Big Data is a broad term used to refer to any kind of techniques to process large amount of data which are not related to traditional analyses or tools. This concept comprises many ideas and approaches, nonetheless sharing a common goal: extracting valuable information out of data to support business decisions and processes.
The Big Data concept is closely related to other terms such as Data Science, Analytics or Data Mining, which also include the objective of extracting value out of data. The Big Data system has been widely defined with the three Vs, representing the large Volume of data that must be dealt with, the Velocity that can be applied to process the data and the Variety of formats they may come in. Particular emphasis has been placed on the objective of Big Data, adding a fourth feature: the Value obtained alongside with the information taken from data.
Any project focused on Big Data is based on three incremental technological layers:
Storage
The hardware and software resources that allow having distributed and redundant storage of data, making it easier to manage and access the data and avoiding information silos. They account for where to store the data.
Processing
The data processing tools that work as a basis to handle large amounts of batch information or fast data flows in real time. They account for how to work with data.
Analysis
Finally, the algorithms and methodologies that perform the data analysis, producing valuable information for business. They account for what to do with data.
The core of the IIC experience and career during these 25 years has focused on the data analysis layer. Using existing storage and processing technologies, our firm commitment is to develop tailored algorithms and analysis techniques so that they make up Big Data solutions that are best adapted to the problems of every client.
Our capacity to work closely with the client while developing a tailored solution is our distinctive approach in contrast to the usual data analysis products.
How does it work?
Solutions based on Big Data have the common purpose of extracting valuable information while performing analysis of big sets of data. This analysis is based on mathematical techniques —mostly statistical—, that are derived from varied fields such as data mining, machine learning, time series analysis or operational research.
These techniques can help exploit any kind of data: databases, numerical records, free text, activity in a social network, audios, images, videos… Any kind of data is susceptible of being analysed if appropriate methodology is applied. Scenarios in which there is a wide variety of data in different formats can also be treated with strategies to integrate information, thus enhancing the solution.
According to the value of the information obtained, three kinds of analytics can be found:
Descriptive Analytics
Descriptive Analytics consists in the storage and aggregation of historical data, visualising them so that they can help understand the current and past states of the business. Descriptive Analytics tells us how the business has worked up to the present day.
Predictive Analytics
Predictive Analytics builds itself on the basis of descriptive analytics and, using advanced statistical models, adds to our information base data that are unknown to us. This results in techniques such as prediction of future values in historical series of prices and demands, but also automated texts or operations classification, or client segmentation. As a result, predictive analytics tells us how the business will work.
Prescriptive Analytics
Prescriptive Analytics is the top level of analytics, the prescription, which exploits the previous levels along with operational optimisation strategies to indicate which business actions will render the best results. Prescriptive Analytics helps us to obtain automated recommendations for the best time to place orders, carry out maintenance or other business operations. This level of Analytics helps us know what to do in order to optimise our business.
B I G D A T A
IIC has the capacity, expertise and experience to provide Data Analysis Consultancy at any of these three levels.
It is estimated that only 13% of firms make full use of Predictive Analytics, while less than 3% obtains the real value Prescriptive Analytics may provide. Potential for growth using advanced analytics is clear.
Benefits and value of the IIC Big Data solutions
Big Data solutions provide:
Accurate knowledge
Accurate knowledge of the business through Descriptive Analytics techniques.
Better organisation and planning
Better organisation and planning of the business using Predictive Analytics.
Higher return of the investment
Higher return of the investment of the business processes as we employ Prescriptive Analytics techniques that recommend actions based on solid data on expected costs and profits.
Guaranteed efficiency of IIC Big Data solutions
Big Data constitutes a tool for business improvement, and as such, its effectiveness depends on it being correctly applied. Given that it is not an end in itself, but a means, it is necessary to perform a detailed analysis prior to implementing Big Data solutions effectively.
At IIC we have over 25 years of experience developing solutions based on the extraction of value out of data. In addition, we play an active role in ongoing training and research processes based on the most recent technologies in the world of data, both in machine learning statistical systems and software technologies for massive data processing. All of this allows us to be a leading-edge organisation developing Big Data solutions that knows how to apply the most effective technologies at the time.
We work closely with our clients with data consultancy both designing and developing our solutions and while they are being used, making sure they are suitable for each individual case.
Why invest in Big Data and advanced analytics?
Investing in advanced Analytics and Big Data implies:
- Competitive edge: Business transformation on the basis of the data is already a fact, it is increasingly expanding to other sectors, and the organisations which have not adopted it so far are at a competitive disadvantage.
- Tangible benefits: Big Data techniques may yield a higher profit to the existing activities, whether it is in savings at the business processes, or a higher effectiveness of the marketing actions, or tasks automatization.
- Profitability: Using Predictive and Prescriptive Analytics techniques may increase the ROI of already existing solutions based on more simple analysis
