This article dives deep into the world of business intelligence integration. We’ll explore the basics, walk you through the integration process and unveil the benefits BI brings to your organization.
Industry experts share their valuable insights, and there’s a curated list of BI tools. We’ll examine the key features to consider and explore the future trends shaping the BI landscape. Let’s dive in!
Article Roadmap
- What Is Business Intelligence Integration?
- Steps
- Benefits
- Best Practices
- BI Software
- Features to Consider
- BI Dashboards
- Future Trends
- Next Steps
What Is Business Intelligence Integration?
BI integration is the process of combining data from business systems, external sources and BI tools. Integrating systems becomes necessary when you need more information than what your software offers.
Business intelligence integration incorporates data mining, visualization, analysis and reporting. It’s like a software handshake made possible with APIs, middleware and other methods.
In an exclusive interview, Anurag Sanghai, Principal Solutions Architect at Intellicus Technologies, explained the term and how it relates to self-service analytics.
I envision data intelligence extending to end users through the development of a cohesive ecosystem. This ecosystem would integrate disparate applications across the organization, breaking down silos and enabling seamless correlation of data to create a single source of truth. With everyone accessing the unified system, users save time and effort, fostering efficiency.”
Steps
Since BI integration is about using data from other systems, planning involves sources and connectivity.
Identify Your Goals
Identify your goals to determine your BI software requirements. Do you want to uncover money sinks? Customer acquisition is a primary cost center, and knowing where you can cut back would help.
Perform a data assessment using the five Vs. What variety, velocity and volumes of data do you expect? Will there be fluctuations? Which are your sources? Which data will be of value, and how will you determine its authenticity?
Determine the techniques to cleanse, transform and store data.
Choose a BI Platform
When selecting a business intelligence solution, prioritize data access, interactivity and self-service BI. Deploy your BI system by establishing connectivity with your enterprise system, or CRM, in this case.
Read more in the Features To Consider section.
Gather Data
This step involves data preparation, an umbrella term that includes quality management and data cleaning.
Data preparation is the process of getting data ready for analysis and further processing. It involves data collection, data cleaning, transformation and validation.
Collect data using any of the following techniques.
Say you want to use your business intelligence functionality within your CRM tool.
Note: For this discussion, we’re using a CRM reference, though it applies to other domains as well.
- APIs: Your BI tool can access your customer data by proving its credentials to the CRM API using an API key. Once you map the CRM data to the BI system’s data model, it will interpret the data correctly.
- Middleware: It’s a program acting as an abstraction layer between the BI tool and the CRM API. Middleware helps in the flow of data CRM and BI tools.
- Data Warehouse: It acts as a bridge between your BI and CRM tools. After ETL, the warehouse transforms the CRM data to fit its schema and uploads it. Your BI tool connects to the data warehouse using connectors and APIs.
- Data Replication: This technique involves continuously copying data from the CRM to the BI tool. But the BI tool cannot write data back to the CRM.
- Data Virtualization: This method creates a logical view of your CRM data without moving it from the source. Virtualization hides the database structure while providing single-point data access.
- Semantic Layer: This intermediate layer retrieves data from the CRM and transforms it into a business format. It maps CRM data elements to business terms using data models.
According to Nate LaFerle, Principal at Remisphere Digital, data quality management is severely underrated.
Successful BI projects include a significant data quality component. Organizations often overlook the business processes that contribute to bad data quality and instead focus on cleaning up bad data once it’s already in their systems. This allows data issues to flow to the BI platform before they can be fixed, and this can undermine trust in the solution. You need to develop a holistic strategy to prevent the most common issues from happening in the first place while proactively intercepting anything that slips through the cracks — before it makes it into (an) analytics solution. The old adage ‘garbage in, garbage out’ has never been more true, and this will become increasingly important as organizations move to leverage these datasets for AI/ML training.”
Refer to our data preparation software product directory for more information.
Analyze Data
Design BI dashboards and reports that can pull data from your business systems. BI integration gives you the tools to analyze data beyond basic CRM information.
Imagine viewing website traffic, social media data and marketing campaign results within your CRM.
Read our BI Adoption and Implementation Strategies article for tips.
Besides CRM, how does BI integration elevate your game?
Primary Benefits
While your business systems focus on a particular area, BI tools can pull in external data that relates to your metrics. BI tools are scalable and have better analysis abilities.
- Track Inventory: Using your inventory system data within your accounting platform helps you track the supply chain. Plus, it helps match the inventory value on your financial reports with the available stock.
- Perform Resource Planning: ERP systems offer reporting but are limited in analysis. BI tools identify trends that basic ERP reports might miss. BI reporting includes comparing time periods and product lines.
- Drive Marketing & Sales: Grouping customers will give you data to personalize sales pitches. You can reel in buyers who are turning away. BI reports will uncover the most profitable marketing channels and campaigns.
- Delight Customers: While a CRM report presents a snapshot of the data, BI tools offer more interactivity. Want to analyze the correlation between wait times and customer satisfaction scores? A BI tool will have the answers.
Best Practices
Follow data best practices to get the most out of your integration.
Don’t Overdo It
When connecting your business systems to your BI tool, looking in the rear-view mirror too long can be disastrous.
LaFerle explained.
It’s important to consider the relevancy of the data you’re migrating to your BI solution. Be careful of relying too heavily on your past historical data for your future business insights, particularly if your organization has evolved or grown through M&A. You can’t rewrite history to apply to your business today — and there’s probably much more to learn from the last 12 months than from the last 12 years.”
Secure Data
Grant users minimal access and require strong authentication to access the application. Encrypt data at rest and in transit to make it unreadable without a key.
Use API protocols to monitor access and identify suspicious activity. Conduct regular vulnerability scans and fix any weaknesses.
Start Small
Introduce BI for some use cases to validate the integration points. Technical issues are easier to resolve when you don’t have a whole lot of ground to cover.
You can get tangible results earlier and assess the integration’s value earlier in the project lifecycle. But, it’s essential to keep your eyes on the prize when starting small, so watch out for scope creep.
BI Software
Opt for a reporting tool to get a handle on your metrics. Or do one better — get a big data platform that delivers information within your systems using embeddability.
Browse our product directories, which are linked here, to select a suitable BI platform.
- Reporting Software
- Business Analytics Platforms
- Big Data Software
- Predictive Tools
- Embedded Software
Features To Consider
Connectors and APIs are the keys that unlock data from databases, files and cloud systems. But it’s unusable without data management for high-quality insight.
With great power comes great responsibility. Maintaining data integrity requires restricting access to only the necessary users. But, they should still have access to data when needed. Which brings us to live data.
You need real-time data as outdated metrics can skew results. AI-ML can deliver timely insight if you set it up.
Add these BI capabilities to your requirements checklist.
- Data connectors and APIs for integration
- Data management
- Dashboarding
- Data governance
- Live analytics
- Location data
- AI-ML
- NLP
Account for fluctuating data loads by verifying if the BI tool works with Apache Hadoop.
BI Dashboards
Building effective dashboards requires careful consideration and planning.
Define your goals and keep the users in mind. Who will be using them — the sales team, marketing department or accounting executives? What are their technical skills, roles and data needs?
Identify the relevant KPIs for your target audience that align with your overall business objectives. Give users the option to link to other dashboards or reports.
Choose appropriate visualizations — some BI tools suggest suitable graphics. Add filtering, sorting and time period selection options. Include drill-down options to view hierarchical data.
Read our Dashboard Best Practices article for handy tips.
Future Trends
Staying informed about upcoming BI trends will help you select a platform for the long term.
Active Metadata
Metadata helps locate data; it’s like your address book. So what’s new? Active metadata accelerates data discovery by quickly pulling data from sources. Additionally, it enables team collaboration.
Anyone with basic computer skills and knowledge of business terms can search for datasets using standard terms. And that’s not all. Active metadata is self-learning.
We saved the best for last. You can set it up to send data-related alerts and classify sensitive data automatically. What’s more, it drives action by making decisions for you, like halting workflows if something goes wrong.
Domain-Centric LLMS
Thanks to the semantic layer, companies with developer resources are developing purpose-specific language models. An LLM that understands manufacturing KPIs would be helpful for companies engaged in production.
Sanghai clarified:
While LLMs are capable of being trained on raw data, the presence of a semantic layer offers a more organized and consistent format, potentially enhancing training efficiency… It’s essentially a synergistic relationship where the semantic layer and LLMs complement each other effectively…. The semantic layer provides the structured data necessary for training, while LLMs, in turn, deliver insights in a human-readable format…. This symbiotic relationship amplifies the value of both components, resulting in more effective data processing and analysis.”
Next Steps
When selecting a BI platform, remember to focus on features that match your specific needs. Get our free BI requirements template to start your software search.
What are your takeaways from your business intelligence integration? Share your thoughts in the comments section below.
Contributing SMEs
Nate LaFerle has been a trusted advisor to some of the world’s largest organizations tackling complex data migration & governance challenges, leading high-impact global project teams at clients including 3M, American Airlines, and Johnson & Johnson. As a consulting talent leader, he oversaw the career & learning programs for over 600 global data consultants and partners at Syniti, a leading data management software and services provider. He continues to advise clients and deliver solutions through his independent consulting practice, Remisphere Digital (www.remisphere.com).
Anurag Sanghai, Principal Solutions Architect with Intellicus Technologies, is an experienced IT professional and thought leader with over 14 years of expertise in consulting, architecting and implementing state-of-the-art data analytics and business intelligence solutions for global clients.
He’s worn many hats, including those of Full Stack Engineer, Data Architect, Engineering Manager and Solutions Architect. His experience includes developing enterprise data warehouses, analytics, trends and forecasting systems, and data visualization initiatives.
An avid innovator, Sanghai is a U.S. patent holder.