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Business Intelligence and Big Data

The requirements for analytics and business intelligence are changing. Business intelligence is now being extended to a technology to accommodate huge amount of data generated by social networks.  Let us understand the business intelligence and its extension called Big Data.

Business Intelligence

Businesses employ different strategies to grow. “5 force analysis” is one of the popular frameworks used as a starting point to develop strategy. It considers five different forces namely new entrants, substitute products, buyers, suppliers, competition.   These forces directly affect company’s ability to serve customers needs and make profits. If one carefully looks at the names, all these are external forces and every business needs to keep track of them not only while creating a strategy but to make it successful too.

Motive of most of the businesses is to make profit. Data about products, customers, competitors, environment, and policies etc. becomes very important in decision making. Rather businesses are increasingly looking for data outside their businesses to keep the competitive edge. When one wants to process this huge amount of data, classical paradigm and tools of databases fall short. Databases are designed to store data which is transactional and for the current time or year. When one wants to take more strategic and futuristic decisions, data would be required in large quantity and for some years. Putting this data in the transactional system would slow down the system and hence a different approach is needed to handle this data. Data warehousing is an approach for this. Data marts are created on top of conventional databases.

There are three core steps of a business intelligence solution.

Extract, Transform and Load (ETL)

Data warehouse is the database created for the purpose of data analysis and reporting and not to store day-to-day transactions. It is a central storage for data from disparate system across enterprise. ETL is a process which is used to source this data from different systems in different formats, data cleansing is done and it is loaded into a target system which is called Data warehouse. Data warehouse stores current and historic data (may be for years) for creation of trend analysis etc. which helps senior management take strategic decisions.

Data Mining

Data mining is a process of deriving trends and patterns from an existing large data set using advanced mathematical modeling techniques. This is used to forecast sales, running a campaign for specific customer segment, predicting customer behavior patterns for buying multiple products etc.

Business Intelligence and Reporting

Business intelligence is a technology which helps store and analyze data to help making better decisions. Data collected can be analyzed on multiple dimensions like time, geography, product, customer etc. Reporting tools may use data warehouse for generating some of the reports.

Following table shows list of popular tools in the space of data warehousing and business intelligence.

ETL tools Informatica, Data Stage, Ab initio, Oracle warehouse builder, Microsoft SSIS and SSAS,
Data Mining SQL Server 2012 Data Tools, Oracle Data Mining , SAS Analytics
BI Reporting Business Objects, Cognos, Hyperion, Micro strategy, Microsoft SSRS, Oracle BI Publisher

If one wants to do career in business intelligence and subsequently Big Data, strong foundation of knowledge of Database concepts, database design, SQL, PL-SQL or T-SQL with 1-2 years of experience of application development will help anyone to learn a tool in the above categories.

Now-a-days businesses are getting more complex and demand for advanced business intelligence and advanced analytics is increasing day to day. The analytics and business intelligence (BI) software market revenue in India is expected to reach $304mn in 2018, an 18.1% year-on-year increase, according to research firm Gartner, Inc. Indian organizations are increasingly moving from traditional enterprise reporting to augmented analytics tools that accelerate data preparation and data cleansing, said Gartner, Inc.