Home The Webmail Blog
 

Keywords :   


Overcoming the top four barriers to actionable data insights

2021-04-08 19:23:56| The Webmail Blog

Overcoming the top four barriers to actionable data insights nellmarie.colman Thu, 04/08/2021 - 12:23   According to IDC, the amount of data created over the next three years will be more than all the data created over the past 30 years. And the world will create three times more data over the next five years than it did in the previous five. As data volume and variety increase and data sources proliferate, new opportunities will arise opportunities to deliver superior customer experiences, drive better business decisions and enable greater agility and resiliency. New technologies and approaches such as the Internet of Things (IoT), cloud native development, AI and machine learning, and the modern data fabric offer a path to this intelligent business vision. Despite these opportunities and new approaches, businesses are struggling to manage data and generate meaningful analysis. Theyre weighed down by issues like dirty data and misaligned data collection and governance policies. These companies risk falling behind competitors, who are using data intelligence to adapt to their customers needs quickly and proactively. To gain actionable insights from your data, youll need to address some common barriers. Lets take a look at each of these, as well as what you can do to overcome them.   Barriers and solutions to running a smarter and faster business In a recent study of 1800+ IT leaders, we explored why businesses either fail to move their data analytics projects into production or experience quality and availability issues when they do. The study revealed several key barriers that are common to most businesses:   1. Data discovery challenges Data discovery is difficult when you have unknown data sources, poor data quality, data silos and compliance restrictions. These issues can trace their origin to data used or generated by a specific application stored in a siloed data platform, typically found in an early 2000s web application architecture or in 1990s UNIX applications. Additionally, incomplete views of customers and other business entities, duplicated data and a general lack of understanding around what data is available (for building new applications or updating existing ones) results in less-effective services, insights and customer experiences. Solution: With a holistic view and understanding of your data estate, plus a modern data architecture that makes your data accessible, you can make data discovery and utilization a more natural part of your DevOps processes and culture. DevOps drives speed and quick turnaround. And your data if its known and accessible and in a useful format can be fully incorporated into your DevOps culture, development and deployment processes.   2. Excessive costs When your infrastructure isnt structured for utility and elasticity, your talent is expensive, and youre facing large, ongoing investments with no guaranteed return, costs can grow out of control. Costs also become excessive as you continue to rely on on-premises data solutions for your worst-case scenarios as youre stuck servicing older virtualized applications and data infrastructure. And your on-premises data platforms servicing cloud-based applications may incur higher than needed ingress/egress fees. Solution: By moving data platforms to the right public and private clouds in a multicloud architecture, you get the benefits of multicloud including elasticity, self-service, optimized economics and cloud native services so you can develop modern applications and host a modern data architecture.   3. Complexity Choosing the right mix of technologies, identifying architectural best practices for deployment, and integrating cloud, on-premises and edge these are all complex responsibilities. Yet theyre made even more difficult if your data platform mix isnt optimized. For example, your data may have been forced into a traditional relational data management system, or worse, into unstructured files even though that is not the optimal place for data use and analysis. This makes developing applications using this data more difficult and less effective. IoT dramatically increases the data coming into your business. But it must be analyzed and intelligently separated into data flows that support the business such that your applications get the data they need when they need it. Many organizations either do not leverage the IoT or do so in a manner that overly restricts their data being used from the IoT. While these approaches result in preventing data flooding with its reliability, security and availability issues, they eliminate the benefits of using all of the appropriate data in their business ecosystem. Solution: Dealing with the explosion of data variety, velocity and volume is complex. But by putting this data into the right data platforms in the right clouds configured into a modern data architecture, your data can be more readily used, be more cost effective and set the foundation for modern analytics and superior business insights.   4. Skills gaps Most organizations dont have the skills necessary in-house to optimize their data architecture for modern AI/ML use cases and cloud native applications. To create a modern data fabric, you need specialized education, training and experience not organically available in typical IT teams. This skills gap also contributes to data integration architecture that is scattered and opportunistic preventing applications from getting the right data at the right time and leading to less-than-optimal experiences, results and insights. Solution: Work with a partner whose team has the right skills, career paths and continuous work experience where theyre always busy solving problems and building expertise across many different industries and use cases. This helps ensure that theyre able to attract and retain the best data people.   Achieving actionable data insights With a modern data architecture, your data can help drive better business processes, experiences and decisions. And with a fully integrated data environment supported by DataOps and MLOps, your business and IT teams can make intelligent business and IT decisions that will drive the most value to your customers and have the greatest impact on your businesss bottom line. Modern data architecture coupled with AI and machine learning enables your business to get the right data to the right application at the right business moment while delivering a new level of business insights and intelligent applications. To learn more about how organizations are managing and modernizing their data, check out our report, Data modernization: R

Tags: top data barriers insights

Category:Telecommunications

Rethinking support models for a cloud native world

2021-04-07 18:11:10| The Webmail Blog

Rethinking support models for a cloud native world nellmarie.colman Wed, 04/07/2021 - 11:11   Most IT teams fall somewhere in-between traditional IT Operations and modern, cloud native ways of doing things. The defining characteristics of traditional IT operations include: clear handoffs between build and operate, emphasized infrastructure and operations and a waterfall development and release approach. To evolve to a cloud native operating model, there need to be no boundaries between build and operate. This is where DevOps comes in. A DevOps approach blurs the line between build and operate and enables you to drive real outcomes. And as part of the shift towards cloud native, the focus should move further up the stack to applications, allowing these to dictate infrastructure decisions rather than the other way around. Think of it as having applications in the drivers seat, with infrastructure along for the ride.   Shackled to old ways Many businesses are still operating in the cloud as if it were a data center. We often get questions about patching and backups, which are concepts that should be handled very differently in a cloud native application than one running in your data center. To reach the pure cloud native nirvana state, customers need to make a more conscious effort to move away from a traditional VM-centric approach. Part of the problem is that you are probably being held back by your MSP partners. Traditional MSPs were built for the old world and theres no incentive for them to encourage your evolution. They are set up to fill the gaps in functional areas between build and operate silos and have narrowly defined and SLA-driven scopes. The truth is the IT support model is constrained to fit inside the boundaries of the past.   How MSPs can encourage evolution As businesses move towards a cloud native model, they start to build highly accountable small teams that are responsible for the application's full lifecycle. Support models need to shift to provide services that properly support those teams. These services should look more like engineering services and less like a pure operations play. And these engineering services should deliver against a customers initiatives rather than arbitrary SLAs. The nature of cloud engineering work means it should be an ongoing engagement. There's always something that can be automated or fixed. Support models need to be flexible, and they should be able to seamlessly integrate with customer teams. Only then can an MSP be fully aligned with customer goals and provide enhancements the right way.   Slowly let go of the past Evolving IT organizations need a modern cloud support model that is free from the siloed boundaries of the past, emphasizes applications over infrastructure and guides their teams toward an agile, DevOps-driven approach to IT operations. It should also be the responsibility of MSPs to help customers remove VM management from each application that becomes more and more cloud native. The older VM management tasks will need a high level of support to make sure they are handled correctly but with integrated and highly specialized teams on the same mission, the path towards cloud native will become much clearer.   How can Rackspace Technology support this emerging world? The evolution to cloud native requires a new way of thinking. Rackspace Elastic Engineering aims to revolutionize the support model and drive your outcomes as you evolve to cloud native. With Elastic Engineering, you get on-demand access to a pod of cloud engineers for ongoing infrastructure and DevOps engineering. You set the outcomes and control the roadmap and the pod delivers via a do with approach, partnering with your internal operations and development teams in an agile sprint-based model. And you always work with the same dedicated pod who knows your environment and your goals. Our dedicated team of multi-disciplinary cloud experts will support a broad range of outcomes including migrations, architecture, automation, optimization, reliability and security. The elastic in Elastic Engineering comes from the premise that with a multidisciplinary set of capabilities your agile Pod can stretch and shape to deliver your cloud outcomes.   Rethinking support models for a cloud native worldThe evolution to cloud native can transform your business, but old support models can hold you back. Discover how to revolutionize your support model and accelerate your cloud native journey. Revolutionize your support model. https://events.rackspace.com/fundamental-shift/Discover Elastic Engineering IT TransformationProfessional ServicesCloud InsightsJosh PrewittRethinking support models for a cloud native worldApril 7, 2021

Tags: support world models native

Category:Telecommunications

Sites : [1]