A new era for data handling?
Over the past few years, the combination of ‘IoT’ devices & AI / Machine Learning has been used to collect huge amounts of big data. Companies from the IaaS (Infrastructure as a Solution) & PaaS (Platform as a Solution) markets are continually releasing new products to meet client needs.
Many solutions allow businesses to increase the ways in which they can operate and the magnitude of data collectable.
5 Trends to look out for
Hiring a Chief Data Officer (CDO)
10 years ago a data scientist was unheard of in most organizations, but CDO’s have become essential to businesses that have gathered enough data to demand a new C-level position.
Leading a varied team of data scientists, engineers and developers the CDO reports directly to the CEO and operates towards these main goals and responsibilities:
- Deliver tools and insights from the collected data to better improve company sales
- Ensure the company is up to date with the governance of data collection and handling
- Continually manage collected data (strategic asset management)
- Handle enterprise risk management, regulatory compliance and finance
As the necessity for this position grows it’s fair to assume that similar roles will become available. A team for managing business risk, or providing sales with insights to client needs may be the direction data gathering leads.
It’s now possible for the collection and preparation of data to be for the most part, automated. And with augmented analytics, the combination of AI & Machine Learning removes these once time-consuming tasks faced by Data Analysts.
Businesses that implement augmented analytics are able to spend more time on how to move forward with data collection and because the process is automated, decisions can be made based on any time frame, even the same day.
Cloud Computing put simply is on-demand access IT resources such as stored data and computing power through the internet. With this solution being cloud-based it, in turn, becomes a mobile data centre that users (with the right permissions) can access from everywhere.
Enterprise-class businesses in all industries can use Cloud Computing to better handle their resources and in some cases product task management/backup.
The use of Cloud Computing allows the mobility for applications to seamlessly move between public and private environments.
One way that this is possible is by using Kubernetes which are also known as “K8’s” and “Kube”. Acting as a central source or Open Container, Kubernetes play a big role in the deployment and management of data applications that are being hosted on Linux.
The ‘Hybrid Cloud’ infrastructure system is made up of the combination of a private server and public cloud environment.
This strategy is used to better manage workloads and operations often towards the same goal. The hybrid-cloud combination enables the ability for businesses to offload chunks of algorithm/data processing and large tasks.
By using the public cloud to manage the weight of processing and to test ongoing deployment, businesses can keep important areas such as data portals on their servers protected behind a firewall.
Find out more about Hybrid-cloud here
The ‘Multi-cloud’ infrastructure is made up of multiple cloud vendors which can be both private and/or public. The “multi” strategy allows companies to provide the highest amount of uptime for applications and resources to their clients. A current example where this strategy would be useful is a streaming service.
When you open YouTube or Netflix, the device that is used will connect to the closest public cloud via the internet, providing lower latency and a quicker request return optimizing the user experience.
You can learn more about the technical side of the Multi-cloud strategy here.
So what’s next for data collection & protection?
Considering the rate in which technology advances, businesses that adapt and find better ways of managing data will always have the upper hand against their competitors. But they will also become targets.
With technological advancements come new opportunities for cybercriminals. Frequent and advanced attacks can leave large businesses vulnerable to malicious hackers, all with the end goal of stealing data.
Data Fabric is the latest answer to solving the challenges of sorting and understanding the data landscape. Made up of 3 categories the data landscape pulls and dumps Real World (IoT / IoB), Data Centre (Private Server), and Cloud Application data into a large data pool.
With so much data falling into one place, the threat of security risks must be taken seriously.
Data fabric not only relieves the issues mentioned above but is also scalable, being able to continually manage the growing amounts of data collected is a must, and by filtering the categories, and slicing deeper into data sources the data fabric generates overarching patterns but also security.
Example: Many internal operations may move between online environments, leaving digital footprints all which may be forcefully accessed.
By having a master view of all the data collected it’s possible to spot interesting patterns or outlying instances that may indicate business opportunities or a possible attack.
How have these trends changed, shifted and possibly become more popular since the break of the new decade?
Take a look at the ‘Top 5 Data Management Trends for 2020’ to find out more!