One of the most intriguing potentials of a forward-thinking company would be the capability to harness big data to explore new strengths, opportunities and hidden wherewithals of the business by using advanced analytics tools. Analytical technologies are used by establishments that are housed in almost every industry sector, including healthcare, manufacturing, automobile, utility services, robotics, aerospace, and many more.
As a business’s goals vary- whether it is faster decision-making, cost-cutting, innovation of newer trends of services or production, competing with a competitor or more- every critical decision can be made by effortlessly using an optimal analytical tool.
However, there are 4 types of main analytical natures that answer different questions and provide different levels of answers for an individual or business. They are:
- Descriptive Analytics Technology: What is happening now?
- Diagnostic Analytics Technology: Why did it or why will it happen?
- Predictive Analytics Technology: What could happen in the future?
- Prescriptive Analytics Technology: What should we do when it happens?
Today we will understand the disparity of these 4 levels of analytics comprehensively, thus, helping you decide what analytics method suits your analytical purposes and what benefits you can receive from each type of analytics technology.
4 Analytical Approaches; 4 Questions Answered
Future-ready data cultures are dominating the commercial and industrial world by leveraging the power of advanced analytics. Data specialists and technology experts are now brainstorming every day to develop an analytics model better than the other and meet their client’s unique and variating analytical requirements with better, faster and mightier technology tools. Harvesting the wealth of information absorbed in big data is crucial, thus making an investment in an analytical tool a critical decision for many businesses, so let’s cut to the chase and learn the differences of the 4 analytical approaches that you can take based on the analytical requirements of your business (these technologies are explained in the order of their evolution).
Descriptive Analytics Technology
Descriptive technology (as the name suggests) describes the statistics that are already being collected by the data sources in the past- including the very recent past. It helps the business answer the question, “What is and what has been happening in the company in the past”. This information can be used by the users to interpret how the business has been performing so far and what are the KPIs that the business should take note of to augment the organisation’s approaches in the future. Descriptive analytics tools can enable insights of historical data in any form, including charts, reports, graphs, maps and dashboards.
For example, suppose a manufacturing manager (let’s call him Jason) wants to learn about how a factory has performed in the past three days. In that case, a descriptive analytical tool will display an analysed version of the performance, resource expenditure, idle time, productivity levels and other essential statistics that concern the manager. Jason can visualise this data on a scalable dashboard and filter out the most important information that he wants to learn about the factory in the past three days. This is when he notices that the statistics of a machine in Section A is performing poorly.
Diagnostic Analytics Technology
At this point, Jason knows what happened, but he still has no clue as to why it happened in the first place. This is where diagnostic analytics come into play. This technology is a form of advanced analytical technique that allows the user to drill down deep into the issues that happened or will happen in future to diagnose the impacts that cause something risky. This technology can be used right after descriptive analytics too, for it can help the analyser to hover the issue and analyse it down to segments to better understand the dynamics behind the issues.
Jason found out that the first machine in Section A of his factory is malfunctioning, using the descriptive analytics tool. Now he can utilise diagnostic analytics to find the root cause of the malfunction using data mining, data discovery and drill down and drill through tools. So instead of trusting his ‘gut-feeling’, Jason can skim through the facts (a diagnostic like a machine section A has a faulty pump system, or the fuel tank is suboptimal), understand why the machine is faulty and why it will end up with casualties if an action is not taken proactively.
Predictive Analytics Technology
This is the preemptive technology that anticipates highly probable events based on the facts portrayed by descriptive analytics tools. This technology analyses historical data and uses AI-driven algorithms to map down trends of targeted parameters to use facts to anticipate the future occurrences. It investigates the statistics, machine learning influxes, computational models and determines when a problem can rise in the future under given circumstances. It analyses the likelihood of certain events in the future to give the user a heads-up to plan better and set realistic goals in the future without meeting unhealthy casualties.
Now Jason, the factory manager, can use this technology to understand when the machine in Section A of his factory will break down again, now he can notify his asset maintenance teams, invest in the proper inventories and track down the overhauling requirements of the machines without allowing it to affect the productivity of the factory. Based on the demand in the future, the potential of the factory and the financial viability, he can now ensure that his machines are performing up to standard and all his production will run seamlessly in the future.
FUN FACT: In the context of asset data analytics, visualisation tools can take a step further by using digital twin and simulation technology, where the user can interact with a virtual model of a physical asset or system to set parameters of a hypothetical or highly-likely event to understand how the real counterpart of the model will behave under certain environments. Therefore, the user can simply manipulate a virtual replica and analyse the future behaviour of an asset or system and tailor cost reduction, risk mitigation and performance upkeep initiatives that suits the future events of the company.
Prescriptive Analytics Technology
Unlike descriptive and diagnostic analytical technologies that only looked at the past, prescriptive analytics (and predictive analytics technology) looks into the future. It allows the user to take the predicted insights of a certain event to the next level by prescribing what needs to be done when the predicted event occurs or what needs to be done to prevent the predicted event from occurring. It investigates the potential implications and suggests the necessary courses of action.
Thanks to prescriptive analytics, Jason now not only knows how his machine will work in the future, but he also understands the mandatory actions and procedures he has to initiate to ensure the machines work in constant top-notch condition and the useful lifetime will be spent optimally without any unnecessary cost and time-wasting efforts. He can also take event-driven approaches and keep complete reliance on all his factory machines using the power of AI, ML, IIoT and other data technologies.
Cerexio- The Home for Analytical Excellence
Cerexio offers a range of data expert solutions that facilitate new age industrial establishments in harvesting the wealth of data and utilising it to make faster, innovative and success-guaranteed decisions. Team Cerexio is known by data-dependent conglomerates and public organisations in Australia and Singapore for enabling data solutions with cutting-edge tools for analytics and visualisation. Cerexio offers technology suites and platforms that are embedded with edge, cloud and platform-based analytics tools that serve the respective decision-making purposes of the clients.
Cerexio offers an out-of-the-box Data Analytics Solution that allows the user to utilise pre-trained analytics models self-reliantly to manage, store, share, utilise data to gain actionable insights that guide the business to its best form of efficiency, productivity and success. This solution:
Cerexio offers an out-of-the-box Data Analytics Solution that allows the user to utilise pre-trained analytics models self-reliantly to manage, store, share, utilise data to gain actionable insights that guide the business to its best form of efficiency, productivity and success. This solution:
- Allows the execution of event-driven algorithms
- Allows users to meet unique analytics requirements without depending on a data expert
- Can be deployed on any form of cloud platform (private, public or hybrid)
- Can be integrated into all data-rich sources despite the complexity of the data system
- Focuses on data science rather than the infrastructure
- Saves the unnecessary time wasting, costs and unnecessary efforts related to the analytical processes and many more advantages.
Meet The Future Faster With The Right Analytical Initiative
A unique analytical approach can help any organisation to morph their business to their preferred stats and ensure that no casualties are met on the way. Analytics technologies help them to smoke out the risks, retain the satisfaction of stakeholders and spend time, money, effort and other resources intelligently when meeting goals. With data at businesses’ disposal, they are capable of implementing fact-based approaches to solve a multitude of problems self-reliantly. Thus, making the investment of the correct analytical technology is one of the most important decisions your business should make because the future of your establishment depends on it.
