How to bridge the skills gap and get more value from enterprise data with MongoDB

Enterprise data can be one of your most valuable assets, but are you getting the value and insights you should be from it?  Cynozure research found 94% of organizations say using data effectively is central to running a successful business. Despite this, one third (35%) say the data world is too complex to understand, and three in ten (31%) bemoan a lack of data literacy in the business. 

As a document/NoSQL database, MongoDB has become a go-to database to manage unstructured data, scale, and power modern applications. But skills gaps can prevent business leaders from gaining maximum value from enterprise data with MongoDB.

The consequences of failing to keep up with MongoDB skills development

Whether it’s developers transitioning from relational databases, data engineers unfamiliar with MongoDB’s schema design principles, or teams lacking experience in query optimization, skill disparities can have a negative impact. It’s important for all businesses to be across the latest best practices for MongoDB optimization. Consequences of failing to do so may include:

• Performance bottlenecks 
• Inefficient data management
• Increased operational costs
• Missed opportunities
• Increased risk
• Greater employee turnover 

Why it pays to invest in MongoDB skills development

Organizations that prioritize upskilling their workforce can gain a competitive advantage and improve their data-driven decision making. Bridging the MongoDB skills gap isn’t just about improving individual expertise, other benefits include:

• Optimized performance: Better schema design and indexing, more efficient queries and minimized infrastructure costs all lead to cost savings
• Future-proofing: Skilled teams, with a knowledge of MongoDB’s scaling capabilities, can adapt and innovate faster and build databases capable of growing with you
• Better data-driven decision making: A well-trained team can help you take advantage of features like analytics and the aggregation framework to help you achieve greater value from enterprise data with MongoDB
• Stronger security and compliance: Teams make fewer mistakes, reducing data risks
• Increased productivity and retention: Well-trained teams deliver features more quickly, meaning faster development cycles and less frustration 
  

Four steps to upskill your team in MongoDB

• Identify MongoDB skill gaps within your team
• Implement structured training and enablement programs
• Foster a culture of continuous learning and knowledge sharing
• Equip teams with the right tools

Let’s go into more detail on each of those below.

Identify MongoDB skills gaps

To deal with skills gaps, it’s worth identifying the areas and issues your teams struggle with most when working with MongoDB. These can vary from business to business, but typically include some of those outlined below. Do any of these sound familiar to you? 

Data modeling challenges 

Teams used to using relational databases can find it difficult to model data effectively in MongoDB. Poor schema design can cause performance bottlenecks and inefficient storage structures.

Aggregation framework complexity 

Writing and optimizing queries using MongoDB’s aggregation framework can be challenging, particularly when dealing with complex data transformations and performance tuning.

Indexing strategies 

Developers often struggle with implementing effective indexing strategies, which are essential for optimizing query performance and ensuring smooth database operations.

Managing large datasets 

Moving data between environments, handling backups, and ensuring efficient data retrieval at scale can pose significant difficulties, especially as datasets grow.

Scaling with sharing and reputation 

Many teams lack expertise in MongoDB’s advanced scaling features, such as sharding and replication, which are crucial for handling high-volume workloads and ensuring database reliability.

Security and best practices 

A lack of understanding about MongoDB’s security features and best practices can lead to misconfigurations that could expose databases to potential threats.

Schema validation  

Some teams struggle with MongoDB’s schema validation capabilities, preferring to enforce data integrity at the application level instead of leveraging built-in database validation mechanisms.

How to scale MongoDB expertise across teams

Training and knowledge sharing

Appropriate training and knowledge sharing is one of the fastest ways to upskill teams. This could range from MongoDB certifications and workshops to mentorship initiatives pairing experienced MongoDB users with newer users. 

Think about the skills different members of the team need. For developers, you could focus on schema design, querying, and transactions, while data analysts would benefit more from mastering aggregation pipelines and analytics. 

Consider other ways to share knowledge, such as documenting internal best practices and encouraging cross-team discussions. You could even set up an internal MongoDB community to allow team members to work through issues and learn from each other. 

Encourage hands-on learning

Theoretical knowledge is important, but it’s worth remembering that many people learn best by doing. Here are a few measures you could introduce to encourage your teams to get more hands-on:

• Set up sandbox environments where developers can experiment without risk
• Run internal hackathons focused on solving real-world MongoDB challenges
• Introduce incremental learning paths, moving from CRUD operations to advanced performance tuning

Invest in technology and productivity tools 

While training and mentorship play a critical role in upskilling teams, the right tools can also significantly ease the learning curve and improve efficiency. 

From MongoDB developer productivity tools to AI-assisted learning and search functionality, there are all kinds of ways technology can reduce complexity and improve efficiency. Here are some of the top options to consider:

Visual query builder

A visual query builder tool can help teams construct, test, and optimize queries without the need for technical expertise. The result? Developers can find data more easily. 

Schema visualization 

Understanding how data is structured within MongoDB can be challenging, especially for those transitioning from relational databases. Visualization tools allow teams to explore and analyze schemas, making it easier to optimize data models and understand relationships between collections.

Aggregation query support 

Aggregation editors make it easier for teams to build complex aggregation pipelines, while also reducing errors and optimizing query performance. This approach is particularly useful for those who find traditional nested queries difficult to construct.

Performance tools

Tasks like identifying data inconsistencies, troubleshooting performance issues, and making targeted updates, are not always easy to manage through standard database dashboards. Dedicated database and performance management tools give you better visibility and control. 

AI-assisted learning 

AI-powered tools are becoming increasingly common, with options out there to help you generate sample queries, explain MongoDB concepts, and troubleshoot issues. Its use in natural language query tools means even users with no prior experience with MongoDB can instantly generate complex queries and get insights from enterprise data. All good news for accelerating the learning process.

Time to take action 

Investing in MongoDB skills is a move that makes sense. From reducing performance issues to increasing innovation, it’s the most effective way to get more value from your enterprise data with MongoDB and drive better business outcomes.