In the dynamic world of modern business, data reigns supreme. Its impact cannot be overstated, especially when it comes to making critical decisions. It might be surprising, though, only a select few retail brands have managed to leverage data effectively, generating value and gaining a massive competitive advantage.
Data-driven businesses have successfully implemented the concept of considering data as a corporate asset, much like physical assets, financial assets, or other forms of immaterial wealth, and have integrated data into their core operations. Meanwhile, others find themselves struggling on the opposite end of the spectrum. They may not yet have realized that, just like any other valuable resource, data also requires investment, proper management, and governance.
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What is Data Driven Decision Making?
Data Driven Decision Making (DDDM) is an attitude and an ongoing process that involves collecting and analyzing various types of data. Its purpose is to guide decisions by highlighting problems, showing correlation between things and helping to offer options..
Embracing data-driven decision making is crucial for leaders who understand that, in addition to their exceptional intuition and talent, validating their instincts with data can further elevate their performance. Acknowledging that no one is infallible, these leaders ask themselves, “How do I know I’m making the right decision?” By integrating data checks into their decision-making process, they blend intuition with analytics to achieve superior results.
In the early stages of data analytics, the primary goal of introducing DDDM was to lead the market, but as its use has become more widespread, it has now become a must-have for not lagging behind.
Challenges in Implementing DDDM
When done right DDDM is a powerful tool in the hands of the organization. But like any tool that is powerful, it should be handled with caution. If a DDDM strategy is executed incorrectly, for example, some data is incorrect, or it is misinterpreted, decision-makers will have a false sense of knowledge.
When you make decisions while knowing your blind spots, you will definitely take them into account and be cautious. However, receiving false data can misguide a decision or even a series of decisions. This problem is well illustrated by the classic parable of a few blind men first encountered by an elephant. Each man touched a different part of the elephant and then described what he believed the elephant was like. The one who touched the trunk thought the elephant was like a snake; the one who touched the ear thought the elephant was like a fan, and the one who touched the leg thought the elephant was like a tree. Each man was convinced he was right based on his limited experience, but none of them had the full picture of the elephant. They all had partial truths but were biased by their own limited perspective.
Long story short, correct data is essential, but on its own, it does not guarantee the desired outcome. When analyzing data, context is key. For example, knowing that a product’s sales have increased is useful, but understanding the context – such as a recent marketing campaign, seasonal trends, market economics, or a launch in a new market – explains why the increase happened and how to replicate it in the future. A well-structured DDDM process can ensure that the analysis is not biased or subjectively influenced.
WHY SHOULD RETAIL OPERATIONAL DATA BE INCLUDED IN THE DDDM STRATEGY?
Global brands spend a substantial portion of their revenue on operating their retail networks. This includes rent, utilities, staffing, renovations or placing and removing campaign materials. The percentage can vary significantly by industry and company, but research shows that retail operations typically account for around 20-30% of a retailer’s total revenue. This is a huge expenditure. To put it into perspective, retail brands on average spend 9% of their revenue on marketing and advertising costs.
What justifies this spending? The retail network is often the primary and the most important touchpoint for customers. It is not just a money pit but can also be a goldmine: when managed properly it can make or break the overall performance of the brand, and therefore it is a crucial area for collecting data and turning it into a valuable corporate asset. An often cited statement from a research conducted by Quri is that as much as 25 percent of retail sales are lost due to poor retail execution practices. It is easy to see that exercising greater control over retail operations significantly affects the performance indicators of retail brands from both cost and revenue perspectives.
Incorporating retail data in strategic decision-making can widen the understanding of leaders who want to see their business from as many perspectives as possible. Retail data encompasses a wide range, from sales, customer, product, inventory, marketing, e-commerce data, to retail operations and deployment data. These perspectives help retail brands better understand their customers, optimize operations, and strategically position themselves in a competitive market.
Integrating Machine Efficiency and Human Skills
Efficient Data Management at Scale
Data Management is a critical competency for data-driven organizations. Machines and algorithms are increasingly capable of performing tasks that were traditionally done by humans. Automated data collection and processing have several advantages over manual methods, making them more efficient and reliable in many contexts.
Automated systems significantly reduce human errors, process large volumes of data much faster than manual methods, and provide real-time insights and visibility into operations. To give an example, when you receive your sales data at the end of every quarter, via excel sheet, you face the following problems:
- 1. You have no visibility over what is happening in your business until the end of the quarter, therefore you cannot make adjustments
- If data arrives in Excel documents (manual work) you need a team of people to collect the numbers, crunch them into a single sheet. These people cost money and may make mistakes – the larger the data set is, the more mistakes are predictable.
- Every person who modifies the data can make changes that are difficult to track, such as updating or ignoring data points and introducing biases into your report without your knowledge.
While the initial setup cost for automated systems can be high, they generally have lower long-term costs compared to manual data entry and can easily scale to handle increased data volumes without significant additional resources. These systems can be programmed to ensure consistent compliance with industry regulations and standards, supporting tracing data for audits and quality control. Automated data collection integrates well with digital workflows and analytics tools, making it easier to analyze data, identify patterns, and make informed decisions.
Using Advanced Technologies: Are You Doing It Right?
The transition towards more automated systems may increase the need of professionals with the skills to work alongside advanced technologies, but in most cases it is not rational to develop and continuously maintain specific technological knowledge and a large staff within the company. What is important is to own and maintain proper documentation of these systems to ensure they can operate safely, regardless of who is managing them.
It is most effective for an organization to focus on its core competencies and entrust specific tasks to experts with extensive previous experience. The success of the transition can be ensured by a reasonable ratio of technological investment, workforce training, and the involvement of external experts. New processes and methods may create new opportunities and tasks in areas where human judgment and creativity are essential. In essence, the key is finding the right balance between leveraging machine efficiency and maintaining the unique value that human skills bring to data management.
It’s time to proactively integrate this approach into core business strategies, as demonstrated by top-performing retail brands like Nike. Nike’s flagship stores offer a blend of digital and physical experiences. They use data analytics to personalize customer experiences and have invested in training their staff to use these tools effectively. Additionally, they collaborate with external experts to develop innovative retail solutions, such as their Nike Fit app, which uses AI to recommend shoe sizes.
In Conclusion
Data-driven decision making is not just a method and an ongoing process, but also an attitude towards data as a valuable corporate asset. Incorporating retail data in strategic decision-making can broaden the perspective of understanding the business and make brands more profitable. Successful data management lies in finding the right balance between leveraging machine efficiency and the unique value of human skills, ensuring it is used with care and ethically.
Wherever you are on your journey to Data Driven Decision Making, we are here to assist you every step of the way. Drawing on our specialized expertise in retail management, we assess your digital transformation possibilities, verify data integrity, provide tailored advice on data organization and structuring, create human-readable data models, and guide you in leveraging advanced business intelligence (BI) tools – ensuring sustainability as analytics capabilities continue to expand into the future. We’ve got your back(end) covered, so you can focus on making the business decisions that drive your success.
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