Putting data lakes to work in 2017

Businesses today are gathering vast amounts of data on a daily basis but where does all this information go? The data, from sales figures to social media stats, can be stored in data lakes. But while these data lakes can be put to work it’s an element that businesses often overlook in the big data revolution but you could change that in 2017.

What is a data lake?
If you’ve not come across the term data lake before it can be confusing. But it’s essentially a storage place for raw data – it’s where your information is stored before you begin using it. Data lakes retain all the data as they remain unstructured and support all data types, these are the main differences between data lakes and data warehouses, where the necessary data from a data lake is taken and stored to provide insights that can guide management and business decisions. Some businesses choose to operate both a data lake and a data warehouse that complement each other.

Making data lakes work for you in 2017
Have you started using data lakes in your business yet? After reading why you should consider data lakes and how it can support your business operations you might be asking yourself why you haven’t taken advantage of them already or start using them even more.

Using data lakes means that the data you’ve gathered can be preserved, rather than either being incorporated into your data warehouse or scrapped. It gives you the flexibility to go back to the original data and perform different analyses on it at a later data and gather more insights as a result.

A data lake can house all types of data, both structured and unstructured and can, therefore, act as a single access point for all a business’ data. It means that an organization’s analysts can mine all the data from a single point when the data may otherwise be stored in multiple locations to account for the different types. The integration of a data lake can make it an invaluable tool for businesses that really want to get the most out of the information they are gathering and storing.

Of course, there are pitfalls to using data lakes too and it’s vital to understand how best to use it. For instance, as there is no oversight for data lakes it can mean large data sets that are not useful being added, making it more difficult for analysts to source the information that they can use to create informative insights. However, despite setbacks, making data lakes work for a business in 2017 could be transformative.

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