From Big Data to Artificial Neural Networks

Many concepts have been defined so far, but the core question has not yet been answered: Why do you need to apply Artificial Neural Networks to your Big Data? Let's Explore with us


What is Big Data?

What is Big Data?

Big Data is the enormous amount of structured, semi-structured and unstructured data that are exponentially generated by high-performance applications in many domains:. Large-scale data gathering and analytics are quickly becoming a new frontier of competitive differentiation. Organizations want to use large-scale data gathering and analytics to shape strategy. Data-related threats and opportunities can be subtle. BISEES is here to offer an innovative approach to overcome these threats.

Our Big Data Solution

The increasingly competitive landscape and cyclical nature of the business requires timely access to accurate business information. Technical and organizational challenges associated with "big data" and advanced analytics make it difficult to build in-house applications; this ends up as ineffective solutions and become paralyzed into inaction. In short, BISEES solution is used to develop applications that could perform deep analysis on huge amounts of data utilizing Artificial Neural Networks.

Analytics on Big Data

BISEES is member of the MIT Enterprise Forum of Cambridge, MA and a Red Herring top 100 Europe Winner! We have developed an innovative solution specifically for Financial Institutions. Our flagship product is the Banking Performance Management System (BPMS). It is based on a banking business information model that provides a holistic approach. It satisfies the need for fast, accurate, and responsive decision-making. We expand to other sectors bringing our experience to your company.


Why your Company Should Use Artificial Neural Networks on Big Data?


Better Forecasting

One of the major application areas of ANNs is forecasting. There is an increasing interest in forecasting using ANNs in recent years. Forecasting has a long history and the importance of this old subject is reflected by the diversity of its applications in different disciplines ranging from business to engineering. The ability to accurately predict the future is fundamental to many decision processes in planning, scheduling, purchasing, strategy formulation, policy making, and supply chain operations. As such, forecasting is an area where a lot of efforts have been invested in the past. Yet, it is still an important and active field of human activity at the present time and will continue to be in the future

Learning from examples

Before a neural network can be used for forecasting, it must be trained. Neural network training refers to the estimation of connection weights. This “learn from data or experience” feature of ANNs is highly desirable in various forecasting situations where data are usually easy to collect, but the underlying data-generating mechanism is not known or pre-specifiable. Second, neural networks have been mathematically shown to have the universal functional approximating capability in that they can accurately approximate many types of complex functional relationships. This is an important and powerful characteristic, as any forecasting model aims to accurately capture the functional relationship between the variable to be predicted and other relevant factors or variables. The combination of the above-mentioned characteristics makes ANNs a very general and flexible modeling tool for forecasting.


Applications on every sector

A wide range of business forecasting problems have been solved by neural networks. Some of these application areas include accounting (forecasting accounting earnings, earnings surprises; predicting bankruptcy and business failure), finance (forecasting stock market movement, indices, return, and risk; exchange rate; futures trading; commodity and option price; mutual fund assets and performance), marketing (forecasting consumer choice, market share, marketing category, and marketing trends), economics (forecasting business cycles, recessions, consumer expenditures, GDP growth, inflation, total industrial production, and US Treasury bond), production and operations (forecasting electricity demand, motorway traffic, inventory, new product development project success, IT project escalation, product demand or sales, and retail sales), international business (predicting joint venture performance, foreign exchange rate), real estate (forecasting residential construction demand, housing value), tourism and transportation (forecasting tourist, motorway traffic, and international airline passenger volume), and environmental related business (energy consumption of buildings, air quality).

Outperform statistics

ANNs has significant advantages over statistical models when both are relatively compared. In ANN models there are no assumptions about data properties or data distribution. Therefore, ANNs are more useful in practical application. Also, unlike some statistical models that require certain hypothesis for testing, ANN models do not require any hypothesis. ANNs are very flexible, data reduction models, encompassing nonlinear regression models, and discriminant models. More also, unlike the support vector machine, extreme learning machine, and random forest, ANNs are more fault tolerant. That is, they can handle incomplete data and noise, they can solve non-linear problems, Also, trained ANNs, can generalize at high speed and make predictions. Furthermore, ANNs are scalable when relatively compared to the support vector machine, extreme learning machine, and random forest.


Improve Management

ANN-approaches are a very attractive new tool for the managers and can be used to solve a number of di!erent problems on a quite sophisticated level. They are particularly useful for modelling underlying patterns in data through a learning process. They can be quite useful in pattern recognition problems, such as the modelling of product innovation project. Artificial neural network decision support systems use learning process to approximate this practical experience. Because of the highly connected, non- linear structure of artificial neural networks and their impressive performance in other applications, they provide a superior predictive system for use in the product innovation project. Thus, they provide cutting edge scientific method for reducing risks in product innovation project.

Get better insight

The future of any business from banking, e-commerce, real estate, homeland security, healthcare, marketing, the stock market, manufacturing, education, retail to government organizations depends on the data and analytics capabilities that are built and scaled. BISEES can help you today to take advantage of the hidden value of your data, create the appropriate structure of Big Data and provide insight using Artificial Neural Networks. Having the right information at the right time will enhance not only the knowledge of stakeholders within your organization but also will provide them with a tool to make the right decision at the right moment. It is no longer enough to rely on a sampling of information about the organizations' customers. The decision-makers need to get vital insights into the customers' actual behavior, which requires enormous volumes of data to be processed. We believe that Big Data infrastructure is the key to successful Artificial Intelligence (AI) deployments and accurate, unbiased real-time insights. Big data solutions have a direct impact and changing the way the organization needs to work with help from Artificial Neural Networks.


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