Some Game-Changing Database Trends to Expect in 2019
The new-generation technologies are reshaping the data management practices as we can see. In order to explore the game-changing data technologies and management practices, we have to actually know how the new-generation enterprises are handling data and what expectations they have from it. Here, we have compiled the inputs from various industry experts to identify the impact of changing data technologies.
DaaS or Data as a service is a concept which offers instant access to all bits of data across a global network through a third-party service layer, which is a very prominent development in data technology. As industry experts explain it, in order to leverage the complete competitive potential of data, a fundamental change is needed to conventional data management practices and the way how enterprises organize and utilize their data.
DaaS is the solution for this, which can rapidly integrate data across a range of sources and deliver it real-time to the end users, which was a real challenge to the traditional data management practices. Before the introduction of DaaS, most of the data platforms used to store information in a relational model as using tables with rows and columns, which was a very complex and less flexible approach to database structuring. It was also time-intensive, and the transactions were very slow. DaaS is a fluidic approach which fits into any business model that is built on a predefined structure, which the users need to fit into.
Tool for data governance
Nowadays, the capability of database administrators to prepare the available data for analytics and business intelligence has a huge impact on any enterprise’s ability to leverage data. Unlike olden times, data is now produced at a huge volume from varying sources and analytics, and AI had become ingrained to businesses. So, it is essential for organizations to empower analytical data procedures with the help of data engineers and data scientists.
A well-structured data governance process has to be put into practice to achieve these goals. However, the structure of data governance should be complicated and needed to be presented to all with an easy-to-use user interface in order to enable self-service data governance. This type of effort must be empowered by the analytical experts to access and use every bit of data, including dark data, in the original form to validate and use it for analytical purposes. Now, many enterprises started viewing data as a departmental asset than being a corporate asset. The modern tools for data preparation and interpretation with self-service interfaces move an organization closer to its vision with data for all than being restricted to top few.
Real-time data streaming
The movement towards real-time enterprise administration is also having an influence on the new-gen data solutions, which can enhance the organizational abilities to instantly respond to the opportunities or identify business issues on time to address it at the first point. There is a surge of real-time data streaming platforms now, which can handle distributed data queues. This further contributes to the machine learning capabilities and real-time analytics for enterprises.
As RemoteDBA.com points out, real-time analytics enabled through live streaming of data is now changing the way how businesses respond to market needs. This approach liberates and democratizes data, which become accessible to a greater number of users. However, many of the enterprises struggling with this as they still use the legacy database management systems which only have limited solutions for these. Enterprises have made a huge investment in these legacy applications and are reluctant to move away from such proprietary technologies.
Even though live stream data analytics is still at the infancy stage, it is proving out to be one of the most powerful approaches to data management of all times, which is capable of monitoring, analyzing, and reacting to real-time market events. This also enables a live focus on quality with detection and correction on time and also helps optimize the identification and utilization of any market opportunity on time.
AI, Deep Learning, Machine Learning
The data landscape is now getting shaken by the movement towards cognitive computing. When it comes to business intelligence and data management, there is no discussion happening nowadays without mention of artificial intelligence and machine learning. Machine learning tends to serve as the root of uncovering the patterns and indicators from a huge pile of structured and unstructured data, collected over time. When properly used, enterprises can differentiate themselves and automate the decision-making process for an increased level of efficiency.
Experts identify deep learning as a technique which will be responsible for the global adoption of AI. In the form of business intelligence, artificial intelligence can be adopted by small businesses also. Deep learning is now effectively woven into the applications of the leading data companies like Amazon, Microsoft, and Google, etc. Deep learning is considered to be the future of machine learning which include recommenders, forecasting, and search ranking, etc. In light of this deep learning initiatives, there is an emphasis on GAM (generative adversarial networks) and RNN (Recurrent Neural Networks), etc., which acts as extensions of the deep learning technology.
The other AI – Augmented Intelligence
Another AI is also gaining momentum along with artificial intelligence, which is Augmented Intelligence. This also forms part of the modern-day analytical platforms and helps organizations to address the current skills deficiencies in the existing artificial intelligence platforms. The major limiting factor which reduced the speed of digital transformations is the data literacy of the workforce. On the other hand, the analytical platforms which integrate augmented intelligence will be able to bridge this gap effectively and change the way how enterprises compete with the data.
Augmented intelligence will combine the power of machine intelligence and human intuition along with artificial intelligence to expand the range of business insights. The more users engage with data; the better analytical platforms can learn and ensure associations between various data sources and unveil the untapped business opportunities for productivity and profit.
All these point towards the fact that the modern-day data managers now recognize the essentiality of multi-modal data management. Business enterprises now tend to embrace new models of data management, and unlike before, even the smallest organizations are able to leverage the benefits of data-centric business management.
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