Data preparation: Compiling multiple data sources, identifying the dimensions and measurements, preparing it for data analysis. This involves Extract-Transfer-Load (ETL) tools that import data from one data store into another.
Online analytical processing (OLAP): It is an offshoot of online transaction processing (OLTP). The key value of OLAP lies in the fact of its multidimensional aspect, which allows users to look at problems from a variety of perspectives. OLAP can be used to complete tasks such as CRM data analysis, financial forecasting, budgeting, and others.
Analytics: Analytics is the process of examining data and drawing out patterns or trends to make key decisions. It can help uncover hidden patterns in data. Analytics can be descriptive, prescriptive, or predictive. Descriptive analytics describe a dataset through measures of central tendency. Prescriptive analytics, as the name implies, prescribes specific actions to optimize outcomes. It determines a prudent course of action based on data. Predictive analytics is the use of statistical techniques to create models that can predict future or unknown events. Predictive analytics is a powerful tool to forecast trends within a business, industry, or on a more macro level.
Data mining: As the name implies it is the process of looking for hitherto unknown valuable insights in the data using databases, statistics and machine learning to uncover trends in large datasets. End users might also use data mining to construct models to reveal these hidden patterns. For example, users could mine CRM data to predict which leads are most likely to purchase a certain product or solution.
Reporting: Sharing data analysis to all stakeholders so they can draw conclusions and make decisions. Reports can take many forms and can be produced using several methods. However, business intelligence products can automate this process or ease complexities in report generation.
Performance metrics and benchmarking: Comparing current performance data to historical data to track performance against goals, typically using customized dashboards.
Querying: Asking the data specific questions, BI pulling the answers from the datasets.
Data visualization: Turning data analysis into visual representations such as charts, graphs, and histograms to more easily consume data.
The above are all distinct goals or functions of business intelligence, but BI is most valuable when its applications move beyond traditional decision support systems (DSS) to make the organization as Intelligent Enterprise. The advent of cloud computing and the explosion of mobile devices makes it possible to support the business users demand analytics anytime and anywhere.
Why is business intelligence important?
Business intelligence equips companies to make well-informed decisions by displaying present and historical data within their business context. Analysts can leverage BI to provide performance and competitor benchmarks to make the organization run smoother and more efficiently. It is also possible to easily identify market trends to increase sales or revenue. The insights churned out of right data can help organizations with anything from compliance to hiring employees. BI can help companies make smarter, data-driven decisions instead of gut-based decisions in the following ways
- Recognize ways to increase profit
- Study customer behavior
- Compare data with competitors
- Keep tab on key performance indicators
- Optimize operations
- Predict success
- Identify market trends
- Discover issues or problems
Reporting is the principal activity of business intelligence and the dashboard is the archetypical BI tool. Dashboards are hosted software applications that automatically pull together available data into charts and graphs that give a sense of the immediate state of the company – the real-time snapshot. BI is not solely confined to generating reports. Though, business intelligence does not tell business users what to do or what will happen if they take a certain course, but it offers a way for people to examine real-time data to understand trends and derive insights by streamlining the effort needed to search for, merge and query the data necessary to make sound business decisions.
For example, a company that wants to better manage its supply chain needs BI capabilities to determine where delays are happening and where variability exist within the shipping process, That company could also use its BI capabilities to discover which products are most commonly delayed or which modes of transportation are most often involved in delays.
Similarly, BI helps to keep track of customer acquisition and retention and answer queries such as how many members have we lost or gained this month.
It can automate generation of sales and delivery reports from CRM data.
A sales team could use BI to create a dashboard showing where each rep’s prospects are on the sales pipeline.
Popular Vendors of BI
There are many vendors and offerings in the arena of Business Intelligence. Some of the major players are enlisted below.
Power BI– Microsoft Power BI is an analytics tool that assists in reporting, data mining and data visualization to provide business insights. Through its simple interface, users can connect to a variety of data sources.
Tableau – a self-service analytics platform provides data visualization and can integrate with a range of data sources, including Microsoft Azure SQL Data Warehouse and Excel.
Plunk-a “guided analytics platform” capable of providing enterprise-grade business intelligence and data analytics
Alteryx-which blends analytics from a range of sources to simplify workflows as well as provide a wealth of BI insights
Qlik-which is grounded in data visualization, BI and analytics, providing an extensive, scalable BI platform
Domo-a cloud-based platform that offers business intelligence tools tailored to various industries (such as financial services, health care, manufacturing and education) and roles (including CEOs, sales, BI professionals and IT workers)
Dundas BI-which is mostly used for creating dashboards and scorecards, but can also do standard and ad-hoc reporting
Google Data Studio – a supercharged version of the familiar Google Analytics offering
Einstein Analytics – Salesforce.com’s attempt to improve BI with AI
Birst-a cloud-based service in which multilple instances of the BI software share a common data backend
The Future of Business Intelligence
Competition is at all time high. It is mandatory for companies to be proactive than reactive. It is their predictive insight that is going to make them different from the others and hence business intelligence would play a pivotal role in this aspect. Solid business intelligence is essential to making strategic business decisions, but many organizations struggle to implement effective BI strategies, mainly due to poor data management practices, tactical mistakes and more. Currently, companies are also seriously into Competitive Intelligence which is a subset of business intelligence. Competitive intelligence is the collection of data, tools, and processes for collecting, accessing, and analyzing business data on competitors. Competitive intelligence is often used to monitor differences in products. Moving ahead, expert analytics sees a third wave of disruption on the horizon, something the research firm calls augmented analytics, where machine learning is baked into the software and will guide users on their queries from the big data.
