When done right, Data-Driven Decision Making (DDDM) is a powerful tool in the hands of the organization. But like any powerful tool, one should handle it with caution. It requires a strong commitment from the C-suite to transition to a data-literate culture and openness to implementing new systems.
In this article, we delve into the question of why implementing a Data-Driven Decision Making strategy remains a challenge – even for well-established retail operations. The difficulties arise from a combination of technical, organizational, and cultural factors. Buckle up as we explore the road to creating and implementing DDDM in retail.
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The Twists and Turns of the Data-driven Journey
Cultural Resistance
A significant barrier to data-driven decision-making is cultural resistance within the organization. By the time a retail brand develops and reaches global-scale operations, it often has long-standing traditions of relying on intuition, experience, or hierarchical decision-making. Transitioning to a data-driven approach often requires a cultural shift, where decisions are based on evidence rather than seniority or gut feelings. This shift can be met with resistance from employees who are uncomfortable with or distrustful of data-driven approaches. Overcoming this resistance requires strong leadership, clear communication, and even a gradual change management process to build trust and buy-in across the organization.
Data Quality and Accessibility
One of the fundamental challenges is ensuring the quality and accessibility of data. The quality of the data can only be as good as the quality of the data collection. Many global brands struggle with fragmented data systems, where information is siloed across different countries, departments, or platforms. This fragmentation makes it difficult to aggregate and analyze data comprehensively. Additionally, the data may be incomplete, outdated, or inconsistent, leading to unreliable insights.
Let’s see it in practice and try to answer the question ‘Who are our suppliers?’ The data we need to answer this question might be scattered across different departments, such as procurement, finance, marketing and logistics. This can lead to inconsistencies and difficulties in effectively managing supplier relationships, understanding their actual performance, identifying overlapping services, or recognizing when a single supplier is serving multiple departments, resulting in higher business volume. Once you have faulty data, and it starts to cause problems, it can undermine the trust as we are back to the problem resistance.
Complexity of Data Analysis
Even when quality data is available, analyzing it effectively requires specialized skills and tools. Data analysis is not just about running numbers; it can involve sophisticated techniques like predictive modeling, machine learning, and statistical analysis, which require a deep understanding of the business context, and the technical aspects at the same time. Let’s take the example. Let’s say we see a drop in the sales of a product and want to understand the cause of it. Was it due to a lack of demand? Or is it due to the supplier’s consignment being delayed because of geopolitical issues, or maybe something else? The person seeking the answer must understand the data available at the company, and which one to use, which one to ignore, etc. These decisions have a profound impact on the outcome.
Moreover, choosing the right analytical tools and technologies can be overwhelming given the vast array of options available.
Numerous companies face a shortage of skilled data analysts or data scientists who can interpret data accurately and translate it into actionable insights and individuals often lack the essential skills and knowledge required to work effectively with data. They struggle to understand, analyze, or communicate data in a meaningful way. This data illiteracy is like stumbling through a foggy forest without a map – it’s challenging to navigate and make informed decisions!
Integration with Existing Processes
Integrating data-driven decision-making into existing processes is another major challenge. Traditional business processes may not be designed to incorporate data analytics seamlessly, leading to disruptions or inefficiencies.
As an example, up until today your sales team relies mostly on personal relationships and intuition to target clients. You implement a system that analyzes customer data to identify high-potential leads. With this implementation you might need to change the decision making process to incorporate the analysis, might need to change the mindset of the sales team to combine the results of the data analysis with their experience, and establish the process of maintaining and managing data.
Expected Return on Investment
Investing in Data Driven Decision Making involves substantial upfront costs, including investments in technology, talent, and process redesign. Companies must invest in robust data management systems and processes to clean, unify, and standardize data, which can be both time-consuming and costly. However, the return on investment (ROI) is not always immediately clear. Companies may find it difficult to quantify the benefits of data-driven decisions in advance, or in the short term.
On the other hand, not investing and missing out on the transition can backfire in the mid to long term when important information is missing from the decision-making process, resulting in poorer outcomes compared to competitors.
Research on the dissemination and adoption of data-driven decision-making systems shows that despite all challenges, their integration across industries is accelerating. The global uptake of these systems is driven by the need to manage information asymmetry – where lower-level managers have more relevant information than top-level executives -, and DDDM systems help centralize this knowledge by making data more accessible across the organization.
Greater insight into relevant information for top executives obviously has an invaluable impact on the overall business performance of the company. No leader wants to fall behind their competition.
Data Governance
Lack of data leadership and control leads to unclear ownership and accountability.
The most common problems we see are:
- No clear owners of the data (such as a dedicated person, team or a system) that maintains the given data. If no one is responsible for maintaining e.g. a store’s opening hours, outdated or incorrect information may be displayed, leading to customer frustration and lost business. Without clear ownership, updates might be missed, especially during holidays or special events, creating confusion for both staff and customers.
- No single point of truth (owner system), thus if there is any ambiguity of a value across systems, it becomes unclear, which value should be accepted. For instance, if the sales system shows X dollars in sales and the BI platform Y for any reason, the sales system should prevail.
- Distinct definition behind the data is missing. The definitions should be consistently understood and globally agreed upon within the company without any ambiguous or subjective elements. A good rule of thumb for a solid definition is that a new team member, with limited knowledge about the company, should be able to read this definition and understand what this piece of information represents, in the same way as long-standing team members.
- Not clearly defined who is allowed to access a given data and for what purpose.
Privacy Concerns
As companies increasingly rely on data, they bear the responsibility to manage it both ethically and legally. Beyond implementing measures to optimize and streamline data governance for their own benefit, businesses must also comply with an expanding array of legal regulations.
Balancing the demand for more and more accurate data-driven insights while respecting privacy and regulatory measures is a delicate task. Companies must navigate these concerns carefully, which can add another layer of complexity to the process.
How to Conquer Difficulties?
Adopt Step by Step
Adopting a Data Driven Decision Making mindset and implementing its processes may initially seem like an overly complex task, but it is not necessary to solve and implement everything at once. Conducting a preliminary assessment of the entire innovation process and then breaking it down into smaller units helps to proceed step by step, giving time to develop improvements and allowing the organization to adapt to new tasks. This approach also helps keep people engaged by delivering quicker results, allowing them to celebrate the success of each step along the way.
Start with a Common Language
Many companies lack proper definition behind data, thus different groups understand them differently which means that some of these definitions have ambiguous, overlapping or contradictory statements. The first thing is to create clear definitions with the involvement of both the business stakeholders and data specialists. Regular data quality validation to identify and correct errors, gaps or inconsistencies and the automation of this process help to achieve accurate and reliable information.
Let’s imagine a meeting where the term “customer engagement” was used, but each department interpreted it differently. Marketing saw it as the number of social media interactions, while sales considered it the number of follow-up calls made. Because the term was so ingrained in corporate lingo, no one questioned its varying meanings. This inconsistency can lead to inconsistent reporting and misguided strategies. Common language is the foundation for any team that wants to collaborate and work towards a common goal.
Communicate the Anticipated Advantages Continuously and Clearly
Strong leadership support and commitment are the key to foster DDDM culture within the organization. Management workshops and training programs for the employees can ensure a better understanding of the essence and benefits of the transition and how to use data in decision-making effectively. It must be understood that collecting meaningless, outdated, calculable, redundant or irrelevant data makes the workload heavier, while semi automated processes can reduce the unnecessary loops or wasting time on searching for the ones in charge and forgotten tasks. It must be clearly communicated to employees that these methods are not meant to replace them, but to help them focus on more meaningful work, enabling greater personal and organizational success.
An empowered, ongoing executive program management can ensure the processes are kept under control, and the lack of internal experience or capacity can be bridged by involving external experts.
In conclusion
While data-driven decision-making offers enhanced accuracy and competitiveness, achieving it is not without its challenges. Companies must navigate challenges related to data quality, analytical complexity, cultural resistance, data governance, process integration, and ROI uncertainty. Overcoming these challenges are well worth it, but requires a strategic approach that combines technical investments with organizational change and strong leadership. Only by addressing these topics can companies fully leverage the power of data to drive better decisions, achieve long-term success, and gain advantage over competitors.
Wherever you are on your journey to Data Driven Decision Making, we are here to assist you every step of the way. We specialize in overcoming data fragmentation, enhancing data quality, and empowering your organization with the tools and insights needed to drive smarter, faster decisions. We’ve got your back(end) covered, so you can focus on making the business decisions that drive your success.
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