Your enterprise must take to ensure security, governance, and compliance over the content and communications that take place through your enterprise collaboration tools. The good news is that this work takes place at one level, and what comes from that work can become a set of standard policies to govern team-level collaboration sites. It’s essential to create corporate policies and training for teams that may be opening their collaboration sites to external parties.
How do you know when you have achieved DevOps? and you are likely to get different answers. A practical answer to how do you know when you have DevOps depends on having a definition. While defining DevOps itself has been elusive, an enterprise definition is needed for alignment and progress measurement. Considering that DevOps needs continuous flow to accomplish business goals, it can be said you have DevOps when you have implemented continuous flow for at least one model application.
Leadership teams tend to have innate biases for certain asset types, and that these preferences drove business model. Like a good driver, a leader needs to know when to speed up to catch the competition, when to shift investment into the right kinds of capital, and when to refuel with new skills, mental models and board members. Just as the human genome offers the prospect of personalized medicine, the value genome offers the prospect of tailored capital editing—refocusing companies on high-value, scalable assets.
When creating the product team, ensure you listen to feedback from the individuals likely to be impacted, don't just do a paper exercise of moving people into new organisational structures. The teams on the ground know what skills and resources they need in their product teams, they know the issues they face and will have good ideas on how to address them, the key aim is to maximise the flow of valuable work into the team.
While DevOps offers immense value for software deployment, the adherence to best practices is essential to reduce risk and assure security. Each organization is different and has different security postures. This blog enumerates best practices for security across nine pillars of DevOps: Leadership, Collaborative Culture, Design for DevOps, Continuous Integration, Continuous Testing, Continuous Monitoring, Elastic Infrastructure, Continuous Delivery/Deployment and Continuous Security. Examples of best practices for each pillar are listed. These practices can be used to assess an organization’s maturity within the journey to Continuous Security, often referred to as DevSecOps.
Multi-model databases (MMDB) have rapidly gained importance in the market. However, is MMDB the right choice for your project or enterprise? While there are plenty of advantages, multi-model is not the ultimate solution for every situation. It is not a way to force developers to use a variety of data models, nor can one layered or native multi-model database integrate every data model efficiently. It’s more about enabling developers to leverage the advantages of different models for different aspects of their applications.
As the frequency of execution increases, dev teams find themselves between a rock and a hard place. While the growing demand for faster turnarounds isn’t poised to slow down anytime soon, teams struggle to integrate a set of tools into an efficient pipeline to get the job done within the time allotted. With demand on the rise, how can teams work together to fast-track their release cycles? With spring cleaning season upon us, dev teams across industries should take time this season to tune up agile processes and continue the work of advancing their shift toward DevOps.
New agile digital disruptors have realised the importance of centring their business objectives on value and technology, making it vital for traditional incumbents to follow suit. If waterfall organisations ignore this challenge, their ability to innovate, and react quickly to market changes and new customer demands is threatened. To change and succeed, the active involvement from the C-suite is essential to instil a cultural shift, placing value and people - employees and customers - at the centre of any transformation.
Complexity can plague the success of DevOps within an organization. Complexity cannot be avoided, as DevOps is complex and will likely continue to be. However, the key to avoiding failure through your DevOps journey is to engage the complexity by using DevOps tenets to implement DevOps. Do not try to boil the ocean. Instead, at each leg in the journey, take inventory of where you are in terms of current goals, state, and best practices. Fine-tune your direction and build your solution using proven continuous delivery methods.
Since DevOps involves a collection of team members from all parts of the software delivery lifecycle (SDLC) process, the central platform needs to meet the needs of all team members. As you work to build your next test analysis toolbox, consider the five features to efficiently evaluate the data, act upon it and deliver iterations and features with confidence. Let’s explore these five essential tools that enable DevOps teams to quickly and efficiently analyze data, triage issues and act upon failures with the best possible insights.
Today’s IT landscape is dominated by cloud, edge computing, IoT, AI and other disruptive technologies, and the datacentre remains at the heart of the organisation. Its role is key to delivering IT services and providing storage and networking to an increasing number of networked devices, users, and business processes. The explosions of data, as well as businesses embracing digital transformation, are all factors that play a part in not only storage strategies, but also the evolution of the datacentre.
As the technology gets easier to deploy, and the Cloud Vendor data services mature, it becomes much easier to build data-centric applications and provide data and tools to the enterprise. This article is aimed at helping big data systems leaders moving from on-premise or native IaaS (compute, storage, and networking) deployments understand the current Cloud Vendor offerings. Those readers new to big data, or Cloud Vendor services, will get a high-level understanding of big data system architecture, components, and offerings.
Introduction to Big Data provides a broad introduction to the exploration and management of large datasets being generated and used in the modern world. A solid understanding of the basic concepts, policies, and mechanisms for big data exploration and data mining is crucial if you want to build end-to-end data science projects. Many datasets are too large to fit on a single machine. Unstructured data may not be easy to insert into a database. Distributed file systems store data across a large number of servers.
Everyone that talks about scaling should understand that they are referring to the ability of their work – whether a system, a tool or some other innovation – to cope and perform under an increased or expanded workload. Something that scales well will be able to maintain, or even increase, its performance or efficiency when tested by larger operational demands. How do I increase the impact of my work? There are typically five steps needed to scale one’s work.
The plethora of automation tools available out there can be extremely confusing for organisations wanting to embark on a digital transformation process. Two-thirds of global service organisations were engaged in digital transformation, with 16% claiming to have already completed the process. it is important for organisations to take a holistic view of what they are hoping to achieve, before deciding on which automation approach to take. This is not always easy, as enterprise architects have to choose from a confusing range of process automation options as a foundation for the transformation journey.
IT discovery is the foundation to your IT asset management (ITAM) solution. If discovery is unreliable, then all of the asset information you are trying collect will not be reliable. Don’t let IT asset discovery become a stumbling block to your ITAM solution. Be sure to have clear objectives and a clear vision of the reports that will be needed to support those objectives. Set your discovery tools to discover and monitor assets relevant to your objectives. Most important, don’t overwhelm your IT employees with unnecessary discovery information, especially during the early phases of the project.
Going forward, access to data and the ability to derive new risk-related insights from it will be a key factor for competitiveness in the insurance industry. New approaches to encourage prudent behavior can be envisaged through Big Data, thus new technologies allow the role of insurance to evolve from pure risk protection towards risk prediction and prevention. Using Big Data analytics, insurance can offer personalized policies, precisely assess risks, prevent fraudulent activities, and increase the efficiency of internal processes. Let’s take a closer look at several Big Data solutions for insurance.
The term Big Data has been around 2005, when it was launched by O’Reilly Media in 2005. However, the usage of Big Data and the need to understand all available data has been around much longer. While it looks like Big Data is around for a long time already, in fact Big Data is as far as the internet was in 1993. The large Big Data revolution is still ahead of us so a lot will change in the coming years. Let the Big Data era begin!
The old way of doing system architecture will not disappear entirely, but it is already past time we started thinking about how to improve the efficiency of our system architecture practices so they better support today’s rapidly evolving business climate. The next major effect of networks on the evolution of system architecture was the desire to integrate systems. It did not take long to realize that entering the same data into different systems was time-consuming and error-prone, so we began to try and integrate systems so they could share data.
Increased access to vast amounts of information, social media, and ubiquitous computing makes it seem as if the complexity of our world is increasing faster than we can comprehend it. But technology is not driving complexity; it is only making complexity more visible. Trying to eliminate complexity is an impossible task, and traditional approaches to enterprise architecture have proven ineffective in dealing with it. By thinking about enterprise architecture in a new way, we can make that complexity work for us, and harness emergent behaviors to help achieve an organization’s goals.
IT as a whole has really moved from the things that help the business do day-to-day jobs, to becoming the engine that actually drives the organization. Often, new products and services can’t be launched without IT, and it’s also becoming the point that IT is the product or service being launched. Nearly two-thirds of CIOs say that driving revenue through the creation of new products and services is among their responsibilities today. CIOs offer seven steps to shifting to revenue-driven IT.
In recent years, the development of massive computing and storing capacities in the hand of a few internet juggernauts led to the rise of the cloud economy. Companies of all sizes have been moving their mission-critical servers and operations to the data centers. On the face of it, the development of Infrastructure as a Service (IaaS) should be good news for the state of cybersecurity. In this context, it is easy to believe that moving to the cloud could mean solving many of your cybersecurity issues.
Many organizations are realizing the value of their data so they are beginning to treat their data as a company asset; hence, the rise of the Chief Data Officer (CDO). Data provided from IT service management reports and metrics will be vital information for the CDO as he/she defines strategy for new technology, process, policy, security, and IT architecture. ITSM managers should expect the CDO role to have a direct impact on how IT service management will be implemented, delivered, measured, and most importantly, integrated with other IT solutions within the organization.