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You're struggling with data management. What are the common reasons for failure?

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Data management is the process of collecting, organizing, storing, and using data to support various business functions and goals. It can be challenging and complex, especially in the era of big data, cloud computing, and analytics. If you're struggling with data management, you might be wondering what are the common reasons for failure and how to avoid or overcome them. In this article, we'll explore some of the most frequent pitfalls and offer some tips to improve your data management skills and practices.

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LIN Nicole

Financial Director
Aashima Sharma

Senior Digital Marketing Specialist- Data Dynamics
КАРПУСH Д.

1 Lack of strategy

One of the main reasons for data management failure is the lack of a clear and coherent strategy that aligns with your business objectives and priorities. Without a strategy, you might end up collecting too much or too little data, storing it in inefficient or insecure ways, or using it for irrelevant or outdated purposes. To avoid this, you need to define your data goals, identify your data sources, establish your data quality standards, and design your data architecture and governance.

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LIN Nicole

Financial Director
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Lack of clear data governance leads to confusion and errors, while inadequate data quality undermines decision-making. Insufficient security measures increase the risk of breaches, and data collaboration. Incompatible formats complicate integration, and poor lifecycle management results in clutter and compliance risks. Limited accessibility stifles innovation, and non-compliance exposes organizations to legal liabilities. Misalignment with business goals creates inefficiencies, while outdated tools hamper performance. Resistance to change hinders progress, and effectively managing complex data demands expertise.

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Aashima Sharma

Senior Digital Marketing Specialist- Data Dynamics
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You're absolutely right, having a data management strategy is critical. Research shows that a whopping 90% of businesses flounder without one. This often stems from unclear goals, inconsistent data quality, and teams working in isolation. The good news is, this can be overcome! By setting specific, measurable objectives, we can ensure everyone is working towards the same vision. Prioritizing data quality through defined standards and checks will make our data trustworthy. Finally, by fostering open communication and collaboration across teams, we can leverage the collective knowledge to make data-driven decisions with confidence.

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Oleh BOHATOV

Data Analyst & Engineer | AWS Certified Cloud Practitioner | Microsoft Certified: Azure Data DP-900 | Google Cybersecurity Certificate
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Imagine a pirate ship filled to the brim with treasure: chests of gold, maps with secret routes, artifacts of incredible power. Data adrift, decisions adrift. Without a captain's map (strategy), your data hoard becomes a pirate's labyrinth. A strategy is a map that: Shows you the way to your destination. Helps you find the "treasures" you need. Protects your "treasures" from pirates. Unites your team and helps them work together. With a data management strategy: You will be able to make more informed and effective decisions. You will be able to optimize your business processes. You will be able to increase your competitiveness. You will be able to build a more sustainable future.

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2 Poor data quality

Another common reason for data management failure is poor data quality, which can compromise your data analysis, reporting, and decision making. Poor data quality can result from various factors, such as human errors, inconsistent formats, missing values, duplicates, or outdated information. To prevent this, you need to implement data quality checks, validation rules, cleansing procedures, and audits throughout your data lifecycle.

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3 Inadequate skills and tools

A third reason for data management failure is inadequate skills and tools to handle the complexity and volume of data. Data management requires a combination of technical, analytical, and business skills, as well as the right software and hardware to collect, store, process, and use data effectively and efficiently. To overcome this, you need to invest in training, hiring, or outsourcing data professionals, and in upgrading or adopting data management platforms, tools, and systems.

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4 Insufficient collaboration and communication

A fourth reason for data management failure is insufficient collaboration and communication among the stakeholders involved in data-related activities. Data management is not a siloed function, but a cross-functional one that requires coordination and alignment among data owners, producers, consumers, and users. Without collaboration and communication, you might face issues such as data silos, conflicts, duplication, or inconsistency. To improve this, you need to foster a data culture, create data teams and roles, and use data sharing and reporting mechanisms.

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5 Resistance to change

A fifth reason for data management failure is resistance to change, which can hinder your data innovation and adaptation. Data management is not a static or one-time process, but a dynamic and ongoing one that requires constant evaluation and improvement. With the rapid changes in technology, business, and customer expectations, you need to be flexible and agile to leverage new data opportunities and challenges. To achieve this, you need to embrace a data-driven mindset, experiment with new data methods and solutions, and learn from your data successes and failures.

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Resistance to change,is often because "Strategies" dont often translate,or relate to issues ( either visible or latent) that are relevant for ground level stakeholders.This results in lack of appreciation for the whole exercise.Often in my role i had to sift through HUGE amount of data to show and quantify to people-the amount of time & money they are wasting because of lack of(or issues around)data management.Even if the top leaders & mid management are convinced often,we must present such moment of truths in a more innocuous friendly way,to ground level stakeholders. Else the entire project duration is wasted in re-convincing the team members.This bottom level heads up needs to be subtle,more like an under-current.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Oleh BOHATOV

Data Analyst & Engineer | AWS Certified Cloud Practitioner | Microsoft Certified: Azure Data DP-900 | Google Cybersecurity Certificate
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Shortsighted leadership: Even with a strategy, if leadership doesn't prioritize data or see its value, the plan can easily stall. This can manifest as a lack of funding, resources, or buy-in from other departments.

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You're struggling with data management. What are the common reasons for failure?

1

2

3

4

5

6

1 Lack of strategy

2 Poor data quality

3 Inadequate skills and tools

4 Insufficient collaboration and communication

5 Resistance to change

6 Here’s what else to consider

Data Management

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You're struggling with data management. What are the common reasons for failure?

1

2

3

4

5

6

1 Lack of strategy

2 Poor data quality

3 Inadequate skills and tools

4 Insufficient collaboration and communication

5 Resistance to change

6 Here’s what else to consider

Data Management

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