Apps will gain prominence in 2014

In 2014 there will be a significant increase in the use of analytical apps compared to the use of traditional information analysis tools. But we are not talking about the “analytical applications” developed by manufacturers, which bring with them standard data models and universal reporting packages, but rather apps like those we have on our mobile phones, lightweight, interactive, business-oriented and easy to use without training. Those are the kinds of apps people want to use. If satisfactory results are obtained in this field, from a speed and flexibility standpoint, the die will be cast for traditional analytical tools, increasingly difficult to justify. The advent of the machines. Many experts discussing Big Data focus on unstructured data, generated by human beings, such as that coming from social networks. Although emphasis will continue to be placed on this field, next year data generated by machines (including that coming from the `Internet of Things´) will gain more relevance, growing faster than any other Big Data source used for analytical purposes. This reality will be especially evident in the industry and healthcare sectors. Data Steward will be the next job in the IT universe. The position of Data Scientist -Scientist of Data- generated quite a bit of discussion in 2013, but considering that data volumes will continue to grow unstoppably within companies, more emphasis will have to be placed on the management and supervision of information, and not only on the scientific side. As a consequence, new positions will emerge, such as that of Data Steward (Data Manager), which, ultimately, is nothing more than a professional with a business profile who understands data and knows how to qualify it over the course of a project. Data discovery tools have achieved significant success in the industry in recent years, which has resulted in substantial benefits for providers of this type of technology. However, there is increasing frustration among users of stagnant data discovery tools. Many of them do not know how to manipulate data to obtain the expected results, and, on numerous occasions, they find that the data does not come from the appropriate system, contains errors or is not in a format suitable for integration with other data. To prevent this type of situation, providers must be able to integrate their customers' trusted data with existing visualization programs. Data quality, a growing problem. The number of incidents regarding information integrity will grow significantly, especially concerning Big Data analytics. Considering that more and more organizations are making their strategic decisions based on raw data, this problem will increase. In 2014 the focus on data quality will intensify notably. The convergence of predictive analytics, data discovery, geographic information systems (GIS) and other analytics solutions will command the new era of analytical automation through machine learning, intelligent ETL and other automated processes. Different technologies are turning in the same direction, but two examples are worth highlighting. First, analysts are encountering the existence of large volumes of data, useless in themselves, and are being forced to extract the most relevant subsets in order to develop any type of analysis. Thanks to the convergence of statistical analytics with ETL functionalities and data extraction, analysts will be able to use the precision of predictive analytics to discern which data sets should be extracted. On the other hand, machine learning technologies are bringing together large data sets and placing them in pre-aggregated groups so that data scientists have the possibility to analyze them in a more optimal and faster way.