ThinkTWENTY20

ESG reporting has been with us for some time. And will likely continue into the future. The focus has recently shifted however, to net-zero reporting, i.e. how companies are planning to achieve net-zero emissions in their business activities. Net-zero involves identifying all the activities of a business that produce emissions and those that mitigate it. The objective is to achieve a net of zero between these two forces.

Mark Carney put it well when he said, in an interview with CPA Canada, “We cannot get to net-zero without proper climate reporting. Full stop. It is just too complex. [Getting to net-zero] involves every company in every sector, every region of the world. We need that information. We need to know who has a plan, who needs capital, and who is lagging behind.”

The CPA Canada survey involved reviewing the net-zero disclosures of 20 companies. The results show that 65% of the companies had some disclosure of net zero goals. 60% have established long-term forward-looking transition plans and 35% have aligned their plans with the Paris Agreement. There were other important findings and the survey includes areas where improvement can be made. The report is available from the CPA Canada website.

Another incentive for companies to act in this area comes from the Canadian Securities Administrators, which recently issued a paper for comments on the subject of climate related disclosures, with the intention of addressing some of the shortcomings of existing disclosures and moving towards more consistency. The paper can be found here.

A foundational document for much of these activities is the report of the Task Force on Climate Related Disclosures issued in June 2017 and chaired by Mark Carney. It’s available on the TCFD website.

The CPA Canada report is definitely worth reviewing, along with the other documents cited in this brief write-up. 

For more information, click here.

Gartner released their report on strategic technology trends for 2022. It makes for some interesting reading. Major trends are:

• Data Fabric – a process of integrating data so it is available across platforms. It uses metadata and embedded analytics to discern characteristics, leading to more efficient data management.
• Cybersecurity Mesh – Extending beyond perimeter protection to integrate security systems across platforms and data systems
• Privacy-Enhancing Computation - (PEC) approaches allow data to be shared across ecosystems, creating value but preserving privacy. Approaches vary, but include encrypting, splitting or preprocessing sensitive data to allow it to be handled without compromising confidentiality.
• Cloud-Native Platforms - Lift-and-shift cloud migrations focus on taking legacy workloads and placing them in the cloud. Because these workloads weren't designed for the cloud, they require a lot of maintenance and don't take advantage of any of the benefits.
• Composable Applications - These are made up of packaged-business capabilities (PBCs) or software-defined business objects.
• Decision Intelligence - improves organizational decision making by modeling decisions through a framework. Integrating data, analytics and AI allows the creation of decision intelligence platforms to support, augment and automate decisions.
• Hyperautomation - a business-driven approach to identify, vet and automate as many business and IT processes as possible. It requires the orchestrated use of multiple technologies tools and platforms, including RPA, low-code platforms and process mining tools.
• AI Engineering - the discipline of operationalizing updates to AI models, using integrated data and model and development pipelines to deliver consistent business value from AI.
• Distributed Enterprise - is a virtual-first, remote-first architectural approach to digitize consumer touchpoints and build out experiences to support products.
• Autonomic Systems - self-managing physical or software systems that learn from their environments. But unlike autonomous or automated systems, they can dynamically modify their own algorithms with no software updates. This allows rapid responses to change, enabling management at scale of complex environments.
• Generative AI - a form of AI that learns a digital representation of artifacts from sample data and uses it to generate new, original, realistic artifacts that retain a likeness to the training data but don’t repeat it. That allows generative AI to be an engine of rapid innovation for enterprises

Get the full report here:
https://www.gartner.com/en/information-technology/insights/top-technology-trends