Assignment Sample on Data Driven Decisions for Business

Introduction

The Computerized Age has introduced a remarkable period of data proliferation, where each snap, exchange, and association create important data. This abundance of information, whenever tackled really, can enable organizations to explore intricacies, anticipate drifts, and improve processes (Andronie et. al. 2021). Data-driven decisions encompass a wide spectrum of business activities, from customer behaviour analysis and supply chain optimization to market segmentation and risk management.

The fundamental premise of data-driven decision-making rests upon the notion that insights drawn from data are more reliable and objective than traditional intuition-based approaches. By incorporating data analytics and cutting-edge technologies, businesses can uncover patterns, correlations, and anomalies that might have otherwise remained hidden (Shahat et. al. 2021). These bits of knowledge act as the foundation for contriving procedures that resound with the needs and preferences of the target audience.

The power of data-driven decision-making has become paramount. As companies strive to leverage data insights for strategic growth, the role of a skilled data analyst has gained prominence (Pillay et. al. 2021). This narrative sets the stage for an exploration into the world of data analytics within the context of Wood from the Trees, a reputable consultancy with a decade-long legacy.

Task 1: Report summary and project plan

PPDAC Project Framework:

The PPDAC framework is commonly used in various fields, particularly in data analysis and problem-solving contexts. It stands for Problem, Plan, Data, Analysis, and Conclusion and it provides a structured approach to tackling challenges or projects (Fleig, 2020).

Applying the PPDAC framework to a project involving “Bangles International Jewelry” would involve following the steps to address a specific problem or challenge related to the jewelry business. Here’s how you might apply the framework to this context:

Problem: Define the specific problem or challenge you want to address in the context of Bangles International Jewelry. For example, the problem could be decreasing sales of a particular type of bangle or identifying trends in customer preferences for jewelry design.

Plan: Develop a plan to address the problem. This might involve outlining the approach you’ll take, such as collecting customer feedback, analyzing sales data, and studying market trends.

Data: Gather relevant data for your analysis. This could include sales data for different types of bangles, client feedback reviews, market research intelligences and information about current jewelry trends.

Analysis: Perform the analysis using the collected data. This could involve examining sales patterns, identifying customer preferences and comparing your products to competitors’ offerings. You might use data visualization to showcase trends and patterns in the jewelry market.

Conclusion: Draw conclusions based on your analysis. For example, you might find that a particular style of bangle is no longer popular among customers, or you might discover that there’s a growing interest in a specific type of gemstone. Based on these insights, you could recommend changes to your product offerings, marketing strategies or design aesthetics.

Remember that the PPDAC framework is iterative, so your conclusions might lead to new questions or ideas. You could cycle back through the framework to refine your problem definition, plan, or analysis based on the new insights you’ve gained.

Project Plan for Delivering the Project:

Project Initiation:

– Define project scope, objectives, and deliverables.

– Identify stakeholders and establish communication channels.

– Create a project timeline and allocate resources.

Data Collection and Preparation:

– Identify relevant data sources and establish data collection processes.

– Clean, validate, and integrate data to create a unified dataset.

Data Analysis and Modelling:

– Apply appropriate analytics techniques to extract insights from the data.

– Develop predictive and prescriptive models to address business challenges.

Insights Presentation and Recommendations:

– Create visualizations and reports to communicate findings.

– Generate actionable recommendations based on insights.

Implementation and Monitoring:

– Collaborate with BIJ teams to implement recommendations.

– Monitor key performance indicators (KPIs) and adjust strategies as needed.

Project Evaluation and Closure:

– Assess the project’s success in achieving objectives.

– Document lessons learned and provide recommendations for future projects.

Jewellery KPIs and Analytics

In the context of a jewellery business like Bangles International, Key Performance Indicators play a crucial role in measuring the business’s success and guiding its strategies (Lee et. al. 2023). Hypothetical Indicators for Bangles Industry include Sales Revenue, Customer Lifetime Value, Conversion Rate, Inventory Turnover, Average Order Value, Website Traffic, and CSAT.

Hypothetical KPIs for Bangles International:

Sales Revenue: The total revenue generated from the sales of jewellery products.

Customer Lifetime Value: The projected total value a customer will bring to the business over the course of their relationship (Hristov et. al. 2019).

Conversion Rate: The percentage of website visitors or potential customers who make a purchase.

Inventory Turnover: The number of times the entire inventory is sold and replaced within a given period.

Average Order Value: The average amount spent by customers on each order.

Website Traffic: The number of visitors to the company’s website.

Customer Satisfaction Score: A metric indicating how satisfied customers are with their overall experience.

How Improved Analytics Enables Improvements:

Sales Revenue: By analysing sales data and trends, the business can identify top-selling products, peak buying seasons, and customer segments with high purchase rates.

Customer Lifetime Value: Improved analytics can help segment customers based on their purchasing behaviours and preferences (Hinderks et. al. 2019). By understanding which customer segments have higher CLV, the business can tailor its marketing efforts and loyalty programs to target and retain those valuable customers.

Conversion Rate: Analytics can provide insights into customer behavior on the website, such as where they drop off in the sales funnel (Al Dakheel et. al. 2020). By identifying these pain points, the business can optimize the user experience, streamline the purchasing process, and increase the conversion rate.

Inventory Turnover: Analytics can monitor inventory levels and product demand, helping the business make data-driven decisions about restocking and inventory management. This prevents overstocking or stockouts, leading to improved turnover rates.

Average Order Value: Through analytics, the business can identify cross-selling and upselling opportunities based on customer purchase histories. This can be used to create personalized product recommendations and bundle offers to increase AOV.

Website Traffic: Analytics can reveal which marketing channels and campaigns drive the most traffic to the website (Domínguez et. al. 2019). This information enables the business to allocate resources effectively and invest in the most successful channels to attract more visitors.

Customer Satisfaction Score: Improved analytics can analyse customer feedback, reviews, and interactions to understand pain points and areas for improvement (Soós et. al. 2020).

Task 2: Data quality issues and remedies

Data Analytics for BIJ:

Figure 3 Data Quality

(Source: AISmartz, 2019)

Proposed Approach to Address Data Problems and Extract Knowledge:

The data analytics process begins with Data Cleaning, a fundamental step aimed at addressing the essential data quality issues that often exist. This involves handling missing values, correcting inaccuracies, and resolving inconsistencies. Techniques like insertion, ascription, and exception expulsion are utilized to refine the dataset, making way for exact examination.

Data Cleaning: Assuming that data quality issues exist, the first step would be to perform data cleaning. This involves handling missing values, correcting inaccuracies, and resolving inconsistencies. Techniques such as interpolation, imputation, and outlier removal can be used here.

Exploratory Data Analysis: Leading EDA helps in figuring out the construction of the information, distinguishing designs, and investigating connections between factors. Visualizations and summary statistics can aid this cycle. EDA can feature patterns, connections, and potential anomalies that could require further investigation.

Feature Engineering: Depending on the nature of the data and the business problem at hand, you might need to create new features or transform existing ones to enhance the predictive power of your models. This step involves domain knowledge and creativity.

Data Modelling: When the data is clean and appropriately prepared, it can move on to building predictive models or performing statistical analyses. Techniques such as regression, classification, clustering, and time series analysis can be employed based on the nature of the problem.

Predictive Analytics: Utilize historical data to predict future outcomes and trends. This can aid businesses in making informed decisions related to inventory management, production planning, and investments.

Segmentation and Targeting: Employ clustering algorithms to segment customers based on behaviour, preferences and demographics. This segmentation enables targeted marketing campaigns and personalized customer experiences.

Root Cause Analysis: The bottlenecks or inefficiencies, data analytics can help identify their root causes. Whether it’s in the production process or supply chain, understanding the underlying issues is crucial for implementing corrective measures.

Optimization: Data analysis can reveal opportunities for process improvement. For example, identifying underutilized capacity or excessive inventory can lead to resource optimization and cost reduction.

In summary, the process of extracting knowledge and insights from data involves several key steps, from data cleaning and exploration to modelling and prediction. The specific approach will depend on the characteristics of the data and the business problem. It’s essential to address data problems early on to ensure the accuracy and reliability of the insights gained from the analysis.

Data Problems in Jewellery

Missing Product Attributes:

Identified: Incomplete records with missing attributes (e.g., weight, dimensions, materials) for certain products.
Solution: Impute missing attribute values based on similar products within the same category or through consultation with BIJ to obtain accurate information (Fernando and Hamil, 2022).

Inconsistent Pricing:

Identified: Variations in pricing data, possibly due to currency conversions or inconsistent pricing strategies.
Solution: Standardize pricing data by converting all prices to a common currency or establish clear guidelines for pricing conversions and consistency.

Product Categorization Discrepancies:

Identified: Different categorization of products across datasets, leading to confusion in analysis.
Solution: Create a standardized product categorization system and map existing products to the new categorization to ensure consistency and comparability (Priskila and Darma, 2020).

Duplicate Entries for the Same Product:

Identified: Multiple records for the same product, potentially leading to inaccurate analysis.
Solution: Detect and remove duplicates using product identifiers (e.g., SKUs, UPCs) or timestamps, and retain only the most recent or relevant entry (Tu al. 2020).

Inaccurate Sales Data:

Identified: Errors or inconsistencies in sales records, affecting the accuracy of analysis.
Solution: Validate sales data against financial records, conduct manual verification if necessary, and correct any discrepancies to ensure accurate analysis (Phittayanon and Rungreunganun, 2019).

Data analysts can ensure that the Bangles International Jewellery (BIJ) dataset is cleansed, accurate, and suitable for meaningful analysis, leading to reliable insights and informed decision-making.

Task 3: Data analysis and commentary

Table A

Sales Data 2018-2020

The data covers three years: 2018, 2019, and 2020.
Each year is divided into two categories: “Sales Volume” and “Sale Value.”
“Sales Volume” refers to the number of units sold each month (January to December).
“Sale Value” represents the total monetary worth of sales for each month.
In 2018, the highest sales volume was 1144 units in September, with a corresponding sale value of 1380851 (Hamilton and Sodeman, 2020).
2019 saw varying sales volumes and values, peaking at 1333 units in June and a sale value of 1775571.
2020 displayed fluctuations, with the highest sales volume at 1234 units in June and a sale value of 1446306.
Overall, the data provides insights into monthly sales trends and fluctuations over the three years.

Table B

Sales Performance Benchmarking

Benchmark comparisons of category performance based on sales volume and sale value.

Data covers quarters and years, spanning the analysis period from 2018 to 2020 (Jason et. al. 2020).

Each year is divided into four quarters: Q1, Q2, Q3, and Q4.

Product categories within the “Accessory” group: “Ankle bracelet,” “Bracelet,” “Hair band,” “Necklace,” and “Ring.”

For each category and quarter:

Sales volume and sale value figures are provided.

Year 2018:

Accessory: Sales volume and value for ankle bracelets, bracelets, hair bands, necklaces, and rings in each quarter.

Year 2019:

Accessory: Sales volume and value for ankle bracelets, bracelets, hair bands, necklaces, and rings in each quarter.

Year 2020:

Accessory: Sales volume and value for ankle bracelets, bracelets, hair bands, necklaces, and rings in each quarter.

Data enables analysis of trends and performance across categories, quarters, and years

Table C

Sales Benchmark Comparison Analysis

The data compares sales volume and value among different markets (Japan, United Kingdom, USA) for quarters in the years 2018-2020.
For 2018, Japan had varying sales volume and value each quarter, while the UK and USA showed more consistent trends.
In 2019, Japan’s sales volume fluctuated, the UK had relatively steady sales, and the USA saw some fluctuations.
2020 witnessed a decline in Japan’s sales volume, the UK experienced varying sales, and the USA had mixed sales trends (Tao al. 2020).
The data provides insights into market performance across the three years, highlighting patterns and changes in sales volume and value.

Task 4: Data charting and commentary

Chart A

Sales Trends Analysis

The data represents monthly sales volume and corresponding sale values across different markets. Over the course of 12 months, the sales volume and value exhibit fluctuations. Market 1 had peak sales in month 6, while Market 2 peaked in month 7. In contrast, Market 3 reached its highest sales in month 2. Despite varying trends, Market 2 consistently had the highest sale values, while Market 3 experienced the lowest. Overall, the data indicates dynamic sales patterns across the markets, showcasing potential market-specific factors influencing sales performance.

Chart B

UK Market Impact Analysis

2018:

Overall, accessories had varying sales volumes and values throughout the year, with the highest sales in Q3.
Ankle bracelets had minimal impact, with only notable sales in Q2.
Bracelets exhibited fluctuating sales, with Q2 having the highest sale value.

2019:

Accessories had a slow start in Q1 but experienced a significant increase in sales volume and value in Q3.
Ankle bracelets remained negligible throughout the year.
Bracelets maintained relatively stable sales, with Q3 having the highest sales volume.

2020:

Accessories started with moderate sales volumes and values, with Q2 having the highest sales.
Ankle bracelets remained insignificant throughout the year.
Bracelets had varying sales, with Q2 showing the highest sales volume and value.

Chart C

UK Marketing Impact Analysis

The marketing campaign’s impact on sales is evident across quarters and markets.

In the UK, sales volume started at 577, reaching 448 by Q4, while sales value began at £470,865, peaking at £368,748. Comparatively, Japan and USA showed mixed patterns (Wang et. al. 2022).

Japan’s sales volume and value fluctuated, with the UK surpassing in value. In the USA, sales volume decreased while value remained relatively stable.

Task 5: Conclusions and recommendations

Conclusions

The advent of the Computerized Age has ushered in an era of unprecedented data proliferation, fundamentally altering the landscape of business decision-making. Data-driven decisions, spanning a wide spectrum of business domains such as customer behavior analysis, supply chain optimization, market segmentation, and risk management, have become a cornerstone of contemporary strategic thinking. The underlying principle driving this paradigm shift is rooted in the belief that insights derived from data hold a higher degree of reliability and objectivity compared to traditional intuition-based methods.

The ascendancy of data-driven decision-making cannot be overstated. As organizations fervently seek to leverage data insights to drive strategic expansion, the role of adept data analysts has risen to the forefront. The narrative outlined here paves the way for an in-depth exploration of the realm of data analytics within the context of Wood from the Trees, a distinguished consultancy with a legacy spanning a decade. By delving into the intricate interplay between data, analysis, and informed decision-making, Wood from the Trees stands poised to harness the transformative power of data to chart a course toward sustained success in an increasingly data-centric business landscape.

References

Aad, G., Abbott, B., Abbott, D.C., Ambroz, L., Artoni, G., Backes, M., Balunas, W.K., Barr, A.J., Beresford, L., Bortoletto, D. and Cooper-Sarkar, A.M., (2020). ATLAS data quality operations and performance for 2015–2018 data-taking. Journal of instrumentation, 15(04).

Ajmi, H., AbdulAziz, H., Kassim, S. and Mansour, W., (2019). A literature review of financial contracting theory from the Islamic and conventional overviews: Contributions, gaps, and perspectives. Journal of King Abdulaziz University: Islamic Economics, 32(2).

Al Dakheel, J., Del Pero, C., Aste, N. and Leonforte, F., (2020). Smart buildings features and key performance indicators: A review. Sustainable Cities and Society, 61, p.102328.

Andronie, M., Lăzăroiu, G., Iatagan, M., Hurloiu, I. and Dijmărescu, I., (2021). Sustainable cyber-physical production systems in big data-driven smart urban economy: a systematic literature review. Sustainability, 13(2), p.751.

Domínguez, E., Pérez, B., Rubio, Á.L. and Zapata, M.A., (2019). A taxonomy for key performance indicators management. Computer Standards & Interfaces, 64, pp.24-40.

Fernando, F. and Hamil, A., (2022). A Study on the Impact of Women Entrepreneurs Doingcostume Jewellery startup Business in Tirunelveli.

Fleig, C., (2020). Design of data-driven decision support systems for business process standardization (Doctoral dissertation, Dissertation, Karlsruhe, Karlsruher Institut für Technologie (KIT), 2020).

Hamilton, R.H. and Sodeman, W.A., (2020). The questions we ask: Opportunities and challenges for using big data analytics to strategically manage human capital resources. Business Horizons, 63(1), pp.85-95.

Hinderks, A., Schrepp, M., Mayo, F.J.D., Escalona, M.J. and Thomaschewski, J., (2019). Developing a UX KPI based on the user experience questionnaire. Computer Standards & Interfaces, 65, pp.38-44.

Hristov, I. and Chirico, A., (2019). The role of sustainability key performance indicators (KPIs) in implementing sustainable strategies. Sustainability, 11(20), p.5742.

Jason, W.C., Ng, C.Y. and Brook, R.H., (2020). Response to COVID-19 in Taiwan: big data analytics, new technology, and proactive testing. Jama, 323(14), pp.1341-1342.

Kumar, S., (2023). FUNDING ISSUES AND CHALLENGES OF CONVENTIONAL, ENERGY MARKET IN THE ESG CONSCIOUS VVORLD. Academy of Marketing Studies Journal, 27(1), pp.1-10.

Lee, V.H., Dwivedi, Y.K., Tan, G.W.H., Ooi, K.B. and Wong, L.W., (2023). How does information technology capabilities affect business sustainability? The roles of ambidextrous innovation and data‐driven culture. R&D Management.

Manni, M., Berkeley, M.R., Seppey, M. and Zdobnov, E.M., (2021). BUSCO: assessing genomic data quality and beyond. Current Protocols, 1(12), p.e323.

Phittayanon, C. and Rungreunganun, V., (2019). Analyzing Factors Influencing Industrial Competitiveness of Thai Silver Jewelry Industry Using Analytic Hierarchy Process. International Journal of Mechanical Engineering and Technology, 10(3).

Pillay, K. and Van der Merwe, A., (2021). Big Data Driven Decision Making Model: A case of the South African banking sector. South African Computer Journal, 33(2), pp.55-71.

Priskila, S. and Darma, G.S., (2020). Employee Perception of Brand Value in the Jewelry Industry. Journal of Economics, Business, & Accountancy Ventura, 23(2), pp.267-278.

Shahat Osman, A.M. and Elragal, A., (2021). Smart cities and big data analytics: a data-driven decision-making use case. Smart Cities, 4(1), pp.286-313.

Soós, G., Ficzere, D., Varga, P. and Szalay, Z., (2020), April. Practical 5G KPI measurement results on a non-standalone architecture. In Noms 2020-2020 IEEE/IFIP network operations and management symposium (pp. 1-5). IEEE.

Tao, D., Yang, P. and Feng, H., (2020). Utilization of text mining as a big data analysis tool for food science and nutrition. Comprehensive reviews in food science and food safety, 19(2), pp.875-894.

Tu, J.C., Chan, H.C. and Chen, C.H., (2020). Establishing circular model and management benefits of enterprise from the circular economy standpoint: A case study of Chyhjiun Jewelry in Taiwan. Sustainability, 12(10), p.4146.

Wang, F., Zhang, R., Ahmed, F. and Muhammed Shah, S.M., (2022). Impact of investment behaviour on financial markets during COVID-19: A case of UK. Economic research-Ekonomska istraživanja, 35(1), pp.2273-2291.