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6 Urgent Reasons to Replace Internal Metrics with a Unified Data Platform

The costs of continuing down an internal build path are high, while the risks of inaction are rising. The AI revolution signals it's time to take a fresh look at adopting an integrated data platform.

Naomi Lurie

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November 17, 2023

Software engineering leaders at large enterprises often spearhead considerable internal efforts to build custom metrics and productivity dashboards.

While well-intentioned, these projects tend to deliver limited value due to the inherent challenges of metrics fragmentation, data silos, lack of benchmarks, and stale insights.

The costs of continuing down an internal build path are high, while the risks of inaction are rising. This AI moment signals it's time to take a fresh look at adopting an integrated data platform from a trusted provider.

The benefits are compelling: superior insights, lower costs, reduced risks, and future-proofing.

This AI Moment Demands A New Approach

Six reasons are driving the urgency to replace internal metrics with a unified data platform.

Image lists the six reasons to replace internal software engineering metrics with a unified data platform, summarizing the text in the blog

Six reasons to replace internal metrics with a unified data platform

#1 The Strategic Importance of Engineering Velocity

Metrics tied to engineering productivity and developer experience are vital for meeting time-to-market and growth targets.

Engineering velocity correlates directly to business performance in today's software-driven business environment. Faster time-to-market for digital products and services is a competitive advantage. Optimizing developer productivity to maximize output and minimize waste is a strategic imperative.

#2 The Rapid AI Transformation of the Engineering Discipline

Navigating from the old world of manual coding to the new reality of AI-driven development requires metrics extensibility to new data sources, use cases, and tech stacks.

Advanced techniques like AI-assisted coding, testing, security scanning, and ops are being embedded into developer workflows. Measuring the impact and optimizing these AI-infused processes requires instrumentation and data platforms specifically designed for it. Internal metrics initiatives conceived before the rise of AI lack the flexibility and extensibility needed.

#3 The Failure of Siloed Data and Metrics

Siloed metrics fail to capture dependencies and hand-offs between teams that impact end-to-end velocity.

Most enterprises have data spread across disparate systems for work management, source code, builds, tests, deployments, and more. Internal build efforts struggle to overcome the fragmented and inconsistent metrics coming from a large portfolio spread over a diverse tech stack.

#4 The Endless Struggles with Standardization

Normalized data and industry benchmarks are essential to understand performance gaps and improvement opportunities.

A chronic challenge with internal metrics initiatives is the lack of standardized data models even within Jira alone. With no common definitions or schemas, the same entities end up represented differently across projects and teams, resulting in considerable manual effort spent normalizing and mapping data points to make metrics comparable across teams.

#5 The Soaring Costs of Custom Development

Hard-coded internal platforms often require extensive rework with each new tool or process change.

Large enterprises spend millions of dollars over multiple years trying to build internal metrics platforms, often with disappointing outcomes. The opportunity cost is high, as precious engineering resources are diverted from delivering customer value. Maintaining custom metrics platforms also incurs significant ongoing expenses.

#6 The Growing Risks of Inaction

Manual analysis of fragmented metrics cannot deliver the real-time insights needed in today's fast-changing environments.

As metrics initiatives stall, developer experience, and productivity suffer. Engineering leaders lack the timely insights needed to identify and remove bottlenecks. Falling further behind on monitoring key metrics increases business risks.

Buy-and-Build Is the Safer Choice

Many organizations value the flexibility of building metrics in-house, particularly the ability to get exactly what they want. Unfortunately, that's not how it typically pans out due to lack of domain expertise, focus, and resources.

But you don't have to abandon the dream.

Leading third-party data platforms allow enterprises to buy proven technology and build on top of it.

The combination of an open data platform with proprietary customizations gives the best of both worlds: Commoditized capabilities are handled by the platform, while specialized needs are addressed internally, resulting in faster time-to-value and a better business fit.

So, how is it done?

Buy the Foundation

Buying a turnkey platform eliminates the undifferentiated heavy lifting of data connectors, normalization, analysis, AI and machine learning, and visualization. Purpose-built for engineering data, leading solutions offer:

  • Connectors to ingest data from disparate tools with minimal setup
  • Normalized data models to standardize and interrelate cross-tool data
  • Attribution mechanisms to resolve board, repository, and application ownership
  • AI to detect anomalies and correlations and provide recommendations
  • Industry benchmarks providing context for internal metrics
  • Pre-built dashboards giving rapid visibility into key metrics
  • Automation to trigger actions based on data triggers
  • Security and access controls to enforce security and privacy policies
  • Mature APIs to access the raw data and analytical datasets
  • Domain experts that act as an extension of your internal teams

Build the Special Sauce

With a unified data foundation in place, engineers are freed from data drudgery and instead are able to focus their energy on building the business-specific customizations that leverage institutional knowledge:

  • Ingesting data from proprietary or uncommon sources not covered by standard connectors
  • Mapping proprietary tools and processes to normalized data models
  • Applying business logic and transformations to enrich the data
  • Building custom hierarchies, tags, and flows tailored to the organization
  • Data science and advanced analytics leveraging the full data set
  • Creating custom dashboards and metrics specific to internal objectives

Realize the Benefits

Choosing a purpose-built platform over partial internal solutions results in multiple benefits:

A honeycomb diagram from Faros AI lists the benefits of a unified data platform, including cost savings, lower risk, benchmarking, visibility, focus, reliability, and agility.

Benefits of a unified data platform

Greater visibility: Holistic data and dashboards foster end-to-end insights spanning teams, tools, and the entire delivery lifecycle.

Improved benchmarking: Normalized data and industry comparisons provide context to better understand performance.

Increased focus: With undifferentiated data tasks automated, engineers focus on high-value analytics and improvements.

Enhanced agility: With future-proofed data models, changes to tools, processes, and org structures are easy to accommodate.

Lower risk: Credible benchmarks and identification of bottlenecks drive engineering productivity gains.

Reliability: Scalable and performant data pipelines grow and expand with your business.

Cost savings: The total cost of buying and building is far lower than internal custom development.

Get Insight on Your Timeline, Not a Vendor's

Many questions emerge as a natural by-product of running a business, and no leader wants to wait weeks or months for an answer.

The unified data models and benchmarks provided by the data platform allow tailored analytics that quickly address pressing and nuanced business needs with minimal effort. And unlike a completely off-the-shelf solution, you are not beholden to a vendor to get them answered.

Examples of such questions are:

  • Do developers need more AI training?
  • Is our new vendor delivering the expected value compared to FTEs and other vendors?
  • What percentage of the engineering workforce hasn’t contributed the minimum threshold of code this month?
  • Has a new test automation suite improved quality?
  • Are engagement scores correlated with pipeline improvements?
  • Is our technology migration on track?
  • Are some contractors working two jobs?

A buy-and-build approach makes it possible to generate new analytics at the speed of the business, leveraging the unified data platform and its full BI layer.

Don't Let Sunk Costs Hold You Back

Some engineering leaders understandably feel reluctant to shift strategies after investing heavily in internal metrics platforms. Why walk away after spending millions of dollars and years of work?

The key is to avoid falling victim to the "sunk cost fallacy." Just because time and money have already been spent does not justify continuing down the same path if it is not yielding the desired outcomes. The sunk costs are real, but piling more resources into a failing initiative rarely makes sense.

The good news is prior effort is not wasted when shifting to a unified platform. In platforms like Faros.ai all the work engineering teams have done to date integrating and normalizing data can be migrated. The key difference is the undifferentiated heavy lifting is now handled by the platform, freeing engineers to focus on high-value analytics and improvements.

Think of it as technical debt that can be written off by shifting to a modern architecture. The burden is lifted off internal teams. Time and energy can be redirected towards capabilities that truly differentiate the business.

The time for change is now. Improving engineering velocity requires replacing siloed internal metrics efforts with an integrated data platform purpose-built for the challenges enterprises face. The outcomes benefit both IT leaders and the overall business.

Reach out to the Faros AI team if you're ready to start the conversation.

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