You're struggling to be more assertive in data management. What's the biggest mistake you're making?

Proper documentation of data assets is crucial for understanding their structure, usage, and lineage. Without comprehensive documentation, it's difficult to make informed decisions about data management and ensure its integrity.

A mistake you might be making in your struggle to be more assertive in data management is focusing solely on tactical solutions rather than considering strategic improvements. While tactical solutions may provide short-term benefits, they fail to address the underlying root causes of your data management shortcomings. To enhance your assertiveness in data management, it is crucial to incorporate strategic solutions alongside tactical approaches

One of the biggest mistakes might be making in data management, is struggling to be more assertive, not setting clear boundaries and priorities. If you haven't defined specific objectives for data management, it becomes challenging to assertively prioritize tasks and make decisions about what data to focus on. To improve assertiveness in data management, it's crucial to address these areas by setting clear goals, communicating priorities effectively, being willing to have difficult conversations, advocating for resources, and enforcing policies consistently.

I find that at a new organization, your recommendations need to show that you understand the demands and pain points of the current project. After that, the team is more likely to ask you for an answer. If you still have trouble, be sure to clearly communicate the impact of failing to comply or update to current CDM standards. Generally, team members will not want to accept these impacts, so they will come back to you to solve the problem

Lack of clarity about data requirements can lead to collecting unnecessary information, missing important data, and ultimately, ineffective data management. How to fix it: Define your goals: Determine what you want to achieve with your data. Identify stakeholders: Understand who will be using the data and what their needs are. Identify data types: Determine what types of data you need to achieve your goals. Determine data volume: Determine how much data you need.

Neglecting to enforce data standards poses significant challenges in maintaining data integrity and consistency. Inconsistent formats, inaccurate data entries, undocumented procedures, and compliance risks arise. Addressing this requires consistent enforcement through clear guidelines, training, audits, and automated tools.

Lack of data standards can lead to inconsistency, low quality, and inaccessibility of data. How to fix it: Develop data standards: Define rules and conventions for data naming, file formats, metadata, etc. Enforce compliance: Implement processes and tools to ensure compliance with data standards. Train employees: Train employees on the importance of adhering to data standards.

Another significant oversight in data management is the failure to enforce data standards consistently. Without robust enforcement mechanisms, data quality can suffer from inconsistencies, errors, and duplication, impeding reliable analysis and decision-making. Failure to adhere to established standards can lead to confusion among users, hinder interoperability between systems, and compromise the integrity of data-driven processes. By implementing and enforcing stringent data standards, organizations can maintain data accuracy, consistency, and completeness, thereby enhancing the reliability and utility of their data assets for driving business insights and achieving strategic objectives.

Failing to report data results impedes insight sharing, decision-making, and stakeholder communication. For example, overlooking customer behavior insights can result in missed marketing opportunities. Clear and concise reporting informs decisions, engages stakeholders, and fosters organizational success.

I did a 6-months probability analysis for a stakeholder with the objective to derisk an investment of a $xx-million investment project. The analytics results came close to a 50% chance of success; which is the worst outcome to present to the board (the result of months could have depended on a coin). No scenario would allow another iteration of expensive analytics/derisking. A choice needed to be made whether to: #1 = bend to the arts of visualization following a stakeholder's agenda, or #2 = stay true to the science and standards of data practices The project investment didn't go through but a case was made to address data quality, integrity, frequency, and create rules-based pipelines removed the dilemma of even considering the art.

Failing to report on data results prevents you from demonstrating the value of data and gaining leadership support. How to fix it: Develop a reporting system: Determine what metrics you will track and how you will measure them. Create insightful reports: Provide clear and actionable reports that demonstrate the value of data. Regularly present reports: Regularly present reports to stakeholders.

I've learned that not reporting data results well can waste efforts and miss opportunities. To improve, present findings clearly and confidently. Use easy language and visuals like charts. Back up conclusions with evidence, and offer practical recommendations to persuade action. This ensures your work has an impact and showcases your skills effectively.

One common mistake in managing data is not reporting the results effectively. When data results are not communicated clearly, it can lead to missed opportunities and confusion. Reporting data findings is essential for sharing insights and making informed decisions. Without clear reporting, stakeholders may not understand the significance of the data or how it impacts their goals. By improving data reporting practices, organizations can ensure that valuable insights are shared in a way that is easy to understand and act upon, ultimately driving better outcomes and fostering a data-driven culture across the organization.

Be proactive: Don't wait to be asked for data. Be proactive in providing the data your organization needs. Be communicative: Communicate with stakeholders about your successes and challenges. Be persistent: Don't be afraid to stand up for your data requirements. Remember: Assertiveness in data management is crucial to achieving your organization's goals. By defining your data requirements, setting standards, reporting on results, and being proactive, you can become more assertive in data management. Improvement notes: The response is more concise and to the point. The language is more clear and easy to understand. The examples are more relevant and specific. The overall tone is more professional and objective.

Based on my experience, I would suggest adding 2 additional points, to begin with (in the journey of becoming more assertive in #datamanagement): 1. As Simon Sinek has highlighted: Not starting with the "#WHYs" - The "Whys" would be the business challenge(s) or business use case(s) that your data project intends to help with - to improve speed or quality of business outcomes. 2. Not involving the management stakeholders, or getting their skin in the game - Once the "Whys" are confirmed, find out "who" will benefit from those. Follow the chain up to the senior-most person (reporting to the CEO) and secure her/his/their involvement.

Make sure everyone understands and uses data to make decisions. This involves training, collaboration, and seeing data as a valuable asset. When everyone is on board, it leads to better insights and keeps the organization competitive. making sure everyone understands and uses data to make decisions. This involves training, collaboration, and seeing data as a valuable asset. When everyone is on board, it leads to better insights and keeps the organization competitive.

Be sure to always be clear and concise. In data reporting many analysts and managers get carried away by showing all data and not telling a story out of it. Think of the big picture and break it down into subsets to create a better understanding of how the data will affect strategic and operational decisions.

Receiving feedback on our work is crucial for maximizing our efficiency and productivity. To improve the effectiveness of data management, it's important to communicate with our team and experts, seek their feedback and ideas, and proactively work on implementing their suggestions.