deFacto Global Blog

4 Steps Your Organization Must Take To Become More Data-Driven

In today’s data-driven world, organizations that do not harness the power of data and advanced analytics risk falling behind their competitors. Becoming more data-driven involves a deliberate shift in culture, strategy, and operations, and requires the adoption of specific steps. Here are some key steps organizations must take to become more data driven. By adhering to these steps, you can unlock your data, make informed decisions, and gain a sustainable competitive edge.  

1. Establish a Data-Driven Culture

Developing a data-driven culture is essential for leveraging information to achieve business success. A core tenet of this culture is to prioritize the value of data and embrace the policy of blending data analytics with experience to make decisions. To support this culture, there are several specific measures an organization can take: 

  • Establish clear business goals: Develop strategic, yet practical, game plans for how data analytics will help to achieve those goals.   
  • Empower decision makers: Put the power of data analytics in the hands of every decision maker across the entire organization. This will enable them to make the best decisions for their business and the entire organization.  
  • Identify the data that is most valuable and relevant to business goals: Once it is pinpointed, focus on collecting that data. Once data is collected, it is essential to ensure its quality.  
  • Protect data: Invest in measures that shield data, especially from the risk of cyber-attacks and data breaches, unauthorized access, theft, or loss.  

2. Recognize Common Barriers and Combat Them

While the benefits of becoming data-driven are compelling, most organizations are victims of their own habits and policies. Many of these represent obstacles organizations must address as part of their overall data analytics strategy.  Here are a few common barriers that organizations should look out for: 

  • The lack of relevant, quality data: It all starts with data. No strategy or technology platform is going to replace high quality, relevant data. Organizations typically have enough of this data to begin their journey to becoming data-driven, but many times it’s in siloed systems and needs to get integrated with data from other sources to be of value.  
  • Limited data literacy within your organization: Data literacy is the ability to read, understand, and use data to make informed decisions. Don’t assume your employees already have this skill. Train data literacy and encourage collaboration to ensure your team possesses effective data analytic skills. 
  • Lack of leadership and willingness to share data and decision-making power: Leaders must lead by example, encouraging open communication and data sharing while providing support and guidance to employees as they adapt to a more data-driven approach. Organizations must foster a culture of trust, transparency, and collaboration.  
  • Insufficient resources and technology: Organizations should prioritize investments in data, analytics tools, and employee training.   

3. Ensure You Are Ready to Commit to Data-Driven Operations

Before transitioning to a data-driven approach, organizations should undergo an assessment to objectively determine their state of readiness. Key considerations include:   

  • Management is committed to the organization’s vision, definition, and plan for taking on the transformation: Stakeholders must be identified, goals and objectives defined, and a governance framework established to ensure compliance with regulations and ethics.  
  • The necessary data is available: Organizations must ensure they have access to the right data. Quality, comprehensiveness, and security are essential for a data-driven approach. This involves establishing data processes aligned with organizational goals, ensuring data is accurate and reliable, and implementing security measures.  
  • Technology infrastructure that is well-designed, usable, and accessible by everyone: User-friendly and consistent interfaces, integration of data sources and systems, appropriate tools and software, scalability, availability, and flexibility for future changes are some of the components of productive infrastructure. 

4. Invest in The Right Tools for The Job

No technology will make up for the lack of quality data. But the choice of technologies will determine the ultimate success of an organization’s data analytics strategy. Ideally, use interfaces your employees are already using. 

  • A spreadsheet program: Tools like Excel are a good option for organizations just starting out with data analytics, but most organizations quickly outgrow their capabilities. Using a spreadsheet as a database is a bad idea, but using a spreadsheet as an interface to a secure database can be a great idea. This puts users in a comfortable environment, but they get the security and normalization of data.  
  • Data visualization tools: Tools like Power BI or Tableau are great for allowing organizations to get to know their data. They offer more advanced features over spreadsheets including dashboards, data blending, data modeling, and predictive analytics, although they require more technical expertise than spreadsheet programs. 
  • Planning and analysis products: These are essential for data-driven organizations. They are specialized tools designed for automating business and decision-making processes. Many extend the capabilities of spreadsheet and data visualization tools. While some are focused on specific functions like financial planning or demand planning, others, like deFacto Power Planning, are designed to address planning and analysis across an entire organization. 


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