Expanding Software Compliance to Software Asset Intelligence
The modern pace of change, combined with the disruptiveness of new technologies and increasing digitalization, creates a perfect storm for many organizations. No matter how the macro economical winds are blowing, there will always be a need for organizations to navigate efficiently. Software Asset Management (SAM) has the potential to be an additional supporting compass, helping decision-makers, teams, and management to navigate.
Historically, the need for License Administration and Management, and subsequently Software Asset Management, arose from the desire to honor agreements with software vendors and avoid the costly consequences of non-compliance. The focus was centered around the agreements and their metrics, and how well the organizations managed to steer and use them accordingly. As a result, practitioners focused heavily on compliance with agreements and metrics stated by software vendors and third-party organizations.
Today, compliance is still of great importance, as the cost of non-compliance has skyrocketed over the years. It makes significant sense to ensure compliance, especially for top-tier software.
This article argues for the need to increase focus on the questions and data that will help organizations ascend from mere compliance. The data will help decision-makers understand the past, present, and future of digitalization, how and which resources are used, and when, or how transformations into new or diversified technology are evolving. In the modern organization, there is a lot that revolves around software and software asset data, no matter if it is on-premises or in the cloud. And no matter if it relates to resources or people or how it is being utilized and how it evolves over time. This data creates understanding and helps to both govern and steer the organizations.
A major point here is that only a fraction of this highly potent data can be found within the metrics of the software vendor agreements. The SAM community needs to reflect on this.
In conclusion, while compliance is important, this article proposes the idea to expand from a compliance focus to include Software Asset Intelligence, to make more informed decisions and optimize software investments from a much broader business perspective.
Aiming for Software Asset Intelligence
Aiming for Software Asset Intelligence serves as a catalyst in the transformation of the SAM program. With a common and easy-to-understand goal, the SAM team and internal stakeholders can more easily align and work towards the common goal and outcome of the changes and improvements. When making the pursuit of Software Asset Intelligence a common goal, it makes sense to drive for improved automation of data and to focus on internal data as an essential area for SAM improvement.
While automation will take a more technical approach, investigating internal data will require an in-depth understanding of your business, IT, current challenges, and transformations.
The transition from manual processes and external compliance to automated and internal data focus is crucial for creating Software Asset Intelligence. Collecting data over time will generate insights that allow for projections and informed strategic decision-making within organizations.
Software Asset Intelligence can significantly increase the relevance of the SAM program and make SAM more data-driven. It allows SAM teams to address strategic issues and support the organization in a more meaningful way than just chasing metrics for third-party software agreements.
Stuck with compliance
SAM and its practitioners serve organizations by providing diligent support for the daily and continuous work related to optimizations and compliance. Compliance work around renewals, annuals, and audits is often performed manually, either at the request of internal stakeholders or through proactive efforts by someone from the SAM team.
Many SAM teams spend a significant amount of time on manual labor, including fulfilling contractual obligations related to metrics from third-party software vendors. This can include tasks such as registering software entitlements, performing compliance calculations, and generating reports, all of which must be repeated for each vendor and kept up to date. This is due to the lack of automation.
Expanding from Manual to Automation
Over the past few years, the sought-after silver bullet within SAM has been semi-automated optimization and automated decommissioning of user licenses or client software. It is a good ambition, but there is a severe lack of successful implementations that have truly scaled to make an impact for larger organizations, other than as a symbolic win. Additionally, this silver bullet often turns out to be a steel bullet that tends to rust, as fully automated decommissioning or revoking licenses are rarely scalable enough to have a significant impact. This is due to the sensitivity and criticality of such setups – if (or when) they go wrong, they can be devastating to operations, especially in cloud services.
Despite downplaying the importance of automated actions, it’s worth mentioning the fundamental importance of structures and processes for decommissioning or revoking licenses as a part of changes when people join, leave and move within organizations. Moreover, adding or removing technology must have well-functioning processes and supporting services for compliance and optimization.
However, this section aims to highlight the importance of automating software asset data. While automation discussions in SAM often focus on automated activities, automation of data is often overlooked. SAM tools are put in place, a high inventory rate is established, the data is validated, and compliance reports are generated, but many teams struggle with getting stakeholders to act on the data they already produce. Perhaps therefore some SAM teams do not prioritize the automation and distribution of data.
But as SAM practitioners we see the need to shift from focus on automation of activities towards focus on automation of data. Because we do not always own decisions, budgets, formal mandates, assets, and management, especially not in the largest organizations. In these situations, SAM is often responsible for providing central intelligence over Software Assets to enable aggregated data for holistic understanding and a decentralized knowledge of the Software Assets.
If the management of agreements and decisions of software is outside of the SAM team, then the team must provide intelligence to the decision-makers. For example, there might be 150 stakeholders making decisions, then ad hoc or on-request data will not be possible, even if there is an extensive SAM team. The need for automation increases in proportion to the diversity of software and decentralization of mandate and management within the organization. In short manual labor is not fit for purpose in an organization with a large amount of different software and stakeholders.
Expanding from External Data to Internal Data
The SAM teams have always been champions of providing accurate data for annual reporting or renewals of software agreements. Providing data for external parties and their contracts is an important task, but we must understand that this data only serves to measure the organizations according to the metrics of the commercial software models. The external data provided to external parties only answers third-party questions.
To be strategically relevant and provide C-level support, SAM practitioners must shift their focus to internal data. This data will be for internal use to answer questions within the organization, both at the aggregated management level and in understanding the specific needs of individual departments and teams. For example, internal data could be used to track the shift from on-premises maintenance and support spending to SaaS spending over a three-year period, or to monitor the deviation in full-time equivalent employees compared to the increase in specific user software.
In the same way that FinOps focuses on internal questions and cost control, SAM practitioners should expand their focus to internal data and intelligence. This can help solve explicit problems that are important to CIOs and other decision-makers as they transform organizations and technology.
Internal data can be monitored through dashboards and key performance indicators to provide alerts and answer detailed questions. Additionally, a manual approach to data analysis can be used to explore more complex and ambiguous challenges within the organization. By focusing on internal data and asset intelligence, SAM practitioners can provide valuable support to the organization and enable informed decision-making on strategic, tactical, and operational levels.
Expanding to Software Asset Intelligence
SAM teams achieve Software Asset Intelligence by aiming for it, but intentionally investing time and resources in increased Automation and an increased focus and emphasis on Internal Data. Software Asset Intelligence often involves utilizing automated data sets over time, although not always. The data in focus aims to support the organization’s internal stakeholders, both centralized and decentralized, in understanding the business, tracking changes, and making an impact on the full range of strategic, tactical, and operational decisions.
Software Asset Intelligence is not a replacement for compliance but an additional field that organizations must master to meet the increased demands of digitalized organizations.
SAM program that utilizes resources effectively
Adding software tiers to this model provides guidance to SAM teams on how to prioritize manual labor for improved optimization of resources and greater impact.
Implementing automation will initially require a significant amount of effort, as well as coordination across technologies and processes. However, with successful automation, the SAM team will have access to correct data on demand for all stakeholders, which will not consume significant resources. This frees up the SAM team for manual labor where it can make the most impact.
This article emphasizes that in a typical SAM multi-tier software model, manual labor will have the greatest impact on higher software tiers. Automation is typically applied to lower tiers, as they are the most manageable through automation. As automation is expanded, it is also rational to include higher tiers, with all software tiers benefiting from increased automation and including both external and internal data.
However, manual labor will only have a significant impact if it is focused on higher tiers. The manual work put in by the SAM team for periodic compliance, optimization, and reporting will have a greater impact on the top ten software vendors with the highest spending than on the bottom ten. Additionally, manual work of analysis of internal data, such as extra reporting, anomaly detection, process reengineering support, and other internal stakeholder issues, only makes sense when the impact is significant.
This model suggests, given the arguments above, that there is a strong case for focusing Manual work on tier 1 and tier 2 regarding External Data. However, regarding Internal Data only Manual work is carried out for tier 1.
Naturally, all tiers should be represented in the Automation layer for Software Asset Data and Software Asset Intelligence.
Hopefully, this article brings inspiration to evaluate old ways and the purpose of current SAM programs. Then what could be a reasonable first next step?
One approach is to take a step back and reflect on the current SAM program, here is a few examples of questions:
- How automated, and of course how reliable, is SAM data?
- How well distributed and fit for purpose is the SAM data?
- Are people spending time on manual labor where it makes the most impact?
- Does SAM serve the organization besides software agreement metrics?
- What internal data and questions can be addressed or supported?
- What are the organizational big bets or transformations that we can support?
Expanding from compliance into Software Asset Intelligence is, in many ways, a strategic decision for the SAM team. However, as with many other situations, the key to getting started is to take small steps, but to take many of them.