A Complete Guide on Business Intelligence for Enterprises

The evolving landscape of business intelligence offers enterprises endless possibilities to improve decision-making, streamline operations, and gain a comprehensive understanding of their markets, customers, and internal functions.

Furthermore, enterprise Business Intelligence tools and strategies are continuously evolving to handle the increasing number and variety of data sources. The incorporation of artificial intelligence and machine learning into BI systems allows for predictive analytics, anomaly detection, and the discovery of complex patterns within datasets.

This empowers businesses to anticipate market trends and customer preferences. Real-time analytics further improves decision-making capabilities, enabling companies to quickly respond to changing market conditions and emerging opportunities.

In this guide, we have brought you an A-Z list of details that you need to incorporate business intelligence in your operative model with complete confidence.

Starting with the basics of what business intelligence stands for, we will traverse through the different facets of the technology to then look into the role that BI plays in making businesses successful.

What is Business Intelligence?

Business intelligence, or BI as it’s usually called, uses services and software to convert data into actionable insights. These insights are used by the organization to make better tactical and strategic decisions.

Use cases of BI in corporate setup can be seen in two different ways. For instance, companies aiming to enhance their supply chain management utilize BI to pinpoint the reasons behind delays and detect any inconsistencies within the shipping process. In addition to this, BI also helps in monitoring member retention, creating sales reports, and presenting a comprehensive overview of customer journeys and prospects’ statuses.

There are several BI tools, like Tableau, Microsoft Power BI, etc., that gather and analyze data sets to present their analytic findings through summaries, reports, charts, maps, etc.

Gartner’s market research reveals that approximately 80% of surveyed businesses are utilizing BI and data analytics software. Industries that stand at the forefront of this adoption include inventory management, marketing, advertising, engineering, insurance, and IT services. The increasing adoption of enterprise business intelligence can be attributed to the imperative to improve data accuracy, manage risks effectively, and uncover new revenue-generating opportunities.

Now, even though business intelligence for enterprises has established itself as a key part of attaining business objectives, there is still some confusion around the meaning of BI and business analysis. Let us answer that for you today.

Enterprise Business Intelligence vs Enterprise Business Analytics

The biggest difference between enterprise business intelligence and enterprise business analytics lies in the questions that they answer. BI focuses more on descriptive analytics that provides the gist of present and historical data to show what is happening now and what has happened earlier. It answers the how and what side of a business so that the managers can replicate what works and change what doesn’t.

Enterprise Business analytics, on the other hand, looks into predictive analysis. It makes use of data mining, machine learning, modeling, etc., to know the likelihood of any future outcomes. It answers the why side of the business queries, helping managers make sound predictions and anticipate the outcome of a new business decision.

Two Primary Types of Business Intelligence

Understanding the diverse landscape of Business Intelligence involves recognizing its two primary types. Let us look at them in detail below:

Strategic Business Intelligence

Also known as auto-delivered intelligence, it is a type of business intelligence for enterprises that is related to generating reports from the data warehouse or data source. It improves business processes by analyzing predetermined datasets that are relevant to the specific process and offers a historical outlook of data. Additionally, the strategic intelligence model offers a base for planning, goal setting, forecasting, etc.

Strategic BI emphasizes showing the output in graphs and charts to show the opportunities, trends, and problem areas. It runs on four key parameters:

• Gathering and storing the data
• Optimizing data for analysis
• Identifying key business drivers
• Seeking answers to crucial business questions

Operational Business Intelligence

This type of business intelligence is related to the operational and transactional data source. One way to identify this type is to see if data that is generated from the analysis directly helps finish an operational task. Operational BI offers relevant, time-sensitive information to the operation managers and front-line customer-facing employees to aid them in their everyday processes.

Since the operational BI is heavily task-focused, there is less need for graphs and charts. For instance, in scenarios where informing a client about overdue payments is necessary within operational domains, a graph may not be as effective as a concise message. This is the reason why communication devices like instant messages, email, dashboards, etc., play a key role in operational BI. The output that one gets from operational business intelligence consists of schedules, invoices, shipping documents, and financial statements.

How Business Intelligence Works?

Even though business intelligence gets used in multiple ways for multiple objectives by businesses, the process is more or less the same for all industries –

• Data gathered from different sources – consisting of internal company data and external market data – is integrated and stored in a data warehouse.
• Data sets are made and set up for analysis by creating robust data analysis models.
• The data analysts then run queries against the models and the data sets.
• The query results are used for creating visualizations in the form of graphs, charts, histograms, etc., in addition to BI dashboards and reports.
• The decision-makers use the reports to make key business decisions in terms of what is working and what needs to change.

Understanding the Components of Enterprise Business Intelligence Architecture

Enterprise Business Intelligence (BI) architecture forms the backbone for establishing strong data management practices and technological standards in analytics. It acts as a blueprint that guides the consolidation and accessibility of data for visualization and reporting. Let us now look into the key functions of enterprise BI architecture:

Data Integration

Establishing a temporary staging area is essential for maintaining data quality and integrity during the extraction, transformation, and loading process from different sources. This integration enables data deduplication, pre-aggregation, and standardization of information across multiple sources. In addition to this, it also helps in the identification and removal of invalid data from various sources.

Data Storage

This layer consists of subsets designed for different business lines. It includes a data lake for storing, processing, and securing various data formats. Additionally, there is a data warehouse for reading and optimizing large amounts of data for analysis. Data marts are also present for specific business data queries. Lastly, an operational data store is included to integrate current operative data from diverse sources.

Data Analytics

One of the most vital components of enterprises BI, it encompasses the use of OLAP tools to analyze historical and real-time data and the creation of predictive models through machine learning tools, allowing for the execution of what-if scenarios collected over time.

Data Reporting

This layer involves creating diagrams, heat maps, graphs, and charts using visualization tools. It also allows for running ad hoc queries through self-service enterprise BI tools.

Data Visualization

This includes generating pre-designed and custom reports, scorecards, and dashboards to visualize business information. It also involves ML-driven prescriptions, recommendations, push notifications, and alerts. The visualization aspect provides intuitive dashboards, interactive portals with search filters, configurable views, and drill-down capabilities with real-time data access.

Multiple Benefits of Enterprise Business Intelligence

The benefits of enterprise business intelligence are widespread. Every domain comes with a use case for the technology. Let us dive into them.

Speedy and Correct Reporting

Through BI, the employees can use customized reports or templates to monitor the KPIs through a  variety of data sources, which include operational, sales, and financial data. These reports can be generated in real-time and be used for businesses to act quickly.

Gathering Business Insights

Businesses can easily measure their revenue, employee productivity, and department-specific performances through BI tools. It can help reveal the weaknesses and strengths of the business while giving them insights into what is working and what is not. In the BI platforms, businesses can even set notifications to keep on top of the movement in KPIs that matter to them.

Better Customer Satisfaction

Business intelligence tools can provide enterprises with an inside-out view of their customers’ patterns and behaviors. Since the tools are designed to track customers’ feedback in real-time, they can help them retain existing customers and reach new ones by taking timely actions and anticipating the needs of the customers.

Identifying New Opportunities

Identifying trends and creating strategies backed by data can give businesses a competitive edge. The employees can merge the external market data with their internal sales journey to identify the trends in both customer experience and market conditions.

Better Operational Efficiency

Business intelligence platforms merge multiple data, which helps the complete organization across domains. What this leads to is that the managers spend less time tracking down the information and focusing on creating timely and accurate reports.

For the employees, this means that they can focus on how their performance is impacting the short and long-term business goals.

Better Revenue

High revenue is the end goal for any enterprise or startup. The data gathered from BI tools can help businesses ask better questions and identify weaknesses in a timely manner. The BI tools can help with analyzing the gaps in revenue while highlighting the ways to expand the margins. All of this together can help businesses make better strategies around where the budgets should be spent to get the biggest revenue.

Features of Robust Business Intelligence Platforms

Let’s delve into the key features that define an Enterprise Business Intelligence Strategy:

Data Integration: Integration from diverse sources, including ERPs, databases, spreadsheets, and cloud applications.

Data Warehousing: Centralized storage for managing and storing organizational data.

Analytics and Reporting: Custom report generation, interactive dashboards, and visualizations for insights.

Advanced Analytics: Utilizing data mining and machine learning algorithms to detect hidden patterns and trends.

Collaboration: Facilitating teamwork for shared data analysis and insights among users.

Mobile Support: Accessibility from various devices, including smartphones and tablets.

Security and Governance: Robust security measures ensure authorized data access.

Scalability: Ability to expand and support increasing data volumes and users.

Real-Time Analytics: Providing up-to-the-minute data insights for immediate decision-making.

Predictive Analytics: Forecasting future trends based on historical data and models.

Self-Service BI: Allowing non-technical users to access and analyze data independently.

Data Visualization: Graphic representation aiding in quick data comprehension and decision-making.

Real-World Examples of Enterprises Using BI Capabilities to Grow

Let us look at some real-world instances showcasing how enterprises leverage BI capabilities to foster growth and success:

Twitter

The leading social media company is using BI along with Artificial Intelligence (AI) to counter potentially dangerous and inappropriate content on the platform. The algorithms together have proven to identify 95% of terrorism-related accounts.

The technology is also used by them for fine-tuning the overall user experience of the application. The business intelligence tools see the live feeds and categorize them on the basis of their subject matter. They also use this data for bettering their search capabilities and identifying the videos/content the users will be interested in.

Uber

The company makes use of business intelligence to identify the different vital aspects of the business. An example of this can be seen in surge pricing. The BI-led algorithms track the traffic conditions, time of journey, availability of the driver, and customer demand – all in real time. This feature of dynamic pricing is also used by hotels and airlines to adjust the price on the basis of the customers’ needs.

Walmart

 Major brands such as Walmart use retail business intelligence software to understand the impact of online behavior on in-store and online activity. Through the analysis of simulations, Walmart is able to understand customers’ purchase patterns. For example, they can understanding how many people searched for a piece of furniture and then bought it from the Walmart app/website on the same day. This way, they are able to pinpoint the busy days and the exit points in their users’ journey.

Starbucks

Through a mix of its mobile app and the famous loyalty card program, Starbucks gets the purchase data of millions of customers around the world. Using that information and the BI tools, the company then predicts purchase trends and sends personalized offers of what a customer would prefer through email or application.

This system helps in drawing the existing customers into the stores more frequently, thus leading to high sales volumes.

Lowe’s

The home improvement company makes use of business intelligence to merge what customers tell them with the actual online and in-store behavior. They use the data to discover insights that lead to staffing and product assortments in stores. The technology helps with driving sales and also with serving the customer. For example, Lowe’s uses predictive analytics for loading trucks specific to the different zip codes. This way, the right store gets the correct type and amount of the product.

Coca-Cola

With over 1.1 million Twitter followers, Coca-Cola uses BI to benefit from the social media data. Using AI-driven image-identification technology, the brand notices when the photos of its drinks are posted online.

This information, when merged with BI, gives the brand insights into who is drinking Coca-Cola and in what context they are mentioning the brand. This information helps the brand hit its user base through targeted advertising, which results in a lot more clicks than a generic advertisement.

These are just a few examples of the adoption of BI capabilities in the business world. The one takeaway that you can take from here is how the technology has a use case across a range of different industries. Let us look into the ways you can implement the capabilities in your business.

Also Read: How Business Intelligence is Driving Data-Driven Decisions in Manufacturing

Understanding the Process of Enterprise Business Intelligence Implementation

Discover the step-by-step process of integrating Business Intelligence (BI) into your business model and optimizing its potential for informed decision-making and enhanced performance:

Build a Business Intelligence Strategy

A business intelligence strategy is a roadmap that enables a company to measure its performance, know its shortcomings, better its competitive advantage, and use analytics to make well-thought-out business decisions.

In order to build a sound business intelligence strategy, it is important to get answers to these questions:

• What is the business objective?
• What resources do you have to meet that objective?
• What do you require to meet the objective?

Set up the Key Performance Indicators

Once you have enough information and a strategy in place, the next step would be to set the KPIs that you are going to track the company’s growth against. The KPIs have to be measurable, should match the objectives, and must be crucial to achieve the business goals.

Some examples of KPIs in the business intelligence can be –

• 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

Build a BI team

When you are out to build a business intelligence team, you get two options: hire an in-house team or outsource the BI project. Either way, here are the different professionals who will be involved in implementing your 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

However, outsourcing the BI project to a dedicated BI services provider like Appinventiv offers several advantages. These include access to a larger talent pool, cost-optimization, and faster project initiation. It also allows for flexibility in scaling resources based on project requirements, expertise from specialized professionals, and the benefit of leveraging established infrastructures and technologies without significant in-house investment.

Know the BI Software Requirement

The choice of the best BI software tool will depend on the budget and the business requirements. However, there are some key considerations to consider when buying a enterprise business intelligence solution:

• Do you have a simplified view of the data?
• Does the software offer integration with the existing system?
• Can you collaborate with others on the data analysis part?
• Will you be able to discover insights on your own?

Pick Data Storage, Platform, and Environment

If you do not have an infrastructure, it will be a good start to look for data storage options. Usually, a data warehouse is seen as the best choice for business intelligence implementation since it offers analysis of data coming in from both business apps and online transaction processing. Once you fix the best data storage platform for your BI project, the next step would be to fix an environment – in-cloud, on-premise, or hybrid.

Prepare Your Data for Quality Enterprise Business Analytics

The success of a good business intelligence and analytics project is dependent heavily on the quality of the data.

To ascertain data readiness for analysis, it’s crucial to ensure your data meets the following checklist criteria:

• Wholeness and completeness
• Validity and correctness
• Uniqueness and absence of duplicates
• Consistency and uniformity
• Timeliness and relevance
• Accuracy and precision

Implement a Pilot Project

Once all the processes are in place and the data has been vetted, the last step that remains is to run a pilot test. While it can be enticing to launch the project across the entire organization, we recommend testing it across a small group of users to see how receptive they are towards it.

After the pilot project is live, review the results and measure them against the KPIs we had set up in Step 2. If you don’t see them supporting the expectations, revisit the architecture.

Up until this point, you must have gathered how complex it can be to integrate BI capabilities in an enterprise or a startup. When you outsource the project to a team of expert business intelligence engineers, they handle the complexities and deliver you a product that is personalized according to your business needs. You have to go through all the complexities of BI project development if you choose to build it in-house.

Let us look into what those organizational challenges are.

The Business Intelligence Challenges that Enterprises Face

Discover how the challenges associated with Enterprise Business Intelligence strategy impact decision-making within the business landscape:

Gathering and Refining of Data

Business data is spread across a range of platforms and systems, both on-premise and in-cloud. The growth in data sources means that businesses have to plan out a system to get all the data in one place. Now, you can make use of BI tools and deploy a data warehouse in a common place for the BI data.

The next phase of data-related business intelligence lies in refining the data, ensuring that quality-wise, it is good enough to base key business decisions on. It is extremely important to not just run data through a checklist of key considerations but also categorize them into the right groups so that every team knows which data is of what use.

Training the End Users

Failed company-wide adoption of business intelligence capabilities is one of the biggest challenges of BI integration. It is crucial to get a buy-in from every stakeholder and the people who are responsible for maintaining them.

Right from the beginning of the development process, it is important to loop in all the team-wise key people. A set of clear objectives and KPIs that come from them will help with the development of a well-thought-of BI product that is built for success.

On the one hand, it is important to get buy-in from key people in the organization; on the other hand, it is equally important to train the staff.

Not Using the Right Performance Indicators

One of the biggest adoption challenges of the enterprise business intelligence solution cycle lies in the fact that after putting in a lot of time and money behind the entire BI development cycle, entrepreneurs fail to see any real value coming out of it. This situation majorly arises out of the reason that there is a lack of correct key performance indicators in place.

It is necessary to understand the capabilities of enterprise business intelligence projects and what they can achieve. Only when you know the exact processes it can impact, will you be able to measure it against. As a dedicated BI service provider, Appinventiv can offer comprehensive support in navigating the intricacies of your BI project.

With this, you now know everything there is to know to get started with the implementation of BI capabilities in your business and things that you should have a lookout for. The only thing left to understand is what the technology has planned in store for itself in the coming years. Knowing this would help you set your BI project off the right ground.

The Key Business Intelligence Trends in 2024 and Beyond

Let us explore the key trends that are reshaping the way users harness data for insightful decision-making and strategic innovation in 2024 and beyond:

AI-Driven Analytics

AI integration in BI enables deeper insights, improving forecast accuracy, operational efficiency, and decision-making. This leads to better resource allocation and allows enterprises to create proactive strategies.

Augmented Analytics

Leveraging AI and machine learning offers businesses simplified data analysis. This enables quicker decision-making processes with the help of streamlined predictive and prescriptive insights.

Data Democratization

Making data accessible to all empowers the employees of the enterprises to make informed decisions. This helps in fostering a culture of data-driven decision-making and efficiency across all levels of the enterprise.

Cybersecurity in BI

Robust security measures across the enterprise safeguard sensitive business data, ensuring compliance and trust. This is vital for maintaining a secure data ecosystem and customer confidence.

Cloud-Based BI Solutions

Cloud adoption reduces the related infrastructure costs, provides scalability, and ensures continuous access to real-time insights. This enhances the overall agility and adaptability.

IoT Data Integration

Incorporating IoT data enriches the overall BI insights. This makes way for comprehensive real-time analytics, which helps in enhancing operational efficiency.

Natural Language Processing (NLP)

NLP simplifies data interaction, allowing non-technical users to access insights more easily. This helps in enhancing user adoption and data-driven decision-making.

Explainable AI (XAI)

Ensuring AI models’ transparency increases trust and understanding, enabling better decision-making based on AI recommendations. This is vital across regulated industries.

How Can Enterprises Evolve with Appinventiv as a BI Services Partner?

The Business Intelligence ecosystem is rapidly evolving, offering enterprises across the globe unprecedented opportunities for data-driven success. With the transformative potential of BI in mind, Appinventiv’s dedicated Business Intelligence services can comprehensively offer tailored BI services to navigate these evolving trends.

With our expertise, innovative solutions, and commitment to empowering businesses, we can guide enterprises in harnessing the full potential of Enterprise Business Intelligence. This enables them to make informed decisions and gain a competitive edge in an ever-evolving market landscape.

Get in touch with our experts to maximize the advantages BI can bring to your business and stay ahead in the competitive market landscape.