This is a grouped information concerning a business’s customers, competitors, partners, competitive environment and internal operations that gives the business the ability to make efficient, significant, tactical and strategic decisions. Big data is huge amounts of unstructured and semi-structured data from the web, sensors, stock market, social media and so on. Big data is of massive interest because it can display more patterns and interesting aberrations than smaller volumes of data. It has the potential to provide novel understanding into such areas as financial market activity, weather patterns, consumer behaviour, tidal movements and so on. To obtain value from big data, we need to use new tools which are able to work with non-traditional data along with traditional data.
These tools include the following;
- Data warehouses
- Data marts
- In-memory computing
- Analytical platforms
Data warehouse: Stores present and past standardised data. It also provides analysis and reporting tools
Data marts: This provides a subset of the data warehouse’s data with an emphasis on a single subject or line of business.
Hadoop: Provides parallel processing of big data across cheap computers. It’s main features are;
- Hadoop distributed file system.
- MapReduce which breaks data into clusters to works on.
- Hbase which is a NoSQL database.
In-memory computing: This uses RAM for data storage to make data retrievable at a faster rate. This can speed processing times from hours/days to just seconds.
Analytical platforms: These are high speed platforms that use both relational and non-relational tools for big data sets. One of these tools is OLAP (Online Analytical Processing), It has the following capabilities;
- Supports multidimensional analysis of data.
- It views data using multiple dimensions.
- It can provide instant online answers to ad hoc queries.
Another analytical tool is data mining which performs the following functions;
- Looks for hidden patterns in sets of data.
- Generates rules to predict behaviour.
- Produces data by associations, sequences, clustering and forecasting.
Text mining is also a common analytical tool; this extracts important elements of information such as facts, opinions and dates from large data sets.
There are six key elements of any effective business intelligence environment. These are the following;
- Data from the commercial domain
- The business intelligence infrastructure
- Business intelligence analytics
- Managerial users and functions
- The delivery platform – Management Information System (MIS), Decision Support System (DSS), Executive Support System (ESS)
- The user interface
The main objectives of business intelligence and analytics is to produce the following outcomes in real-times and also highly precise manner;
- Production reports – For routine-type decisions e.g. Marketing, human resources, financial accounts
- Parameterized reports
- Dashboards to help the user experience
- Search/report creation
- Forecasts and scenarios
This is the use of various tools to forecast future trends and behaviour. These tools include the following;
- Statistical analysis
- Data mining
- Historical data
Predictive analytics has numerous BI applications for sales, financial markets and fraud detection to name but a few.
Operational and middle managers utilize MIS (running data from TPS- Transaction Processing System) for routine production reports.
Super users and business analysts utilize DSS for more sophisticated analysis and custom reports and semi-structured decisions.
“What-if” analysis, Sensitivity analysis, Multidimensional analysis / OLAP and pivot tables are all examples of DSSs.