Today, practically every company can access large data sets, whether their own or from other sources – but, without the capacity for exploration, carrying out data analysis and transforming that raw material into real knowledge – this is, the evolution of big data into smart data – the expected added value is not achieved.
Along this road, which leads to displaying a successful data strategy, organizations seek to incorporate from data analysis solutions and BI, to artificial intelligence capacities and automatic learning. But not everything happens through technology.
To illustrate the aforementioned, we deem convenient to review the results of a study which found that 73% of executives consider that data assets are fundamental for the commercial strategy of their company. To be able to profit from the potential that lies within data, 51% of the surveyed said they will be displaying a multi-cloud strategy, that is to say, that will distribute applications, workloads and data in different public, private and hybrid Cloud environments. The intention is to be able to benefit from “new and better options of storage, workload management and specialized data sets, and analytic tools from a variety of Cloud suppliers”.
Nevertheless, the investigation also highlights that organizations face a series of challenges to extract authentic value from the data distributed in different types of environments which constitute a multi-cloud architecture.
Between the most pressing barriers are “the lack of interoperability between different environments, data blocking and data silos”. The report emphasizes that all these inconveniences can deepen if companies do not count with an integral data management strategy for multi-cloud environments. But the reality is that only 34% of the surveyed mentioned counting with a truly effective strategy of this kind.
Complex scenario
The majority of companies today create, assemble, move and store data in different sites and different clouds. When opting for hybrid architectures and multi-cloud, organizations expect being able to decide which analytic workloads to execute, where and how. To that end, they must be able to unite the best data resources with the best analytic tools when distributed in various clouds, and to follow up on who is doing what with the data.
Companies need to strengthen and orchestrate their policies and safety practices and governance in a context signed by the use of different storage systems and infrastructure, and in which users need mobility and accessibility from different standpoints. But well: in such scenario, in which data migration could also be frequent, the sole fact of having data lineage already constitutes an important challenge.
Because of that, besides complying with normative subjects, companies need to establish an ample data governance policy, which includes the ways in which people, processes, and different technologies work with data in a compatible, auditable and safe manner. Only in this way, problems such as redundancy, errors, difficulty to understand where data is and who is using it and lack of trust in their own data, could be avoided.
A proposed solution is to create a catalogue of company data, “not only for the data, but as well for algorhythms and the people who use them, and the relevant privacy and safety policies”.
In brief: to obtain value from data, companies need to be able to process them, and analyze them from multiple standpoints – as their own data centers, different clouds (cloud platform) or in different end points. And for that purpose, it is key to have a coherent governance and safety policy, framed within an integral data management strategy.
To explore the details of the investigation we have just reviewed, we invite you to click on this link.
Does your company work with a multi-cloud strategy? Do you count with an effective data management strategy?