Extract
Business intelligence (BI), supported by good data mining, science and visualization, is emerging as a fundamental pillar of business innovation that allows business leaders to make informed decisions based on data, rather than relying solely on intuition.
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Business Intelligence: The Hidden Business Treasure
In today's digital age, data are not simply figures or records; they are constantly expanding elements that contain patterns, reveal trends and hide valuable opportunities in plain sight. These treasures of information, when discovered and understood, can become the master key to achieve high performance in the organization and achieve sustained business profitability.
In this dynamic reality, business intelligence (BI) has emerged as an innovative and crucial tool that serves as the driving engine of innovation and efficiency in the modern business environment, enabling business leaders to make decisions based on facts, rather than relying solely on intuition.
Benefits of Implementing Business Intelligence: Relevant and Informed Business Decisions
Business intelligence presents an unparalleled opportunity for companies to make informed and relevant decisions that drive their business success throughout their business journey. Through data analysis and visualization, business intelligence enables organizations to better understand their internal operating environment, identify key trends, and anticipate changes in their external competitive environment in two approaches:
In Retrospective form, allows organizations to carry out a measurement and evaluation of business performance of the business that gives rise to continuous improvement that drives business growth and competitiveness.
In Prospective form, allows organizations to carry out a proactive risk management to anticipate and respond effectively to market changes in an increasingly complex and dynamic business environment.
Challenges When Implementing Business Intelligence: Paralysis Analysis and Data Integrity
Although business intelligence is a powerful tool for driving business success, there is an inherent risk in wading into a sea of information without a clear focus. This phenomenon, known as "analysis paralysis", leads companies to get lost in the abundance of reports that, ultimately, do not provide significant value to decision making. Therefore, one must discern between what is simply intriguing and what is truly important for business strategy.
At the same time, business intelligence can be used to push agendas and power plays with the use of incomplete, inaccurate or inconsistent data which can lead to erroneous conclusions and incorrect decisions. This is why it is important to guarantee data quality and integrity through effective cleaning, integration and verification processes to ensure good handling of sensitive data, its security and privacy.
Keys to Good Business Intelligence: Clarity in Objectives
When implementing business intelligence, you should seek to establish a data-driven culture. This may involve changing mentalities and transforming existing processes, as well as promoting collaboration between different teams and departments. It is essential to have clarity in the objectives and define the key questions that are sought to be answered from senior management to operational levels to ensure that data collection, analysis and presentation are aligned with the organization's strategic goals. Some key questions that business intelligence can answer are:
1. Management Area:
What are the indicators that most impact the profitability of the business?
How can we improve efficiency in strategic decision making?
What is the current performance of our investments and how can we optimize them?
What potential risks does the company face and how can we mitigate them?
How does our current strategy align with market trends and shareholder expectations?
2. Commercial Area:
What are the most profitable products or services and what pricing strategies should we apply?
How can we improve customer acquisition and retention?
What are the market trends and how can we adjust our offering to take advantage of them?
What is the effectiveness of our marketing campaigns and promotions?
What opportunities for geographic expansion or new market segments should we consider?
3. Administrative Area:
How can we optimize internal processes to increase operational efficiency?
What is the resource utilization in different departments and how can we improve it?
What are staff performance indicators and how can we foster a more productive work environment?
How to improve inventory management and reduce associated costs?
What security and regulatory compliance measures should we reinforce or update?
4. Operational Area:
How can we improve project planning and execution to meet deadlines and budgets?
How efficient are our production processes and how can we minimize downtime?
How can we improve the quality of the product or service delivered?
What is the level of customer satisfaction and how can we improve it?
What sustainability and energy efficiency measures can be integrated into our operations?
5. Human Resources Area:
What are the indicators of employee performance and retention?
How can we improve staff training and development to increase productivity?
What are the areas for improvement in the work environment and how can we address them?
How to evaluate the impact of benefits and compensation policies on employee satisfaction?
What recruitment and retention strategies are most effective in the current labor market?
By identifying and defining Key Performance Indicators (KPIs) that are directly aligned with business objectives and related to the key questions being asked, distraction with superfluous data is avoided and attention is focused on the information that really drives business success. Constantly asking yourself how data contributes to predefined objectives helps keep the focus on valuable information.
Data Mining: Excavating the Past to Chart the Future
Data mining is similar to digging for buried treasure since companies usually have their information dispersed across multiple systems and platforms. These data sources will vary depending on the nature of the company, the industry in which it operates, and its Technological Architecture. Some of the most common sources are:
Performance of campaigns, promotions and web behavior related to digital marketing.
Electronic Communications of chats, messaging, forums and shared documents.
Sensors and IoT Devices (Internet of things) connected to machinery and equipment.
Internal Databases of clients, collaborators, suppliers and banks.
Log Files of access to websites and system activity.
Management systems for operational, commercial, logistical and financial purposes.
External Surveys of customer satisfaction.
Internal Surveys of job satisfaction.
In order for these data sources to be used, they must be integrated in a coherent and understandable way; which can be a challenge, especially when dealing with legacy systems or incompatible platforms.
These massive sets of collected and integrated data are often presented unstructured and require cleaning and refinement to get a complete and accurate view of business information. As well as to perform data analysis that identifies patterns, trends and opportunities based on a more complete view of internal operations and customers.
It is essential to ensure that the collection is carried out ethically and complies with the privacy and information security regulations in force and applicable to each country and sector.
Data Sciences: Information Cleansing and Refinement
Data science allows companies to realize the analysis necessary to make information more than just numbers. By using advanced algorithms and predictive models to classify, sort, and clean mined information, data scientists can identify correlations and causal relationships, providing a more complete understanding of business processes and results. This not only helps keep up with market demands but also provides the opportunity for companies and organizations to design transformation strategies that allow them to lead the competition by proactively adapting to changes.
However, analyzing large volumes of data effectively can be a technical challenge as companies need to have tools and platforms that allow them to process, analyze and visualize data in an efficient and scalable way. In addition, it is important to have personnel trained in data analysis and statistics to correctly interpret the results.
Data Visualization: Presenting Trends
Data visualization tools can help condense large amounts of information into clear, concise visual representations. This makes it easier to identify important patterns, trends, and relationships, allowing for more informed and efficient decision making.
The appropriate choice of text and graphics plays a crucial role in the effective communication of information. This election depends on the type of information you want to communicate and the message you want to convey. taking into consideration clarity, accuracy, and accessibility when selecting visual representation of data. Some of these graphics are:
Bar Graphs to compare values between categories.
The allocation of resources in each phase of the process.
The efficiency of different crews and production lines, highlighting the production of each shift.
Sales and/or rentals of different solutions.
Line Charts to show trends and changes in data over time.
The temporal progression of activities on a schedule, highlighting key milestones.
The trend in daily production over the course of a month.
The evolution of the company's income over several quarters.
Pie Charts to represent proportions of a whole.
The distribution of costs in a project and/or budget.
The proportion of time spent in different phases of the production process.
Scatter Charts to visualize relationships between two variables.
Task duration and team performance on similar projects.
The speed of production and quality of the product.
Histograms to represent the distribution of data and frequencies to visualize the variability of a data set.
Material delivery times.
The production times for a certain item.
Pareto diagrams to identify the root causes of a problem.
Identify the most frequent types of problems or delays.
Prioritize the most common quality defects.
Bubble Diagrams to show relationships between three variables through the position, size and color of the bubbles.
The magnitude of the impact of potential risks on a project, considering probability and consequences.
The performance of different products in terms of sales and customer satisfaction.
Heat Maps to represent the density or intensity of geospatial data.
Patterns of use of spaces.
Energy consumption patterns.
Gantt charts to view schedules and project planning.
Specific tasks in a project, showing overlapping activities.
Preventive maintenance.
Boxplots (Box and Whisker Plots) to show the distribution and dispersion of a data set.
Variability in costs of similar projects
The dispersion of cycle times in different production shifts.
The power of data visualization is undeniable, but should be used sparingly. Graphics should be tools that facilitate understanding, not complicate it. Opting for simplicity and clarity in data presentation ensures that information is accessible and easy to interpret for all team members.
Conclusion: Achieving Relevant and Informed Business Decisions in a Constantly Evolving World
Mining and data science represent the cutting edge of business transformation in the digital age, revealing the hidden treasure in the data that flows through organizations. These tools not only allow for deep digging into massive sets of information, but also refine and present trends significantly through data visualization that allows us to achieve true and positive business intelligence.
However, the power of these tools comes with challenges, especially the risk of falling into "analysis paralysis." The key lies in clearly defining the objectives and key questions that will guide data collection and analysis, avoiding distractions on interesting but irrelevant information.
Ultimately, data mining and data science are not only analytical tools, but also catalysts for informed decision making. By addressing critical questions in management, business, administrative, operational and human resources areas, organizations can leverage these troves of information to drive efficiency, innovation and competitiveness in today's business environment.
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About Pablo Tellaeche (Author):
Owner and main consultant of TACs Consultores, Speaker and University Professor; seeks to bring a true and positive Lean Culture and Digital Transformation to every company with which he has the pleasure of collaborating.
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