In the early years of “Big Data”, Data Governance topic was certainly not in hype. The disruptive opportunities of the huge amount of data generated and the heterogeneity of the available data sources have led many companies to try to navigate in this “data deluge” (remembering the term used by the Economist), without the appropriate tools to chart the route.
Data Governance, meanwhile, has remained linked to traditional areas, perceived as a brake on innovation brought by the IT function or as a set of legal limits, especially in the financial sector. In a new maturity of Big Data enhancement, this is no longer possible. Mature companies are aware of the centrality of monitoring data quality activities, managing information security and controlling data operations. These activities are not only required, but they form the basis of the data-driven transformation process.
Data Governance activities indeed allow us to achieve truthful, certified and easily interpretable insights. Consequently, they enable trust in data, and they create engagement in final users, i.e. the decision makers of the business lines.
International mature companies are trying to integrate, from an organizational point of view, Data Science area (innovation engine) and Data Governance function (responsible for providing the right fuel to the Analytics “machine”).
Big Data & Business Analytics Observatory, part of the School of Management of Politecnico di Milano, presented, during the Conference held on November 24th, an in-depth analysis about Data Strategy. The Observatory described the data strategy from two points of view, both as activities and figures dedicated to Data Governance and as ways of accessing the analysis results for non-specialist users. For the third year in a row, Datrix participated as a Partner of the research edition. Let’s see some of the results presented.
About 4 out of 10 large Italian companies consider their own data quality inadequate. This is a huge obstacle to innovation: poor quality and integration of data emerges as the first obstacle to the implementation of advanced Analytics projects. Moreover, the research highlights the issue of data integration. Both integrating external and internal data and unstructured and structured data are activities particularly challenging: more than one large company out of two does not consider adequate the technologies currently in use. Finally, despite having invested in advanced data visualization software, companies struggle to use massively these tools. Non-specialist users are reluctant to directly interact with data, such as the creation of ad hoc views or the development of simple predictive models. As for small and medium-sized enterprises, the research highlights even greater difficulties: only 24% carries out the integration of internal data coming from different business processes.
The aforementioned limits – often underestimated in the rush towards analytics business impact – have indeed a direct impact on the ability of companies to create value through data.
But how to overcome Data Governance and Integration problems, with limited cots and without slowing down the innovation processes that Data Modeling or Data Science activities can bring? Simple answer, that can apply to small companies as well. Two keywords: Cloud Computing and Augmented Analytics.
Cloud Computing services are a must among mature companies in the Analytics field. They allow quick access to state-of-the-art technologies, including for example high-performance data warehouses or data lakes, which are tools that facilitate the integration of heterogeneous data. Thanks to the technologies offered by Google, for instance, even a small or medium-sized company can enhance all the data collected on its website, optimize its advertising investments, or customize the relationship with its customers. Furthermore, if you rely on the right partner, the projects, aimed at achieving these goals, goes to speed in less than one year.
Looking at more advanced technologies, we cannot fail to mention Augmented Analytics. As our Claudio Zamboni (Chief Revenue Officer, Datrix) and Matteo Bregonzio (Head of R&D, 3rdPlace) recalled during the Observatory final Conference, augmented analytics can represent a real turning point for all those companies that have not moved on these issues yet. Augmented Analytics means the use of end-to-end solutions that automates, thanks to machine learning algorithms, all those time-consuming activities such as, for example, data quality monitoring, integration of heterogeneous formats, exploratory data analysis to verify cleanliness. Augmented Analytics means revolutionizing the final phase of insight discovery, with innovative solutions that take advantage of natural language analysis and are aimed directly at the end user, offering business suggestions starting from the data entered into the systems.