Advanced analytics

Advanced analytics use data science techniques, such as machine learning, data mining, predictive analytics, and location analytics, to give companies insight into trends they might not get.

By examining raw data, advanced Analytics allows conclusions to be drawn about the information that this data contains. Used in many industries, Advanced Analytics enables companies, organizations and individuals to make the most strategic decisions. Data Analytics can be descriptive, predictive or prescriptive.

Advanced analytics brings out the most important aspects of large volumes of data and highlights information that is difficult to observe by direct analysis. Above all, it is the possibility of graphical representation that is interesting in data analysis. This makes it easier for decision-makers to interpret their data and make decisions accordingly. Advanced Analytics can be used in many fields such as: energy management, health, trade, industry, meteorology, politics ...

Business Intelligence uses historical data to find out about the business evolution, and people in the company can leverage from this to predict competitive response, and changes in their customer behavior. Advanced Analytics give the possibility to do better prediction and provide better insight to future changes, so organizations or companies can be more responsive and better forecast and plan their business in an effective way.

Long time ago,I have work for  a company managing buildings and we did data mapping and advanced analytics in order to:

·      Obtain a global vision of our building stock in order to improve its energy performance.

·      Identify consumption anomalies to put an end to them with action plans that allow us to save energy.

To achieve the above objectives, we have collected the data below:

·      Asset data such as the surface area of our buildings, their year of construction, information relating to technical equipment, works, etc.,

·      Energy data relating to our energy consumption and the amount of waste emitted,

·      Activity data on building usage, opening hours, number of employees, etc.

Thanks to data mapping and advanced analytics, we collected data from various sources and cross-reference them to learn lessons. For example, the collection and centralization of data such as the address of buildings and the list of meters of a building stock makes it possible to have a global vision of its stock and to understand the consumption of each building while minimizing its time analysis.

As technologies like artificial intelligence spread more and more in supply chain analysis, companies will likely see more benefits. Information that was not previously processed due to the limitations of natural language analysis can now be analyzed in real time. Artificial intelligence can quickly, comprehensively read, understand and correlate data from disparate sources, silos and systems. It can then provide real-time analysis based on the interpretation of the data. Businesses will have much more extensive supply chain information. They will become more efficient and avoid interruptions, while using new business models.

References:

Kartik (2018), What is Advanced Analytics and What Are the Benefits of Advanced Analytics? [Online]. Available from:     https://www.dataversity.net/what-is-advanced-analytics-and-what-are-the-benefits-of-advanced-analytics/

IBM report (2019), Information Supply chain [Online]. Available from:      https://www.ibm.com/ca-fr/supply-chain/supply-chain-analytics

Datanergy report(2019) ,Data-Analytics[Online]. Available from:       https://www.datanergy.fr/glossaire/data-analytics-analyse-donnees/