The terms business intelligence and data analytics have become buzzwords in recent years, but what do they really mean? This article will equip you with the right knowledge to intuitively understand their specific characteristics and use cases. As you read the post, keep the following list in the back of your mind as a reference point.
- BI is more accessible with abstracted tooling; Data Analytics requires specialized talent.
- BI focuses on what is possible now. Data Analytics strategically focuses on the future.
- BI tells you what; data analytics tells you why.
- BI is coarse, which is useful for day to day operational decision making. Data analytics is granular.
- It is only possible to interpret unstructured data with analytical methods.
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Business Intelligence and Data Analytics aims to support better business decision-making and facilitate data-driven Decision Support Systems (DSS). However, they are not the same, and this article will explore the key differences; before we dive deeper, let's set the stage by defining both terms.
“BI is about providing the right data at the right time to the right people so that they can make the right decisions” – Nic Smith with Microsoft BI Solutions Marketing.
Business intelligence (BI) is essentially the procedural and technical infrastructure that collects, stores, and analyzes its data. BI leverages software and services to transmute data into actionable insights; it is a broad term that encompasses data mining, process analysis, performance benchmarking, and descriptive analytics.
“BI is looking in the rearview mirror and using historical data. Data Analytics is looking in front of you to see what is going to happen.” – Mark van Rijmenam, CEO / Founder at BigData-Startups.
Data Analytics is a methodology utilizing quantitative and qualitative processes that enhances productivity and business gain. It involves many functions that include extracting data and categorizing it to derive various patterns, relations, connections, and other valuable insights.
Table of Contents
Present vs. Future
Business intelligence operates in the present; data analytics is more focused on the future - it's worth exploring this idea further. The BI process provides reporting mechanisms to help businesses monitor their businesses in the present moment. The process answers questions about historical business performance; BI focuses on making sense of past data and concentrating on what actions are feasible at this moment in time to improve the business - BI tools utilize variables that already exist. In this context, descriptive analytics provide a summary of historical data, distilling it in a visualized form to facilitate data-driven decision making.
Data Analysis Stack.
Data analytics allows companies to mitigate the uncertainty of the future by making predictions of future performance. BI is generally structured; data analytics operates in the unstructured realm, dealing with messy and incomplete data that is not immediately useful without careful data wrangling. Data analytics generates predictive insights for a company, picking out patterns and future trends to guide decision-making. BI is oriented for immediate action, while data analytics is orientated strategically in the timeline of years. The data analytics toolkit is more technically sophisticated than the business intelligence toolkit, with data scientists utilizing tools such as Seaborn, Pandas, Hadoop, Matplotlib, and NumPy. Our post on Data Engineers will clear things up if you are unsure about the specifics of these tools and their applications.
Business Intelligence Use Cases
BI processes can identify the most profitable customers; for example, a supermarket may introduce a loyalty scheme. Customers would be given discount cards for data collection. The company would apply BI tools to analyze and visualize disparate data points to find the ideal profile: age, sex, geographical location, marital status, number of children, etc.
Business Intelligence tools allow enterprises to analyze their energy consumption patterns through integrated automation systems powered by IoT. For example, energy patterns and distributions based on building characteristics, geographic regions, and power consumption. This data empowers enterprises to identify their consumption patterns and shows them a path to reduce their energy usage and implement smart energy control protocols.
Developing Investment Strategies
Asset managers utilize BI strategically for investing; e.g., a model can focus on various social media platforms. Investors uncover sentiment and generate trading signals. Other use cases include satellite imagery to understand the global supply of commodities, e.g., oil and gas, or to triangulate consumer spending based on the number of cars in shopping center parking lots. Whole new categories of investing are emerging from leveraging analytics and BI applications.
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Using BI, marketers can analyze Customer relationship management (CRM) data by analyzing a series of interaction criteria to uncover the most profitable customer profile. The customer base can also be analyzed to identify and develop new cross-sell and up-sell opportunities and target online marketing campaigns. According to research, it is five times more expensive to sell financial products and services to new customers than existing customers (banking and finance industry) hence the need for precision/boutique sales and marketing techniques, only possible with BI tools.
BI also plays a vital role in enhancing customer loyalty and retention by discovering why customers change institutions; it is possible to implement new processes and pipelines to reduce customer churn. Tracking customer habits, preferences, and behaviors through BI tooling allows an organization to modify its products and services on the fly to meet customer needs.
Pro-Tip: Xplenty's scalable platform lets BI architects move/transform data across various data stores (MySQL database to PostgreSQL database, for example) for better collaboration and communication among teams. And check out our post on the Top four BI platforms to boost profits.
Data Analytics Use Cases
Data-Driven Products and Services
Launching new offerings, products, and services is crucial for any business's survival. Customers have needs and desires, and in the current environment, if not met, another company will be on hand to be at their service. Companies utilize data analytics to create data-driven service and product offerings, harnessing disparate data sources such as CRM, Transaction, social media, geo-location, IoT, device, third party, and product data. Data is malleable, and using advanced analytics, new products, and bundles can be quickly built and iterated upon, probing the customer for feedback until it is perfect. Gone are the days of waterfall style products; it's too slow. Data analytics products require an agile approach.
Data is a crucial ally for security, fraud prevention, and compliance. It is possible to identify red flags and address issues before mutating into more significant problems - the security and compliance landscape requirements are continually evolving, and so are the bad guys. The bad guys are often highly proficient technically and are always changing, and data analysis is the most potent weapon in the fight against cybercrime.
Data-driven insights uncover what’s hidden and suspicious – and in time to mitigate risks - for example, data models would see the purchase of a one-way flight ticket as a red flag and a significant indication of airline fraud. Analyzing data can help an organization reduce the operational costs of fraud investigation and anticipate and prevent fraud. Regulatory reporting and compliance (i.e., HIPAA) can be automated into pipelines reducing manual resource drain. The pipeline would automatically identify and stop rogue actors using anomaly detection.
Of course, no analysis would be possible without an adequate data pipeline to move raw data from a source to a destination; check out our post on data pipelines for more information.
Inventory and Supply Chain Management
Insufficient management of supply chains leads to crippling bottlenecks. Supply chains must be continuously optimized to improve efficiency. Using data analytics, a supply chain management team knows what to store, what to keep, and what to discard; stocking up on slow-moving products or running out of popular ones are both problems. Such insights optimize performance and reduce costs.
IoT and Healthcare
IoT is making a considerable impact on the healthcare sector and is working in unison with patient-centered analytics. Patients' apps and devices are becoming ubiquitous; sensors embedded in diagnostic equipment, surgical robots, personal health and fitness equipment, drug dispensing systems, and implantable devices. Sensors embedded in medical devices are helping doctors understand medical emergencies even before they arise. The data is collected and analyzed for real-time monitoring. Additionally, the equipment themselves self-monitor and heal, minimizing downtime and avoiding potential failures.
How Xplenty Helps
A robust Business Intelligence and Data Analytics strategy must have a robust ETL solution at its core. That's where Xplenty comes in. BI is essential for day-to-day operations, while analytics helps to shape strategy. Rather than keeping BI and analytics siloed, companies should consider merging aspects of the two into generic data pipelines; both are data-hungry and ingest from the same sources; collaboration results in optimization. Xplenty's powerful on-platform tools allow its customers to effectively transform and analyze its data, all while adhering to compliance best practices. With Xplenty, you can integrate, process, and prepare critical data for analytics on the cloud. To experience the Xplenty platform for yourself, contact us to schedule a demo.