What is Business Intelligence
and how can process intelligence maximize BI's value?
Big data has earned its name. By 2025 global data is estimated to reach 181 zettabytes – that's 181 followed by 21 zeros. If you stored that data on Blu-ray discs, the stack would reach from the Earth to the Moon…1,332 times.
Business intelligence (BI) seeks to extract valuable insights from this avalanche of data. But translating BI insights into meaningful process improvements requires something more — process intelligence.
1. What is business intelligence?
Business intelligence (BI) is a collective term for the strategies, technologies and processes that organizations use to turn their data into actionable insight. With BI, companies collate, integrate, analyze and visualize data from across their operations - unifying multiple (often unconnected) datasets into a coherent, interrogable source of business truth.
Business intelligence serves a number of key functions, including:
Equipping business leaders with precise data - both historical and real-time – supporting both tactical and strategic decision-making.
Providing dynamic, digestible business-wide reporting and performance measures.
Highlighting the interdependence and the interconnectedness of different business functions on one another.
Identifying market trends, opportunities and potential challenges.
Enhancing operational efficiency and productivity.
Gaining competitive advantage through data-driven insights.
If business data is a raw material, business intelligence is what transforms it into a useful, user-friendly foundation for strategic decision-making.
2. How does business intelligence work?
There is no single cookie-cutter approach to business intelligence. The range of BI tools and BI software provides multiple routes for a business intelligence analyst to slice and dice the data to generate business value. However, typical BI solutions tend to include the following components:
De-silo the data
The more relevant information included in a BI solution, the more impactful the insight it provides. The first step therefore is to identify and collate the data to be included in the BI model across both internal and external sources. Everything from databases, system logs, and transactional systems, to spreadsheets and third-party data flows.
Centralize and integrate the data
Data integration is the next crucial step, aggregating the different data sets into a central location such as a data warehouse, data mart, or cloud business intelligence platform. However, because this data is held in multiple disparate fields and formats, it must first be cleansed and aligned. BI tools will commonly use the ETL (extract, transform and load) methodology to carry out this process. Raw data from each source is extracted, cleaned, aligned and loaded into the central repository. This allows the data to be queried easily, creating an efficient business analytics process.
Dig into the data
With data integration complete the data discovery phase commences, and the process of gathering business intelligence insights begins in earnest. Different BI tools offer different data mining and data analysis solutions designed to identify trends in the information and provide insight into business performance. Some business intelligence software will go a step further with their data analytics, offering predictive modeling and forecasting to suggest future trends for the business.
Bring the data to life
Data visualization is a key phase in BI reporting – it’s how data becomes business intelligence insight for the business user. BI solutions typically provide interactive dashboards and visualizations (such as charts and graphs) to make outputs from data discovery easy to understand. It also provides business-wide stakeholders with a shared understanding of past, present, and (potentially) future business performance.
Build on the data
Comprehensive, accurate insights, combined with business analytics to forecast future performance based on current parameters, provide business leaders with a solid data foundation on which to build or refine company strategies.
3. Types of business intelligence analysis
While the specific executions vary, BI solutions provide business intelligence analysis across four main categories:
Descriptive analytics: This is the process of using current and historical data to identify business trends and to spot relationships between functions that might otherwise remain hidden.
Diagnostic analytics: If descriptive analytics identifies what’s happening, diagnostic analytics seeks to find out why – establishing the root causes of business performance outcomes or metrics.
Predictive analytics: This describes the process of using historical and real-time business data to project or model likely future business outcomes. Commonly leveraging artificial intelligence (AI) and machine learning (ML), these forward-looking models assist strategic planning and business transformation initiatives.
Prescriptive analytics: This is the essence of data-driven decision making, collating all data points in the BI model to suggest what a business should do to maximize its chances of achieving its targets. Again, this form of BI solution tends to leverage AI’s capacity to analyze big data and apply contextual understanding in order to suggest what an organization ‘should’ do.
Not every business intelligence tool is created equal. So it's crucial to clearly define your BI requirements and desired outcomes in order to identify the system that aligns best with your specific needs.
4. Business intelligence tools
The business intelligence tool box is crammed with different means to extract, analyze and present important insight. Here’s a snapshot of some of the most common BI tools:
Data mining
As the name suggests, this is the process of digging into organizations’ datasets and analyzing the information in order to identify significant patterns and relationships. These can underpin strategic decision-making and forecasting with data driven insights.
ETL – Extract, Transform and Load
ETL is the process by which many BI tools consolidate multiple data sources into a single repository of business information that serves as the foundation for analysis. Businesses extract the data (from transactional systems and databases for example). They then cleanse and align (or transform) into a consistent workable format, before finally loading it to a central BI resource.
OLAP – Online Analytical Processing
OLAP technology enables business users to query multidimensional data – such as a centralized BI data warehouse – to reveal insight from a range of different perspectives. For example, to analyze sales performance across different product categories, regions, and time periods, a business intelligence analyst might use OLAP to slice and dice consolidated sales data cube from all distribution channels.
Dashboards
Dashboards are the poster child for business intelligence, collating tailor-made KPIs, visualizations and reporting summaries in intuitive, interactive insight overviews. Typically BI dashboards enable users to filter, drill-down and query the data in real-time – enabling a broad cross-section of users to access the information they need.
Monitoring and alerts
Business intelligence professionals use BI tools to monitor key performance metrics and provide issue alerts if these metrics exceed specified thresholds. For example, if a key customer’s order volumes fall below agreed levels for two months in a row, a notification can be sent to the account manager to investigate with the client. BI alerts provide early warning of potential issues or significant shifts in trading conditions, enabling business leaders to take proactive mitigating action.
Reporting
BI solutions enable users to create and distribute standard or bespoke reports and insight summaries. These can be both static snapshots or dynamic reports that refresh as the data changes. The consolidated, cleansed data source ensures BI analysts can filter reports to address specific KPIs, business functions, and hierarchies quickly and easily.
Visualizations
The axiom that a picture is worth a thousand words finds a happy home in business intelligence. It’s often easier for people to process and understand potentially complex business information when it’s rendered graphically rather than verbally. Seeing a trend can be simpler than reading about it. Data visualizations allow time-poor executives to access key insights instantly. Most BI platforms offer a wide range of standard and specialist visualizations – including charts, graphs, diagrams, plots, maps, gauges, timelines and tables – that can be filtered or used as filters to drill down into the data.
5. Benefits of business intelligence
By turning business’ big data into actionable insights, business intelligence delivers significant benefits. These include:
Better business decisions: BI puts data-driven insights at the heart of business strategy. The provision of comprehensive, accurate business data helps business leaders make smarter, faster decisions in pursuit of strategic objectives.
Uncovering value opportunities: Business intelligence software lets users visualize data from multiple departments all in one place, providing filters and drilldowns based on parameters and metrics spanning the different functional areas. This can reveal interdependencies and opportunities for creating value that would not be apparent when looking at any one department's data in isolation.
Improved efficiency and productivity: Providing a clear view of business-wide operations, business intelligence helps pinpoint areas of inefficiency, cross-functional bottlenecks and opportunities to enhance internal processes.
Enhanced customer experience: Collation and analysis of customer data within a BI platform enables organizations to provide more personalized experiences, address areas of friction and promote customer loyalty.
Employee satisfaction and collaboration: Business intelligence tools democratize business insights. Data visualizations and reporting enable all authorized business users to see the impact of their work on business performance in real-time. Similarly, BI’s shared perspective of business data fosters greater cross-departmental collaboration and cooperation.
Gain competitive advantage: BI reporting can be used as a market intelligence hub, using data analysis to identify market developments, customer trends, and competitor initiatives to help shape business strategies as well as product or service innovation.
One version of the truth: A well-executed BI strategy delivers an authoritative, shared, desiloed, and trusted source of operational and strategic insight. This helps promote common success measures, shared understanding and prevents interdepartmental data veracity disputes.
Offsetting operational risk: Reporting real-time shifts in customer, competitor, market or operational data, BI can act as an organization’s listening post or early warning system. This enables them to identify potential risks early and take action to offset or minimize negative impacts.
Compliance covered: Business intelligence solutions also help adherence to industry regulations by monitoring and reporting developments in data impacting regulatory compliance.
6. Business intelligence challenges or disadvantages
The previous section outlines the many advantages to using a BI tool or tools, but there are a number of disadvantages to business intelligence solutions – or at least challenges to consider. These include:
Cost: For many small businesses, the biggest disadvantage to BI is upfront cost – be it licensing, personnel training or hardware. Even self-serve models, cloud-based solutions and mobile BI can incur too much of a financial burden.
Data quality: Just like databases, CRMs, and artificial intelligence (AI) systems everywhere, business intelligence is only as effective as the data that feeds it. Incorporating poor quality or less relevant data into a BI model can slow the analysis process and potentially corrupt results.
Data volume and structure: Businesses generate and parse a glut of data every day. Some BI systems can struggle to integrate and analyze data from diverse sources presented in diverse formats. Further, as much as 80% business data is buried in unstructured formats (like emails, contracts, or free text fields) that BI software finds difficult to use.
Data security: Consolidating the treasure trove of sensitive business data that drives BI necessitates stringent security measures to protect it – both from a business continuity and regulatory perspective.
Resourcing: To set up, maintain, and generate maximum ROI from BI solutions requires specialist expertise (such as a business intelligence analyst). This places a strain on resourcing or training budgets.
Multiple systems: Organizations often require more than one business intelligence solution to cover their needs. This increases integration complexity and cost.
Conflicting interpretations: Even though BI draws its insights from a single source of the truth, different stakeholders are likely to draw different conclusions. Business intelligence software can’t draw definitive, irrefutable conclusions.
Change management and user adoption: Employees may resist adopting business intelligence solutions. Introduction of a centralized insight resource like BI can be perceived as an imposition or a dilution of function-specific reporting. A BI deployment may require a change management component.
None of these challenges constitute reasons not to deploy a business intelligence system, but rather serve as considerations business leaders need to factor in ahead of investment.
7. Examples of business intelligence
Business intelligence is no longer the preserve of data scientists or business analysts. By summarizing, simplifying and visualizing consolidated business data, BI provides ready access to business insights for subject matter experts from all business functions. Here are some common business intelligence use cases.
Marketing and customer experience
Marketing teams orchestrating omnichannel campaigns can use business intelligence solutions to track and visualize customer engagement success metrics across each channel (such as views, clicks, engagements, purchases, survey completions or form fills). This not only enables marketers to compare return on advertising spend (ROAS) between different channels but also between media suppliers in the same space – such as two rival programmatic demand-side platforms (DSPs) – and even between different creative campaigns. This insight is used to rechannel budgets for current and future campaigns. It also provides greater understanding of customer media and message preferences, helping marketers create more personalized, tailored customer experiences.
Sales
Sales professionals commonly use BI dashboards to access insights on both business-wide KPIs and granular sales performance metrics (by customer or sales personnel). At the macro end of the scale, this might include tracking and analysis of profitability, customer lifetime values or discounts, revenues versus targets, or visualization of the overall sales pipeline. A real BI superpower, however, is to be able to drill down into the company figures to monitor sales performance KPIs per region, team or individual and, where necessary, revise activity targets and training provision.
Supply chain and logistics
BI provides significant insights for both supply chain businesses and supply chain management functions. For logistics businesses (such as air, road, rail or sea freight companies) business intelligence allows close monitoring of customer volumes and profitability metrics – both individually and collectively. These can be segmented and filtered by key data points such as customer type, cargo type or geographical bias.
These insights identify, for example, customer volumes versus contracted commitments, key drivers of profitability, and identification of ideal customer profile sub-segments. For logistics functions, BI reporting also enables close monitoring of stock or raw material inventory levels, supplier compliance metrics, returns volumes and vehicle utilization metrics.
Finance
Business intelligence can be a powerful tool for businesses to monitor – and gain deeper insights into – the financial health of their organization. Finance teams can use interactive dashboards and visualizations to provide a dynamically updated, instant overview of KPIs like revenue, costs, profitability, days sales outstanding (DSO), days payable outstanding (DPO), revenue, and cash flow, in real-time.
Any of these metrics can be sliced and diced within the dashboard by the performance metrics from all business functions. In this way business leaders can see some of the operational drivers of financial performance – for example the degree to which on-time in full deliveries impact repeat customer business.
IT
While typically responsible for the smooth operation of business intelligence software, IT professionals are also among its beneficiaries. This can include visualization of support ticket data to identify consistent infrastructure performance issues or monitoring network traffic patterns and capacity to minimize downtime and spot anomalies. BI also enables IT professionals to identify over-utilized or under-utilized assets and re-allocate resources like servers, storage, and cloud instances more effectively, based on demand patterns.
8. Business intelligence vs Process Intelligence
There’s no question that there are similarities and overlaps between business intelligence and Process Intelligence. They are complementary techniques and technologies. Both practices involve the collection, integration and analysis of business data. Both use dashboards and visualizations to make insights accessible, with the aim of identifying value opportunities and supporting data-driven decision-making.
But there are important differences of scope and impact. Where BI combines current and historical data sets to describe what has happened, is happening and might happen, primarily during single process steps, Process Intelligence goes much further than that.
Celonis Process Intelligence uses process mining techniques such as object-centric process mining (OCPM) to extract process data from any source (ERPs, CRMs, SCMs, data warehouses, etc.), but not just for individual processes – for end-to-end, cross-functional workflows. This process data enables businesses not only to monitor business performance, but also to:
Perform root cause analysis.
Identify otherwise hidden opportunities to create value.
Understand which processes to optimize in pursuit of strategic targets.
The Celonis Process Intelligence platform, with the Process Intelligence Graph at its heart, then augments this process data with standardized process knowledge and best practices evolved over thousands of Celonis deployments. Consequently, Process Intelligence shows business leaders how processes can be enhanced to maximize value, providing an enablement layer for intelligent process automation and technologies like AI.
The Process Intelligence Graph creates a living, breathing digital twin of an organization that not only enables it to simulate process changes before enacting them, but more fundamentally allows businesses to fully understand their own complex workflows. It provides a common language for the business. A connective tissue between business units and processes.
9. How Process Intelligence supercharges business intelligence
Process Intelligence increases the insights and impacts BI can deliver by connecting it to an organization’s greatest lever for change: processes. By enriching and empowering business intelligence data with detailed process insights, Process Intelligence is a force multiplier for business intelligence. It helps pinpoint anomalies negatively impacting KPIs and drives the discovery of new value opportunities.
And because Process Intelligence uses process knowledge and AI, it provides organizations with a clear understanding of how and where specific processes should be improved to address KPI concerns identified in BI reporting.
Crucially, this all shortens the time to value realization from BI reporting insights – as the following case studies illustrate.
Celonis business intelligence case studies
Vetter Dr. Samuel Kunze, Head of Process Excellence, at life sciences organization Vetter emphasized the speed to value impact Celonis made to its business intelligence function. Sometimes it took months to get relevant insights with common business intelligence tools before Celonis was implemented. "This has improved drastically. We can define performance indicators quickly and feed them back to our operating business," he said. But providing more than just insights, using Celonis Process Intelligence enabled Vetter to enhance its AP processes – with 99% of potential cash discounts being realized and cycle time of its deviation process reduced by 15%.
Globus Luxury retailer Globus deployed Celonis to gain full transparency over its shipping and eCommerce processes as well as to maximize execution capacity. "The devil is in the details," said Globus Chief Digital Officer Andreas Hink. "We found many little inefficiencies – cumulatively fixing them led to a noticeable reduction in our cancellation rate…This simply wasn’t possible with the tools we used before. We have many different business intelligence systems, and it took a lot of time and effort to manually analyze the data."
Process Intelligence insights delivered straight to Power BI
Additionally, business intelligence professionals can now access Celonis Process Intelligence insights directly from Microsoft's Power BI platform thanks to Celonis' Connector for Power BI.
"With the Celonis Connector for Power BI", said Marta Pohl-Steinhausen, product manager for Celonis Power BI Development, "we make it easier for millions of employees to improve the way they work with Process Intelligence from the Celonis platform, delivered natively in their familiar Microsoft Power BI environment. The Connector will further enlighten an organization’s reporting to help make better operational decisions."