Machine learning, block chain and the Internet of things, exciting technology trends that are disrupting entire industries.
In this article I’ll explore these topics and discuss how they might impact the future of the ITAM industry.
Potential future growth areas for the ITAM industry or speculative gobbledygook? Please let me know in the comments.
The IT press often refers to the Internet of Things as a future concept.
Journalists like to refer cynically to the fridge that knows the lettuce should be thrown out or when to restock itself with beer. The reality is IoT is happening right now and many ITAM Review readers will be experiencing their supply chains and assembly lines being digitized and connected to the Internet.
Research by AT&T and Eye for Transport in 2016 suggested 87% of respondents were looking to expand IoT capabilities. Manufacturing, transportation and supply chain are all obvious candidates for IoT – primarily for asset monitoring.
4G enabled tags can be assigned to company assets to track movement, vibration, location, temperature and so on. For example delivery of refrigerated goods can be measured in real time to ensure delivery on time and within contractual temperature guidelines. As with IT assets; the better the information the smarter the decisions.
As IT Asset Managers, I believe we have the transferrable skills to manage this form of networked device and Internet connected asset. IoT also represents the intersection of ITAM and the broader discipline of physical asset management (Managing estates, air craft carriers, fork lift trucks and ISO/IEC 55,000).
Today as an asset manager, you might be managing a 20,000 desktops, servers, and mobile devices – tomorrow it might be 50,000 when your supply chain gets digitized.
A potential growth area for the profession of ITAM – do you agree?
Blockchain is to value what the Internet is to information. Whilst the majority of current focus with Blockchain is crypto currency, the potential is enormous in managing anything of value that would benefit from transparency and efficiency.
The crypto currency Bitcoin replaces the conventional bank with an independent and secure Internet connected ledger. What if we were to apply this logic to license entitlement records?
Rather than being dependent on archaic software records and painful administrative updates from software vendors, what if all purchase history was stored in an independent, secure and anonymous ledger visible by both the customer and software manufacturer?
The advent of -2 and -3 ISO/IEC 19770 tags means that, in the future, software entitlement and inventory records could be machine readable and fully automated. Whilst it involves a significant leap of faith to imagine this in action in 2018, it could be a way of fully realizing the potential of ISO/IEC 19770 and fully digitizing SAM. Do you agree?
Is this pie in the sky speculation or something of potential for the future?
Finally, Machine Learning is an area of Artificial Intelligence based on systems improving their performance based on feedback rather than simply following instructions.
Every single SAM tool on the market today has been coded by a human being telling the SAM Tool how to compute and interpret software product use rights. What if machine learning was applied to license optimization and process improvement?
“For decades, machines operated on responding to direct user commands. In other words computers were designed to perform set tasks in response to pre-programmed commands. Now, computers don’t strictly need to receive an input command to perform a task but rather input data. Specifically, the machine creates a predictive model based on previous experiences captured in the data. From the input data the machine is then able to formulate a decisions on how, where, and when to perform a certain action.” ~ Machine Learning for Absolute Beginners by Oliver Theobald.
AlphaGo learnt 3,000 years of human knowledge in 40 days by, essentially, learning from its mistakes. It was taught the rules of the complex board game “Go” and started experimenting until it reached a level of competence to beat a grand master.
What if this machine learning logic was applied to product use rights and historical contracts and license records? Another leap of faith based on today’s current norms – but what if license records were machine-readable using ISO -2 and -3 tags? What if it could learn from previous audit settlements to highlight potential risk?
What if machine learning could be applied to shift infrastructure across different cloud platforms automatically based, not just on elastic cloud computing requirements, but also cost optimization and risk avoidance?
More day-to-day ITAM spadework handled by machines, more strategic ITAM handled by ITAM professionals.
Do you agree? What could smart machines do for ITAM?