Check out this short but insightful piece the collaboration between IBM Watson and Pokemon Go at work and what can be using the IBM Watson's platform.
Showing posts with label IBM. Show all posts
Showing posts with label IBM. Show all posts
Wednesday, July 27, 2016
Saturday, December 19, 2015
(The Big Disrupt) IT: The Double Edged Sword That IS Being A CISO
In an age where IBM CEO
Virginia Rometty well-worn phrase that data is the natural resource of the 21st
century is looking less and less presumptuous, companies across the aboard are
investing an awful lot of money securing their data and their IT infrastructure
from attacks which should be good news for CISO’s who are solely in charge securing
both as the role has risen to prominence in light of large data breaches
however, this puts a lot of pressure on CISO’s to get things right when the
odds are firmly stacked against them.
Sure you might think CISO armed
with growing budgets, years of experience dealing with cyber-attacks and threats,
a rash of tools offered by security vendors, and a strong team behind them
would put in a great position to stave off the threat of hackers but CISO’s,
despite all these advantages are still at a disadvantage as they face an enemy that
outnumber them and are often as good or better at breaching security systems as
CISO’s are at protecting them. While attackers can get caught and prosecuted, the
cost barrier to entry is almost insultingly low given how much companies have to
spend to deal with a data breach. To give you an idea, TalkTalk’s data breach
in October will likely cost the company 30- 35 million while the attackers
would be breaking the bank if their efforts broke into the thousands[1].
A good chunk of that 30 million
will likely go to their CISO’s budget as the company stated that they will give
their CISO “carte blanche over security investments” which was likely to happen
anyway given the company’s managing director Charles Bligh revealed that they
were discussing spending more on security before the breach happened [2].
TalkTalk’s renewed commitment to security may reveal the company intention to
avoid being breached again but this new focus in various organizations across
many fields is leading to a strange occurrence of CISO’s budgets increasing
despite companies experiencing breaches.
While security is obviously
going to become a top priority for organizations after experiencing a breach,
it’s highly unlikely a costly failure in any other role in the C-suite would be
rewarded with an increased budget. You don’t have to be rocket scientist to
find out what would happen if a CMO burned a 30 million hole in his budget on a
marketing campaign that failed horribly or a CEO presided over sustained period
of no or low growth as both would be out of a job before long. However, Unlike
CEO’s or CMO’s, a CISO’s job is largely about planning for worst as opposed for
the best working to stop multiple threats which means they negotiate a higher
degree of risk of failure.
The high risk of failure seems
to be growing by the month as CISO’s experience their responsibilities expand
at a rapid rate with organizations embracing new technologies such as wearables,
mobile, and the internet of things which CISO’s have to secure. This should
prove good news for CISO’s as more responsibility means greater stature in the
organization but they also have contend with a notable increase in cyber-attacks
and a much talked about lack of talent
in the cybersecurity field which makes covering their growing remit that much
harder.
In sum, like a number in the
C-suite, CISO’s find themselves subject to a growing budget, greater responsibility
and yet overwhelmed by their role but whatever happens, expect CISO’s to be
prepared for it.
Wednesday, December 31, 2014
(The Big Disrupt) Predictive Analytics: "Hotspots" and "Heat Lists" or the Future of Modern Policing Right Now and Later
Much has been written by us at
The Big Disrupt about why Big Data and the Internet of Things will enable the
largest and most comprehensive data grab in the history the species which is
terrifying but the development that scares us the most is that it will makes
humans easier to predict which is great for governments and corporations but
not so much for Joe public who’s already tracked, watched, and monitored at
every turn.
We could write a whole book on
how problematic predictive analytics can be particularly when used to combat
crime (some concerns we’ll cover later) but, like any a good Kantian will tell
you, it’s always wise to criticize things according to their limits. Predictive
analytics can tell us much about crime from where it most frequents and who’s
most likely to fall victim to a crime but to a certain degree police
departments everywhere and the public at large know who is most likely to fall
victim to crime: the poor and vulnerable. Predictive analytics may help police
departments protect people most likely to fall victim to crime but what it can’t
address, or more to point, what it’s not designed to address, is why such
people are more likely to fall victim to crime in the first place.
Predictive analytics, like many
modern technologies, cannot address social problems but it can address inefficiencies
in processes that can address social problems but not directly. For example, predictive
analytics may inform police departments where a crime is likely to take place
and allow them send units to potentially stop crimes but this scenario is
likely to reveal department and officer biases as it’s likely that units will
engulf poorer areas that usually have a less than cordial relationship with
police departments and their officers in the first place.
This runs into another problem
with predictive analytics, what is actually being analyzed. Predictive analytics
is good at parsing through large datasets but not so much at identifying
department attitudes and tactics used towards certain neighborhoods. Because of
this, what predictive analytics is most likely to reveal is not only where and
when crime is likely to be committed but the biases of departments and their
officers, the not so flattering socio-economic and historical make up of a city,
and the inability of predictive analytics interpret the effect of both factors
have on the data it analyzes.
Another limitation of
predictive analytics being used to fight crime is that it won’t make
departments or officers any better at dealing with the public especially innocent
members of the public who live in areas deemed as “hotspots” by predictive
analytics. This is a very important point as the last few weeks and months have
shown, arming cops with data that will most likely buttress their already deep
set biases towards certain groups and areas can and will have deadly
consequences.
In sum, the all too social dynamics
of crime are more complicated than the useful but limited answers predictive
analytics can provide which is concerning as predictive analytics is in some
respects already informing policing decisions that clearly neglect the
complexities police officers are neither empowered or equipped to deal with or the technology itself can even recognize.
We’ve always subscribe to the
view that the most interesting thing about any technology is why it is being
implemented at any particular time and the implications that come with it and predictive
analysis is no different. While companies like IBM and Microsoft (both invested
to predictive analytics) would point to the great returns in crime prevention
and other efficiencies, the truth is that while predictive analytics requires a
serious investment in software, hardware, hiring and training, it can and
mostly likely will in the future lead to a drastic cull of cops on the beat. Police
work for the most part will become technocratic but at the same time simpler as
cops will likely follow crime hotspot maps that highlight where certain crimes
take place and simply wait for something to happen.
This might not seem like much
of a problem but just imagine the sight of cops just hanging around where you
live waiting for a crime to take place just because their predictive analytics
software deemed your area a “hotspot” for a certain type of crime or crime in
general. While we’ve already cited the potential of predictive analytics
serving as a confirmation of departmental biases, we must also consider that decisions
by cops in the field can and will be influenced by predictive analytics. For
example, cops are more likely to prepare for hostility in areas where crime
takes place more often than areas that rarely have instances of crime according
to data provided by predictive analytics.
This may seem like an obvious
observation but given that cops have the power of arrest and are armed, information
that confirms or even creates new biases among officers before they even engage with members of the public can prove problematic. Cops in certain areas
are likely to ask otherwise innocent members of the public for information or
conduct stop and search procedures due to the perceived prolific nature of
crime in certain areas and the proximity of residents in that area to it.
The scenario above may already
sound familiar to anybody who lives in an area with a reputation and that’s
because, to a certain extent, crime fighting is already a data driven
enterprise. A key reason why predictive analytics is being used by law
enforcement in the first place is due to the vast amount of data departments collect
and need to interpret in order to combat crime. Another key reason why there
has been an almost widespread embrace of the application of predictive analytics
in a number of police organizations across the US and elsewhere is that
departments are facing budget constraints and thus are forced to operate under
what has to be the most depressing creed of the modern age: doing more with
less.
Even in the widely cited
success stories of predictive analytics bringing crime down, the reasons why
local departments invested in predictive analytics revealed an awful lot more
than its impressive results. In The City of Lancaster in California, forced to “do
more with less” in light of sharp budget cuts and had to “deploy resources more
efficiently”, made an investment in predictive analytic systems that helped yield
an excellent 35% reduction in “ part 1” crimes in 2010 and 40% in 2011[1].
These numbers are impressive and have served as an effective sales script for
IBM (the numbers used above were cited from a IBM case study by Nucleus
Research) trumpeting the effectiveness of predictive analytics to police organizations
across the US and overseas.
James Slessor, Accenture’s
Managing director of Accenture Police services, pretty much made the same points
that IBM are making as he cited another success story in California among
others this time in Santa Cruz where law enforcement “applied predictive
analytics to burglary data in order to identify the streets at greatest risk –
it resulted in a 19 per cent drop in property theft without the need for additional
officers”[2].
In both instances, Accenture
and IBM are in effect selling predictive analytics systems not only as an
effective tool in fighting crime, which it may well prove to be, but as an efficiency
measure to deal with cuts to budgets and resources which is not a bad thing but
this is hardly the most noble motivation driving a revolution in how police
work is done in 21st century.
So far we’ve largely focused on
predictive analytics being used by departments to predict where and when
certain crimes happen but, in truth, the most concerning thing is not so much
how the technology is used to fight crimes in cities but how it can and is
being used against people. We’ve already mentioned that we are the most
watched, tracked and monitored age in the history of the species and predictive
analytics will ensure we will be the easiest to predict. Police organizations
are already using predictive analytics against criminals as Computerworld
reported back in October that the Metropolitan Police Service ran a project
with Accenture that “merged data from the Met’s various crime reporting and intelligence
systems and applied predictive analytics, generating risk scores on the
likelihood of known individuals committing violent crimes”[3].
While you might not lose sleep
over police units using predictive analytics against “known” criminals, how
easy would you sleep if a police commander came to your front door and warned
you that they’ve got their eye on you because, as The Verge Matt Stroud reported,
your name cropped up on “an index of the roughly 400 people in the city of
Chicago supposedly most likely to be involved in violent crime” predictably termed
a “heat list”[4].
I don’t know your tolerance
regarding invasions of your privacy but it surely sent shivers up Stroud’s
spine who gave his article a provocative title that speaks loudly to many of
the points quietly made in this piece.
In sum, predictive analytics can
and will play a major role in how crime is fought in cities in the 21st
century and beyond but with concerns about it’s potential to confirm or create
new biases, compromise individuals’ right to privacy and the motivations and
interests driving this push towards analytics, the need for pause must be met
with a sober debate about what predictive analytics means for the public as
well as law enforcement.
[1]
Nucleus Research, 2012, ROI Case Study IBM SPSS City of Lancaster, http://public.dhe.ibm.com/common/ssi/ecm/yt/en/ytl03131usen/YTL03131USEN.PDF
[2]
Accenture, 2012, Smarter Policing, http://www.accenture.com/SiteCollectionDocuments/PDF/Accenture-Smarter-Policing.pdf
[3] C.Jee,
2014, Met Police pilot analytic tool to fight gang crime, http://www.computerworlduk.com/news/public-sector/3582701/met-police-pilots-analytics-tool-to-fight-gang-crime/
[4] M.
Stroud, 2014, The Minority Report: Chicago’s New Police Computer Predict Crime,
But is it racist?, http://www.theverge.com/2014/2/19/5419854/the-minority-report-this-computer-predicts-crime-but-is-it-racist
Wednesday, November 12, 2014
(The Big Disrupt) IBM: What's The Matter With Big Blue?
While the title of this article poses a somewhat leading question suggesting there something wrong with IBM, It's quite obvious that something is clearly not right.
The heart of IBM's problems is not it's dismal performance or even bad management (though it has been woeful as far as strategy is concerned) but the mindset among its leadership which is heavily fixated on chasing returns for its shareholders as opposed to providing top quality services for its customers. This ill advised drive towards boosting bottom line growth was initiated by former CEO Sam Palmisano "roadmap 2015" initiative (then continued until recently by current CEO Ginny Rommety) to boost the company's earning per share which has instead seen the company lose top line growth, make significant cuts to its increasingly frustrated workforce, lose their shareholders money (Warren Buffet among them with his 7% share in the company) and fall desperately behind faster and more agile competitors in markets they should of had a strong foothold in.
This corrosive focus on bottom-line growth has seriously affected the business standing in the tech industry and has led to prominent figures in the tech space to question whether the company has lost the plot. On the surface of things, the answer to that question is a resounding yes as IBM has, on the face of it, forgotten that it's a tech company for the best part of a decade.
The intense focus on buybacks and on boosting their profit margins at the expense of real investment in the company has left the company seriously behind the curve in markets they could have dominated. Of late, It seems that penny has finally dropped in the heads of big blue's leadership that a lack of investment in an industry that that's rapidly changing is dangerous as the big bets on big data and the cloud has shown that IBM is realizing that providing quality services for the ever changing needs of it's customers is only way forward.
IBM has made a significant investment in its big data business with $24bn invested so far and 17bn spent on acquisition of big data companies.It has also made a serious commitment in innovating in big data with one third of it current research focused on big data. It's big investment already looking like a sound one as it's data analytics business brings in 16bn in revenue.
The company has also been investing in their cloud services with 7bn invested so far and has already achieved a dominant position with 80% of the fortune 500 making use of the company's cloud services. Its' investment in its cloud service has brought in 4.4bn in revenues and recorded a staggering growth in revenues of 69%.
However, despite it large investments in big data and the cloud, what could be its most lucrative investment is in the mobile space. Evidence of this can be gleaned from its growth in this space as IBM has already recorded 69% growth in mobile and 45% in social business. However for all this positive growth in areas the company identifies as future sources of growth, all of these areas are highly competitive and despite the company's considerable investment in skill and talent in these areas, the dynamics in these markets will force the company to move at a clip it's not used to and is likely to make the company's high hopes for all these areas difficult to realize.
But the biggest problem at IBM is currently how the organization is run because of its frankly foolish decision to focus on boosting shareholder value in an industry that has historically punished short term thinking. The strategy of focusing on the bottom line has led to the company not only making cuts to its workforce but alienating the staff it has with middle and lower level employees at the mercy of executives, clearly out of ideas, looking to squeeze every buck it can out of the company rather than address the company's dated business model and avoid the financial Armageddon to come if it doesn't.
In sum, IBM does have major problems but for the most part these are relatively simple to remedy but as things stand, IBM are going to look real shaky if it continues its current course which not only reveals the incredibly bad strategy and overall lack of management savvy among its top executives but the company leadership clearly running out of ideas at a juncture where this damning predicament can be fatal. IBM can be the great company it once was and if the company can scrap its current corporate strategy cooked up in its C-suite in favor of a more customer focused philosophy, IBM will once again be the company setting trends in tech rather than following them
Monday, September 8, 2014
(The Big Disrupt) Driverless Cars: Why Driverless Cars Will Hit London Streets Sooner Than You Think
Three months ago, a sea of
black cabs clogged the heart of London for an afternoon and stage was set,
Black cabs drivers were showing their teeth against Uber and media outlets both
sides of the pond were already framing London Cabbies show of force as another
instance of cabbies, in the words of its statement in response to Germany
banning the service nationwide “put(ting) the brakes on change”[1].
However the truth is darn more complex than Uber and proponents will have you
believe. The real reason behind cabbies showing up in number in the heart of London
had nothing to do with the threat Uber offers to their business but Transport
for London (TFL) apparently preferential treatment towards the San Francisco
cab hailing firm.
Just two months before the
protest, the Licensed Private Hire Car Association (LPHCA) publicly urged TFL
to force Uber to follow the same laws and regulations it’s members have to
comply with and the TFL is tasked with enforcing in the first place. However, Leon
Daniels, TFL’s Managing director for surface transport, pretty much sent a clear
message that black cab drivers in the capital should adapt to the new
innovations in a bid to “offer passengers the potential of better and more convenient
services”[2]
However, as Daniels knows, offering passengers a better service wasn’t the
issue a hand as a growing number of cabbies are already using app friendly
bookings to get fares.
In May, the Licensed Taxi
Drivers Association (LTDA) expressed their dismay against Uber as from their
viewpoint the the taxi hailing app company was circumventing a law that’s allows
only taxis to have meters by through the use of their app that according to
LTDA, basically mimicked the function of a taximeter and was therefore illegal.
However, just like the LPHCA, the LTDA real aminus was reserved for the TFL who
in their eyes has so far demurred to the Google and Goldman Sachs backed
company. Steve Mcnamara, predicted a month in advance that the protest was going
to happen as he made that his problem wasn’t with Uber but TFL and their treatment
of the company as he stressed “"I'd be happy if Uber complied with the
same rules as everybody else. All we are asking for is a level playing
field." To complain about these issues, there's going to be a mass
demonstration in central London of between 8,000 and 12,000 black cabs, who
will cause "chaos, congestion and confusion"”[3].
After a month of pressure by
the LTDA and LPHCA, instead of ruling outright whether Uber’s use of their app
to calculate costs in their driver’s vehicle was permitted, TFL sought a High
Court was whether Uber’s app was legal or not. This not only confirmed what the
LTDA and LPHCA already suspected, TFL was less than willing to take firm hand
against Uber but as the BBC reported “ TFL
does not believe the app breaks the rules”[4].
TFL were of the view that Uber weren’t breaking the law by virtue of their apps
not being part of their vehicle as meters are in taxis despite the app in
practice performing the same function as a taximeter.
This central contention along
with others is why Uber has become so controversial across of number of states
to the point that the app service has met a litany of legal and political
pushback wherever it went. Regulators in other countries have been more
deliberate in dealing with Uber with some outright banning the service but TFL
has been notably weak leaving the question left worth asking: why?
TFL, like all big
organizations, are less than forthcoming about their reasoning behind their
behaviour towards certain actors and groups but more than in a mood to share its
grand plans and in this respect we can speculate upon TFL’s and indeed the
Mayor of London’s office careful and borderline preferential treatment of Uber.
In 2012, the Mayor of London
office setup the Roads Task Force (RTF) tasked with coming up with a vision of
the roads of London that’s fit for the 21 century. after a three month
consultation period with various stakeholders, the RTF published a forward looking
report in 2013 suggesting a number of changes to prepare the roads of London
for 21 century that could usher the use of alternative services among them
ridesharing companies like Uber, deliver drones, and the driverless car.
While the report was mostly about
making London easier and safer to get around and improving the city’s road
infrastructure to handle the obvious pressures of a growing population, one of
the main aims was to clearly to encourage less road usage which could usher in
a number alternatives, including, rideshare services (like Uber), delivery
drones and even driverless cars which all would help lower the use of cars on
London roads. The report was also done in conjunction with TFL who had three
high ranking members on the RTF and not too longer after published its own
report that was largely in agreement with the view and suggestions made by the
RTF.
With the Mayor of London
office, the RTF and TFL all on the same page for the need to tackle congestion
in the city, it’s clear that all three parties are interested in reduction of
car use. All this helps explain why London mayor Boris Johnson has been eager
to bring driverless cars to London streets as well as the government announced
that it will driverless cars will allowed on British roads as early as next
year. Last year IBM, who had a representative on the RTF and until recently was
running London’s congestion charge systems after winning a contract with TFL
back in 2007, has partnered up with Google, Cisco Systems and German car parts
supplier Continental AG to work on “autonomous driving systems for cars” which
could see the advent of the driverless car come sooner than expected as many
thought Google weren’t willing or able to get the driverless car on roads
across the globe on its own[5].
Johnson got himself into some hot
water while caught talking up the benefits of Google’s technology in bringing
about driverless vehicles such as buses which he quick shot down after his
published report, which no longer available online, caught wind[6].
While the Mayor’s report revealed his support for driverless cars and greater
automation, his support for driverless cars is nowhere near as bullish as the
TFL .The Guardian reported that Peter Hendy, TFL’s commissioner, wrote a foreword
for a ClearChannel commissioned study talking up the potential of driverless
vehicles in the capital. Professor David Begg, a former TFL board member and author
of the report entitled “A 2050 Vision for London”, even forecasted the death of
the taxi driver as he wrote “ "Taxi fares are expensive in London.
One of the main costs is the wage/return to taxi drivers. Passengers in an AV (Autonomous
Vehicle) world will be able to remotely call a driverless taxi to take them to
and from their destination …”[7].
While there’s nothing wrong
with Professor Begg throwing out predictions about the death of the taxi drivers,
there is clearly something wrong with a high ranking TFL official writing a foreword
for a report that predicts the death of a profession and industry it regulates.
However LTDA’s Steve Mcnamara doesn’t seem too concerned about the Mayor and
the TFL being in favour of driverless vehicles but should be given Uber’s long
term plan to replace its human drivers with driverless cars as last year the
San Francisco company “committed to invest up to $375 million for a fleet of
Google’s GX3200 vehicles”[8].
Uber CEO Travis Kalanick has
publicly stated the company’s intention to increase the use of driverless cars
in the company growing fleet of cars and trumpeted this development as a boon
for customers as he said “ "When there's no other dude in the car,
the cost of taking an Uber anywhere becomes cheaper than owning a vehicle. So
the magic there is, you basically bring the cost below the cost of ownership
for everybody, and then car ownership goes away””[9].
In sum, to the chagrin of its own drivers, never mind Taxi and private hire cabs everywhere, Uber seems on
track to bring about Professor Begg’s grand vision and awful lot earlier than
2050 and with the Mayor or London, TFL and a gaggle of powerful corporations
and business groups in their favour, whose to bet against them.
[1] http://blog.uber.com/uberonEN
[2] http://www.huffingtonpost.co.uk/2014/04/16/uber-cars-cabs-london-uk-banned_n_5158331.html
[3]
Ibid
[4] http://www.bbc.co.uk/news/technology-27674773
[5] http://www.fastcompany.com/3016204/where-are-they-now/google-ibm-and-continental-team-up-to-make-self-driving-cars
[6] http://www.theguardian.com/politics/2014/jul/31/boris-johnson-tries-distance-himself-idea-driverless-buses
[7]
Ibid
[8] http://techcrunch.com/2013/08/25/uberauto/
[9] http://www.theverge.com/2014/5/28/5758734/uber-will-eventually-replace-all-its-drivers-with-self-driving-cars
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