4 most common obstacles to data-driven decision-making and how to overcome them

Learn about the 4 most common obstacles for data-driven decision-making in every organization. Understand the challenges and also how to tackle them before it is too late.

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Data-driven decision-making (DDDM) is becoming increasingly crucial for the future success of businesses. By using data and analytics to inform decisions, organizations can gain valuable insights that help them make better decisions faster and more accurately than ever. However, many companies struggle to adopt a data-driven approach due to several common obstacles that impede their progress or even completely block the integration of data-driven management (DDM).

The most common reasons are limited access to data, difficulties in interpreting complex data sets, a lack of knowledge among employees about how to use data effectively, a lack of a data-oriented corporate culture and an inadequate technological infrastructure. In addition, cultural barriers within companies prevent a data-centric mindset from taking hold. Overcoming these challenges and implementing a successful data-driven strategy requires leadership commitment and sufficient resources to train employees in the skills needed to use Big Data. With the right mindset and support at all levels of an organization, companies can reap the rewards of sound decisions based on accurate analysis of available information and the resulting insights. This article presents some of the most common problems in data-driven decision making (DDDM) and strategies for overcoming these obstacles.

Obstacle 1: Lack of Data Quality

Most companies think they have a lot of data, but the reality is that they don’t have enough good quality data. Data quality is important because it affects the accuracy of decisions, and inaccurate data can lead to incorrect conclusions. In this context, poor data quality refers to low accuracy, completeness, consistency and relevance of data. It is a major challenge for organizations that rely on data to make informed decisions.

Common causes of poor data quality

The following are common causes of poor data quality:

  1. Lack of standardization: The absence of a uniform set of rules and guidelines for data collection, storage, and management. Without standardization, data may be inconsistent, leading to confusion and incorrect conclusions.
  2. Missing values: This occurs when important data is not captured, recorded or entered into the system. Missing values can result in incorrect or incomplete analysis and decision-making.
  3. Outdated Information: Data can quickly become outdated, leading to inaccurate or irrelevant analysis and decision-making. Organizations must regularly update their data to ensure its accuracy and relevance.
  4. Unstructured datasets: Unstructured data, such as text, images, or audio files, can be challenging to manage and process. Data can become messy and difficult to use without proper organization and structure.
  5. Lack of data governance: Effective data governance is essential to ensuring the quality and consistency of data. A lack of governance can lead to inconsistent and incorrect data and difficulties in tracking and controlling data access and usage.
  6. Lack of data validation: The absence of checks and controls to ensure that data is accurate, complete, and consistent. Organizations may rely on incorrect or incomplete data without verification, leading to poor decision-making.

Strategies for improving data quality

Implementing data governance policies is one of the best strategies for improving data quality. These policies should include a framework for governing data collection, use, storage, and disposal. They should also include policies for data sharing, access control, security, and legal and regulatory compliance. In addition, organizations should establish standard procedures for collecting and managing data to ensure its consistency and accuracy. They should also implement data validation procedures to detect errors and verify the completeness of data before it is used to make decisions. Finally, organizations should regularly review their data architecture to ensure that appropriate systems are in place to store, access and manage data.

Here are some common fixes for poor data quality:

  • Ensure data is standardized, validated, and governed.
  • Regularly audit and cleanse datasets to ensure accuracy.
  • Establish procedures for the proper collection, storage, and use of data.
  • Invest in automated tools to help monitor, identify, and rectify incorrect or missing data.

Obstacle 2: Insufficient Data Access and Analysis Capabilities

Data access and analysis are critical components of a successful data-driven decision-making process. Access to the correct data is essential to making informed decisions that positively impact the business. Performing accurate analysis or gaining meaningful insights may be impossible without access to the right data and analytical capabilities necessary to extract useful information from their data.

Common issues that organizations face with data access and analysis

These are the most common issues when we are speaking about data access and analysis obstacles:

  1. Lack of data access: Poor access to data can limit an organization’s ability to make informed decisions. Organizations need appropriate access to the right data to analyze and interpret it effectively.
  2. Lack of data integration: Integrating data from multiple sources is essential to gain a comprehensive view of the data. Without integration, organizations are limited to working with siloed datasets that do not provide an accurate picture of their data landscape.
  3. Limited analytical capabilities: Organizations may lack the skills and tools to analyze data accurately and gain meaningful insights.
  4. Lack of data visualization tools: Visualizing data helps organizations understand trends, track progress, and identify opportunities for improvement. Without these tools, gaining valuable insights from complex datasets is challenging.
  5. Lack of benchmarking tools: Benchmarking allows organizations to compare their performance against industry standards and track progress over time. Without these tools, it is more difficult for organizations to measure the success of their decisions. Platforms like MoreThanDigital Insights will help to evaluate and compare relevant datasets.

Strategies for improving data access and analysis capabilities

Improving data access and analytics capabilities involves optimizing data storage, retrieval and processing. Organizations should deploy solutions that facilitate access to data across multiple platforms and locations. These include technologies such as ETL (extract, transform and load), databases and middleware that help integrate data from multiple sources.

In addition to tools, employees should also be involved to ensure successful data access and analysis. Companies need employees with the right knowledge and skills to analyze data, interpret results and make decisions based on the insights gained. Companies should also invest in training programs that provide their employees with these skills, or hire external experts as needed.

Here are some common fixes for better data access and analytics capability:

  • Implementing proper data tools to ease access to data and analytics
  • Investing in analytical software and giving employees access
  • Utilizing data visualization tools for easier understanding of the accessed data
  • Establishing benchmarking tools (e.g., MoreThanDigital Insights)
  • Developing a data-driven culture to ensure everyone sees the value of data and capability is built across the entire organization

Obstacle 3: Lack of Data Culture

A data-driven culture is one in which decisions are made based on data, rather than relying solely on intuition or experience. This culture emphasizes the use of data to make informed, evidence-based decisions that ultimately lead to better outcomes for the business. Unfortunately, many companies lack this culture and rely heavily on gut feelings and subjective opinions to make important business decisions. As a result, they miss valuable opportunities to optimize their operations and maximize profits. A lack of a data-driven culture can be a major obstacle for most companies.

Common reasons why organizations lack a data culture

These are the most common issues why there is no data-driven company culture:

  1. Lack of Leadership Support: Organizations often struggle to implement a data culture from the top down, as executives and senior management are often hesitant to embrace a data-driven approach. Without strong leadership support, it is difficult for organizations to implement and sustain a data culture. As such, organizations should ensure that their leaders are well-versed in the importance of data-driven decision-making and can effectively communicate this message across the organization.
  2. Organization structure and hierarchy problems: Poorly defined organizational structures can be an additional obstacle to implementing a data culture. Without proper roles and responsibilities, it is difficult for organizations to ensure that employees understand why data is important and how it can be used to make informed decisions. Organization structures should be designed so that data flows efficiently and all stakeholders know their respective roles in the data-driven process.
  3. Lack of Data Literacy Among Employees: A lack of understanding and appreciation for data within an organization can also prevent it from establishing a data culture. Organizations should invest in training programs that equip their employees with the skills and knowledge they need to utilize data effectively. Data literacy could include a combination of technical, software-related, and analytical skills.
  4. Resistance to Change: It is also common for organizations to face opposition from individuals when implementing a data-driven culture. People may be hesitant or unwilling to accept the changes that come along with this shift. Organizations must recognize and address these issues to ensure a smooth transition and successful implementation of data-driven decision-making processes.
  5. Lack of Data Governance: Without proper data governance, it can be difficult for organizations to access the data they need to make informed decisions. Data governance is crucial to ensure that data is appropriately collected, stored, and analyzed to maximize efficiency and utility.
  6. Poorly Defined Processes for Collecting, Analyzing, and Utilizing Data: Without well-defined processes for collecting, analyzing, and utilizing data, organizations may not be able to take full advantage of the data they have available. Organizations need to develop transparent processes and guidelines to use their data effectively.
  7. Lack of Reliable Data and Insights: Organizations may struggle to find reliable data sources and insights. Without access to high-quality, accurate information, it can be difficult for decision-makers to make informed decisions. Organizations should invest in data sources (internal, e.g., Websites, CRM and ERP) or external analytics providers (e.g., Gartner, Forrester, MoreThanDigital Insights) and tools that can provide reliable insights to enable better decision-making.

Strategies for improving and fostering a company culture for data

Any good data-driven culture must build a data-driven leadership team that understands and acts on data and insights. Leaders should ensure that their organizations have the appropriate resources and people to make data-driven decisions. The leadership team should also ensure that data-driven initiatives receive the attention and support (perhaps even priority) they need. This includes incentivizing employees to use data to inform their decisions.

For a data-driven culture to work, it is important that ALL employees are trained in data literacy. Companies should invest in programs and training that teach employees how to interpret data insights and incorporate them into decision-making processes. A simple trick is to integrate KPIs and insights into employees’ daily work tools, e.g., displaying KPIs relevant to them on their intranet portal or sending automated data reports daily/weekly so it becomes natural.

In addition, it is important to foster collaboration between departments and teams to share data and more. This can be achieved by developing a shared understanding of how data can support business goals and by identifying and nurturing “data ambassadors” who understand the power of data-driven decisions.

Here are some common fixes that help you build a company culture for data:

  • Make sure that the leadership team is becoming data-focused
  • Invest in training and education to increase data literacy among employees
  • Creating a sense of ownership and accountability when it comes to data and analytics for every employee
  • Identify and encourage “data ambassadors” who act as an internal voice
  • Make Data a “new normal” by implementing KPIs and Reports it into intranets, dashboards, other reports, meetings and management decisions
  • Encourage collaboration between departments and teams by developing a shared understanding of how data can support business objectives

Obstacle 4: Difficulty Interpreting Data Complexity

Analyzing complex data sets can be daunting, especially for less experienced personnel. That’s because of the sheer volume of data available, which makes it difficult to see patterns or make sense of it. To overcome this challenge, companies should invest in training and data visualization tools that enable staff to quickly and accurately interpret complex data sets. Companies can also host workshops or seminars on data-driven decision making that help employees understand the importance of using data to make informed decisions.

Here are some common fixes that help you interpret data complexity:

  • Invest in training and data visualization tools that enable personnel to analyze complex datasets quickly
  • Develop data-driven decision-making workshops or seminars that help employees better understand complex data
  • Leverage “Data-Ambassadors” who can act as internal “Translators” for other employees
  • Consult with external experts on how to present better or organize your data to reduce the complexity
  • Evaluate if the complexity is needed and find simpler solutions if necessary
  • Regularly check in with your employees if they experience complexity and difficulty – Get feedback fast and often.

Conclusion

Organizations must remove the most common barriers to data-driven decision-making to foster a thriving data culture that leads to better decisions, faster results, and less friction. Leaders should ensure their organizations have the resources and staff to make data-driven decisions and train their employees on how to interpret and act on insights from data. In addition, collaboration between departments and teams can be fostered by developing a shared understanding of how data can support business goals and by identifying “data ambassadors” who understand and communicate internally the importance of data-driven decisions. By addressing these issues head on, organizations can create an environment where everyone understands the importance of using data when making decisions.

Benjamin Talin

Benjamin Talin is founder of MoreThanDigital, a serial entrepreneur and innovator. He has founded countless businesses, ranging in age from 13 to the present. His passion is using technology and innovation to change the status quo, and his experience covers everything from marketing to product development to new technology strategy. One of Benjamin's great desires is to share his expertise with others, and he frequently speaks at conferences on a variety of topics related to entrepreneurship, leadership, and innovation. Additionally, he advises governments, ministries and EU commissions on issues such as education, economic development, digitalization, and the technological future.

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