Critical Data Management Mistakes to Avoid

 

 

For any business to maintain relevancy and stay abreast with the current trends in the business landscape of the 21st century it has to implement a robust data-driven approach.

By 2025, the global data market is projected to reach a value of $249.3 billion. Organizations that realize the importance of big data and treat it as an asset tend to have a competitive edge. But this is only possible if they are able to properly manage data & draw useful insights from it to further their business goals.

Every company that deals with vast amounts of data from countless sources & platforms needs proper data management if it wishes to leverage it for increasing efficiency.

However, there are some common but key mistakes that businesses make when implementing data management strategies. Not addressing these can lead to wasted time & resources along with the effort that you put into it. Assistance from data management service providers in Dubai, UAE can help prevent these critical mistakes.

Following are some critical data management mistakes that you must always strive to avoid.

 

What is Data Management?

The secure, efficient, and cost-effective practice of data collection & usage is known as data management. The main purpose of data management is to enable businesses to optimize data usage for maximizing operational efficiency as well as revenue.

Data management within an organization involves a wide range of tasks that need your immediate attention and focus. Typically it involves

●      Data creation and updates across diverse data tiers

●      Managing data access

●      Data storage on the cloud and on-premises

●      Working on robust data security and privacy measures

●      Providing high availability, disaster recovery, & backups

●      Archive and destroy data based on compliance requirements

 

Critical Data Management Mistakes to Avoid

Following are the main data management mistakes that you need to avoid at all costs

 

1.    Not Defining a Clear Data Management Strategy or Process

One of the most common mistakes businesses make is that they don’t clearly define data management processes like data capture and storage. They also don’t have a fixed plan in terms of data reporting and analysis. This results in confusion & duplicated efforts which leads to poor data quality.

 

2.    Neglecting Data Quality

Poor data profiling, cleansing & validation lead to bad data quality. Most organizations do not use quality data tools to take care of the aforementioned aspects of data management. Having data with poor quality results in inaccurate insights and that can directly damage your revenue stream as well as your reputation.

  

3.    Poor Data Security Measures

This is another common mistake where businesses ignore the importance of robust security measures to protect their data. They do not invest in things like sensitive data encryption and resisting access to only the most relevant personnel. They also make the mistake of not regularly updating their data security protocols.

This leads to devastating repercussions like financial losses & legal or regulatory penalties. Data management service providers like Alpha Data can help set up robust security measures to minimize these issues.  

 

4.    Lack of Data Governance

The absence of the right policies & procedures to ensure proper data usage by an organization is another common mistake. The efforts to ensure the data is used securely and in compliance with relevant regulations fall under data governance.

 

      

5.    Ignoring Your Business Goals and Objectives

Businesses tend to ignore their business goals and objectives when defining a data management strategy. They tend to use the same approach as other businesses without realizing that their operations significantly differ in terms of size or capacity.

Having a data management strategy that aligns with your business goals allows you to ensure all your business needs as well as customer requirements are met.

 

6.    Using Data Silo Approach

In the data silo approach, there is an inefficient or minimized sharing of key information between employees or different departments. A data silo is stored data that is only available to a fixed number of people within an organization.

Siloing data can lead to poor decisions & missed opportunities. This issue can be solved by establishing cross-functional teams that include members from all departments.

 

Conclusion

Effective data management requires extreme care and caution on your part. You need to develop a data management strategy that aligns with your business and its future goals.

Moreover, you need to ensure the data quality & security are never compromised. By avoiding the mistakes we have mentioned above organizations can create a robust data management strategy that delivers real value to all the stakeholders.

So to harness the full potential of your data assets you need to work to develop a data management strategy that is free from the above mistakes and is based on the best practices.