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Enterprise Business Intelligence: Definition, Architecture & How to Start


Trinh Nguyen

Jan 12, 2024

Enterprise Business Intelligence: Definition, Architecture & How to Start

Historically, business decisions were often based on prior experience, collected knowledge, or intuition. In other words, enterprises manually gathered information about customers, the market, or the competition over the years, along with transpired situations to take action.

While these methods still do play a role in present-day decision-making, the advent of business intelligence (BI) technologies has greatly shifted the way businesses determine their path. What’s more, incorporating artificial intelligence (AI) and machine learning (ML) into enterprise BI solutions opens the door for predictive analysis and identification of complicated patterns within datasets.

All of this enables organizations to predict industry trends and customer preferences. Real-time analytics boosts decision-making capabilities even further, helping businesses respond promptly to dynamic market conditions and emerging opportunities.

As a matter of fact, almost 80% of the companies surveyed have utilized business intelligence and data analytics tools at some point.

Today, we’ll discuss enterprise business intelligence, covering all the basics you need, from enterprise business intelligence’s definition and architecture to a step-by-step guide to kickstart your own BI implementation.

What Is Enterprise Business Intelligence?

Business intelligence refers to all the procedures and methods of gathering, storing, and analyzing data from business operations to provide a holistic view of a company. Enterprise business intelligence can be defined as the application of BI principles throughout a big organization. Through customer, market, sales, financial, and production data analytics, enterprise BI solutions can provide valuable insights that can guide your company’s strategy.

BI has a wide range of practical applications in corporate settings. For instance, businesses seeking to improve supply chain management use business intelligence to identify the causes of delays and any irregularities in the shipment process. BI software also assists in producing sales reports, keeping track of customer retention, and providing a thorough summary of prospects’ and customers’ journeys.

Inventory management, advertising, marketing, engineering, insurance, and IT services are among the industries leading this adoption. Coca-Cola is a case in point. The company takes advantage of AI image recognition technology to identify when photos of its drinks are shared online. Leveraging these data sources along with enterprise BI software allows the company to gain further insights into the identity, location, and motivations of beverage consumers. This data will fuel personalized ads, quadrupling the click rate compared to generic ones.

2 Types of Enterprise Business Intelligence

2 Types of Enterprise Business Intelligence

To comprehend the complex landscape of enterprise business intelligence, it’s necessary to recognize its two kinds in practice.

Strategic Business Intelligence

Strategic business intelligence primarily produces reports from an analytical data source, data mart, or data warehouse.

It focuses on four main elements: (1) massive data collection, organization, and storage; (2) data optimization for quick reporting and analysis; (3) identification of important business drivers through historical data analysis; and (4) finding answers to important business questions.

Here are some questions that strategic business intelligence can help answer:

  • Who are the most valuable customers?
  • Which customers are most likely to purchase additional products or services?
  • Which products are suitable for bundling?
  • Which territories or regions have the greatest projected growth?
  • What is the optimal price for our products?
  • What is the overall cost of customer acquisition?

Operational Business Intelligence

On the other hand, operational business intelligence generates reports from a transactional or operational data source, seeking to aid operational decision-making. This kind of BI also empowers operational managers and front-line customer-facing personnel with relevant, time-sensitive insights to help them in daily tasks.

While strategic BI often relies on graphs and charts, operational BI is more about providing clear, straightforward information to help with immediate tasks. Let’s say you need to notify a client about late payments. A simple message would be more helpful than a graph in this case. Then, tools like instant messaging, emails, and dashboards are crucial for operational BI.

Some tangible outcomes of operational business intelligence include:

  • Invoices
  • Meeting schedules and badges
  • Receipts
  • Shipping documents
  • Financial statements
  • Marketing mailing lists

Architecture of Enterprise Business Intelligence

Architecture of Enterprise BI

Enterprise business intelligence architecture is the foundation for building solid data management practices and analytics standards. It represents a roadmap for data consolidation and data accessibility.

Following are the 5 key elements of enterprise business intelligence architecture:

1. Data Integration

Data integration is in charge of collecting and combining data from multiple data sources into a processing and storage system. Data here comprises information from an organization’s transaction processing and operational systems, such as enterprise resource planning (ERP), customer relationship management (CRM), finance, human resources (HR), and supply chain management applications. External data sets such as market data, and customer lists can also be gathered and used for analysis by enterprise business intelligence platforms.

Effective data integration results in a cohesive and thorough understanding of every nook and cranny of your business operations. It enables more reliable and precise analysis later on.

A common method to integrate data is via ETL tools (Extract, Transform, Load), which gather data using batch processing. To put it simply, they take data out of the source systems, format it, and then load it into a data warehouse. This process allows for data deduplication, the detection and elimination of erroneous data from different data sources.

Other methods of integrating data are real-time data integration and data virtualization. The former involves updating data instantly as it changes, while the latter creates combined views of different data sets without storing them in a data warehouse physically.

2. Data Storage

Data storage serves as the basis of a BI platform, housing the transformed and processed data. It makes sure that business data is easily accessible for analysis and reporting.

Typically, data from source systems is consolidated and transferred to a data warehouse so that an enterprise BI solution can access it. For certain business data queries, there are also smaller data marts available.

A data lake could also become a part of a BI infrastructure. Similar to data warehouses, data lakes are centralized repositories, but they are used to store unstructured data in its original and raw format.

Moreover, there could also be an operational data store, which serves as a temporary holding area for data before it is transferred to the data warehouses.

3. Data Analytics

Data analytics is like the brain of enterprise BI platforms, which analyzes and interprets data. This element processes historical and real-time data, finds patterns, and produces insights using various tools and technologies, such as data mining and OLAP (Online Analytical Processing) tools.

It facilitates sophisticated data analysis, predictive analytics, and well-informed decision-making by allowing users to create predictive models and execute what-if scenarios via ML models.

4. Data Reporting

Reporting proves essential for the production and distribution of analytical summaries. It gives users an organized and thorough overview of the data analyzed, making it simple to comprehend the insights drawn from the data.

Furthermore, data reporting tools provide customization and automation, ensuring the right information reaches the right people at the right time.

5. Data Visualization

To present information straightforwardly, visualize data in dashboards, reports, graphs, and even recommendations, push alerts, and notifications.

This way, decision-makers can gain a clear and comprehensive understanding of business performance, trends, risks, opportunities, etc., and make rational decisions accordingly.

7 Steps to Start Enterprise Business Intelligence Implementation

Enterprise Business Intelligence Implementation

Almost every business has already employed enterprise BI to some extent. However, in many cases, that only means utilizing business apps or spreadsheets with limited built-in reporting features. These are essential tactical tools but not strategic.

Below are 7 fundamental steps to successfully incorporate enterprise business intelligence and elevate data analysis to a more strategic level.

Step 1. Build a Business Intelligence Strategy

To begin with, you have to draw up an enterprise business intelligence strategy. This means a blueprint that identifies your company’s weaknesses, strengthens its competitive advantage, and leverages analytics to reach insightful business decisions.

To put together a solid business intelligence strategy, it’s important to answer these questions:

  • What’s your business objective?
  • What resources are at your disposal to accomplish that objective?
  • What else do you need to achieve that objective?

Additionally, identify problems and obstacles your company has in day-to-day operations and conduct an audit of any data analytics solutions you currently have in place.

Step 2. Set up the Key Performance Indicators

After having a strategy on the plate, you need to set the KPIs to track your company’s growth. The KPIs should be measurable, attainable, and geared towards achieving the overall business goals.

Some KPI examples in enterprise business intelligence consist of:

  • Financial: Liquidity ratio, net income vs net earnings
  • Marketing: Customer acquisition cost, conversion rate
  • Project management: Returns on investment, productivity
  • Customer service: Customer effort score, net promoter score
  • Human resources: Net income per employee, cost per hire

Step 3. Build a BI team

Next up, it’s time to assemble a business intelligence team. Here, you can choose between partnering with an outsourcing BI service provider or forming an in-house team. Either way, below is a list of professionals who are typically involved in a BI project:

  • Application developer
  • BI Infrastructure architect
  • Business representative
  • Data administrator
  • Data mining expert
  • Data quality analyst
  • Database administrator
  • Metadata administrator
  • Project manager
  • Subject matter expert

In most big enterprises, nearly all the roles above are part of business intelligence teams. Smaller businesses with tighter funds and resources, however, might need to downsize and merge several positions into a single post.

Outsourcing the BI project to a dedicated BI services provider offers a multitude of advantages. This approach lets you leverage a broader talent pool, optimize cost, and ensure a quicker project launch. Furthermore, it grants you flexible scaling of resources, the expertise of specialized personnel, and access to already set-up infrastructures and advanced technologies, eliminating significant in-house investment.

At Neurond AI, we have experience building end-to-end enterprise business intelligence solutions. We will help you identify market trends, extract advanced analytics from your data, integrate it into your Enterprise Data Warehouse, and transform it into easily accessible dashboards.

We understand each business is unique, and so is their requirement. We begin by identifying and gathering requests from stakeholders, then together with you, agree on short-term and long-term goals, followed by choosing the right BI tool for your business. After that, we collect and centralize data from many sources before transforming and cleaning it. Then, we can derive business insights and provide interactive and customizable dashboards full of easy-to-understand information to help you make tactical and strategic business decisions. Above all, there is no one-size-fits-all BI solution; we personalize BI solutions to your specific business needs and goals.

Step 4. Design the Business Intelligence Architecture

After forming a BI team, the next step is to map out the business intelligence architecture. This design phase defines how well the enterprise business intelligence platform will meet your organization’s objectives, adjust to changing business requirements, and integrate with other enterprise systems.

Here, you have to decide which hardware and software components make up your business intelligence architecture as well as the process in which data will flow from the original source systems to a data warehouse or data mart and finally to BI tools. Plus, you’ll need to choose whether to deploy the environment in the cloud, on-premises, or hybrid. However, cloud deployments currently account for the majority of BI platforms.

A new architectural alternative is the DataOps approach, which aims to accelerate the production of data pipelines for BI and analytics applications. Apart from incorporating Agile software development principles, it also leverages automated testing, containerization, orchestration, and monitoring. A well-designed DataOps framework not only enhances data quality but also expedites the business’s delivery of data insights.

Similar to the Agile approach, DataOps often requires major organizational and cultural change. Examples of such shifts are the increasing collaboration among line-of-business stakeholders, data scientists, BI analysts, and engineers; reduced organizational silos between software development teams and IT operations.

Step 5. Select Enterprise Business Intelligence Platforms

Now, choose enterprise business intelligence software, an essential data visualization tool for reporting and analysis. While picking enterprise business intelligence tools, you should look into their scalability, pricing, necessary features, and easy navigation. Evaluate how well the platform accommodates future business needs and the level of vendor support. But overall, make sure that the enterprise BI solution you choose satisfies your unique business needs.

If your personnel for data analysis are all non-technical, you should prioritize an enterprise business intelligence solution that facilitates self-service analytics. Self-service BI tools enable users of various skill levels, not just those with programming or SQL expertise, to look into the data and draw insights without consulting IT. Data analysts can thus save time and IT resources to focus on more value-added projects. This generally boosts data access and analysis for every user in the organization, leading to stronger collaboration and quicker decision-making.

Some of the top enterprise business intelligence platforms available today are Microsoft Power BI, Qlik, Tableau, Sigma, Looker, Domo, and IBM Cognos Analytics.

Step 6. Plan and Conduct End-user Training

A crucial yet oftentimes overlooked element in implementing BI projects is training for end-users. More than anyone, business users should be trained to familiarize themselves with enterprise BI tools and processes to be aware of data governance, privacy, and security policies.

Step 7. Deploy and Test the BI System

Before launching, it’s vital to perform extensive testing on the environment to ensure everything functions as intended. Validate that all the components have been integrated successfully, that the data has been accurately represented, and that the solution satisfies your predetermined requirements.

After the pilot project is live, it’s time to assess the outcomes and compare them to the KPIs set in Step 2. In case your expectations are not met, we recommend reconsidering the architecture and making adjustments accordingly.

Revolutionize How You Make Business Decisions

Integrating BI into an enterprise can be quite a hassle, but it’s an inevitable move. However, if you outsource the project to an experienced team of BI engineers, not only will they handle all the difficult steps from building a strategy to deploying the enterprise business intelligence solution, but they will also provide you with a customized service based on your unique needs. No need to go through all the complexities of enterprise BI implementation anymore!

So, what are you waiting for? Neurond AI is here to help! With a strong track record of successful BI projects delivered and a team of highly technical experts, we’re confident in our BI consulting and implementation services.

Contact us today to uncover the full potential of your data!