| title | seoTitle | seoDescription | datePublished | cuid | slug | cover | tags |
|---|---|---|---|---|---|---|---|
Data-Driven Decisions Made Easy: Simple Guide for Business Growth |
Business Growth Blueprint: 4 Steps to Data-Driven Decisions |
Stop guessing, start thriving! This guide unveils a 4-step framework for data analysis to unlock actionable insights & drive business growth. |
Sun Mar 24 2024 10:58:59 GMT+0000 (Coordinated Universal Time) |
clu5ep8ib000408l4b3xwez9m |
data-driven-decisions-made-easy-your-guide-for-business-growth |
data-analysis, business-analytics, exploratory-data-analysis, 2articles1week, data-driven-insights, data-driven-decisions, business-data-analyst |
In today's fast-paced business landscape, data reigns supreme.
Every decision, every strategy hinges upon the insights gleaned from data analysis. Yet, the impact of poor data analysis cannot be overstated. According to a recent study by Gartner, companies lose an average of $15 million per year in losses annually due to poor data quality. This staggering statistic underscores the critical importance of robust data analysis practices in driving business success.
In the realm of engineering, the German concept of Kochrezept—a recipe for success—parallels the meticulous standards of the Deutsches Institut für Normen(DIN). Similarly, in the world of Business Data Analysis, there exists a blueprint for excellence, a methodical approach that transforms raw data into strategic insights.
In this blog post, we’ll embark on a journey to unlock the potential of data analysis with practical example.
![Data Analysis Memes. What my friends think I do vs. What I think I do.](https://cdn.hashnode.com/res/hashnode/image/upload/v1711277112388/393bca98-aa21-4abc-a67d-d77d871f9429.gif align="center")
Before diving into the realm of data analysis, it is imperative to clearly define our project goals. What are we seeking to achieve? What insights are we hoping to uncover? By establishing a clear problem statement from the outset, we lay the foundation for a successful analysis.
In our hypothetical scenario, let us imagine ourselves as analysts at a leading bicycle retail company. Our objective? To create a comprehensive sales dashboard for the executive team, providing invaluable insights into sales performance and customer behavior.
The first step in our journey involves data collection as explained in the video down below. Drawing upon the company's relational database, we utilize SQL to extract relevant data pertaining to sales transactions, customer details, and product information. By combining multiple datasets, we lay the groundwork for a holistic analysis.
%[https://youtu.be/Lb6Gi6IR-Kc?si=mOtMP-JoJDwOjLhi]
Best Practices:
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Clearly articulate project goals to align analysis efforts with business objectives.
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Ensure data integrity by cross-referencing multiple datasets and verifying accuracy.
Real-World Example: A leading e-commerce giant leverages similar data analysis techniques to optimize its product recommendations, resulting in a 10% increase in sales revenue.
“If You Fail to Plan, You Are Planning to Fail” — Benjamin Franklin
With our raw data in hand, the next crucial step is data cleaning and preparation. Often likened to polishing a rough diamond, this phase involves removing inconsistencies, addressing missing values, and standardizing formats to ensure uniformity.
Excel has 1,048,576 rows by 16,384 columns, However, the practical limit is often lower, as performance can degrade with very large datasets or complex calculations.
Based on my experience, if your data does not exceed half the size of Excel total rows, you’re better of cleaning it using Excel for faster result. Otherwise, SQL or Python is the way to go without loosing too much time in computing time. Tom Mitchell seems to agree with me:
%[https://twitter.com/TomMitchellData/status/1767129113351819558?s=20]
Let’s say we default to SQL. In our SQL script, we meticulously comb through the dataset, identifying anomalies and outliers that may skew our analysis. By employing data cleansing techniques, such as imputation and outlier detection, we enhance the quality and reliability of our dataset.
Best Practices:
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Develop a systematic approach to data cleaning, prioritizing critical fields and addressing outliers promptly. Python excels in this field with its Scikit-learn ML library.
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Document data cleaning procedures to maintain transparency and reproducibility.
Real-World Example: A financial services firm resolves data inconsistencies in its client database, leading to more accurate risk assessments and substantial cost savings.
Armed with a clean dataset, we can start on the exploratory data analysis (EDA) phase, delving deep into the intricacies of our data to uncover meaningful patterns and trends. Visualization tools such as Excel and Tableau serve as our trusted companions, allowing us to transform raw numbers into insightful charts and graphs. Open-source enthusiast? Look for Metabase, Superset and Redash. Pythonista? Hop on board, you’re very well welcomed and well-equipped. In fact, you’re gonna love this:
%[https://youtu.be/Liv6eeb1VfE?si=U0Fw88WMbT7JJbc2]
In our sales dashboard, we employ pivot tables and charts to visualize key metrics such as total revenues per year and month, revenue breakdowns by product category, and top-performing sales reps. By harnessing the power of visualization, we provide stakeholders with a clear and intuitive snapshot of sales performance. Just applicable insights, cut off the fluffs.
Best Practices:
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Choose appropriate visualization techniques that resonate with your audience and convey insights effectively.
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Incorporate interactive elements to encourage exploration and engagement.
Real-World Example: A retail giant leverages interactive dashboards to monitor store performance in real-time, enabling swift decision-making and proactive intervention. Kibana excels in this viz.
Moving on to advanced analytics and predictive modeling. Armed with sophisticated tools and techniques, we can unravel the underlying drivers of sales performance and forecast future trends with precision.
![Credits to Qlik: Predictive Modeling.](https://cdn.hashnode.com/res/hashnode/image/upload/v1711277556661/468cd4bc-f547-422d-8414-382335eed6bb.jpeg align="center")
For example, when using Tableau, we implement in our Tableau dashboard predictive modeling algorithms to anticipate customer demand and identify opportunities for revenue growth. By harnessing the power of data-driven insights, we empower decision-makers to stay ahead of the curve and seize competitive advantages.
Best Practices:
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Continuously refine predictive models based on feedback and evolving business dynamics.
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Collaborate with domain experts to contextualize analytical findings and derive actionable recommendations.
Real-World Example: A telecommunications company utilizes predictive analytics to anticipate network outages, reducing downtime by 30% and enhancing customer satisfaction.
Qlik summarizes this pretty neatly here.
Informed decision-making is transformative, optimizing sales strategies and predicting market trends.
Embrace the power of data in your decision-making process. Explore further resources, engage in continuous learning, and share your data analysis challenges with the community.
Confused where to start and which community to join? Just start with any and we’ll go from there. Let's cover Business Analysis and Process Management before diving deeper into practical business data analysis. This way, we can focuses on optimizing processes first, before leveraging data to identify trends and opportunities for improvement. Both works in tandem.
Warning: May cause increased data enthusiasm. Read the next post in Data Series at your own risk.
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