MongoDB (MDB) is a database software company which is benefiting from the growth in unstructured data and leading the growth in non-relational databases. Despite MongoDB's recent rise in share price, its current valuation is modest given its strong position in a large and attractive market.
There has been an explosion in the growth of data in recent years with this growth being dominated by unstructured data. Unstructured data is currently growing at a rate of 26.8% annually compared to structured data which is growing at rate of 19.6% annually.
Figure 1: Growth in Data
(source: m-files)
Unstructured data refers to any data which despite possibly having internal structure is not structured via pre-defined data models or schema. Unstructured data includes formats like audio, video and social media postings and is often stored in non-relational database like NoSQL. Structured data is suitable for storage in a traditional database (rows and columns) and is normally stored in relational databases.
Mature analytics tools exist for structured data, but analytics tools for mining unstructured data are nascent. Improved data analytics tools for unstructured data will help to increase the value of this data and encourage companies to ensure they are collecting and storing as much of it as possible. Unstructured data analytics tools are designed to analyze information that doesn't have a pre-defined model and include tools like natural language processing.
Table 1: Structured Data Versus Unstructured Data
(source: Adapted by author from igneous)
Unstructured data is typically stored in NoSQL databases which can take a variety of forms, including:
Unstructured data can also be stored in multimodel databases which incorporate multiple database structures in the one package.
Figure 2: Multimodel Database
(source: Created by author)
Some of the potential advantages of NoSQL databases include:
Common use-cases for NoSQL databases include web-scale, IoT, mobile applications, DevOps, social networking, shopping carts and recommendation engines.
Relational databases have historically dominated the database market, but they were not built to handle the volume, variety and velocity of data being generated today nor were they built to take advantage of the commodity storage and processing power available today. Common applications of relational databases include ERP, CRM and ecommerce. Relational databases are tabular, highly dependent on pre-defined data definitions and usually scale vertically (a single server has to host the entire database to ensure acceptable performance). As a result, relational databases can be expensive, difficult to scale and have a relatively small number of failure points. The solution to support rapidly growing applications is to scale horizontally, by adding servers instead of concentrating more capacity in a single server. Organizations are now turning to scale-out architectures using open software technologies, commodity servers and cloud computing instead of large monolithic servers and storage infrastructure.
Figure 3: Data Structure and Database Type
(source: Created by author)
According to IDC, the worldwide database software market, which it refers to as structured data management software, was $44.6 billion in 2016 and is expected to grow to $61.3 billion in 2020, representing an 8% compound annual growth rate. Despite the rapid growth in unstructured data and the increasing importance of non-relational databases, IDC forecasts that relational databases will still account for 80% of the total operational database market in 2022.
Database management systems (DBMS) cloud services were 23.3% of the DBMS market in 2018, excluding DBMS licenses hosted in the cloud. In 2017 cloud DBMS accounted for 68% of the DBMS market growth with Amazon Web Services (AMZN) and Microsoft (MSFT) accounting for 75% of the growth.
MongoDB provides document databases using open source software and is one of the leading providers of NoSQL databases to address the requirements of unstructured data. MongoDB's software was downloaded 30 million times between 2009 and 2017 with 10 million downloads in 2017 and is frequently used for mobile apps, content management, real-time analytics and applications involving the Internet of Things, but can be a good choice for any application where there is no clear schema definition.
Figure 4: MongoDB downloads
(source: MongoDB)
MongoDB has a number of offerings, including:
Figure 5: MongoDB Platform
(source: MongoDB)
Functionality of the software includes:
MongoDB's platform offers high performance, horizontal scalability, flexible data schema and reliability through advanced security features and fault-tolerance. These features are helping to attract users of relational databases with approximately 30% of MongoDB's new business in 2017 resulting from the migration of applications from relational databases.
MongoDB generates revenue through term licenses and hosted as-a-service solutions. Most contracts are 1 year in length invoiced upfront with revenue recognized ratably over the term of the contract although a growing number of customers are entering multiyear subscriptions. Revenue from hosted as-a-service solutions is primarily generated on a usage basis and is billed either in arrears or paid up front. Services revenue is comprised of consulting and training services which generally result in losses and are primarily used to drive customer retention and expansion.
MongoDB's open source business model has allowed the company to scale rapidly and they now have over 16,800 customers, including half of the Global Fortune 100 in 2017. Their open source business model uses the community version as a pipeline for potential future subscribers and relies on customers converting to a paid model once they require premium support and tools.
Figure 6: Prominent MongoDB Customers
(source: Created by author using data from MongoDB)
MongoDB's growth is driven largely by its ability to expand revenue from existing customers. This is shown by the expansion of Annual Recurring Revenue (ARR) overtime, where ARR is defined as the subscription revenue contractually expected from customers over the following 12 months assuming no increases or reductions in their subscriptions. ARR excludes MongoDB Atlas, professional services and other self-service products. The fiscal year 2013 cohort increased their initial ARR from $5.3 million to $22.1 million in fiscal year 2017, representing a multiple of 4.1x.
Figure 7: MongoDB Cohort ARR
(source: MongoDB)
Although MongoDB continues to incur significant operating losses the contribution margin of new customers quickly becomes positive, indicating that as MongoDB's growth rate slows the company will become profitable. Contribution margin is defined as the ARR of subscription commitments from the customer cohort at the end of a period less the associated cost of subscription revenue and estimated allocated sales and marketing expense.
Figure 8: MongoDB 2015 Cohort Contribution Margin
(source: MongoDB)
MongoDB continues to achieve rapid revenue growth driven by an increasing number of customers and increased revenue per customer. Revenue growth has shown little sign of decline which is not surprising given the size of MongoDB's market opportunity. Revenue per customer is modest and MongoDB still has significant potential to expand the number of Global Fortune 100 customers.
Figure 9: MongoDB Revenue
(source: Created by author using data from MongoDB)
Figure 10: MongoDB Customer Numbers
(source: Created by author using data from MongoDB)
MongoDB's revenue growth has been higher than other listed database vendors since 2017 as a result of their expanding customer base and growing revenue per customer. The rise of cloud computing and non-relational databases has a large impact on relational database vendors with DBMS growth now dominated by cloud computing vendors and non-relational database vendors.
Figure 11: Database Vendor Revenue
(source: Created by author using data from company reports)
MongoDB's revenue growth is relatively high for its size when compared to other database vendors, but is likely to begin to decline in coming years.
Figure 12: Database Vendor Revenue Growth
(source: Created by author using data from company reports)
MongoDB's revenue is dominated by subscription revenue and this percentage has been increasing over time. This relatively stable source of income holds MongoDB in good stead for the future, particularly if customers can be converted to longer-term contracts.
Figure 13: MongoDB Subscription Revenue
(source: Created by author using data from MongoDB)
MongoDB generates reasonable gross profit margins for an enterprise software company from its subscription services, although these have begun to decline in recent periods. Likely as the result of the introduction of the entry level Atlas offering in 2016 and possibly also as a result of increasing competition.
Figure 14: MongoDB Gross Profit Margin
(source: Created by author using data from MongoDB)
MongoDB has exhibited a large amount of operating leverage in the past and is now approaching positive operating profitability. This is largely the result of declining sales and marketing and research and development costs relative to revenue. This trend is likely to continue as MongoDB expands, particularly as growth begins to decline and the burden of attracting new customers eases.
Figure 15: MongoDB Operating Profit Margin
(source: Created by author using data from MongoDB)
Figure 16: MongoDB Operating Expenses
(source: Created by author using data from MongoDB)
Although MongoDB's operating profitability is still negative it is in line with other database vendors and should become positive within the next few years. This is supported by the positive contribution margin of MongoDB's customers after their first year.
Figure 17: Database Vendor Operating Profit Margins
(source: Created by author using data from company reports)
MongoDB is yet to achieve consistently positive free cash flows, although appears to be on track as the business scales. This should be expected based on the high margin nature of the business and the low capital requirements. Current negative free cash flow is largely a result of expenditures in support of future growth in the form of sales and marketing and research and development.
Figure 18: MongoDB Free Cash Flow
(source: Created by author using data from MongoDB)
Competitors in the database vendor market can be broken into incumbents, cloud platforms and challengers. Incumbents are the current dominant players in the market, like Oracle (ORCL), who offer relational databases. Cloud platforms are cloud computing vendors like Amazon and Microsoft that also offer database software and services. Challengers are pure play database vendors who offer a range of non-relational database software and services.
Table 2: Database Vendors
(source: Created by author)
Incumbents
Incumbents offer proven technology with large set of features which may be important for mission critical transactional applications. This gives incumbents a strong position, particularly as relational databases are expected to continue to retain the lion's share of the database market in coming years. Incumbent players that lack a strong infrastructure-as-a-service platform though are poorly positioned to capture new applications and likely to be losers in the long run. This trend is evidenced by Teradata's (TDC) struggles since the advent of cloud computing and non-relational databases.
Cloud Platforms
Cloud service providers are able to offer a suite of SaaS solutions in addition to cloud computing, creating a compelling value proposition for customers. In exchange for reducing the number of vendors required and gaining access to applications designed to run together, database customers run the risk of being locked into a cloud vendor and paying significantly more for services which could potentially be inferior.
Challengers
Dedicated database vendors can offer best in breed technology, low costs and multi-cloud portability which helps to prevent cloud vendor lock-in.
The DBMS is typically broken into operational and analytical markets. The operational DBMS market refers to databases that are tied to a live application whereas the analytical market refers to the processing and analyzing of data imported from various sources.
Figure 19: Database Market Competitive Landscape
(source: Created by author)
Gartner assesses MongoDB as a challenger in the operational database systems market due primarily to a lack of completeness of vision. The leaders are generally large companies which offer a broader range of database types in addition to cloud computing services. MongoDB's ability to succeed against these companies will be dependent on them being able to offer best in class services and/or lower cost services.
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MongoDB: Riding The Data Wave - Seeking Alpha