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Month: August 2017

Analytics Ensure Safety in LA and White Plains

Security is top of mind when city CIOs think about the types of analytics they need. However, analytics is also enabling them to improve internal processes and the experience citizens and businesses have.

The City of White Plains , New York stores its data in a data center to ensure security. The City of Los Angeles has a hybrid implementation because it requires cloud-level scalability. In LA, 240 million records from 37 different departments are ingested every 24 hours just for cybersecurity purposes, according to the city’s CIO Ted Ross.

“We didn’t start off at that scale but [using the cloud] we’re able to perform large amounts of data analysis whether it’s cybersecurity or otherwise,” Ross said.

He thinks it’s extremely important that organizations understand their architecture, where the data is, and how data gets there and then put the appropriate security measures in place so they can leverage the benefits of the cloud without being susceptible to security risks.

“If you’re not doing analytics and you’re moving [to the cloud], it’s easy to think it will change your world and in certain [regards] it may. The reality is, you have to go into it with both eyes open and understand what you’re trying to accomplish and have realistic expectations about what you can pursue,” said Ross.

White Plains is on a multi-year journey with its analytics, as are its peers because connecting the dots is a non-trivial undertaking.

“Municipalities have a lot of data, but they move slowly,” said White Plains CIO Michael Coakley. “We have a lot of data and we are trying to get to some of the analytics [that make sense for a city].”

Departments within municipalities still tend to operate in silos. The challenge is eliminating those barriers so data can be used more effectively.

“It’s getting better. It’s something we’ve been working on the for the last few years which is knocking down the walls, breaking down the silos and being able to leverage the data,” said Coakley. “It’s for the betterment of citizens and businesses.”

Connecting data from individual departments improves business process efficiencies and alleviates some of the frustrations citizens and businesses have had in the past.

“If you’re a small business owner who bought a plot of land in White Plains and wants to [erect] a building, you could go to the department of Public Works to get a permit, the Building Department to get a permit and the Planning Department to get a permit and none of those departments know what you’re talking about,” said Coakley. “With the walls being broken down and each department being able to use the data, it makes the experience better for the business or home owner.”

The city is also connecting some of its data sets with data sets of an authority that operates within the city, but is not actually part of the city.

“There’s a reason for their autonomy, but it’s important to start the dialog and show them [how connecting the data sets] will benefit them,” said Coakley. “Once you show the department what they can provide for you, and ensure it’s not going to compromise the integrity of their data, they usually come along. They see the efficiencies it creates and the opportunities it creates.”

In those discussions, it becomes more obvious what kind of data can be generated when the data sets are used and shared and what kind of analytics can be done. The interconnection of the data sets creates the opportunity to get insights that were not previously possible or practical when the data generated in a department stayed in that department.

White Plains is trying to connect data from all of its departments so it can facilitate more types of analytics and further improve the services it provides citizens and businesses. However, cybersecurity analytics remain at the top of the list.

“Cybersecurity is number one,” said Coakley. “We have to worry about things like public safety, which is not just police, fire, emergency, public works, facilities, water, electrical, and engineering. There’s a lot of data and the potential for a lot of threats.

Why Automation and AI are Cool, Until They’re Not

Every day, there’s more news about automation, machine learning and AI. Already, some vendors are touting their ability to replace salespeople and even data scientists. Interestingly, the very people promoting these technologies aren’t necessarily considering the impact on their own jobs.

In the past, knowledge jobs were exempt from automation, but with the rise of machine learning and AI, that’s no longer true. In the near future, machines will be able to do even more tasks that have historically been done by humans.

Somewhere between doomsday predictions and automated utopia is a very real world of people, businesses and entire industries that need to adapt or risk obsolescence.

History isn’t simply repeating itself

One difference between yesterday’s automation and today’s automation (besides the level of machine intelligence) is the pace of change. Automating manufacturing was a very slow process because it required major capital investments and significant amounts of time to implement. In today’s software-driven world, change occurs very quickly and the pace of change is accelerating.

The burning existential question is whether organizations and their employees can adapt to change fast enough this time. Will autonomous “things” and bots cause the staggering unemployment levels some foresee a decade from now, or will the number of new jobs compensate for the decline of traditional jobs?

“I think there will be stages where we have the 10 percent digital workforce in the next two years and 20 percent in three to four years,” said Martin Fiore, Americas tax talent leader at professional services firm EY. “Some will say, ‘Wow, that’s scary.’ Others will say, ‘I see the light I’m going to upscale my capabilities.”

Businesses and individuals each need to change the way they think.

Angela Zutavern, VP at management consulting firm Booz Allen Hamilton and co-author of the forthcoming book, The Mathematical Corporation views intelligent automation as a new form of leadership and strategy as opposed to just technology.

“Companies who understand this and get on board with it will be way ahead and I believe that companies who either ignore it or don’t believe it’s real may go out of business,” she said. “I think it’s better to know about it, understand it, and be a part of making the change happen rather than getting caught off-guard and have it happen to you.”

An old company pioneers new tricks

Despite its 100-year history, EY is actively facilitating the adoption of Robotics Process Automation (RPA) and AI within its own four walls and among its customers.

Its RPA group employs a global team of 1,000 robotic engineers and analysts who are creating new applications. In past 18 months, more than 200 bots have been rolled out in tax operations, which includes work for clients. EY is also using RPA processes internally in core business functions to improve quality and performance while enabling a new sense of purpose among its employees.

“RPA helps us increase our ability to handle high levels of transaction volume (e.g.,tax returns), accelerate on-time delivery and improve accuracy,” said Fiore. “Over time, there will be a positive impact on our workforce model, and we’re planning for that now.”

Similarly, an EY innovation lab recently experimented to see if AI could help to analyze contracts faster and better than people.

“We thought we’d make headway and great progress in a year or two, but in the first 90 days [the machines were] three times more effective in the process,” said Jeff Wong, global chief innovation officer at EY. “You’ll see us increasing our efforts there radically in the next 12 to 18 months.”

Last year, EY “hired” 350 bots, although company spends about a half a billion dollars annually on employee training. Job rotation is also common at EY because the company wants to “teach people to learn how to learn.”

Education will change

Young people entering the workforce already need different skills than their predecessors, and the trend will continue. Param Singh, associate professor of business technologies at Carnegie Mellon University expects grade schools to teach fundamental programming skills and high schools to teach machine learning.

“Typically, managers [had] person management jobs. Increasingly those jobs will have to be good on the technology side,” said Singh. “Few people are good at deep learning, probably less than 5,000. We’ll needs hundreds of thousands when we see major adoption happening.”

Meanwhile, working professionals and their employers should not be complacent. As the levels of intelligent automation increase, individuals and companies will need to understand which jobs will be displaced and which jobs will be created, none of which is static.

“Cloud computers, data lakes and in the future, quantum computing are things that every leader should be conversant about or anyone who aspires to a leadership role in this machine learning age,” said Booz Allen Hamilton’s Zutavern. “People should understand what the possibilities are and know when to pull in the deep experts.”

DevOps Not Working? Here’s Why.

DevOps can help organizations get better software to market faster, when it’s working. When it’s not working, development and operations teams aren’t working as a cohesive unit.  They’re operating as distinct phases of a software development lifecycle.

Part of the problem may involve tools. Either the tools still operate as silos or they don’t provide the kind of cross-functional visibility that DevOps teams require. However, a bigger task may be getting development and operations working together.

What makes DevOps even more challenging is that there’s no one right way to do it.  Of course, there are better and worse ways to approach it, so here are a few suggestions to consider.

Think before automating. Automation is part of DevOps, but it’s not synonymous with DevOps. While it’s true that automating tasks saves time, automation also accelerates the rate at which mistakes can be propagated.

“If you just automate things and you haven’t built the skills to handle high speed, you’re putting yourself in a place where friction and accidents can happen,” said Sean Regan, head of growth, software teams at software development tool provider www.atlassian.com. “Before you automate everything, start with a culture. You’ll have happier developers, happier customers, and better software.”

Test automation is essential for DevOps, and to do that well, developers need to test their software in a production environment.

“DevOps is founded in automation. One of the first things organizations recognize is they need a dynamic infrastructure which most people think is cloud,” said Nathen Harvey, vice president, Community Development at DevOps workflow platform provider www.chef.io Chef Software. “It doesn’t have to be cloud, it means you have compute resources available to developers and the people who are running your production organization.”

With the help of automation and developer access to production environments, DevOps teams are delivering software in days or weeks instead of months.

Cultivate a DevOps culture. Software teams that have gone through an Agile transformation remember they had to change their culture for it to succeed. The same is true for DevOps.

“You need to get your teams collaborating in a way they haven’t done before,” said Harvey. “It becomes much less about a hand-off and more about understanding the common goals we’re working towards.”

One indication of DevOps maturity is whether the shipment of software is considered an end or a beginning. Atlassian used to celebrate after a product shipped, which used to be common for software companies. Now Atlassian celebrates milestones hit after the release, such as the number of customers using a new feature within a given time frame.

Take a hint from web giants. A decade ago, web companies were embracing DevOps and figuring out how infrastructure could be managed as code.  Meanwhile, other companies were operating in business-as-usual mode.

“If you’re coming from a more traditional organization, the idea of managing infrastructure as code may still be new,” said Chef Software’s Harvey. “I think the best way to achieve success is to pull together a cross-functional team that cares about driving a particular business outcome, such as how to deliver this one change out to our customer.”

 Cheat. Companies spend lots of time reinventing what works at other companies. Atlassian memorialized a lot of what it has learned in self-assessments and playbooks, so DevOps teams can identify and address the challenges they face.

“Customers are coming to us saying, ‘Give us playbooks, give us patterns, give us specific actionable ways to move toward DevOps,” said Regan.  “If you’re moving to DevOps, there’s usually an early stage where you wonder if you’re doing it right.”