft a vision for the next step toward data transformation, we recommend a Future State of Data Workshop. Because without equipping the org with rod and reel, the data lake remains unfished, all its prizes hidden beneath the surface.

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