How To Manage Your Data Intelligently In The Cloud Era
The era of cloud data has changed drastically. Data that was once concentrated in one location or a central repository is now distributed, and with data spread out, individuals can access it anytime, anywhere. While accessing data has become easier than ever, however, it has also created chaos when it comes to using that data.
Disparate data storage has scattered data almost to the point of being unusable. Nowadays, with the rapid increase of massive data, the difficulty of multiple cloud platforms, data sources, integration between technologies and integration between platforms has made the road of enterprise data management and analysis more tortuous. It is anxiety-inducing for chief information officers—they are unable to find or understand their most valuable data. Furthering the chaos is the lack of standardization of semantics across data assets. Consequently, companies spend far more time searching for data than actually analyzing the data itself.
This is where intelligent data management becomes a life ring for companies drowning in data they can’t make sense of. When you manage your data intelligently, data becomes a much more valuable asset, easier to connect and analyze. Let’s look at three key areas companies need to consider when they need to make their data work smarter, not harder.
An organization’s analytics and the processes around those analytics are complex, but connecting data is essential in today’s data-driven world. Companies need to be able to help their customers connect all data sources, as disparate data sets with no common thread means losing critical time and worse, the insight that data holds. An intelligent data cloud can automatically identify the most valuable data, and use that to empower business personnel and enable digital transformation.
While intelligent data clouds provide data connection capabilities, many companies are hesitant to incorporate this solution. One reason is a fear of increased security breaches; another hesitation often stems from the varying rules and regulations around privacy of cloud data in different countries.
A recent Gartner survey shows that organizations are increasingly requiring more integrated data and analytics capabilities within their digital platforms—about 70% in 2020, as compared to 57% in 2018 seek this out. So while organizations may have concerns about adopting an intelligent data cloud, they won’t be able to avoid it for long. There are also strategies they can use to mitigate their concerns. For example, scaling an intelligent data cloud to meet a specific organization’s needs over time, rather than running full-force from the start. Understanding the flexible capabilities of what the intelligent data cloud can do will help companies better discern data security.
Artificial intelligence (AI) enhancement has greatly improved the efficiency of data analysis and management. Machines that can take much of the rote work out of the hands of people, freeing humans up to do more valuable tasks like data auditing, is a significant asset.
AI uses query histories to predict how data will be analyzed. That automated data preparation can optimize those predicted analytics for cost effectiveness or for time-to-insight. Today’s AI models can process data in minutes, rather than months. And through this, we can discover common data connecting patterns and recommend data models that will better expose the stories and insight the data provides.
When incorporating an AI system, those that can work with data sets both big and small, protect privacy and adhere to any state, local, or country regulations will be the most useful in managing data.
The Covid-19 pandemic has taught us that if an entire platform cannot become a cloud platform, it will be difficult to adapt to drastic changes. Having an agile and intelligent data cloud ultimately means data is made both accessible and meaningful to the average end user—not just data experts. People will be able to consume data anytime from anywhere, without the knowledge of how to operate or manipulate big data, or databases, or the like.
Agile cloud-native architecture controls the chaos into something manageable for everyone to access and use. As a result, adopting cloud-native architecture helps companies expand and support their business agility, and allows them to meet their customers’ needs anytime, anywhere.
The Era Of Intelligent Data
The data warehouse revolution in the cloud computing era is just beginning. New technology architectures, new ways of using data, and new cost structures will profoundly change the industry. While these changes will throw some companies into chaos, those that turn to the intelligent data cloud will minimize confusion and potential disorder that can cost them valuable time and energy. The future of human use of data should be as simple and convenient as using cloud computing today. Taking intelligent data clouds from a small to global-wide practice allows data users to focus on the data itself, rather than the platform.
This article originally appeared on forbes.com, to read the full article, click here.
Nastel Technologies is the global leader in Integration Infrastructure Management (i2M). It helps companies achieve flawless delivery of digital services powered by integration infrastructure by delivering tools for Middleware Management, Monitoring, Tracking, and Analytics to detect anomalies, accelerate decisions, and enable customers to constantly innovate, to answer business-centric questions, and provide actionable guidance for decision-makers. It is particularly focused on IBM MQ, Apache Kafka, Solace, TIBCO EMS, ACE/IIB and also supports RabbitMQ, ActiveMQ, Blockchain, IOT, DataPower, MFT, IBM Cloud Pak for Integration and many more.
The Nastel i2M Platform provides:
- Secure self-service configuration management with auditing for governance & compliance
- Message management for Application Development, Test, & Support
- Real-time performance monitoring, alerting, and remediation
- Business transaction tracking and IT message tracing
- AIOps and APM
- Automation for CI/CD DevOps
- Analytics for root cause analysis & Management Information (MI)
- Integration with ITSM/SIEM solutions including ServiceNow, Splunk, & AppDynamics