7 Real-world Wins from Companies Using Business Intelligence
Businesses around the world are increasingly relying on their data assets to answer essential operational and strategic questions about their organizations, their markets, and their audiences. However, without proper systems in place to handle the growing data scale that they are facing, much of the information gained from the data is lost due to inability to manage these growing complexities.
There are countless examples of organizations in nearly all industries that have managed these complexities with business intelligence systems:
- A mortgage software provider uses business intelligence to manage its large customer contact list for precision marketing.
- A career-based social network uses business intelligence to process over 50 million system events per day to make real-time product roadmap decisions.
- A global industrial manufacturing company uses business intelligence to produce more precise demand forecasts for efficient manufacturing planning.
- A children’s fashion company uses business intelligence to process retail data from 830 stores to streamline the customer experience.
- A French restaurant operator uses business intelligence to analyze 328 million receipt line items per year to better manage their restaurant operations.
- A sports betting provider uses business intelligence to integrate 60 data sources and manage 50TB of data to help personalize their customer experience.
What is business intelligence (BI)?
Business intelligence (BI) is the set of technologies and processes that organizations use to make more effective, data-driven, strategic business decisions.
With many enterprise data warehouses and data lakes now storing petabytes or more of both structured and unstructured data, business intelligence systems are becoming essential enterprise infrastructure for providing real-time consumption of data by all members of an organization. Access to such data can no longer be siloed to the confines of the data science department alone.
1. Business intelligence enables precision marketing in the banking industry
Ellie Mae provides software to leading financial institutions to automate their mortgage applications — ensuring for regulatory compliance and high loan quality.
With about 35% market share of U.S. mortgage applications, Ellie Mae manages a significant amount of important customer data. However, before implementing a proper business intelligence system, this list was filled with duplicate records and contained a significant amount of stale or incorrect information.
By implementing systems to properly cleanse and deduplicate data, analysts no longer have to rely on siloed spreadsheets dispersed across different divisions. They can now make sound strategic business decisions derived from their shared analyses. Quality data now delivers trusted insights to BI users, while sales, product, and financial analytics enable precision marketing through better segmentation and identification of cross-sales opportunities.
2. BI creates real-time insights for agile decision-making in social media
XING is a career-based social networking platform based in Hamburg, Germany. The platform has more than 15 million members, producing over 50 million system events per day, and the underlying data is heavily regulated by the GDPR.
Managing this type of data scale and governance is complex. XING accumulates a number of small files and diverse file formats from their analytics systems. The company needs the ability to process and aggregate this data to produce consumable formats for analysts and managers.
By bridging and centralizing their various data systems, XING was able to dramatically reduce the processing overhead of their transformation jobs and simplify the integration process to allow for less technical members of the organization to become more involved. By doing so, management can get real-time access into user behavior, allowing them to make much faster decisions on the product roadmap.
3. BI facilitates production planning in manufacturing
SKF is a global manufacturer and supplier of bearings, seals, mechatronics, and lubrication systems. Based in Sweden, the company is represented in more than 130 countries worldwide and has approximately 17,000 distributor locations.
As SKF has such broad geographic coverage and product diversity, they need to constantly forecast market size and product demand to adjust their manufacturing. Traditionally, SKF analysts — like so many analysts — created and maintained those forecasts in complex Excel files. However, the overhead of maintaining and reconciling disparate analyses can be overwhelming. SKF often needed days to produce a simple demand forecast.
“We weren’t able to distribute data from the files in any standardized way. We realized we needed a data vault or data warehouse that would represent a single source of truth, would be easy to maintain and update, and would house data that could be understood and used by everyone in the organization.” — Fritz Ulrich Dettmer, Manager of Business Intelligence.
By centralizing their data assets into a single system, SFK was quickly able to start sharing their data and analyses between a number of different departments within the organization — including sales, manufacturing planning, application engineering, business development, and management. As they produce a large number of product variants, they are now able to quickly aggregate demand forecasts, no longer needing to debate data integrity between departments. This allows management to efficiently plan their future manufacturing.
4. Business intelligence improves sales and stock analysis
Kidiliz Group is an international children’s fashion company that operates in over 40 countries around the world, with 15 in-house and licensed brands. Based in France, the company generates over 50% of its revenue outside its home country and sells through over 11,000 retail locations, including 830 of its own stores.
As Kidiliz began collecting a large amount of retail data in their legacy ERPs, they realized they needed to build better business intelligence systems to allow for more effective analysis of sales and inventory across their retail network. The company is well known for its adaptability to changing fashion trends and thus having a robust data system for tracking customer and product data was essential to operational success.
Kidiliz developed and integrated 20 data flows on retail activity — such as register transactions across their network. They are now able to analyze their sales and stock data to learn how to provide the best experience to their end customers by streamlining their in-store experiences.
5. Modern BI tools accelerate insights in the restaurant business
Groupe Flo owns and operates a number of high quality restaurant brands across France. As part of their technology modernization program, the group set out to build a strong business intelligence system to allow them to recover and process all the receipts for their restaurants, which include over 328 million line items per year across their 300 sites.
Before developing their modern BI system, much of their integrations and analytics were performed manually, which made processing large amounts of receipt data infeasible. Furthermore, legacy systems didn’t allow the marketing department to drill down into more granular data — such as sales of a single restaurant at a specific time of day.
By automating the integration processes, Groupe Flo is able to transmit restaurant data to headquarters within 10 minutes of creation and then consolidate it for consumption by the next morning. In addition to speeding up their analyses, they can now also cross-reference their sales data with other data sources — such as weather and holiday datasets — to get better insight into customer patterns. This provides Groupe Flo valuable insights that they can directly use to adjust operational aspects such as store and labor hours.
6. Business intelligence allows for real-time insights in sports betting
Tipico is a leading international provider of sports betting and casino games for online and retail businesses. Every day, Tipico’s data warehouse processes 675GB of data and receives 150GB of real-time messages from numerous internal and external systems.
Tipico deployed a business intelligence system entirely on AWS that integrates data from as many as 60 data sources, manages about 50TB of data, sends data to 20 external systems, provides real-time alerting to analysts and IT, and allows for quickly deploying and testing AI models.
Both analysts and non-technical managers can easily query a single, self-service system to get comprehensive operational and customer data. This allows them to quickly respond to changes in the business and provide a better product for their customers.
Going forward, Tipico believes they’ll be able to use this integrated data to provide real-time product customization to their end-users to significantly increase customer experience and retention.
The next steps to improving your business intelligence
A good business intelligence system allows managers and analysts alike to quickly answer comprehensive strategic and operational questions about their organization. By integrating data sources and providing central systems for analysis, BI technologies can provide a central source of truth for business data. This both increases efficiency and makes decision making a more scientific process backed by hard data rather than relying on intuition and guesswork.
Properly integrating data sources for real-time analysis of business data has become an essential process for all modern businesses. Talend Data Fabric offers a suite of applications to help businesses properly manage their data in all environments — multi-cloud and on-premises — by providing a unified and collaborative system for securely collecting, processing, and governing large amounts of data at the speed of business.
To see how Talend can benefit your organization, try Talend Data Fabric to learn for yourself how business intelligence can help your business make smarter data-driven decisions.