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Using Data Warehouses with Business Intelligence 

by Aishwarya
Using Data Warehouses with Business Intelligence 

Business Intelligence, at its core, is a technology-driven practice with which you can transform raw data into actionable intelligence. In other words, it just means you can answer complex questions with your data and make informed decisions. But the key requirement of a good business intelligence tool is Data. And in order to gain the maximum from Data having an organized and centralized repository is the Key, which is possible with a Data Warehouse. 

Think about it! Normally it is estimated that we generate at least 2.5 quintillion bytes (18 zeros after 2.5!) of data every day and a large chunk of it is contributed by Enterprise Operations. And Enterprises which don’t have a method or governance for the data are lagging behind in the utilization. Before we go into why Data Warehouses and Business Intelligence are a match made in Cloud (Pardon the pun!). Let’s talk a little about Data Warehouses, how is it different from Data Lakes, and What Data Marts are. With this clarity, it is easier to understand the inherent benefits produced by Data Warehouses for Business Intelligence tools.

What is a Data Warehouse and How is it different from Data Lake?

Most of you might know what a data warehouse is, but for those who don’t – It is like a Warehouse (storing place) for Data, as simple as that. Extending the meaning, Data Warehouse is a place to store (normally large amounts of) Data which is later is used in processing and analysis. You can use meta tags for the retrieval of cloud data in a similar way to using barcodes or special numbers for the goods stored in physical goods. 

Data Warehouses are great for storing data from multiple sources. It can store data that is:

  1. Integrated
  2. Granular 
  3. Historical 

You can access past data in the form of rows and columns, which have a combination of various sources and can be highly detailed. We are mainly talking about Cloud-based Data warehouses in this read, though most of the discussion would be applicable to Hybrid and On-premise data warehouses too. 

Data Warehouses that offer a “Single version of the truth” use a process called OLAP or Online Analytical Processing to perform multidimensional analysis at high speeds. This is required because, most businesses have data in the form of multi-dimensions like Sales by Geography like region, country, state, store, etc; Time like a year, month, week, day, etc., or Product like brand, type, age, gender, etc., and more. For such a multi-dimensional requirement, the standard SQL or relational querying wouldn’t be sufficient. Since we’ve now understood a little about Data Warehouses. Let’s get into how it is different from Data Lake. 

Data Lake and Data warehouses both store data, but the key difference lies in the way it is organized. Data warehouses store data in a structured manner and Data Lakes store data in the original format only. Nowadays a Unified Data Model is emerging as the front-runner which used both Data Warehousing and Data Lake for storage for maximum efficiency, but more on that later. All you need to know is that more than competitors both DWH and DL can be viewed as complements, and can form an additional layer before Data Warehouse in the layer above.

Data Mart is a subset or a subsidiary of the Data warehouse like a Business Unit is a subset of an organization. Data Marts contain data that are related to certain BUs or units specifically. So from now on when we mean Data warehouse to extract data, you can take it to be a Data Mart too. 

The Role of Data Warehouse in Business Intelligence

We’ve already discussed what Business Intelligence is so wouldn’t go into that again. But Business Intelligence’s main purpose is Analysis and Reporting with Dashboards. The Business Intelligence tools like Power BI, Qlik, Tableau, etc. which are available in the market have drag and drop features making them easy to handle. So, with the reporting end becoming more user-friendly, the difficulty and the crucial data points would fall back onto the data. Business intelligence tools need data that can be used or at least be friendly enough to have analytics performed on them. Think about how the addition of the Data Warehousing layer brings OLAP and its analytical capabilities to perform complicated queries on data on which further analysis can be done.

Data Warehouses form the backbone of Business Intelligence. Let’s discuss this with examples, with Data Warehouse you can access data of not only one BU but the entire organization at one place wrt Geography, Time, Sales, Costs, Products, etc. which wouldn’t be possible if you’re working on siloed data. And with direct access linking Business Intelligence tools like Power BI with Azure and Qlik’s Active Intelligence with most of the data warehouse platforms, you can perform Analysis on live data rather than historic data. So by having a Data warehouse layer before the reporting layer of Business Intelligence tools you can provide the added benefit of using real-time data, utilizing all the data that can be required, and therefore use the Business Intelligence to its fullest. 

In the process of companies’ transition from unorganized data to Big Data or structured data or their possible future transition to Small and Wide data, “Data” will always be there. As such Data Governance or Data Ops would become of prime importance. So for being competitive having a Data Layer is important and having a Data Warehouse in business intelligence tools can provide the dexterity organizations might be looking for. 

Final thoughts

Taking a page from one anime, where one character talks about how one might help others, the comparison is made with 10+10. With value addition tools we can take it up a notch like how 10+10 equals 20. But with the right implementation and tools like Data warehouse and Business Intelligence Tools, the value addition can be represented as 10 x 10 which would provide the organization the benefit of 100. All we mean is though the two tools thrive independently too, their combination can provide the organization with a range of experience that was proclaimed to be impossible a few years back.

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