Top difference between Business Intelligence, Data Warehousing, and Data Analytics

With the ever-increasing use of technology in the business realm, it is clear that the companies who will properly use their data will emerge as champions. Big Data Analytics for sure is one of the technologies in-vogue, but it is a good practise to keep an eye on the trends in Big Data Analytics. This will help implement the most feasible trends and strategies to keep your business afloat. In the age of Big Data, there are a lot of terms that are used daily and three of the most common terms used with respect to Big Data are Business Intelligence (BI), Data Warehousing, and Data Analytics.
Let’s now understand the terms one-by-one before the difference between the terms;

What is Business Intelligence?

Big Data Business Intelligence Solutions enable companies to have ideas of presentation. It is also known as BI among the business professionals. BI is the process where you take insights from the available data and use the analytics to produce fruitful action. During the process, Business intelligence deals heavily with data warehousing as the warehouse acts as the source of information for analytics.

What is Data Warehousing?

Data Warehousing is a place where the business data is stored so that it can be used as and when required. Warehousing can happen at any step during the analytics process as the raw data can be acquired and rescanned. Due to prominent big data analytics technologies, the original data in the data warehouse remains safe and potentially unrecoverable.

What is Data Analytics?

Data analytics is a process where computer programming techniques and statistical methods are combined to study the data and derive insights for the betterment of the business. Advanced Big Data Analytics Services include a lot of prep work as the data might be formatted for machine-reading, or filled with errors or other troublesome flaws. Sophisticated data analytics is performed to validate the data with the help of profound toolsets.

To understand the difference between the aesthetics of Business intelligence, Data Warehousing, and Data Analytics, let’s have a one-on-one matchup.

Head-to-Head comparison between Business Intelligence and Data Warehouse

• Basic Usage

Business Intelligence is a system that is used for deriving insights related to a particular kind of business based on the available data. A data warehouse is a place to store historical and current data so that it can be used as and when the need arises.

• Source of Information

In a Data Warehouse, the information is held in fact tables and dimensions. Thus, data dealing with revenue and costs, demographics, or other attributions can be easily placed in a synchronized order. Big Data business intelligence solutions source their data from the data warehouse. Thus, authentic Data Warehousing becomes a must in Business Intelligence.

• Audience

In top-rated advanced Big Data analytics companies, the senior executives and managers have direct access to the analyzed data by Business Intelligence tools. Whereas, data engineers, business analysts, and data analysts use the information from the Data Warehouse to do a competent ‘behind the curtains’ work.

• Output
The output of Business Intelligence analytics is in the form of charts, graphs, and business reports. The output in a Data Warehouse, on the other hand, is in the form of dimension tables. It is used for upstream applications and for the Business Intelligence tools.

• Tools used

Business Intelligence analytics uses tools for data visualization and data mining, whereas Data Warehouse deals with metadata acquisition, data cleansing, data distribution, and many more. The tools used for Big Data Business Intelligence solutions are Cognos, MSBI, QlickView, etc. On the other hand, Data Warehousing uses tools such as Amazon Redshift, Informatica, and Ab Initio software.

How To: Big data is going to be a significant factor in business

Difference between Data Analytics and Data Warehouse

• Time-Variant

In a Data Warehouse, the data collected is actually identified by a specific time period. This is done as a Data Warehouse mainly stores analytical reports and historical data related to the company. For Data Analytics, the best possible approach involves automating insights into a certain data set, in a given set of date and time.

• File System

As in Data Analytics, science of examining raw data is included; it uses the data filing system to save the data for further processing. It takes almost an entire day to complete the processing of the data in a Data Warehouse. Using SAP can be highly instrumental here as it helps in reducing the time taken to process the data. Comprehensive SAP BI/BO solutions providing company can prove to be your apt ally in dealing with this problem.

• Memory Type

Data Warehouse and Big Data both have non-volatile memory. Thus, the previous data never gets replaced or deleted even when the new data adds on. In the Data Warehouse, there is no chance that the operational database will impact the data in the warehouse directly. In Data Analytics, the information is stored in file format for the reference of the researcher to derive conclusions which is solely based on the previous data.

• Data Format

A myriad of structural data that includes relational data can be handled with ease by Data Warehouse. Data Analytics accepts data in spreadsheets, XML, HTML, and PDF format to name a few.

• Preferences

An organization uses a Data Warehouse when it needs to know the whereabouts of the organization, such as the company’s work ethics, planning for the next year, current year’s performance, and much more. Data Warehousing provides authentic data, which proves vital for the company. Data Analytics is used in optimizing business experience, helping organizations to provide personalized services to clients on the basis of their preference, cost cutting and many more. On this note, the companies offering Big Data Intelligence services will provide you accurate information that will increase your profitability, revenue and will bring in more customers.

Comparing Business Intelligence and Data Analytics

All descriptive analytics of business data is Business Intelligence. The analyzed data by Business Intelligence tools is used by managers as it also constitutes predictive analysis. Whereas, Data Analytics requires a more profound level of mathematical expertise.

• Process applied

Big Data Business intelligence solutions use predictive, perspective, and descriptive analysis to visualize, understand and interpret the data so that the processes such as sales forecasting and consumer data score can be done in a precise manner. BI uses tools such as Tableau, Looker, Microsoft powered BI to process the available data in the best possible way so that the outcome of the analysis can be fruitful for the businesses.

• Difference in qualities

Data Analytics mainly relies on algorithms and quantitative analysis to determine the relationship between the available data that isn’t clearly stated on the surface. Business Intelligence, on the other hand, doesn’t rely on a high level of mathematical expertise, forward-looking approach, or predictive reports to do the data analysis.

• Usage of Data

Data modelling is essential for Data Analytics as it helps in the collection of raw data, clean it, validate and transform it into the information that can be used. Clean data also allows the Big Data business analytics and tools used in it to function in a better manner.

Closing Comments
Data is critical in the modern-day business scenario. In the modern business world, working with data can be tedious as it doesn’t have a single action or even a set of actions to work with. Leading Big Data Services providing companies are most-often sought as they can incorporate Business Intelligence, data warehousing, and data analytics with a professional touch to provide the best set of information for the business to work with and scale newer heights in their genre. Skilful analysis done by Big Data Business Intelligence Solution providers will keep the problems such as social and statistical biases at bay and ensure that the information always gets into the hands of the decision makes for better usage of the data.

Ashish Parmar

He is the Co-Founder and CEO of Prismetric . An enthusiastic entrepreneur, interested to discuss new app ideas, rich gadget tricks and trends, and admires signature tech business styles to readily embrace. He enjoys learning most modern app crafting methods, exploring smart technologies and passionate about writing his thoughts too. Inventions related to mobile and software technology inspire Ashish and he likes to inspire the like-minded community through the finesse of his work.