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Obstacles and actions for making better data-informed decisions

Data can be really helpful in the decision-making process to provide context and reduce uncertainty. Figuring out the nuts and bolts of data-informed decision-making – including how and when to use data, as well as what data to use – can be tricky and unintuitive, but it’s an important skill and one that can be learned.
To help you get good at it, I’m going to point the most common challenges around data-based decisions and then share the five-step process that I follow to make better decisions with data. Whenever I can, I always support our decisions with intelligent technology that help us filter through the noise and focus on what’s relevant to the task at hand. And that’s the same what we do for our clients at Untrite – our software empowers clients to gain visibility across their otherwise disconnected, siloed systems by utilising data, enabling better cross-collaboration and data-informed decision-making.

Making data-informed decisions can be challenging due to several factors:

  • Data complexity: Data is often vast, complex, and scattered across various sources and systems within an organisation. Gathering and analysing this data to extract meaningful insights can be a time-consuming and intricate process.
  • Data quality and integrity: Ensuring the accuracy, completeness, and reliability of data is crucial for making informed decisions. However, data may suffer from quality issues such as inconsistencies, errors, or missing values, which can impact the reliability of the insights derived from it.
  • Data interpretation: Interpreting data requires a combination of domain knowledge, statistical analysis skills, and critical thinking. Different stakeholders may have varying interpretations of the same data, leading to potential disagreements and challenges in reaching a consensus.
  • Data bias and subjectivity: Data can be influenced by biases introduced during collection, analysis, or interpretation. Biases can stem from factors such as sampling methods, data collection processes, or preconceived notions of analysts. These biases can lead to skewed insights and potentially flawed decision-making.
  • Information overload: With the exponential growth of data, organisations often face the challenge of information overload. Sorting through vast amounts of data and identifying the most relevant and actionable insights can be overwhelming, making it difficult to extract value from the data.
  • Organisational resistance: Organisations may face resistance to adopting data-driven decision-making practices due to cultural barriers, lack of awareness or understanding of the benefits, or fear of change. This resistance can hinder the effective utilisation of data in decision-making processes.
  • Technological limitations: Leveraging data for decision-making requires robust infrastructure, advanced analytics tools, and skilled data professionals. Limited technological capabilities or insufficient resources can impede an organisation’s ability to effectively collect, analyse, and utilise data for decision-making.

Understanding which decisions are the most important within an organization requires a thoughtful and systematic approach. In the event of available data, I follow these five steps when making a well informed decision:

Step 1: Define the decision to be made

  • You should clearly define the decision you’d like to make, including your goals, available choices, and possible outcomes. This upfront clarity provides a reference point when analysing data and saves time in the decision-making process.

Step 2: Define additional relevant information and constraints

  • Consider additional information and contextual factors that impact the decision-making process, such as timing and business constraints.
  • List out decision-related constraints to align data analysis with practical considerations.

Step 3: Determine the data needed to make a decision

  • Translate decision requirements and constraints into specific data needs.
  • Hypothetically evaluate different datasets to determine the information that would reduce uncertainty and influence the decision.

Step 4: Collect and interpret data

  • Collect the necessary data or collaborate with data subject matter experts to acquire the required information.
  • Share the pre-work and decision context to ensure the collected data aligns with the goals and constraints.

Step 5: Make your decision

  • Combine the collected and interpreted data with the defined goals and constraints to reduce uncertainty and gain clarity.
  • Leverage the structured approach followed throughout the process to make a well-informed decision.

Data-informed decision-making requires investment and a commitment to continuous learning. Mastering the art of data-informed decision-making is a valuable skill that can be developed by following a structured approach. By defining the decision, considering additional context, determining the required data, collecting and interpreting the data, and making the decision, organisations can effectively leverage data to gain valuable insights and make better choices. Untrite’s AI software serves as a valuable tool in this process, enabling clients to access, integrate, and analyse their data effectively, leading to enhanced visibility, improved cross-collaboration, and data-informed decision-making.

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