Data is everything! Currently, 90% of enterprise analytics and business professionals say that data and analytics are key to their organization’s digital transformation initiatives.
Data-driven businesses have a 23 times greater chance of acquiring customers, a six times higher chance of keeping those customers, and a 19 times high probability of being profitable, according to the Mckinsey Global Institute. Businesses can use this data to make better decisions and enhance the consumer experience.
In today’s interconnected world, data is everything, and its significance will only increase in the future. The majority of businesses worldwide, or at least the most successful ones, now rely heavily on data and information, for which they pay substantial amounts to securely store and use.
Managing Data Intelligently In The Cloud Era
The age of cloud data has undergone significant transformation. Data that was once centralised in one place or a single repository is now dispersed, making it accessible to people anytime, anywhere. While getting access to data has never been simpler, utilising that data has become a complete chaos as a result.
Data that is stored in disparate platforms and locations is so dispersed that it nearly renders itself useless. The route to enterprise data management and analysis is increasingly difficult now due to the explosive growth of big data, the complexity of various cloud platforms, data sources, integration between platforms, and interaction across platforms. Chief information officers are concerned because they can’t locate or make holistic sense from their most important data. The lack of semantic standardisation among data assets is a contributing factor to the perplexity. As a result, businesses spend much more time looking for data than really understanding it.
Companies that are drowning in data that they can’t make sense of are in need of intelligent data management at this point. Data becomes a far more valuable asset, easier to link and understand, when it is managed intelligently. Let’s examine three crucial areas that businesses must take into account when they want to make their data function more efficiently.
Data Connectivity
Although an organization’s analytics and the processes that surround them are complex, connecting data is crucial in the data-driven world of today. Businesses must be able to assist their clients in connecting all of their data sources because incomplete data sets result in lost time and, worse, the missed knowledge that the data contains. The most important data may be automatically identified by an intelligent data cloud, which can then be used to empower employees and facilitate digital transformation.
Even though intelligent data clouds enable data connections, many businesses are cautious to use this option. One reason is apprehension about growing security breaches, and another common cause of reluctance is the fact that different countries have different laws and regulations regarding the privacy of cloud data.
Recent studies and surveys indicate that businesses are demanding more integrated data and analytics capabilities within their digital platforms. Organizations may be hesitant to implement an intelligent data cloud, but they won’t have much time to do so either. They might employ several techniques to lessen their concerns. For instance, rather than operating at maximum capacity right away, growing an intelligent data cloud over time to fit the demands, businesses will be better able to assess data security and regulatory requirements as they comprehend the adaptable capabilities of what the intelligent data cloud can achieve.
Artificial Intelligence
The evolution of artificial intelligence (AI) has significantly increased the effectiveness of data administration and analysis. A major advantage of using machines is that they can do a lot of the repetitive work much more efficiently, thus freeing up people to work on more worthwhile activities like data auditing.
In order to predict how data will be examined, AI uses search histories. The expected analytics can be optimised for time to insight or cost effectiveness, using the automated data preparation. AI models of today can process data in minutes as opposed to months. And by doing so, we can identify common data-connecting patterns and suggest data models that will more effectively reveal the narratives and insights the data offers. These models assist data scientists to experiment with multiple options and come out with best-fit recommendations.
The best AI systems for managing and modelling data are those that can handle both large and small data sets, preserving privacy to comply with any local, state, or government regulations.
Cloud Architecture
We learned from the Covid-19 pandemic that it will be challenging to adjust to significant changes if the entire platform cannot be converted or moved to the cloud. Data is finally made accessible and useful for the typical end user—not just data experts—by having an agile and intelligent data cloud. People will be able to access data at any time, from any location, without needing to understand how to use or manage databases, big data, or other similar technologies.
Agile cloud-native architecture reduces the chaos with something that everybody can use and access. As a result, implementing cloud-native architecture enables businesses to expand and support their business agility and to satisfy the needs of their consumers wherever and whenever they may emerge.
The Era Of Intelligent Data
In the age of cloud computing, the data warehouse revolution is just getting started. The industry will undergo significant change due to new cost structures, data-use methodologies, and technology architectures. While some businesses will experience upheaval as a result of these developments, those who use the intelligent data cloud will reduce confusion and potential chaos, which could end up costing them significant time and energy. Humans should be able to use data in the future in a way that is as easy and convenient as cloud computing is right now. Data users can concentrate on the data rather than the platform when intelligent data clouds are adopted from a restricted practise to a global phenomenon.
Source: PC Quest
