Business Intelligence in big data analytics

Business Intelligence in big data analytics

1. Business Intelligence

2. Definition Business intelligence (BI) is a set of technological processes for collecting, managing and analyzing organizational data to yield insights that inform business strategies and operations.

3. Purpose of BI Business intelligence analysts transform raw data into meaningful insights that drive strategic decision-making within an organization BI tools enable business users to access different types of data, historical and current, third-party and in-house, as well as semistructured data and unstructured data such as social media.  Organizations can use the insights gained from BI and data analysis to improve business decisions, identify problems or issues, spot market trends and find new revenue or business opportunities.

4. BI vs. BA Business intelligence (BI)  is descriptive, enabling better business decisions that are based on a foundation of current business data.  VS. Business analytics (BA)  is then a  subset  of BI, with business analytics providing the prescriptive,  forward-looking  analysis. It is the umbrella of BI infrastructure that includes the tools for the identification and storage of the data for decision-making.

5. How BI works? BI platforms traditionally rely on  data warehouses  for their baseline information.  Data warehouse acts as one central system to support business data analytics and reporting. Data warehouses can include an online analytical processing (OLAP) engine to support multidimensional queries. For example: “What are the sales for our eastern region versus our western region this year, compared to last year?”

6. Steps in BI The steps taken in BI usually flow in this order: Data sources : Identify the data to be reviewed and analyzed, such as from a data warehouse or  data lake ,  cloud ,  Hadoop , industry statistics, supply chain,  CRM , inventory, pricing, sales, marketing or social media. Data collection : Gather and clean data from various sources. This data preparation might be manually gathering information in a spreadsheet or an automatic  extract, transform and load (ETL)  program. Analysis : Look for trends or unexpected results in the data. This might use  data mining ,  data discovery  or  data modeling  tools.

7. Visualization : Create data visualizations, graphs and dashboards that use business intelligence tools such as  Tableau ,  Cognos Analytics , Microsoft Excel or  SAP . Ideally this visualization includes drill-down, drill-through, drill-up features to enable users to investigate various data levels. Action plan : Develop actionable insights based on analysis of historical data versus  key performance indicators (KPIs) . Actions might include more efficient processes, changes in  marketing , fixing  supply chain  issues or adapting  customer experience  issues. Some newer BI products can extract and load raw data directly by using technology such as Hadoop, but data warehouses often remain the data source of choice.

8. Benefits of BI Benefits of BI: Clearer reporting:  BI gives organizations the ability to ask questions in plain language and get answers they can understand. Dashboards can prioritize the most important insights, saving time for both data experts and nontechnical team members. Instead of using best guesses, staff can base decisions on what their business data is telling them whether it relates to production, supply chain, customers or market trends. The data can help answer an organization’s pressing questions: Why are sales dropping in this region? Where do we have excess inventory? What are customers saying on social media? Consolidated data:  BI delivers business insights by pulling in and consolidating data from multiple sources, internal and external, for complete analysis. By providing an accurate picture of the business and market, BI provides an organization with the means to design a business strategy. Create new efficiencies:  Organizations can monitor business operations against benchmarks and fix or make improvements on an ongoing basis, all fueled by data insights. Analytics can discover and help eliminate manufacturing or supply chain bottlenecks. Managers can monitor staff performance to help pinpoint where organizational changes can be made.  Supply chain management  can be improved by monitoring activity up and down the line and communicating results with partners and suppliers.

9. Deeper data insights:  BI helps organizations become more data-driven, to continually improve business performance, gain competitive advantage, and locate new customers and new opportunities. They can improve ROI by understanding their business and market, and intelligently allocating resources to meet strategic objectives. New data insights can reveal customer behavior, preferences and market trends. Those insights enable marketers to better target prospects or tailor products to changing market needs. Faster decision making:  As progress is monitored and analyzed digitally, better informed decisions can be made more quickly for faster adjustments in the marketplace. Increase customer satisfaction:  When customer service staff have access to customer data and insights, they can provide requested information and resolve issues more quickly. Increase employee satisfaction:  Self-help access to important business data can optimize workflows so that staff can do their jobs faster, with fewer added or repetitive steps.

10. Challenges in BI Contradictory conclusions:  Self-service BI empowers multiple teams to search for the insights they need, but can also lead to divergent conclusions, which can create more friction instead of a unified plan of action. This can be especially true if human bias creeps into the analysis. Skills shortfall:  The need for data integration might be difficult, given a wide variety of sources, and integration might exceed current capabilities. Expertise in data science, engineering and architecture is required to help ensure that analysis yields insights that reflect reality. Up-front costs:  The initial costs to develop a powerful, modern BI system might appear large, but the cost savings generated by analysis will offset the investment.

11. Use cases of BI Customer service:  With both customer information and product details available through a unified data source,  customer service  agents are able to quickly answer customer questions or begin to solve customer concerns. Finance and banking:   Financial  firms can determine current organizational health and risks, and predict future success by viewing combined customer histories and market conditions. Data can be reviewed branch-by-branch with a single interface to identify opportunities for improvement or further investment. Healthcare:  Patients can quickly get answers to many pressing  healthcare  questions without asking time-consuming questions of staff or medical personnel. Internal operations, including inventories, are easier to track, minute-by-minute. Retail:   Retailers  can boost cost savings by comparing performance and benchmarks across stores, channels and regions. And, with visibility into the claims process, insurers can see where they are missing service targets and use that information to improve outcomes. Sales and marketing:  By unifying data on promotions, pricing, sales, customer actions and market conditions, marketers and  sales  teams are better able to plan future promotions and campaigns. Detailed targeting or segmentation can help boost sales. Security and compliance:  Centralized data and a unified dashboard can improve accuracy and help determine the root causes of  security  problems. Compliance with regulations can be simplified with a single system to gather reporting data. Statistical analytics:  Using  descriptive analytics , organizations can review statistics to spot new trends and uncover why those trends are developing.