Every business collects a massive amount of data, including everything from website and email analytics, to product sales data and customer contact information.
This data is typically collected to fulfil a single, siloed task, for example, product sales data is collected to enable you to bill your customers correctly.
However, this data can be analysed to gain business insights that can drive better decision-making and find trends that can help you outperform the competition. Using the above example, this sales data can be enriched with email open rates and website analytics to understand how these channels impact the likelihood of purchasing.
With so many potential data sources, there is a need to store this data in a single location where the data can be transformed and analysed. This is where the concept of data lakes becomes relevant.
A data lake is a system to store structured and unstructured data connecting to multiple sources, designed specifically for analysis.
Breaking this statement down, the structured data in a data lake may include relational databases, such as the database behind a CRM solution. The unstructured data may include clickstream data from a company’s website.
A data lake can connect to a wide variety of data sources, both cloud-based and on-premises. Finally, as a data lake is specifically designed for analysis, it allows for data to be queried and transformed for machine learning, predictive analytics and visualisation.
The data lake is supported by connectors, which are services that import data from a source into the data lake. These can be either on-premises or cloud-based sources with a wide variety of sync settings, including everything up to real-time data ingestion.
Once the data is ingested, it is then stored, and ready for analysis. While it is possible to have an on-premises data lake, it is more common to utilise a cloud service, as it means that the storage can be scaled indefinitely. Cybersecurity is incredibly important when considering a data lake, as a potential breach would be wide-reaching, with so much data being stored.
Although it is possible to bring any and all data into a data lake, businesses need to decide which data is important and how they will manage it. This includes a schema that makes it possible for users to easily find what they are looking for. If data is not governed effectively, data lakes can become very expensive and difficult to derive insights from.
All data lakes are able to connect to a variety of analytic and visualisation tools. These may include tools for machine learning, data science, business intelligence and data visualisation.
The variety of data sources is what makes data lakes so unique. With a data lake, your business can bring together structured and unstructured data for analysis that helps find insights that can transform business operations and help you grow faster.
Businesses move quickly, and the time it takes to find, store and analyse data without a data lake often means that by the time the analysis is complete, the insights are no longer as relevant. Data lakes enable businesses to quickly adapt to changing conditions and answer new or unforeseen questions with ease.
In the era of AI, having a data lake is essential to perform machine learning. Many modern data platforms have built-in tools to help businesses make the most of the AI opportunity, without necessarily needing to substantially increase their investment.
With most data lakes being cloud-based, they can scale indefinitely, without being overly expensive. This means that businesses can ingest as much data as is required for the analysis, without needing to worry about hardware requirements or over-provisioning.
Historically, many data storage and analytics tools were overly complex, meaning that only database administrators and data scientists were able to extract insights from organisational data. Data lakes democratise data, with advanced features for experts, but a level of simplicity where business users can perform their own simple analysis.
Historically, organisations would have multiple data lakes for different business groups. This made it easier to manage, but also creates challenges, as some data would be duplicated across data lakes, and having silos makes it impossible to extract the most value out of the data.
Microsoft aims to break these data silos with OneLake, a single data lake for the entire organisation. Having a single data lake makes it easier for users to collaborate, with one copy of the data that can be used across multiple analytical engines.
OneLake is a key component that underpins Microsoft’s unified data analytics platform, Fabric. With Microsoft Fabric, all data is stored on OneLake, and accessed from the other Fabric services, such as Data Factory, Data Engineering, Data Science and Power BI.
This unique approach to a data platform has clear benefits for businesses, as it is a holistic solution for managing the end-to-end process of data analytics. With a single data lake, all silos are broken down, enabling businesses to make data-driven decisions, backed up by real-time analytics.
We understand the importance of data storage and analytics for business growth and innovation. Having a data lake solution is essential to find these insights, but the technology is only as powerful as the implementation.
We’ve worked with businesses of all sizes to implement data lakes and Microsoft Fabric to help them make the most of the data and AI opportunity. If you want to find out more about how this would work in your organisation, contact us today.