How Analytics Eliminate Business Costs?

Analytics is the systematic statistical analysis of statistics or data. Its use is especially useful for the discovery, identification, and communication of useful patterns in complex data sets. It also involves applying specific data patterns towards successful human decision making. Companies across all industries use analytics to improve their businesses. They use it to:

Analytics

Today, many companies rely on analytics to discover patterns and trends and then apply specific algorithms or mathematical techniques to those insights. This enables business people and managers to make better decisions. The main objective of analytics is to reveal profitable trends by using large amounts of un-normalized data from a wide variety of sources. To achieve this goal, data scientists must build sophisticated mathematical algorithms.

Analytics is a crucial element of data preparation. Data science, on the other hand, helps data scientists develop and code the mathematical models. There are two fundamental approaches to analytics: traditional data preparation and advanced data preparation. Data science helps managers decide on the best approaches by helping them understand how to appropriately combine descriptive and quantitative indicators, how to collect samples and how to analyze the various model components.

Traditional analytics consists of creating an analytical model, testing it on real time and taking the result of that model into the next step – which is to create a report with the results. Then, what is needed is a system that can tell managers if the model was correct. Data science is different. Analytics in this context is usually accompanied by some prescriptive machine learning methods. Machine learning algorithms are typically designed to create a solution from the raw data that is analyzed.

In order to analyze data and arrive at a robust decision, managers should use both traditional and advanced analytics techniques. The best approach however would be to develop both techniques separately. Traditional analytics will entail the collection of relevant and non-relevant data from the operational environment. Examples of relevant data are the quality ratings of machines and people, the inventory situation in the warehouse and so on.

Traditional data science helps managers identify the business issues, as models and the business drivers. The machine learning techniques will then be used to solve business problems. Machine learning includes such methods as the neural network, supervised and unsupervised networks, recurrent neural networks and artificial intelligence. It also involves domain knowledge, optimization, scheduling and optimization. One can also include reinforcement learning, graphical modeling and decision tree solving in the analytics ecosystem. All these techniques together, can help managers improve the quality of their decisions.

Once a decision has been made about the analytics project, what are the implications for data science vs. traditional data management? First of all, with the introduction of natural language processing (NLP), it is now possible to extract the logic behind natural languages, which is an important part of the creative process. With the logic extracted, managers can specify rules for the application to follow, which will result in improved quality of products and services. This means that once they have a clear idea of what is needed, they can specify a database for the application, train the machine learning model and generate the required analytics. As the analytics emerges, the managers can make any necessary changes in the database and reuse the generated data for other purposes.

Traditional databases used to store information usually contain only the date, time and count tables. These tables are very useful to analysts but they lack the ability to support the complex analytical process and so analytics will always remain at a disadvantage. On the other hand, natural language processing (NLP) will enable the data scientists to take the logic directly out of the natural languages and hence, the logic will not be stored in the database, but will be translated into a relevant gas algorithm. Such a solution will make it easier for the managers to extract the logic for prediction and thus improve the quality of the final analysis.