How Intelligent are Business organizations?

Most organizations are bordered about the bottom line, which a primary indicator of business position, at a given period. It is worthy of note that the real factors responsible for performance are not properly harnessed to exploit possible opportunities and prevent associated risks from crystallizing. It's worth noting that making a wrong decision is normal in any business, and it's important to have a culture that encourages experimentation and learning from failure. Are organizations really learning from failure, especially if it is preventable? The cost of learning failure outweighs the cost associated with its prevention. This is where business intelligence monitoring becomes a must have irrespective of the size of the organization.

According to Harvard Business Review published in October 2015, up to 80% of business strategies fail due to poor decision-making. Factors such as lack of data, poor communication, and lack of experience can contribute to poor decision-making. Additionally, not utilizing advanced analytics and technologies such as Artificial Intelligence and Machine Learning can also lead to poor decision-making. But it's important to note that making a wrong decision doesn't always mean a failure, it can also be an opportunity to learn and improve.

Business operations can vary in terms of their level of intelligence. Some operations may be highly automated and data-driven, utilizing technologies such as artificial intelligence and machine learning to optimize processes and make decisions. Other operations may be less advanced, relying more on manual processes and human decision-making. The level of intelligence in a business operation is often dependent on factors such as the industry, the size and resources of the organization, and the specific goals and objectives of the operation. Additionally, the use of data analytics, automation, and other intelligent technologies can help organizations make better decisions, improve efficiency, and drive growth.

?Global Trend on Business Intelligence

Business Intelligence (BI) has been a rapidly evolving field in recent years, and there are several global trends that have been shaping the BI industry:

• Cloud-based BI: Cloud-based BI solutions have been gaining popularity as they offer lower costs, easier scalability, and increased flexibility. This trend allows organizations to access their data and analytics from any device, from anywhere, and at any time.
• Artificial Intelligence and Machine Learning: Artificial Intelligence (AI) and Machine Learning (ML) are becoming increasingly integrated into BI solutions, allowing for more advanced analytics and automated decision-making. This trend allows organizations to uncover insights and patterns in data that would otherwise be missed by human analysts.
• Self-service BI: Self-service BI solutions are becoming more popular as they empower business users to access and analyse data without relying on IT or data analysts. This trend allows organizations to make data-driven decisions faster and more efficiently.
• Big Data and IoT: The explosion of data generated by the Internet of Things (IoT) devices, social media, and other sources is driving the need for more advanced BI solutions that can handle large amounts of data. This trend is pushing the limits of traditional BI tools and driving the development of new technologies such as Hadoop, Spark, and NoSQL databases.
• Mobile BI: The trend of mobile BI is also gaining momentum as more and more users are accessing data from mobile devices. This trend allows organizations to provide employees with real-time data and insights, regardless of their location.
• Real-time BI: Real-time BI solutions are becoming more popular as they allow organizations to analyse and act on data in near real-time, rather than waiting for scheduled reports. This trend is especially useful for organizations that need to make quick decisions based on current data.

Signs that an organization lacks business intelligence capabilities.

These signs can indicate that an organization is not taking full advantage of the insights and opportunities provided by BI and may be missing out on opportunities to improve decision-making, operations, and performance.

• Reliance on gut feeling or intuition: If an organization relies heavily on intuition or gut feeling to make decisions, rather than data and analytics.
• Inefficient processes and operations: the organization is plagued by inefficiencies and bottlenecks in its operations.
• Lack of visibility and control: Inability to effectively monitor and control its performance and operations.

• Difficulty in making accurate forecasts and plans: Inability to make accurate forecasts and plans.
• Poor customer understanding: poor understanding of customers, including their behaviour, preferences, and buying patterns.
• Limited collaboration: departments and teams’ inability to effectively share data and insights.
• Difficulty in identifying and addressing risks: difficulty identifying and addressing risks associated with its operations.
• No governance and security: lack of governance and security mechanisms in place for its BI systems.
• No flexibility: inability to make updates, changes, and add new data sources to its BI systems.

?How to determine your organization’s intelligence level.

There are several ways to determine if a business is intelligent:

• Use of data and analytics: A business that utilizes data and analytics to inform decision-making and optimize operations is considered intelligent. Is your organization able to track customer behaviours and preferences, and adjust to internal processes and marketing strategies?
• Automation and technology: The use of automation and other advanced technologies such as Artificial intelligence, Machine Learning and IoT can indicate that a business is intelligent. How much of the current processes are automated?
• Adaptability and learning: An intelligent business can quickly adapt to changing market conditions, learn from past experiences and continuously improve its operations. Are there processes in place to adapt to changing business environment and experimentation? Are they processes in place to identify and correct operational inefficiencies?
• Strategic planning and execution: A business that has a clear strategy in place and can execute it effectively is likely to be considered intelligent. Is there a strategic direction that is rigidly followed? Is there growth plan, with specific targets and metrics?
• Innovation: A business that is constantly innovating and finding new ways to improve its products, services, and operations is likely to be considered intelligent. Are there processes to identify new market opportunities? Is the organization able to create new products or services that meet customer needs?

?Business Risks associated with lack of business Intelligence.

There are several risks of not having intelligent business processes, including:

• Inefficient operations: business processes can become bogged down by inefficiencies and bottlenecks, leading to increased costs and decreased productivity.
• Lack of visibility and control: it can be difficult for an organization to effectively monitor and control its performance and operations, making it harder to identify and address issues.
• Difficulty in making accurate forecasts and plans: it can be difficult for an organization to make accurate forecasts and plans for the future, putting it at a disadvantage in a rapidly changing market.
• Poor customer understanding: an organization may have a poor understanding of its customers, making it harder to create products and services that meet their needs and retain their business.
• Limited collaboration: different departments and teams within an organization may have difficulty sharing data and insights, leading to silos and poor collaboration.
• Difficulty in identifying and addressing risks: an organization may have difficulty identifying and addressing risks associated with its operations, putting it at greater risk of financial loss or operational failure.
• Lack of governance and security: an organization may not have proper governance and security mechanisms in place for its business processes, putting sensitive data at risk of unauthorized access or breaches.
• Limited flexibility: an organization may find it difficult to adapt to changing market conditions and customer needs, putting it at a disadvantage in a rapidly changing market.

Benefits of Business Intelligence (BI) Tracking

BI can help organizations make better decisions, improve their operations, and drive growth. Specifically,

• Improved decision-making: BI tools and dashboards provide organizations with a clear and comprehensive view of their data, allowing them to make more informed decisions based on facts and data, rather than intuition or gut feeling.
• Increased efficiency: BI can help organizations identify and address inefficiencies in their operations, leading to cost savings and improved productivity.
• Better forecasting and planning: BI can provide organizations with the ability to analyse historical data and trends, allowing them to make more accurate forecasts and plans.
• Greater agility: BI can help organizations quickly adapt to changing market conditions and customer needs, allowing them to stay ahead of the competition.
• Enhanced customer insights: BI can provide organizations with a deeper understanding of their customers, including their behaviour, preferences, and buying patterns, which can be used to improve products, services, and marketing strategies.
• Risk management: BI can also be used to monitor and evaluate the potential risks associated with a certain business decision or strategy.
• Improved collaboration: BI can help different departments and teams within an organization share data and insights, leading to better collaboration and teamwork.

?The role of Artificial Intelligence in business intelligence

Artificial Intelligence (AI) plays a significant role in business intelligence (BI) by providing advanced analytics and automated decision-making capabilities. Some of the ways AI is used in BI include:

• Predictive analytics: AI-powered predictive analytics algorithms can analyse historical data and identify patterns to make predictions about future trends and outcomes. These predictions can be used to inform strategic decision-making and improve business operations.
• Automated insights: AI-powered tools can automatically uncover insights and patterns in data that would otherwise be missed by human analysts. This allows organizations to quickly identify trends and opportunities that can inform decision-making.
• Natural Language Processing (NLP): AI-powered NLP algorithms can analyse unstructured data such as text, images, and audio, and extract insights from them. This is particularly useful in social media listening, sentiment analysis, and customer service.
• Machine Learning: Machine Learning (ML) is a subset of AI that allows systems to learn and improve from experience without being explicitly programmed. ML algorithms can be used to train models that can predict outcomes, classify data, and perform other advanced analytics tasks.
• Automated decision-making: AI-powered decision-making algorithms can automate complex decision-making processes, such as fraud detection, customer segmentation, and resource allocation. This allows organizations to make decisions faster and more efficiently.
• Chatbot: AI-powered chatbot can provide customer service, sales, or other types of assistance, allowing organizations to automate routine interactions with customers or employees.
• Robotics Process Automation (RPA): AI-powered RPA can automate repetitive tasks such as data entry, reducing human error and increasing efficiency.

Business Intelligence tools selection criteria

Selection criteria refer to the set of standards or guidelines used to evaluate and select potential solutions, products, or vendors. These criteria help organizations to identify the most suitable option that meets their needs and aligns with their goals and objectives.

When it comes to Business Intelligence (BI) solutions, selection criteria may include:

• Data integration: The ability to integrate with the organization's existing systems and data sources, and the ability to handle large amounts of data.
• Analytical capabilities: The analytical capabilities of the solution, such as data visualization, data mining, and reporting.
• Scalability: The ability to scale up or down as the organization's needs change.
• User-friendliness: The ease of use and ease of deployment of the solution.
• Security and governance: The ability to protect sensitive data and comply with regulatory requirements.
• Cost: The total cost of ownership, including the initial purchase price, ongoing maintenance and support costs, and costs associated with training and deployment.
• Support and maintenance: The level of support and maintenance provided by the vendor and the availability of training and documentation.
• Flexibility: The ability to adapt to changing business requirements and integrate with new data sources.
• Technical expertise: The level of technical expertise required to use the solution, and whether the solution can be used by non-technical users.
• Vendor reputation: The vendor's reputation, track record, and experience in the BI industry.

Business Intelligence to Implementation Requirements

There are several key requirements for implementing business intelligence (BI) in an organization, including:

·????????Data: The foundation of any BI implementation is the data that it relies on. The organization must have a robust data management system in place to collect, store, and process data from various sources, such as transactional systems, social media, and external data providers.

• Hardware and software: BI require specialized hardware and software, such as servers, databases, and BI tools, to collect, store, and analyse data. Organizations must have the necessary infrastructure in place to support these technologies.
• Skilled workforce: BI requires a skilled workforce to design, implement, and maintain the BI system. This includes data analysts, data scientists, and BI developers who can design and implement the necessary BI solutions.
• Governance and security: BI systems must be governed and secured to ensure that data is accurate, complete, and protected from unauthorized access. Governance should include data quality, data lineage, data lineage, data governance, data security and data privacy.
• Support and maintenance: BI systems require ongoing support and maintenance to ensure that they are running smoothly and providing accurate, up-to-date information. This includes regular monitoring, troubleshooting, and updating the system as needed.
• User adoption: BI is not valuable if users don't use it, therefore organizations should ensure that the system is user-friendly, accessible and that the users are trained on how to use it.
• Business alignment: BI should align with the company's goals and objectives, and should be integrated with the company's overall strategy.
• Flexibility: BI should be flexible, allowing for updates, changes, and new data sources to be added as needed.

?#BI #businessintelligence #businessanalytics #businessstrategy

References

Why Good leaders make bad decisions.

https://hbr.org/2009/02/why-good-leaders-make-bad-decisions

The secret of successful strategy execution

https://hbr.org/2008/06/the-secrets-to-successful-strategy-execution