Data ingestion and normalization in the context of FinOps represents the set of functional activities involved with processing/transforming data sets to create a queryable common repository for your cloud cost management needs. In this context, data ingestion and normalization occurs when bringing together cloud billing data, cloud usage data, cloud utilization and performance data, on-premises CMDB or ITAM data, business-specific data, and other data points from a variety of cloud providers and IT data repositories to create a collection of cost and usage information which can be queried to support and enable the FinOps Capabilities.
Effective FinOps practice requires access to regular streams of detailed usage, utilization and cost data, which can be categorized and analyzed to drive decision making. Unlike the world of on-premises data centers, there is no shortage of data on cloud usage. Cloud vendors produce massive amounts of very granular usage and cost data on which to base a FinOps practice. Monitoring platforms, security platforms, and business operations applications can also provide data that will inform on utilization, location, value and usage, oftentimes at similar levels of volume and granularity.
So while the issue many data centers experienced was a lack of detailed data, the challenge faced by cloud users is oftentimes that there is too much. An effective data ingestion and normalization strategy should strive to provide the FinOps team with the right combination of data from:
the right source systems
at the right timeliness to support the cadence of decision making
at the right level of granularity to support aggregate reporting and drill down investigation
with the appropriate standardization, augmentation, normalization, etc.
The strategy for data ingestion will be driven largely by the needs of the reporting, cost allocation and optimization reporting needs. Data required to make decisions at a business unit level will not be in need of as much detailed or granular information.
FinOps teams which manage or allocate costs at a resource level may require multiple sources of data to gather resource information for some cloud providers which don’t natively provide it.
The data ingestion and normalization challenge grows as the complexity and diversity of the data increases. The level of granularity, the consistency of cost and rate metrics, mismatches between similar or analogous services, application of various types of discounts, the application of metadata to resources and hierarchies, and many other differences between the billing and usage data provided by the cloud providers can all provide challenges, even before considering the regular change and huge volume of data provided.
WHERE ARE ORGANIZATIONS IN TERMS OF MATURITY
Responses showed there was a shift to supplement cloud billing data with non-cloud centric data. Cohorts taking advantage of business, performance observability and CMDB data sources were operating at a higher FinOps Walk maturity level. FinOps practitioners taking their cloud cost management to the next level by including sustainability data were operating at a FinOps Run maturity
Match granularity of cost and usage data on incoming source files, though reporting separately
Ensure metadata being applied to hierarchy and resources is consistent across cloud providers and data sources
Ingest data from multiple data sources, normalizing cost metrics
Use of a third party FinOps platform to normalize data
Ability to create consistent reports for different clouds, possibly using different reports
Integration of performance/utilization data
Consistent data lake of usage, cost, performance, utilization data
Ability to run single report with multiple clouds
As someone in the FinOps team role, I will…
Determine the list of data sources required to fulfill my current reporting and operational needs
Determine the level of granularity required in each data source
Establish a data model for normalization, mapping fields from various sources to one another
Regularly and proactively validate data source content, and clearly understand when changes occur, react to them, adjust and re-document accordingly, and notify all those affected
Ensure that the data sources and resulting repository of cost and usage information is kept accurately, is appropriately sized, backed up, and managed throughout its useful lifecycle
Provide and ensure everyone with a need to access information can do so
Work with every group to determine the right metrics, measures and metadata that should be included in “official” output
Develop reporting output expectations document (update over time as maturity grows)
As someone in a Business/Product role, I will…
Provide business or product level information as required by the FinOps function to create KPI or other information required
As someone in a Finance/Procurement role, I will…
Provide access to data sources as required by the FinOps function
Ensure Finance is using the most up to date and appropriate data sources for reporting and decision making
Participate in or lead reconciliation and validation efforts to ensure invoices, usage data sources and other information map together as expected (typically monthly invoice reconciliation against usage data, and/or native cloud service provider tool data versus normalized data)
As someone in an Engineering/Operations role, I will…
Provide access to performance and usage monitoring information within the purview of Engineering for use by the FinOps data repository
As someone in an Executive role, I will…
Support the strategy for centralized data normalization, requests for access to information of various sorts as required by the FinOps function
Encourage and communicate clearly regarding the need to have a single source of cloud usage and cost truth for reporting and decision making
Measure(s) of Success & KPI
Measures of success are represented in the context of cloud costs and may include one or more key performance indicators ( KPI ), describe objectives with key results ( OKR ), and declare thresholds defining outliers or acceptable variance from forecasted trends.
cost data from multiple cloud providers queryable from a common normalized data schema
% of total cost available for reporting in normalized fashion
% of matching metadata elements
Cloud provider billing data
Utilization data showing CPU, Memory, Disk, and/or Network utilization at a resource or resource group level
Transactional data from logs or systems which record the number or quantity of use of types of resources (often shared resources)
Value or outcome based reporting which provides data on the number of transactions or increments of value created by or represented by the operation of the systems. This information can provide the denominator for unit economics KPIs