Month: May 2018

[youtube https://www.youtube.com/watch?v=QGS1BfToCNM]

In this podcast Igor Volovich(@CyberIgor) talks about strategic side of cyber security. He shared some practices that business could adopt to keep their infrastructure safe. Igor sheds some light on some easy ways to measure security for your business and understand the leadership commitment needed to establish a security mindset. Igor also share the need for metric lead strategies to quantify the outcome. This podcast is great for future information security leaders to understand data science and metrics led cyber security strategy.

Igor’s Recommended Read:
How to Measure Anything in Cybersecurity Risk by Douglas W. Hubbard, Richard Seiersen http://amzn.to/2BOoK6D

Podcast Link:
iTunes: http://math.im/itunes
GooglePlay: http://math.im/gplay

Igor’s BIO:
Strategist, advisor, advocate, mentor, author, speaker, and cyber leader. Passionate about the craft of cybersecurity and its role in protecting the computing public, the integrity of global commerce and international trade, and defense of critical national infrastructure.

Internationally experienced cyber security executive and senior advisor with 20 years of service to the world’s largest private and public-sector entities, Fortune 100’s, US legislative and executive branches, and regulatory agencies

About #Podcast:
#FutureOfData podcast is a conversation starter to bring leaders, influencers and lead practitioners to come on show and discuss their journey in creating the data driven future.

Wanna Join?
If you or any you know wants to join in,
Register your interest @ http://play.analyticsweek.com/guest/

Want to sponsor?
Email us @ info@analyticsweek.com

Keywords:
#FutureOfData #DataAnalytics #Leadership #Podcast #BigData #Strategy

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Businesses trying to get their big data strategy straight are looking for their move from concept to execution. A study from Oxford and IBM found that of the four big-data phases (Educate, Explore, Engage, and Execute), “Twenty-four percent of respondents were in the educate phase and another 47 percent in the explore phase. Only six percent had reached the execute phase.” So, it is not as easy as it seams and yet it is picking so much steam when everyone is talking about having a strategy in place. Getting big-data project in place is one thing, getting the discovery right is another thing.

Almost everyone I spoke to who has been dealing with big-data confirms that it is not a new problem, but something that has been haunting businesses for eons. It is just now that big-data focus has reached mainstream and generating all the heat and whistles for the businesses.

In my past projects we’ve dealt with problem ranging from ensuring a telecom system will be able to test on wide range of stochastic model, ensuring ad-serve on mobile platform works for millions of users, retail chain fixing their customer experience problem by utilizing customer experience touch points and learning from it, to a mobile manufacturer wanting to utilize it’s data to learn how they could master product innovation. These are one of the few that we dealt with and with almost everyone you could see some or the other side of big-data execution.

In my promising career, I have helped many companies get their automation strategy straight, which many times includes automating the discovery of insights as well as making sure their processes will auto magically generate insights that will help them in their longer span of business. In almost all the interactions, initial conversation is painful but as the value became evident, it is a cakewalk. The good part of big-data project is that it is not all smoke and bone but truth lies in data.

The faster we are getting deep into digital age, the relevance of big-data is all the more important. It is not a fluke that big data has gained so much grounds and so fast. If anything, it will keep on going crazy and higher. So if businesses don’t join the bandwagon, they could find themselves in area of dark and discovery less zone. Thanks for open source vendors around various assets of big-data, it has become extremely easy to dive into big-data capabilities and get the science to work at nominal cost.

Large volumes, blobs of data or Big-data is used to solve fundamental business problems. On one hand it is helping businesses find the answer to their most pressing questions and on the other, if done properly, it is helping business find the most pressing questions as well to find the answer to them. So, it depends on which side of savviness lies your business.

Some businesses, despite carrying boat load of data, are still looming in the ocean of ignorance and dissatisfaction. Take for an example some of top airlines carriers, they must be having so much data running under their nose, but they still stay on worst satisfied customers list. They do get the 3Vs of big data but still when it comes to use the data to build a competitive edge and take their airlines to north in every aspect of business, they fail at its execution.

And, we all know big blob of data is not the problem here. If, anything, these airlines must be dipped in data, and more the data, more the ability to get insights, discoveries, opportunities and results. The problem lies in the inability to translate the data to insights that teams could execute on and act in a way that drives transformational results. Thus, resulting in Big Data projects failures.

For example, in working with mobile carriers to accelerate their Big Data initiatives around reducing churn and driving data revenues, I often emphasize that ‘too much data’ is not a good thing – it’s a great thing. In most cases what businesses need is the right cross-discipline push– the push, that is, to become a truly analytics-driven business guided by decisions that are data-informed. So how do you get there?

So, what does it take to get the big-data strategy right and achieve much-needed discoveries to prove the ROI:

– Focus is the key: Get your focus on KPIs that are directly impacting the business and not focus on too many things or too irrelevant things. If right KPIs are picked, insights will come eventually.

– It’s a leadership, not a management problem: Sometime having several competing task forces to attack a problem is countered productive. Get this problem it’s own dedicated leader and invest in their strategy. It is cheaper/faster to do one thing at a time as compared to contesting things that could create loads of cross dependencies.

– Pick right strategy to get the game face on: In many of my previous blogs, I have rooted on the idea of picking the right strategy (Hiring consultants, outsourcing, building in-house teams) for starting your big-data project. So, get the right strategy on what is most cost effective to your business. If you can’t get to strategic insight, finding a strategy consultant is helpful as they could recommend the best possible way.

– Bring the talent and equip them: Through whichever ways, throw people at the problem and equip them so that they could work through it. This will get the momentum going to fix the problem.

– Get the conversation going: It is all about learning, sharing and optimizing; insight is power and sharing is the only way to move things forward.

Whether you’re a mobile carrier, a brick and mortar retailer, or a high-tech software firm, there are always new ways to connect the data dots to make new discoveries. At the end of the day, it’s the brains behind those technologies that will make the real difference.

So, when you look at your business next, see who is driving the discovery? Are they focused solely on finding the right answer to a question, or are they continually seeking the right questions to ask? It is the iteration between the two is where lies the real insights and discoveries.

Originally Posted at: Hitting the Road to BigData Analytics and Discovery

The quest for perfection often gets in the way of success. For example, Coca-Cola wasted millions on New Coke when the Old Coke was just fine. It might surprise you to learn that a frequently used data science technique can serve as a good reminder for leaders to strive for success, not perfection.

Let Naive Bayes be imperfect

Naive Bayes is a machine learning technique that’s primarily used for classifying text. For instance, Naive Bayes may be used to identify spam in email. The algorithm takes your email text as an input, does its magic, and then determines — or classifies — your email into one of two categories: spam or no-spam.

This is unremarkable for a machine-learning algorithm; however, if you look under the hood, the algorithm makes bold assumptions that would ostensibly render it ineffective. For instance, word order or context is not considered, so the phrases “monkeys drive crazy cars” and “cars drive monkeys crazy” are treated the same. What is remarkable is that, notwithstanding these seemingly reckless assumptions, it works.

Naive Bayes illustrates that real life isn’t always logical or formulaic. The fact that Naive Bayes works irritates many data scientists. A common slang used for Naive Bayes is idiot Bayes. Some data scientists feel Naive Bayes needs to be improved because, even though it works, they know there’s something wrong with how it works.

The trouble with trying to perfect Naive Bayes is that you quickly run into a problem solving mess. Trying to account for all the semantics and contextual nuances of the English language is a fool’s errand. And for what? The algorithm already works well, so what are they hoping to improve? This is the classic Achilles heel of our dear data scientists: Their incessant pursuit of perfection when success is already in-house. We’ll give them allowances because that’s their nature, but leaders and managers, you don’t get the same pass.

When ignorance is bliss for leaders

Naive Bayes teaches us to focus on results, not methods. Methods do matter, but let’s get straight on the means and the ends.

I once heard a comedian tell a joke about the game show, “Who Wants To Be A Millionaire?” The contestant is presented with four answers to choose from, and when they seem stuck, the host (originally Regis Philbin) encourages them to talk through the answer. The joke was that the contestant would talk it through with clearly fallacious and erroneous reasoning — and then select the right answer. Something like, “Well…I know it was an African-American community leader around the turn of the 20th century, so I’ll go with…Jimmy Carter.” As funny as that sounds, as long as Jimmy Carter is the correct answer, the contestant wins the money.

We need to take this naive approach with leadership. Sometimes we get too hung up with our methodologies and forget about why we studied them in the first place. Kotter’s eight-step process for leading change is great to know, but if you can bring about change in the first three steps, you don’t need to finish the other five. If someone achieves self-actualization with low self-esteem, we don’t need to investigate why Maslow’s Hierarchy of Needs isn’t working.

We can use Naive Bayes for practical, everyday purposes even though we know it shouldn’t work as well as it does. Let the data scientists focus on the methods — that’s what they do. But as a leader, you’d be remiss if you joined them on the path to perfection. Stay on the road to success and keep your eyes on the outcome.

Final thoughts

Learning from your environment is the hallmark of a great leader. If your strategy incorporates data science, you probably have a great lesson that’s right under your nose.

Your data scientists are using Naive Bayes for its pragmatics, not its elegance. As a leader, you should be naive in this respect as well. If you’re lucky enough to stumble upon something that works, don’t question it — just appreciate what you have and focus on the next challenge.

There’s nothing classic about Classic Coke — that was just Coca-Cola’s idea of undoing a bad mistake. They should’ve just called it Naive Coke and left it alone.

Originally posted at: http://www.techrepublic.com/article/focus-on-success-not-perfection-look-at-this-data-science-algorithm-for-inspiration/

Source by analyticsweekpick

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Since the consumer purchase funnel is generating great amounts of data, it has become extremely difficult to track and make sense of the data, as consumers add social media and mobile channels to their decision-making. This is fueling the ever mounting pressure on CMOs to show how their budget delivers incremental business value.

Better data management is turning out to be a strong competitive edge and great value generation tools for organizations. So, well managed marketing organization will make adequate use of data.

This has pushed many marketers to stand overwhelmingly towards better big data analytics, as analytics will become a major component of their business over the next several years—according to the Teradata Data Driven Marketing Survey 2013 released by Teradata earlier this year, 71 percent or marketers say they plan to implement big data analytics within the next two years.

Marketers already rely on a number of common and easily accessible forms of data to drive their marketing initiatives—customer service data, customer satisfaction data, digital interaction data and demographic data. But true data-driven marketing takes it to the next level: Marketers need to collect and analyze massive amounts of complicated, unstructured data that combines the traditional data their companies have collected with interaction data (e.g., data pulled from social media), integrating both online and offline data sources to create a single view of their customer.

Visually and McKinsey & Company co published this infographic to illustrate the pressures that CMOs find themselves under and respective potential benefit in leveraging big data.

Source: CMOs’ Journey from Big Data to Big Profits (Infographic) by v1shal

As I write this, hundreds of emergency services, charities, disaster relief agencies and volunteers are doing their best to help people affected by the terrible Nepalese earthquake which struck during the weekend. And Big Data is playing its part, too – with crowdsourced, data-driven efforts to connect people outside the country with their missing loved ones, and assist in getting aid to where it is needed.

Big Data is the name given to our ever-increasing ability to collect more data from a multitude of sources, and analyze it for insights using advanced computer algorithms. Patterns humans can’t see provide a better understanding of situations and solutions to problems. Disasters are big, messy and noisy situations, and exactly the sort of conditions in which Big Data can help to make sense of the chaos. The massive amounts of data that we are generating with mobile phones, satellites and social media can all play a part in providing clues to the best way to respond to a situation.

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