Open Framework, Information Management Strategy & Collaborative Governance | Data & Social Methodology - MIKE2.0 Methodology
Members
Collapse Expand Close

To join, please contact us.

Improve MIKE 2.0
Collapse Expand Close
Need somewhere to start? How about the most wanted pages; or the pages we know need more work; or even the stub that somebody else has started, but hasn't been able to finish. Or create a ticket for any issues you have found.

Archive for July 26th, 2014

by: Robert.hillard
26  Jul  2014

Your insight might protect your job

Technology can make us lazy.  In the 1970s and 80s we worried that the calculator would rob kids of insight into the mathematics they were learning.  There has long been evidence that writing long-hand and reading from paper are far superior vehicles for absorbing knowledge than typing and reading from a screen.  Now we need to wonder whether that ultimate pinnacle of humanity’s knowledge, the internet, is actually a negative for businesses and government.

The internet has made a world of experience available to anyone who is willing to spend a few minutes seeking out the connections.  Increasingly we are using big data analytics to pull this knowledge together in an automated way.  Either way, the summed mass of human knowledge often appears to speak as one voice rather than the cacophony that you might expect of a crowd.

Is the crowd killing brilliance?

The crowd quickly sorts out the right answer from the wrong when there is a clear point of reference.  The crowd is really good at responding to even complex questions.  The more black or white the answer is, the better the crowd is at coming to a conclusion.  Even creative services, such as website design, are still problems with a right or wrong answer (even if there is more than one) and are well suited to crowd sourcing.

As the interpretation of the question or weighting of the answer becomes more subjective, it becomes harder to discern the direction that the crowd is pointing with certainty.  The lone voice with a dissenting, but insightful, opinion can be shouted down by the mob.

The power of the internet to answer questions is being used to test new business ideas just as quickly as to find out the population of Nicaragua.  Everything from credit cards to consumer devices are being iteratively crowd sourced and crowd tested to great effect.  Rather than losing months to focus groups, product design and marketing, smart companies are asking their customers what they want, getting them involved in building it and then getting early adopters to provide almost instant feedback.

However, the positive can quickly turn negative.  The crowd comments early and often.  The consensus usually reinforces the dominant view.  Like a bad reality show, great ideas are voted off before they have a chance to prove themselves.  If the idea is too left-field and doesn’t fit a known need, the crowd often doesn’t understand the opportunity.

Automating the crowd

In the 1960s and 1970s, many scientists argued that an artificial brain would display true intelligence within the bounds of the twentieth century.  Research efforts largely ground to a halt as approach after approach turned out to be a dead-end.

Many now argue that twenty-first century analytics is bridging the gap.  By understanding what the crowd has said and finding the response to millions, hundreds of millions and even billions of similar scenarios the machine is able to provide a sensible response.  This approach even shows promise of meeting the famous Turning test.

While many argue that big data analytics is the foundation of artificial intelligence, it isn’t providing the basis of brilliant or creative insight.  IBM’s Watson might be able to perform amazing feats in games of Jeopardy but the machine is still only regurgitating the wisdom of the crowd in the form of millions of answers that have been accumulated on the internet.

No amount of the crowd or analytics can yet make a major creative leap.  This is arguably the boundary of analytics in the search for artificial intelligence.

Digital Disruption could take out white collar jobs

For the first time digital disruption, using big data analytics, is putting white collar jobs at the same risk of automation that blue collar worker have had to navigate over the last fifty years.  Previously we assumed process automation would solve everything, but our organisations have become far too complex.

Business process management or automation has reached a natural limit in taking out clerical workers.  As processes have become more complex, and their number of interactions has grown exponentially, it has become normal for the majority of instances to display some sort of exception.  Employees have gone from running processes to handling exceptions.  The change in job function has largely masked the loss of traditional clerical works since the start of mass rollout of business IT.

Most of this exception handling, though, requires insight but no intuitive leap.  When asked, employees will tell you that their skill is to know how to connect the dots in a standard way to every unique circumstance.

Within organisations, email and, increasingly, social platforms have been the tools of choice for collaboration and crowdsourcing solutions to individual process exceptions.  Just as big data analytics is automating the hunt for answers on the internet, it is now starting to offer the promise of the same automation within the enterprise.

In the near future, applications driven by big data analytics will allow computers to move from automating processes to also handling any exceptions in a way that will feel almost human to customers of everything from bank mortgages to electric utilities.

Where to next for the jobs?

Just as many white collar jobs have moved from running processes in the 70s and 80s to handling their exceptions in the 90s and new millennium, these same jobs need to move now to find something new.

At the same time, the businesses they work for are being disrupted by the same digital forces and are looking for new sources of revenue.

These two drivers may come together to offer an opportunity for those who spent their time handling exceptions either for customer or internal processes.  Future opportunities are in spotting opportunities in business through intuitive insights and creative leaps and turning them into product or service inventions rather than seeking permission from the crowd who will force a return to the conservative norm.

Perhaps this is why design thinking and similar creative approaches to business have suddenly joined the mainstream.

Tags: , ,
Category: Information Development, Information Strategy, Web2.0
No Comments »

by: RickDelgado
26  Jul  2014

How Machine Learning is Improving Computer Security

If there’s one thing that keeps business leaders awake at night, it’s worries over data security. Nowadays, every company no matter the size uses technology in their operations, whether its using cloud systems for emails, massive server rooms for handling online transactions, or simply allowing employees to access company information on their smartphones. One misstep could end up leading to data loss or even data theft, which could end up costing the company some big money. Even mega-corporations like Target aren’t immune to this unfortunate trend. Businesses are looking for ways to make their information more secure, so to do that, many security systems are turning to big data, or more specifically to machine learning as a way to prevent and combat threats.

 

When you get right down to it, computer security is all about being able to analyze the data. A company’s security is largely dependent on the amount of data analysis they’re capable of, along with the quality of that data. A company that can collect a lot of data at once but doesn’t have the means to analyze it properly for threats won’t get very far. The same goes for a business with excellent analytic tools but without the resources to gather and store that information. These facts are very important because without a lot of data, machine learning simply can’t be as effective.

 

For those who aren’t familiar with machine learning, it essentially means a system that is capable of learning from data. The system is given a task, and from that algorithm can constantly get better, performing the task more efficiently and perhaps even finding new ways to do it. The more data a machine learning system has to work with, the better it will be at its assigned duties. In the case of cyber security, a machine learning system is able to sort through vast sets of big data in order to identify certain complex signals that it has deemed to be particularly damaging or a threat.

 

The machine learning approach has a major advantage over the more traditional way of threat detection. With the traditional way, systems had to look for signatures that had already been determined to be a threat. Once these signatures were identified within a network, the system would have to either stop it from further infiltration, or eliminate it. This method has some rather obvious weaknesses, the main one being its non-predictive nature. A threat that doesn’t fit an existing signature would likely not be identified, opening up the network to an attack. In essence, companies and organizations would always be behind prospective attackers looking to steal valuable data. Machine learning is able to address this major weakness. By looking through data for certain patterns and signals, machine learning is much more capable of predicting future attacks and preventing them, letting the system stay one step ahead of those who intend to do harm. By keeping a database of all existing malware, machine learning can root out problems before they happen, which for obvious reasons can be of great value to businesses.

 

There are plenty of security tools available for organizations that want to employ machine learning as a defensive measure. With these tools, systems are able to detect aberrant behavior, or actions that fall outside the norm, which can trigger an alert sent to security teams. As machine learning security systems constantly improve, they can then narrow down the alerts even more so team aren’t subject to waves of alerts each and every day. One such machine learning tool called Fortscale is able to separate abnormal events and place them in a special inbox, allowing IT security personnel to take a look at the problem and address it as needed.

 

Machine learning is already a common part of security measures. Spam mail filters are one simple example, but other systems exist like antivirus software, intrusion detection programs, and even credit card detection codes. There is much progress still to be made, but machine learning is at the forefront of raising computer security to a new level. With machine learning properly deployed, business leaders can rest easy knowing their data is more secure.

Category: Information Development
No Comments »

Calendar
Collapse Expand Close
TODAY: Mon, April 24, 2017
July2014
SMTWTFS
293012345
6789101112
13141516171819
20212223242526
272829303112
Archives
Collapse Expand Close
Recent Comments
Collapse Expand Close