What Is a Data Lake?
Key Concepts and Benefits

A data lake is a central repository of large volumes of data that’s stored in its original form. Most of that data is raw and unprocessed. Examples include:

• Social media posts and reactions
• Images
• Sensor data
• Log files
• Financial data
• Physician’s notes
• IoT data and all kinds of text data in documents, emails, and product reviews
• And more!

Data lakes can also store structured and semi-structured information. This data can then be processed (i.e., cleaned, organised, and transformed) and used for data analytics, AI/machine learning, and customer experience personalisation.

That all adds up to insights companies can use for a competitive advantage. In fact, data-leading companies experience a whopping 89% improvement in customer acquisition and retention. Now, that’s a solid way for businesses to get ahead and stay there.

Data lakes also make data management easier. Experts estimate that unstructured data makes up 80 to 90% of all data, meaning organisations that cannot process and analyse it aren’t getting the full picture of their business. Additionally, Forrester predicts that the amount of unstructured data enterprises manage will double in 2024. Data lakes provide an affordable, agile environment to store all this information without having to process and structure it first – saving time and money.

Offering convenient storage, scalability, and cost-efficiency, data lakes help businesses realise the full potential of data in numerous ways.

• Centralised data storage
  Data lakes store a range of raw, unprocessed data in one central place. This saves organisations the time and the challenge of performing complex data transformations or organising data into predefined schemas (i.e., a specific order) beforehand, making data storage convenient and accessible.
• Data unification and analysis
  Data lakes are treasure troves of raw information that data scientists can turn into insights for decisions. They bring together data from multiple sources, both internal – like your CRM or ERP systems – and external, such as websites and social media. Unifying all this data in one location breaks down data silos that prevent companies from getting holistic views of their business health and a full understanding of customers. Through tools like Data Cloud, you can unify and activate your data across customer interactions and make the most of your data lake investment.
• Trusted AI enablement
  Data lakes let you build AI initiatives on a vast and diverse data foundation. That foundation is ideal for training AI and machine learning models to personalise customer experiences, make predictions, inform decision-making, and offer real-time recommendations.
• Scalability and cost efficiency
  Data lakes can store structured, semi-structured, and unstructured data without extensive data transformation or schema changes. This flexibility eliminates the need for costly data pre-processing, reducing overall storage and maintenance costs. Data lakes can even provide data lineage (i.e., lifecycle of data), metadata management, and access controls that lower the risks and costs of governance challenges. Cloud-based data lakes provide the flexibility to scale up storage capacity as your data grows. And pay-as-you-go models only charge you for what you use, reducing upfront costs.

Data lakes provide a central repository for storing diverse datasets from sources, ranging from CRM and ERP systems to social media applications and web and mobile applications. With these deep lakes of information, data scientists can perform analysis and advanced queries to expose previously hidden trends and gain business intelligence that can be used for innovation.

The data stored in data lakes provides a great foundation for developing and training machine learning models and AI applications. Nine in 10 analytics and IT leaders agree that AI is only as good as the data it is built on. AI thrives on diverse and significant volumes of data to drive accurate and comprehensive models. Since data lakes integrate with machine learning platforms and frameworks, training and deploying AI models with data lakes can be managed efficiently.

Data lakes help leaders make decisions grounded in a deep understanding of their businesses since they combine data from diverse sources. Plus, they can use tools to search, filter, and visualise data stored in the lake to make informed decisions about things like when to launch a new product, where to cut costs, or how to optimise inventory levels. Additionally, organisations can pinpoint anomalies and get ahead of emerging trends in real time by analysing data continuously as it flows into the lake. And by powering AI and machine learning models with data stored in data lakes, you can get recommendations to streamline decision-making.

Here are a few industry use cases in action:

• Customer experiences (Data exploration) A retailer can collect data from all the different ways a customer interacts with the brand – via a website, in person, on social media, via mobile, and more – to create a personalised omnichannel experience for each customer.
• Customer churn prediction (AI models) A telecommunications business can integrate customer data, call logs, billing information, and social media interactions from data lakes. Then, using machine learning techniques, it can train an AI model to identify factors that contribute to customer churn and make real-time predictions to reduce churn.
• Patient treatment (Decision-making) Healthcare organisations can store many types of data in a data lake, including records, images, and even research papers. Providers can then use predictive modeling to inform patient care.

Data ingestion is the process of collecting and importing data from different sources into a data lake. These sources include structured data from databases, unstructured data from documents or social media, and semi-structured data from logs or sensor readings. The data is stored as is, without any specific order, so it can be explored and analysed in its original state.

Once the data is in the data lake, it can be processed and changed to make it easier to understand and use for analysis. Processing involves filtering, combining, or summarising data to find meaningful insights. Transformation converts the raw data into a more organised format, like tables or columns, allowing quick and accurate analysis.

Data governance and security are critical to maintaining the integrity of data lake architecture. Data governance entails rules and processes to manage data properly and adhere to compliance. These rules include who owns the data, who can access it, and how long it should be kept. Companies use security measures like encryption, authentication, and authorisation to protect the data from being accessed by unauthorised people or stolen.

• Definition: A data lake is a vast reservoir that stores raw and unprocessed data from numerous sources. It allows data to be stored as-is, without predefined structures.
• Use cases: Use data lakes when you need to store and explore vast amounts of diverse data, such as social media feeds, sensor data, or log files. For instance, a healthcare provider might use a data lake to store patient records, medical images, and research data.
• Pros and cons: Data lakes offer the benefit of storing raw data in its original form, which provides flexibility for data exploration. However, they can become a "data swamp" if data quality, governance, and security aren’t properly managed.

• Definition: A data warehouse is a structured database that stores processed and organised data, often following a predefined order. It’s designed for efficient querying and analysis, with data organised into tables and columns.
• Use cases: Use data warehouses when you need to analyse historical data for reporting, business intelligence, and decision-making. For example, a retail company might use a data warehouse to evaluate sales trends, customer behavior, and inventory management.
• Pros and cons: Data warehouses, with their structured approach, provide fast and reliable querying capabilities but lack agility in handling large volumes of unstructured or rapidly changing data.

• Definition: A data lakehouse combines the best of both worlds. It stores raw data like a data lake but also incorporates structured elements like a data warehouse. Data Cloud is a prominent example of a data lakehouse.
• Use cases: Use data lakehouses when you need to combine the flexibility of data lakes with the structured querying capabilities of data warehouses. This hybrid approach is ideal for real-time analytics, machine learning, and data exploration.
• Pros and cons: Data lakehouses aim to bridge the gap between data lakes and data warehouses, pairing the flexibility of data lakes with the structured querying of data warehouses. However, implementing and managing a data lakehouse can be complex and require careful planning.

In a business world where data means differentiation, harnessing its full potential is the key to maintaining competitive advantage. Data lakes help companies do just that, storing vast amounts of unprocessed data that can fuel AI innovation, personalise customer experiences, inform decisions, and mitigate risks. With solutions like Data Cloud, which integrates your data lake with your CRM, you can ensure that data doesn’t sit idle in a silo but rather gets used to its fullest potential. Discover how Data Cloud can help you improve the return on your data lake investment.