September 03, 2016

Informatica CEO: 'Data security is an unsolved problem'

"We think our focus on data can bring a new approach," Chakravarthy explained. "Rather than focusing on infrastructure and networks, you need to focus on the data, wherever it is." The problem requires insight and visibility into data at a detailed level, in other words, and Informatica thinks it can offer that in a way no other provider can. "I don't see anyone else with the same approach," Chakravarthy said. It's been just over a year since Informatica went private in a US$5.3 billion buyout that included investments from Microsoft and Salesforce. Chakravarthy, who had been chief product officer, took the CEO chair at the same time, replacing Sohaib Abbasi. Speeding Informatica's transformation for the cloud and big data was the primary objective in going private, and the past year has brought good progress, Chakravarthy said.

20 of the biggest influencers on US CIOs

Bearing in mind the increasing importance of security to the enterprise, we were surprised by how few security specialists the typical CIO follows. An explanation for this could be that serious security issues and news find its way into the technology press pretty quickly, so the imperative to follow people like Brian Krebs is probably not as urgent as it would be for the CISO. The report is an essential information source for anyone involved in selling to CIOs based in the USA. Whether your role is comms, marketing, social media, advertising, sponsorship, events organisation, etc. this report will make your life easier and help you do your job better. The report is available from our website but please feel free to email me at richard [at] or leave comments here if you have any questions regarding this research.

How to increase profits with digital transformation fueled by mobile

How does digital transformation unlock value and increase profitability? Research from IBM shows that combining mobile with data and analytics unleashes the power of employees. The concept is simple: Deliver the right information to the right person, when and where he or she needs it. This is the concept of the “individual enterprise,” and it represents digital transformation at its best. Think of an employee bringing to bear the capabilities of the enterprise. Digital transformation with the individual enterprise means using technology to work smarter and more productively. Let’s take a look at two companies that are using mobile as a catalyst for digital transformation to empower employees and increase profits:

Prescriptive Analytics: The Ultimate Self Help Tool

What is making prescriptive so attractive is that it does not discriminate between internal and external behaviors. For example, a retailer might leverage prescriptive to determine which sections of a store are receiving the most attention from customers and how to capitalize upon that (i.e. external behaviors). Versus a supply chain manager who uses prescriptive to identify average shipping times which can increase the efficiency of deliveries (i.e. internal behaviors). Furthermore, it democratizes analytics by delivering the information in plain English, right to the person who should see it, rather than requiring a trained professional for interpretation. But prescriptive also has the potential to go beyond simple practice improvement. As solution providers create more intelligent engines, they are able to actively identify problem areas that are costing the organization in revenue.

Will Artificial Intelligence help Big Data deliver on its promise?

One area which will be interesting to observe is the relationship between Data Scientists and AI. As AI and Machine Learning progresses and evolves, some of the more basic and straightforward tasks that Data Scientists perform routinely will become automated and will yield great results in productivity. AI is certainly not going to replace Data Scientists any time soon, and can in fact be a massively helpful tool to utilise, however how will they view it: Friend or Foe? Could this also be one of the many ways that the industry can combat the talent deficit, automating the more basic tasks and reserving the more complicated Data Science processes for the Data Scientists?

What Makes FinTech So Successful and Disruptive?

Traditional financial institutions have been in this game for a while and operate in a vast and complex ecosystem, which now serves a foundation for FinTech growth and development. In fact, some estimates suggest that three of the largest FinTech investors are international financial institutions – Citi Ventures by Citi, followed by Goldman Sachs and JPMorgan. Aside from the largest investors, a range of financial institutions has been actively supporting financial technology startups in one way or another – through substantial money injections, accelerators/incubators, challenges, etc. And although no money can guarantee success, as the saying by Tim O’Reilly goes, “Money is like gasoline during a road trip. You don’t want to run out of gas on your trip, but you’re not doing a tour of gas stations.”

How algorithms rule our working lives

These algorithmic “solutions” are targeted at genuine problems. School principals cannot be relied upon to consistently flag problematic teachers, because those teachers are also often their friends. And judges are only human, and being human they have prejudices that prevent them from being entirely fair – their rulings have been shown to be harsher right before lunch, when they’re hungry, for example – so it’s a worthy goal to increase consistency, especially if you can rest assured that the newer system is also scientifically sound. The difficulty is that last part. Few of the algorithms and scoring systems have been vetted with scientific rigour, and there are good reasons to suspect they wouldn’t pass such tests. For instance, automated teacher assessments can vary widely from year to year, putting their accuracy in question.

5 Traits Effective IT Leaders Need

What are the characteristics that make these and other industry luminaries so revered? The technology they created? The artful design they infused into function? The plethora of free food and other perks they doled out to employees? Turns out there are five characteristics that more than 8,000 IT workers surveyed in North America by Robert Half Technology pointed to as traits that are important for an IT leader to possess. But often challenges crop up that prevent tech executives, managers, and team leaders from reaching such regarded heights. "The most successful leaders are in touch with the needs of the organization and their team, but are also keenly aware of industry trends and factors that impact the tech industry as a whole," John Reed, senior executive director for Robert Half Technology, told InformationWeek.

Why a security team embraces shadow IT

Bartholomy says the end-user technology unit also works with the broader IT unit on corporate technology strategy, including implementing other cloud solutions, such as Workday. While the company consumes a lot of cloud software for a financial services firm, it doesn’t adopt cloud casually. Like any other vendor Western Union works with, SaaS providers go through a risk assessment process to ensure that they meet the company’s rigorous security standards. "Because we are in a financial services organization, compliance is a big part of what we do so making sure that those vendors are doing all of the right things to make sure that we feel good about using them,” Bartholomy says.

Christine Doig on Data Science as a Team Discipline

Data science is about the design and development of solutions to extract insights from data (structured and unstructured) using machine learning and predictive analytics techniques and tools. Data Science as a discipline and Data Scientist as a role have been getting lots of attention in the recent years to solve real world problems with solutions ranging from fraud detection to recommendation engines. Christine Doig, Senior Data Scientist at Continuum Analytics, spoke at this year’s OSCON Conference about data science as a team discipline and how to navigate the data science Python ecosystem. She talked about how to transition from data to models to applications. Christine also discussed the different roles and skillsets needed for the data science discipline: Statistician, Computational Scientist, and Developer.

Quote for the day:

"Together we must learn how to compose differences, not with arms, but with intellect and decent purpose." -- Dwight David Eisenhower

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