Big Data Management is the most important tool for companies: after all, if data have been called "the new oil" it’s for a reason. Awareness of the importance of data is in fact a pillar of data-driven strategies, something that all companies should implement. According to Politecnico di Milano’s Governance Models Observatory for Data Science, in 2018 all companies that adopted a mature data governance strategy rose from 17% to 31%.
The 55% of large companies, however, are still in a traditional situation and have not yet prepared a correct data-driven strategy: they have taken the first step, but do not know how to continue or do not yet have the technological and human tools to do so. Intelligent Data Management starts from here: diversifying data sources and knowing how to recognize new ones; give context and value to Big Data and accumulate information for potential new future business strategies.
Business Intelligence applied to Big Data Management
One of the characteristics that identify Big Data is the heterogeneity of the sources from which they come. Sources that, today more than ever, continue to change. Proper data management starts here: from monitoring sources (from the Internet of Things to social networks) and from the careful evaluation of new Big Data sources. The flow of incoming data is one of the main challenges: the company must build a "digital dam" to ensure that the huge flow of Big Data is controlled and can be conveyed intelligently to all departments of the company. Advanced Big Data Management tools allow the company to channel data, manage high-speed streaming and master the catalog of information in its possession. The company must not feel overwhelmed by Big Data, but needs also be aware that they must be managed with care and strategy, as deserved by every critical asset. For those data to be useful, it needs to be placed into a context. “Raw” Big Data can become constructive information only if legitimized by a series of parameters: where they come from, how they can be used and what it means in relation to other data, for example. At that point, data becomes information and knowledge.
How Big Data Management opens up new opportunities
Big Data is not always useful in the short term. Data relating to GPS or the use of a device, to give just two examples, may seem "useless" for the current core business of the company. But it would be a huge mistake to discard it. Businesses today must be able to grow their “treasure”, using Big Data management to combine structured and unstructured data, cataloging such data with intelligence and a certain foresight so that they can, in the future, be quickly available. A Big Data archive could be exploited to expand a business area or allow the company to enter a new business segment starting from solid market information. The basis of Big Data is the critical asset on which to leverage not only today, but also tomorrow. Intelligent Storage tools, supported by Artificial Intelligence algorithms, are fundamental for advanced data architecture models to correctly manage data, protect it and make it available in an agile and fluid way.
A corporate culture that values Big Data
The mass adoption of Big Data lives its full potential only if the data is made accessible and usable by every department of the company, from sales and marketing, to IT. Big Data Management, in short, ensures that this information becomes an integral part of the corporate culture: the more people have access to it, the more opportunities there will be to give solidity to an intuition. This way, data-driven strategies strengthen the potential of human resources. In this context, the role of Data Scientists gets more and more value, as experts who uses machine learning algorithms and advanced Data Visualization tools. Raw data is analyzed and contextualized, generating information and knowledge improving company performance. BCG's “The Dividends of Digital Marketing Maturity” research on digital marketing found that companies that achieved excellence in marketing driven by Big Data and machine learning saved up to 30% on costs and increased revenues by up to to 20%. Finally, it is essential for Big Data to be mobile and not bound in a "silo": it must always be accessible, in constant motion and available to all company businesses.