Analyzing events and situations should inherently include the dimensions of space, time and nodes in a network. That is, where and when did or might something occur, and what impact did that event have on other parts of a network. A simple example is trying to understand how a traffic accident (at a location and time) affects the flow of vehicles on a freeway and other roadways (in a network) connected to that freeway. These three dimensions are an important part of situational intelligence as without them, a true understanding of a situation cannot be determined. Without knowing either when the above-mentioned accident occurred or where it occurred, it would be impossible to determine its impact on traffic elsewhere.
Below are brief decriptions of how the dimensions of space, time and node just mentioned are important aspects of delivering valuable and actionable insights to users:
Spatial Analytics - provides a geospatial understanding of where assets, resources and situations are geographically located, as well as their relative proximity to each other and other items (e.g. people, buildings, fires, trees, other assets) of interest.
Temporal Analytics - provides insight into when situations occurred or may occur. Temporal analysis utilizes a timeline of what occurred or might occur to show how situations have or might evolve. Note that this is different from just looking at aggregated data at a single point in time (e.g. sales by quarter) – the use of time-series data can be used to identify what happened last quarter and every time interval since then.
Nodal Analytics - provides insight into the logical and/or physical interdependencies and symbiotic impact of assets, resources, and situations. Nodal analysis helps users understand downstream impacts (perhaps on customers) and upstream impacts (perhaps on other assets) of a failure at a specific location in a network. It also helps users determine how critical certain parts of an infrastructure are by analyzing the impact if a failure were to occur.
To summarize, analytics can perform extremely valuable tasks. But not all analytics and approaches are alike. Understanding what business problems you’re trying to solve will help determine what types of analysis your data needs. That brings us to the second question you should ask.
Question 2: What Benefits Will My Organization Realize From Your Analytics?
Analytics is a broad field with differing products and solutions. As such, each vendor promises and delivers technology that provides different output, results and benefits. Understanding which benefits you require from an analytics product enables informed product and vendor selection. You should of course thoroughly examine vendor promises and seek explicit proof points for each benefit that you identify as a critical success factor.
Effective data-driven decision-making with a high degree of confidence is a fundamental benefit that you should realize.
Analytics vendors ideally should collaboratively work with you to develop a quantifiable and defensible business case for investing in analytics. Only you know your organization’s needs, skills and budget, so the vendor should demonstrate how their features and services fit with those needs. The types of analytics discussed are listed below along with examples of how you might use those analytics.
It is worth noting that in some instances, analytics may not yield revealing or extraordinary results; after all, if there is nothing to find or improve upon, that’s positive, worth knowing and reassuring too.
Type of Analytics | Applications |
Prescriptive Analytics | Operational optimization: Which new services would be most effective? What markets should we pursue? How should we allocate capital? What is the most efficient schedule for our crew? |
Predictive Analytics | Preparation for outcomes: Which part is likely to fail first? Should we maintain or replace that asset? How much should we budget for asset replacement? How much capacity will we have available? |
Diagnostic Analytics | Performance assessment: Which part failed? Why was that alarm raised? When did that customer call last? Which assets are performing irregularly? |
Descriptive Analytics | Performance review: Did we meet our goals? In which areas do we need to improve? How many new customers do we have? How much did we save with that new initiative? |
Table 1: Applications of Analytics
Here are just a few of the ways in which using analytics can lead to measurable cost savings:
Loss and theft reduction - Loss of valuable products occurs across supply networks, retail outlets, pipelines, electricity transmission and distribution wires and other distribution networks. Reclaiming even just one or two percent of sales by reducing theft, spoilage, technical loss and other types of loss quickly results in cost savings. Analytics can locate points in a network where losses actually occur and are likely to occur, so that preventative measures can be taken to realize cost savings.