What is a Salesforce Data Lake?

Firstly, all of the above are ways to describe the storage and management of data. 

One of the biggest challenges facing businesses right now is the insurmountable volume of data that can be collected about an audience and the fact that this data is usually siloed across dozens, if not hundreds of platforms. 

From social media, to webinar and event registration platforms, to website analytics, dedicated customer survey tools, shipping and delivery information, payment processing, and plenty more, the crucial data you need to analyse and act on is spread across a variety of mostly disconnected software and applications. 

Learn more about Salesforce Data Cloud with our 101 guide.

And it’s not just data as a result of customer interactions that matters either. Add your business performance metrics and third-party intelligence sources, and it’s easy to see why companies struggle with using data quickly, consistently, and meaningfully.

As we bolt headfirst into an era where AI is at the forefront of business and marketing strategy and decisions, how data is managed becomes even more important. AI depends on complete and reliable data. The efficiency of AI (and more traditional systems) also depends on how you structure your data relationships - this is where data lakes, data warehouses and data lakehouses make a big difference!

Salesforce Data Cloud brings customer data from multiple sources into a single view that everyone in the business can make use of, whether they’re from sales, marketing, customer service, commerce, business development, or another team.

But here’s where those common terms are important to understand.

• Data warehouse: A digital storage space where structured data can be archived for specific business intelligence purposes and reporting. The data is predefined and stored in a specific format, ready to use in a data set.

• Data lake: A digital storage space where unstructured information is stored in raw format, ready for whatever uses we may be able to find for it, now or in the future. A more flexible approach.

• Data lakehouse: A combination of both of the above; bringing together the features and tools of a data warehouse and the unstructured, raw data of a data lake.

Structured data vs unstructured data

If a data warehouse stores structured data and a data lake stores unstructured data, you need to grasp what types of data those could be, so you can assess your business needs.

• Structured data: data points that have already been organised and ‘structured’ in some way i.e. into tables, so they are searchable and clearly defined. This type of data includes numbers, words, and strings. For example, anything that could be formatted into your standard subscriber list with fields and values, or a relational database like Marketing Cloud Data Extensions.

• Unstructured data: basically the opposite. This type of data is more qualitative and made up of images, emails, word processing files, video and audio, for example. It can’t be easily shown in a table of rows and columns and is notoriously more difficult to search and analyse.

According to Gartner, a whopping 80% of enterprise data is unstructured. As you can guess, this means data warehouses are not always meeting the full needs of a business. In order for a data warehouse to receive data from another system it must first extract, transform and load (ETL) which can be time consuming.

Yet data lakes have their drawbacks too. These don’t take into account the other 20% of structured enterprise data and are much more difficult to extract useful insights from without significant data science resources and budgets.

The solution? A data lakehouse - the best of both!

Combining the data management and security of a data warehouse with the flexibility of a data lake, a data lakehouse allows businesses to store and organise all types of data. In turn, this means better insights, improved machine learning for AI tools like Einstein, and the ability to act quickly on both first and third-party data.

Salesforce Data Cloud comes with a built-in data lakehouse. It also has a ready-made data model known as ‘Star Schema’ that helps you to organise your data, and a simpler ‘Extract, Transform, Load’ (ETL) process that can largely be configured in the UI.

Even though it’s so powerful as a standalone product, Data Cloud also integrates natively with data warehouses like Snowflake so you can share data between both and reap the benefits i.e. tapping into the Snowflake Marketplace for third-party data services.

Sound good? The first step to getting started with Salesforce Data Cloud is to ensure your existing data sources are clean and accurate, so the data you ingest can be relied upon.

For support implementing Salesforce Data Cloud, get in touch. We have developers and data scientists certified in Data Cloud who can take the heavy lifting out of your hands.