What do companies that outperform their competition have in common? They are able to generate a lot of business value from their data. According to an Aberdeen survey, organizations that utilize data lakes outperform their competition by 9% in organically derived revenue growth. Data lakes make it possible for an organization to engage in new types of analytics, including machine learning used with new sources such as data from click-streams, social media, log files, and even devices connected to the internet that are stored in the data lake. With new types of analytics, companies can act faster on opportunities for business growth. Data lakes and new analytic activities help attract and retain customers, boost productivity, and provide new insights for making informed decisions.

What Is the Difference Between Data Warehouses and Data lakes

Companies use data warehouses and data lakes for different needs and use cases. Depending on business requirements, most organizations will need a data warehouse and a data lake.

A data warehouse is really a database that is optimized to analyze relational data that comes from various business applications and transactional systems. In this case, the data structure and scheme are defined in advance. This allows for fast SQL queries. Data is first cleaned and then enriched and transformed. This data is generally considered the “singular source of truth” for users of organizational data for reporting and analysis purposes.

Data lakes are a bit different because not only does a data lake store relational data from business applications, it also stores unstructured or non-relational data from various sources like social media, mobile apps, and IoT devices. Because the structure and scheme are not defined when data is captured, all of your data can be stored without any careful design. You don’t even need to know what questions you may need answers to in the future. You can use many different types of analytics on your data, including big data analytics, SQL queries, real-time analytics. Machine learning and full-text search to find new insights from your data.

Organizations are quickly learning the benefits of data lakes, and many with data warehouses are adding or transforming their data warehouse to include data lakes. The real benefit of the data lake is that it provides your organization with the ability to utilize diverse query capabilities, data science use cases, and advanced capabilities to discover new information models. 

Data Lake Benefits

In general, data lakes enable you to collect more data from many more sources in a lot less time. It helps you leverage analytics on external data sources. Because a data lake can combine different sources of customer data, including information from a CRM application, social media analytics, incident tickets, and buying habits and history data, lakes provide much deeper insight into who the customer is, what types of rewards or incentives will increase loyalty, what promotions or partners might be most profitable. It helps your organization learn how to attract new customers and retain current customers.

Another benefit of data lakes is that they can assist your research and development teams to test their hypothesis, fine-tune assumptions and assess results to help identify the right materials for a product design that results in faster performance or more effective medications or what attributes a customer might be willing to pay more to get.

One of the most useful benefits of the data lake is that with more sources of real-time data and the ability to get data from IoT devices, more analytics can be run to increase operational efficiencies, reduce costs and improve quality.

Challenges

Apart from the many benefits a data lake provides, they also present a challenge. The primary challenge of data lakes is that raw data gets stored without any oversight of the contents. For a data lake to make the stored data usable, it needs defined mechanisms to catalog and secure it. If these elements are missing, data cannot be found or trusted and often results in what is termed a data “swamp.”