Drivers of Data Observability Software

As software companies become more data-driven and embrace digital transformation, the reliance on strong infrastructure will be commensurate to support the growing demand for intelligent data. The evolution from traditional Application Performance Monitoring (APM) to Data Observability ultimately allows for a more robust product that goes beyond solving yesterday’s problems and reinforces topline growth for the future. Let’s take a deep dive into how Data Observability software is essential for any growing data-driven company from an investment perspective, along with identifying companies and growth opportunities within this vertical bringing the solution to market.

Understanding the Data Pipeline Architecture

One way of thinking about data pipelines is like a water pipe but instead with digital information traveling. The ability to source raw data from either real-time or batched sources and convert it into usable data for business purposes at scale requires the need for a strong data pipeline. With the rise of Web 3.0 and other major technological trends, traditional data pipelines are no longer sustainable for data-intensive businesses today. Therefore, innovative infrastructure software companies are building data observability solutions on top of existing application performance monitoring products to meet the needs of digital transformation.

Key Drivers of Observability

Increasingly complex IT Infrastructure/data pipelines

As a result of the advancement in the software engineering field, IT and DevOps teams now leverage hybrid and multi-cloud technologies, including but not limited to containers, Kubernetes, and microservices. Despite the combination of different tools enabling software development teams to build and ship software faster and easier, it results in a growing complexity from a data observability perspective.

Businesses leverage high-quality data from a variety of sources to optimize decision-making. These sources often come from real-time or batch data, which is then fed into the data pipeline for preprocessing. As the number of data sources increases along with the respective volume of raw data, these traditional data pipelines are strained and lack capacity, resulting in inaccurate data and unreliable infrastructure for the long term. Only so much data can be fed into the pipeline at a time before it gets processed, qualified, and cleaned. These fragile pipelines can hinder topline growth, output incorrect internal and external KPIs, and ultimately lead to a weaker product. This is even more true for even large enterprises with scale.

Perpetual operational issues

Robust infrastructure with constant monitoring and security is necessary for a software product on the cloud to be used, let alone the extended functionality it may offer. Many software companies have large DevOps teams that invest a significant amount of time to help solve the daily operational issues that occur within data pipelines. Without observability software, much of the time spent is on determining what the issues are and the respective severity it has as it relates to other issues within the data pipeline. These minor issues, in turn, decrease productivity and prevent data teams from focusing on more critical tasks and building a scalable infrastructure.

Different uses of data across teams

As companies grow, the formation of different teams enables the need for stronger collaboration and data sharing. These teams, amongst others, include engineering, data science, product, sales, and customer success. Each of these teams uniquely interacts and engages with their own segment of data from the pipeline to make decisions independently. The process of retrieving accurate and reliable data is where observability comes to fruition. Without the ease of access and/or the accuracy of the data, the different teams will be unable to meet their customer’s needs and generate additional revenue.

Higher investment in customer success

A necessity for software companies is not only to increase their initial sales but to increase their retention metrics, which involve a huge investment behind customer success to enable opportunities for expansion revenue. Here’s a detailed explanation of the criticality of net retention for SaaS companies.

This means users need to be happy with the product they are using. If there are any performance encumbrances or infrastructure faults, it will create a poor end-user experience, which has a higher probability of resulting in churn. Therefore, in order to maintain and increase revenue, having high customer satisfaction is essential. With observability software, companies can invest more time behind high-value-driven issues rather than minor obstacles. Furthermore, observability allows more automation for the backend infrastructure where human engagement isn’t as critical, thus preventing data downtime¹.

Meeting expectations with customers

A key driver to SaaS revenue is the ability to intelligently evaluate infrastructure and development demands², which is how to balance the total cost of service from the SaaS provider while providing an optimal end-user experience to meet and exceed customer expectations. Furthermore, companies are required to provide the level of service as listed in their Service-Level Agreement, and not providing the standards can mitigate trust with the customer³.

Increase in IT Spending

The existing Application Performance Monitoring (APM) market doesn’t meet the needs the same way the Observability market does, particularly in being able to scale with and offer actionable insight across different cloud architectures.

Gartner estimates

• the data observability market to reach $18 billion by 2025, driven by an increase in IT spending⁴.
• a 9% increase in IT spending with the majority of the expenditures behind enterprise software and IT services⁴.
• only about 10% of enterprises are deploying observability software and an expected amount of 30% by 2024⁵.

As IT spending increases to meet the needs of digital transformation, the more the observability market will be able to penetrate data-driven businesses around the world in order to make the most of their data, from top Fortune 500 enterprises down to small-medium businesses.

Data Observability Investment Landscape

In regards to the public data observability landscape, leading infrastructure software companies like Datadog, Splunk, and Dynatrace have shown major growth and demand⁶. Their respective EV/Revenue multiples of 57.5x, 11.4x, and 26.5x are a testament to the necessity for observability software and the future of infrastructure software. All three of these companies have net revenue retention rates significantly above 100%, indicating the dire need for such software and the strong investment behind customer success, sales and marketing, and R&D.

Even private companies like Acceldata and Monte Carlo Data that provide unique and comprehensive observability solutions are being funded at premium valuations as growth investors foresee a massive and unpenetrated opportunity. Today’s frothy and competitive market, huge access to private capital, and the recession-proof nature of this vertical further justify the investment in these high-growth software companies. Additionally, the exit opportunities are larger than ever before, where IPO markets are now more welcoming to technology companies in addition to large public companies being able to make aggressive acquisitions given their robust balance sheets.

Significant Growth Opportunities

A large percentage of companies haven’t fully leveraged observability software, which gives incumbents expansion revenue opportunities and smaller yet fast-growing players an opportunity to enter. Transformational capital along with strategic and operational support are the keys these companies will use to unlock opportunities and markets to ultimately prevail.

References:

¹ | Monte Carlo: Data Reliability delivered. Monte Carlo Data. (2021, September 15). Retrieved November 3, 2021, from https://www.montecarlodata.com/.

² | (https://www.insightpartners.com/), I. P. 2021. (2019, February 13). What are the key drivers to maximize your SAAS revenue model. Insight Partners. Retrieved November 3, 2021, from https://www.insightpartners.com/blog/what-are-the-key-drivers-to-maximize-your-saas-revenue-model/.

³ | Eckerson Group. (2021). The Definitive Guide to Data Observability for Analytics and AI. Eckerson Group. Retrieved November 3, 2021, from https://pages.acceldata.io/rs/634-LZP-040/images/WP-DataObservabilityGuide-Eckerson.pdf?mkt_tok=NjM0LUxaUC0wNDAAAAF_0jJmdq-n7TSZmdmI2xfR-A0lr0_moOCdsshaz5T7WRwSxne6TSeOLRIPi22rIE6VTqn5uaekwCN7fwzPPUX6L9wrQTXGVfwEkgaDLw.

⁴ | Savitz, E. J. (2021, July 8). Three stock picks to play ‘observability’ software. it spending is accelerating. Datadog and 2 More Stock Picks to Play ‘Observability’ Software | Barron’s. Retrieved November 3, 2021, from https://www.barrons.com/articles/three-stock-picks-to-play-observability-software-51625761085.

⁵ | Negus, B., & Jahn, R. (2021, July 6). Gartner: Observability drives the future of cloud monitoring for DevOps and SRES. Dynatrace news. Retrieved November 3, 2021, from https://www.dynatrace.com/news/blog/gartner-observability-drives-the-future-of-cloud-monitoring-for-devops-and-sres/.

⁶ | Woodie, A. (2021, March 4). Who’s winning in the $17B Aiops and Observability Market. Datanami. Retrieved November 3, 2021, from https://www.datanami.com/2021/03/04/whos-winning-in-the-17b-aiops-and-observability-market/.

Disclaimer: All information presented within this site is for informational purposes only. Because this information presented is based on my personal opinion, it should not be considered investment advice.