The best way to understand Business Intelligence, is probably an overview of how it works in action: taking an organization from data-rich but UN-optimized in data usage, to truly data-driven and able to fully reap the benefits of their information and technology.
Place yourself in the shoes of an established organization – hypothetical, of course – from a data-rich industry like healthcare, IT, or retail. By nature of the tools necessary just to conduct your business, you already have massive amounts of raw data on hand, and you probably made a not-insignificant investment to acquire it. This vast store of data might include, as examples – depending on your industry:
For a medical corporation:
- Patient records
- Diagnosis reports
- Patient surveys
For a retail organization:
- PoS data
- In-store sensors
- Footage from security cameras
- Demand data based on customer footprint
This is where business intelligence at the enterprise level comes in. Previously, ETL design and implementation for the data warehouse was ignored by various organizations but slowly they started adopting a modern approach to handle the data mining, analytics, optimization and reporting.
Let’s have a look at the difference between is a look at traditional BI approach vs Modern approach:
Modern BI Approach
By taking a holistic view of your entire organization and the data gathered in every function, enterprise BI knocks down silos and provides advanced solutions, with a deep understanding of how disparate data from across the enterprise can, when used the right way, provide maximum benefit to the entire enterprise.
While the examples above are broad and hypothetical, many major companies have already benefited from enterprise BI, ranging from giants like Amazon and Netflix, to relatively smaller, niche organizations like music analytics platform Next Big Sound, and digital game developer Miniclip. Read on to discover more about how enterprise BI works.
Major Components of Business Intelligence
Although Business intelligence and analytics implementation differs in the details for every organization, there are a several underlying principles that are relatively constant. These include:
A primary component of BI is making data more efficient to access and analyze. This is achieved in several ways:
- Data aggregation (a centralized data warehouse enabling faster querying);
- Data cleaning (standardizing data so that it’s better able to “communicate” with other data);
- OLAP (online analytical processing, a way of quickly analyzing data via multiple dimensions)
- Denormalization (optimizing data query times in several ways, including allowing redundant data storage)
Real time analytics
True BI is an “always-on” process, enabling agile, nimble responses to inefficiencies or problems as soon as they’re detected. In practice, this means access to real-time metrics and dashboards, as well as an alert system that ensures that no time is wasted in producing a response.
The data from sources like sensors, markets, logs, social interactions, purchase/spends can be processed for Real time analytics.
Your raw data is a record of history: how a process has been performing, how customers have been making purchase decisions.
With BI, the vast amount of historical data on hand is put to use in vast simulations, drawing on statistical inference and making use of ML and AI tools to provide probabilities for future events and behaviors, which can then be put to use to make more informed decisions in a broad range of areas, including development, marketing, sales, budgeting, and even hiring and promotion.
KPIs (key performance indicators) are often seen as an intractable, “conventional wisdom”-driven metric. Enterprise BI can detect surprising data patterns that may change the way you look at, choose, and assess your KPIs – ultimately resulting in improved performance and results.
Unstructured and unconventional data sources
The typical image of big data, and databases in general, is row after row of numbers, along with simple text data like names. A key differentiator in BI is the ability to draw on unstructured data, which is typically in an unconventional, “un-quantifiable” format like long-form text, such as customer reviews or comments.
The benefits of the advanced technology behind BI mean that this data can be analyzed on its own and in relation to more traditional data, providing even more easily accessible, digestible, actionable information.
Comparison of Top BI tools
Choosing the right BI tool means asking a few questions of your organization and your stakeholders: Which tool is right for analyzing the data you have on hand, and the market data needed to make decisions? Which tool makes sense for the people who will be using it? And which one can produce the output and results that you’ll need?
Below, we take an in-depth look at several of the solutions available:
Developed by IBM, Cognos is widely used and delivers one of the most user-focused, intuitive end user interfaces available. It’s ideal for users who frequently make use of data in presentations, such as business cases to upper management.
Key features include real-time analysis, ready-to-present data visualization tools, and the ability to quickly share information with colleagues.
Another broadly-used tool, Domo is designed for relevance to the entire organization for businesses in almost any industry. Domo’s BI reporting tool is especially multifaceted and powerful.
Key features include real-time alerting and a robust mobile app for data access and management from anywhere.
We’re now looking at tools with more specialized benefits. Qlik is ideal for organizations where data might be more limited, difficult to clean, or just considered “incomplete.” It’s ideal for organizing and analyzing even these types of “difficult” data, providing insights which otherwise may not have been possible.
Key features include an associative engine which connects all available data in such a way that makes it possible to infer the conclusions described above, even in sub-optimal data sets.
Yet another tool which is ideal for pulling in data from all areas of the company and enabling it to “talk” to each other to generate useful analytics and reports. Pentaho is ideal for companies involved in production or manufacturing, as it specializes in integrating data from connected, IoT (Internet of Things) devices.
Key features include the above-mentioned IoT focus and advanced visual report- and analytics-generation tools.
Taking a more predictive, probability-driven approach, Spotfire draws heavily on artificial intelligence for organizations in competitive industries where which trend forecasting is a critical need.
Key features include real-time analytics, identification of potential data inconsistencies, and location-based analytics.
How Is Business intelligence being used?
We’ve discussed several business intelligence use cases already, both in the introduction (with our hypothetical companies), as well as some of the optimal uses for the BI tools above. Now, let’s take a closer look at some of the practical benefits of BI across various industries.
BI in Banking & Finance
- Via the data warehouse and BI tool, centralize access to disparate internal KPIs like lead time, cycle time, sales, and more, to analyze and make decisions regarding overall employee performance
- Analyze customer satisfaction in key areas like service and performance, identifying areas for improvement to increase value to customers
- Zero in on process improvements and service offerings that can help court targeted, high-value clients
- Track and process data on internal processes and company culture, using the results to identify process efficiencies and optimize the environment for growth
- Harness the potential of the data warehouse and BI tool to generate reports and other personalized content to improve customer relations
BI in Retail
- Use PoS & beacon data to offers discounts to customers when they enter the store
- Optimize inventory and stock management by drawing on RFID, PoS, and/or beacon data to order, fulfil, and stock merchandise
- Draw on continuously processed data to drive real-time merchandising updates, optimizing the customer path at a granular level and increasing spend
- Tailor inventory orders with demand and fulfillment forecasting informed by real-time supply chain analytics like seasonality, shipping distance, economic and market factors, and more.
- Turn personnel scheduling into an efficient, fast process by utilizing data based on promotions, season, historical sales, and competitor
BI in Healthcare
- Predict specific efficacy scenarios for different medications and treatments on a patient-by-patient basis using data drawn from testing, wearable fitness devices, and the wealth of historic data.
- Centralize and provide easy access, via BI tools, to the entire contents of a patient’s medical history, collected over years from numerous sources.
- Create easily understandable charts and graphs with intricate, visualized details on patient health, treatment plans, test results, medication regimens, and more.
- Identify disease risks with increased accuracy based on both personal medical data and a broad range of environmental factors and data
- Improve and streamline communication between medical care providers when a patient is visiting different facilities and specialists.
BI in Manufacturing
- Store and optimize data pulled from existing connected, IoT devices which previously had separate, isolated data management and storage systems, increasing the ROI for these installations.
- Analyze and classify production alarms and errors in real-time, allowing for faster diagnostics and remedies.
- Install and optimize predictive maintenance protocol based on machine and process data, creating a more effective practice than simply following set schedules.
- More efficient automation through continuous limit monitoring, reducing or eliminatingmissed alarms and allowing for closer limit tolerances
- Data warehouse and real-time analytics are key to the innovative cyber physical systems that will define Industry 4.0 – improvements in efficiency, quality, safety, and more.
BI in Supply Chain Management
- Fully integrate, normalize, and analyze data from all steps of the supply chain, from raw material through to the facility and/or retail location itself, to identify efficiencies and improve forecasting.
- Evaluate current suppliers and identify potential new partners through historical data: on-time delivery, damage rate, customer satisfaction, and more.
- Anticipate, respond to, and neutralize cost fluctuations with historical and real-time global data
- Optimize ordering and delivery schedules more quickly and efficiently
- In real-time, identify and respond to abnormal fluctuations in commodity pricing, adjusting ordering and inventory accordingly and immediately.
The Business Intelligence Process
With an understanding of what business intelligence tools look like, and some examples of how they can be put into use – and what the benefits are – let’s close with a look at the details of the process itself. These steps are the underpinning of what ultimately leads to better processes and results for your organization.
Pulling up the data
In this initial step, all aspects of the available raw data are assessed: scope, type, source, state/suitability, and more. This initial audit defines the methodology and, potentially, the tool or tools that will be used to clean, standardize, aggregate, and optimize data for centralized use.
Tool deployment and installation
Once the most suitable tool or tools have been identified, the service provider will install and deploy the tool, either as a managed solution, a SaaS solution, an on-site installation, or some combination of the above. Client and customer concerns and requirements, such as security and auditing, must be considered here. At this point, adoption and training efforts within the organization may begin.
Big data integration
With the BI tool installed, all existing data can be analyzed to provide the desired analytics and insights. More importantly, new data is continuously and efficiently being integrated alongside existing data, providing the basis of the real-time analysis and alerts that are a key component of BI.
A cloud solution is most often the right choice for the scale of data included and generated in a BI implementation. The right service provider will combine the storage benefits and efficiencies of the cloud with performance-maximizing practices like de-formalizing data for optimal results.
BI dashboards take all the data being constantly analyzed and provide a broad range of visualization and summary choices to make it presentable, usable, and actionable. Visualizations can range from the easily digestible, like charts and graphs (which may appear simple but are driven by advanced BI analytics), to more intricate formats like heat maps, candlestick charts, and beyond.