The sheer volume of information that analytics teams contend with is humongous. With a bevy of dashboards, a litany of KPIs, and a mind-boggling volume of alerts causing almost a ‘paralysis of analysis’ scenario on the operations floor, where exactly do C-suite leaders need to take the call? What specific signals do they need to act on?
In today’s age of AI, new-age tech support organizations are not constrained by a lack of data; rather, they’re constrained by decision latency. As highlighted by Movate’s Point of View (POV), dashboards and KPIs add value when they inform timely, high-impact decisions.
This white paper on “From Analytics to Decisions,” introduces a decision-led operating model that reframes analytics around the questions that CXOs must answer in real time:
- Where should we intervene now?
- Which risks demand attention?
- What should be automated versus governed by human judgment?
Dr. Kiran Marri, Chief Scientist at Movate, draws on decision theory and applied AI to outline 5 decision classes (descriptive, diagnostic, predictive, prescriptive, and cognitive intelligence); these classes are designed to curtail uncertainty and accelerate action.
Dr. Marri’s views showcase how AI enhances ‘situational awareness’, uncovers weak signals in noisy environments and enables faster intervention across tech support ops.
The paper delves into where to intervene, decision latency, decision theory, decision classes, compounding advantage, and the maturity question to ask.
Also check out other blogs by Dr. Marri:
- Blog: Choosing between an SLM vs. an LLM – https://www.movate.com/small-vs-large-language-models-exploring-the-right-fit/
- Blog: The data value chain framework – https://www.movate.com/the-data-value-chain-framework-from-information-to-impact/
- Blog: Unlocking sales magic via M365 CoPIlot – https://www.movate.com/unlocking-sales-magic-with-m365-copilot-upsell-cross-sell-and-soar/