Big data has become part of data-driven processes everywhere, whether it's cognitive computing, multi-cloud, or avant-garde use cases of streaming data.
The ongoing coronavirus pandemic (SARS-CoV-2 - COVID-19) is hopefully coming to an end soon and we are again venturing a first glimpse into a hopeful future with many exciting Data & Analytics topics: Data interoperability, Knowledge Graphs, Cloud automations and Blockchain technologies are (still) topics to be highlighted!
Data interoperability includes the ability to easily share data between enterprise systems and resources on demand to maximize business productivity, without technological constraints. The vision behind it? Everything should be compatible and interchangeable with everything, even technologies!
To achieve the goal of data interoperability, challenges in the areas of Big Data, Data Management, and Data Virtualization are imperative to solve.
The challenge of interoperability has defined the shape of a Big Data ecosystem since the introduction of diverse technologies nearly a decade ago.
The integration and mastery of Big Data means that organizations can address issues of artificial intelligence (AI), increasing reliance on cloud architectures, and the viability of streaming architectures.
The current momentum in Big Data is focused on convergence to realize the long-sought, rarely realized, ideal IT has always pursued of what is called "interoperability." And as technologies begin to support this, the topic of interoperability will become more interesting as well.
The development of solutions to the upcoming challenges in the area of data management should not be underestimated. Future systems should not be justified in their existence solely by their heterogeneous data provision and management, but should offer significant added value through uniform and intelligent access to heterogeneous IT landscapes.
True interoperability requires the sharing of technologies, tools, and management methodologies whose approaches are based on integrating their data to help data stewards and enterprises break down their data silos.
Organizations will be able to accelerate the integrated delivery of information if they adopt new data architectures and platforms.
The data virtualization approach, by definition, unifies data from multiple data sources, creates a unified view, and then makes the data available to consumers.
Cloud automation is one of the biggest challenges - even for basic on premises, hybrid or multi-cloud interoperability. In addition, the current public health crisis has created a situation where remote working is here to stay. Many companies can have their workforce working remotely up to 100 percent indefinitely.
To achieve the goal of cloud automation, it is imperative to solve the challenges in distributed cloud, data mobility, and network resilience.
The building of distributed clouds is being used as another means for the future by which companies can couple their data with their computing resources. The technologies help organizations to make very dynamic decisions to check and adjust computing resources day by day to meet the very fast changing challenges.
For remote access automation, network resilience plays a very important role. It is an important component of interoperability. If the network fails, no artificial intelligence will maintain the cloud.
Network resilience is strengthened by approaches that automatically configure organizations' remote resources at scale "for routers, switches, and all IP addresses that actually reach the site."
The network problems associated with big data interoperability also extend to popular blockchain implementations, such as Bitcoin or Litecoin, of cryptocurrencies. Other projects, such as DigiByte, have already solved the problem of big data interoperability.
Recently, the US payment service provider PayPal announced that it would enter the cryptocurrency Bitcoin trade. The departure after a decade-long blockade stance on the cryptocurrency Bitcoin will also facilitate and significantly accelerate market access to alternative cryptocurrencies.
With cryptocurrencies, privacy concerns must now also be reconciled with regulatory and legal constraints in a timely manner. However, their increasing traction is simply another source of data for IT systems to exchange large amounts of data with for interoperability.
The next stage of development is to first integrate and then freely share data from the myriad tools and technological approaches used across the enterprise.
Topics such as cloud automations, knowledge graphs, data management & data governance, and blockchain are game changers for successful businesses.
These are topics that must be addressed in a timely manner, as they can provide a significant competitive advantage or put you at a competitive disadvantage if you miss the trends.
Markus Begerow has many years of experience in the design, implementation and operation of business intelligence and analytics systems. Over the last 10 years, he has advised renowned customers in this field of activity. As a business information scientist and cloud solution architect, he can draw on extensive practical experience in various industries. As a division manager, he is responsible for the topic of Data & Analytics at CoPlanner Software und Consulting GmbH.