Marketing With Data Lakes and Data Warehouses

Marketing With Data Lakes and Data Warehouses

There are many ways for an organisation to store their Big Data, data lakes and data warehouses being popular choices. However, which one should you be using? The decision comes down to one simple test – who uses it and how.

Big data is getting bigger day by day, and the more data we create, the more space we need to store it. Data Lakes and Data Warehouses are both primarily used to store big data, but they are not interchangeable. Each serves a purpose of its own and knowing this can help you effectively use either for a solid marketing strategy.

Let’s look at the major differences between the two and also see how, as a marketer, you can leverage them.

What is a Data Lake?

Oracle defines data lakes as “a place to store your structured and unstructured data, as well as a method for organising large volumes of highly diverse data from diverse sources”.

A Data Lake, in the simplest of terms, is a dumping ground for data. This stored data is neither structured nor unstructured as it is collected without any segmentation or sorting. A data lake has no scale and no specific agenda besides merely storing data.

Most of the vendors in the market provide data lakes that can be installed on a cloud or on-premises, while some cannot be run on-site. These include products from web service providers, such as Amazon’s Elastic Map Reduce or Microsoft’s Azure Data Lake, which either run Hadoop distributions from platform-focused vendors such as Hortonworks or MapR, or their own distributions. Apart from these, Cloudera, Altiscale, HBR, and Zaloni, too, provide data kale solutions.

Also Read: How AI is Improving Predictive Analytics

What is a Data Warehouse?

A Data Warehouse, on the other hand, is used for structured data that is intended for further study, comparison and analysis. As per Informatica, it as “a technology that aggregates structured data from one or more sources so that it can be compared and analysed for greater business intelligence”.

A data warehouse is more systematic and is modelled in a way that it aggregates structured data from various sources for ease of access and use. The data stored in a data warehouse will be cleansed and standardised.

To avoid some of the challenges of the conventional on-premises data warehouses, many organisation are moving towards cloud-based data warehousing. Some of the top data warehouse service providers include Amazon Redshift, Google BigQuery, Microsoft Azure, and SnowflakeDB.

What are the Pros and Cons of Data Lake and Data Warehouse?

As the data lake stores data without a specific purpose in mind, it’s a perfect place to start when conducting data analysis using AI or machine learning.

The only flip side to the large storage capacity of a data lake is that not only can it be expensive, but also a hindrance in meeting data governance systems. Using a data lake to store your data, means you can extract quickly results from the pool of existing data for relevant cases.

Also Read: Implementing Machine Learning for Data Analysis

When it comes to the data stored in a data warehouse, the processed and structured format makes it easier for any organisation to understand the database it currently holds. So if you’re looking to measure customer satisfaction or product performance based on fixed parameters, then a data warehouse will help you pull out specific information about that need. However, your flexibility to do this is limited to available structured data. You cannot go beyond that looking for deeper insights.

Another point of difference between the two is that data warehouses take much effort to build and maintain, whereas a data lake is relatively straightforward as the data doesn’t need any processing.

What are the Use Cases for Both?

Use Cases for a Data Lake

As explained earlier, data lakes provide an abundant pool of raw data that can be utilised as per the required context. Data lakes are popularly used for AI tech, machine learning and by data scientists.

Here are some examples

1. Classification of Sounds & Images

Machines can be trained to recognise sounds and images with deep learning. A system installed in a plane can be trained to recognise different engine sounds and identify sounds that indicate system failure, triggering a warning before any disaster takes place. To set up a machine learning system like that, you will require access to a large database of sounds and images from a data lake.

2. Text Analytics

NLP or natural language processing can help organisations extract entities (people, places, or things), themes, or sentiment from call centre notes. This information can then be combined with other information about customers to build predictive models. Additionally, data lakes allow organisations to use survey responses to assign entities and enhance predictive analytics.

Also Read: IBM Unveils InfoSphere, a New Data Prep Tool Designed to Help Speed DataOps

Use Cases for Data Warehouse

Data Warehouses are fit for providing enterprises with consistent data for repetitive processes like business reporting, year-over-year analysis and dashboards. A data warehouse enables consistency and clarity within an organisation’s functioning.

Here are some examples:

1. Summarise and Filter Useful Data

A transport or delivery company can collect GPS and delivery data 24 hours a day, but most of that data is of little value as individual data points. The goal is to group those events into trips to show the overall statistics for distance travelled, timeliness of delivery, and other key metrics. A data warehouse can help summarise bulk data and then only filter useful data for improvement of services.

2. Merge Live Data with Historical Data

Financial institutions need real-time access to market data such as interest rates, but they also need to store that market data and show it in the context of historical trends. A data warehouse can facilitate this integration between the two sets of data.

Data Lake or Data Warehouse, Which One is Better?

As a marketer, you must be able to answer this question. However, to answer this question, you must first understand your needs and the final goal you want to achieve. You might realise that although quite distinct, there’s a need for both a data lake and a data warehouse.

Given their ability to store large quantities of data and their suitability for a future of ever-growing database, it might be tempting to think that a data lake is an obvious answer for marketers. However, they tend to turn into not-so-useful data swamps where you can’t find what you need.

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Data warehouses welcome smaller companies into the world of data analytics, while data lakes enable enterprises to transform massively with Big Data. These systems aren’t mutually exclusive, either. As a marketer, if you feel that your analytics needs a change, then you can always add a data lake to your existing data warehouse.