10 best practices for DevOps

10 best practices for DevOpsHere are 10 DevOps best practices that are gaining traction in organizations.

1: Break the silos in IT

Breaking down functional silos between disciplines in IT must come from upper management, since IT has been organized into disciplinary silos for decades. In this environment, application development work has historically taken an assembly line approach, with one department building the app, after which the app is shipped to an operations group to integrate the app, after which the app is tested by a QA group, after which the app goes back to the applications and operations group so it can be deployed.

This separation of functions limits active collaboration, contributing to applications problems that delay deployment. Pressured to deliver today’s apps faster, IT managers have begun to restructure IT into DevOps teams that are a mix of all of the IT disciplines, with each team getting accountability for a specific category of apps.

Continue reading

10 steps to DevOps success in the enterprise

10 steps to DevOps success in the enterpriseThe DevOps movement continues to gain traction among large US enterprises, including Adobe, Amazon, Target, and Walmart.

As TechRepublic’s James Sanders explains, DevOps—a combination of Development and Operations—is essentially a workflow “centered around integration and communication between software developers and IT professionals who manage production operations.” The idea grew out of the Agile methodology, and first gained attention at a conference in 2009.

Many IT departments are siloed between development, operations, support, and management, but a DevOps system seeks to integrate them all for better productivity and a smoother overall workflow. The system allows companies to quickly deliver software and security updates both internally and to customers, Sanders wrote. The ultimate goal is to bring products to market faster, deliver software and security updates more quickly, and make the entire process more reliable.

Continue reading

The Impact of Predictive Analytics in the Industrial World

The Impact of Predictive Analytics in the Industrial World Are there distinctions between how predictive analytics is used in the industrial world vs. consumer applications?

In both cases it boils down to creating algorithms that can help drive and improve value to the business or consumer. It is the degree to which those analytics are used for mission critical applications where there are differences.

In the case of consumer applications, predictive analytics has primarily been algorithms to improve experience from social media engagement to transactional activities like shopping. A retailer knowing in advance when the shopping e-cart app may crash and avoiding such an incident can result in strengthening consumer confidence and loyalty. This is an important business outcome, but is not mission critical. If some piece of an industrial application in the Aviation industry fails, it could potentially cause flight delays with an engine that must be taken offline for maintenance. In the Healthcare space, an MRI machine that has faulty performance can impact important patient results. Providing predictive insights to avoid unexpected downtime, performance issues or even prescribing alternative actions, such as optimum time to service an engine is essential for industrial companies.

Also, security, durability and reliability of the analytics platforms are critical factors in Industrial Internet. In the consumer world, software updates and changes can be rolled out in minutes, but that is not possible in the industrial domain where there is more liability for performance. Updates can involve hundreds, or even thousands of machines and sensors in operation for one single application, e.g. flight management.


View Source

2016 Big Data, Advanced Analytics & Cloud Developer Update: 5.4M Developers Now Building Cloud Apps

2016 Big Data, Advanced Analytics & Cloud Developer Update: 5.4M Developers Now Building Cloud AppsKey takeaways from the study include the following:

  • 6M developers (29% of all developers globally) are involved in a Big Data and Advanced Analytics project today. An additional 25% of developers, or 5.3M, are going to begin Big Data and Advanced Analytics projects within the next six 13% or 2.6M of all developers globally are going to start Big Data and Advanced Analytics projects within the next 7 to 12 months.  The following graphic provides an overview of the involvement of 21M developers in Big Data and Advanced Analytics projects today.
  • 4M developers (26% of all developers globally) are using the cloud as a development environment today. Developers creating new apps in the cloud had increased 375% since Evans began measuring developer participation in mobile development in 2009 when just slightly more than 1.2M developers were using the cloud as their development platform. 4.5M developers (21% of all global developers) plan on beginning app development on cloud platforms in the next six months, and 3.9M (18% of all global developers) plan on starting development on the cloud in 7 – 12 months.

View Source


The Morning Download: Large Enterprise Cloud Adoption Set to Accelerate, McKinsey CIO Study Finds

The Morning Download: Large Enterprise Cloud Adoption Set to Accelerate, McKinsey CIO Study FindsThe shift to cloud computing is about to begin a significant acceleration, with the biggest gains coming from large enterprises that have until now been slower to change, research from management consulting group McKinsey & Co. shows. “In the next three years, enterprises will make a fundamental shift from building IT to consuming IT,” a new report from McKinsey’s Silicon Valley group has found.

The survey determined that 77% of companies in 2015 used traditionally built IT infrastructure as the primary environment for at least one workload, and that the percentage of such deployments will drop to 43% in 2018. While only about 25% of companies in 2015 used public infrastructure as a service as the primary environment for at least one workload, that percentage is expected to rise to 37% in 2018.

Companies told McKinsey that the shift to the cloud primarily was driven by the need for improved time to market and higher quality, but that security was a key factor, too.

The IT as a Service Cloud Survey included about 800 CIOs and IT executives worldwide across a variety of industries, according to McKinsey. The results of the cloud survey, which asked respondents about IT workloads, suggest that companies are adopting digital technology into their business model and operations at a greater rate. The finding also suggest that IT vendors will feel the effects of the change as purchasing patterns change, McKinsey said.


View Source

Big-Data Analytics Plays Big Role in 2016 Election Campaigns

Big-Data Analytics Plays Big Role in 2016 Election CampaignsBig Data Analytics – Assessing the voting public’s mood swings, likes and dislikes to win an election has been a top-line task for political campaigns in the United States for most of its 240 years of existence. But only in the last decade have the internet, social networks and real-time analysis of big data collected from all corners of the country played a central role in helping sway voters to determine the leader of the nation and of the Free World. The

John Kerry-John Edwards campaign in 2004 made extensive use of direct email to likely Democratic voters in its losing cause. However, President Barack Obama’s campaigns in 2008 and 2012 took political connectivity to a whole new level, making extensive deployment of multiple daily emails, targeted webvertising, social networks and conventional television and radio ads to attract likely voters.

In unprecedented fashion, the team then continued fundraising throughout Obama’s eight years in office, even after both campaigns had ended.


View Source

Balancing Agility And Risk At US Bank

Balancing Agility And Risk At US BankBalancing Agile and Risk Mitigation

As a heavily regulated financial institution, U.S. Bank is rightfully risk-adverse. Nevertheless, it must balance day-to-day operational risk with the strategic risk inherent in not changing quickly enough, and thus losing its competitiveness in today’s turbulent digital environment.

Achieving this balance depends in large part on the software development organization, as all banks are becoming software-driven enterprises, yet must maintain a constant focus on compliance and security.

Dealing with auditors, therefore, is an important part of Peterson’s work. “The auditors seek to ensure the steps taken to manage the  book of business is sound,” she says. “We’ve checked and double-checked our numbers and here’s proof electronically.”

In other words, the role software plays in the auditing process has changed – and with it, auditing itself. Auditors don’t simply pore over spreadsheets. Today, they can review reports from software that can guarantee the data in those spreadsheets are in order.

In fact, the more proactive Peterson’s team can be on compliance matters, the better. “We need to identify technology solutions to fill gaps, so we’re able to be audited and align to the auditors’ checklists,” she says. “We’re not only removing manual tasks; we’re making the software efficient enough to do what is needed to support all aspects of the business.”


View Source

The 4 Mistakes Most Managers Make with Analytics

The 4 Mistakes Most Managers Make with AnalyticsThere is a lot of hype surrounding data and analytics. Firms are constantly exhorted to set strategies in place to collect and analyze big data, and warned about the potential negative consequences of not doing so. For example, the Wall Street Journal recently suggested that companies sit on a treasure trove of customer data but for the most part do not know how to use it. In this article we explore why. Based on our work with companies that are trying to find concrete and usable insights from petabytes of data, we have identified four common mistakes managers make when it comes to data.

Mistake 1: Not Understanding the Issues of Integration

The first challenge limiting the value of big data to firms is compatibility and integration. One of the key characteristics of big data is that it comes from a variety of sources. However, if this data is not naturally congruent or easy to integrate, the variety of sources can make it difficult for firms to actually save money or create value for customers. For example, in one of our projects we worked with a firm which had beautiful data both on customer purchases and loyalty and a separate database on online browsing behavior, but little way of cross-referencing these two sources of data to actually understand whether certain browsing behavior was predictive of sales. Firms can respond to the challenge by creating “data lakes”, holding vast amounts of data in their unstructured form. However, the very fact that these vast swathes of data now available to firm are often unstructured, such as in the form of strings of text, means it is very difficult to store them in as structured a way as could occur when data was merely binary. And that often makes it extremely difficult to integrate it across sources.

Continue reading

Asking the Right Questions for DevOps Success

Asking the Right Questions for DevOps SuccessIn today’s fast-paced, ultra-competitive business environment, an IT department’s ability to successfully deliver business outcomes is intrinsically tied to product selection and its partnerships with vendors.

But when it comes to development and operations projects (DevOps for short) the requirements are amplified further still as success in this regard relies on reinvention across numerous other spheres, including IT culture, organisational structure, process automation, software development, and team collaboration.

Simply put, it’s a more complex scenario than a traditional technology project. This is a critical point in understanding why traditional product selection strategies should change when dealing with DevOps products.

IT executives and professionals recognise that transformation through DevOps practices should transcend mere product features; indeed, DevOps practices triggers transformation across people, process, culture, and organisational structure to drive improvements in business speed, quality, and the customer experience.

Continue reading

Big Data Analytics Can Benefit Manufacturers

Big Data Analytics Can Benefit ManufacturersA recent Honeywell survey of manufacturing executives indicates that a majority of respondents (67 percent) plan to use data analytics to address issues with equipment.

The survey shows that anaytics are becoming core to what Honeywell refers to as the industrial internet of things (IioT) as a way for companies to save money on equipment maintenance and repair by providing insight into when machines need service.

The survey, conducted by Honeywell Process Solutions (HPS) and KRC Research, polled more than 200 North American manufacturing executives. The study, entied “Data’s Big Impact on Manufacturing: A Study of Executive Opinions,” showed that a majority of respondents said they are already investing in data analytics technology.

In addition, the survey indicated that manufacturing executives view unscheduled downtime and equipment breakdowns as the biggest obstacles to maximizing revenue. Yet, more than a quarter of respondents said they don’t plan to invest in data analytics in the next year. These respondents cited inadequate resources and lack of understanding as key reasons for their lack of investment.

Continue reading