By Amjid Mahmood
Imagine this scenario. You’re the head of compliance at a leading bank, and an employee who works on the trading floor comes forward with some very serious allegations about a senior trader. According to this whistleblower, the trader has been disclosing confidential information about clients to external parties and sharing information on their trading positions.
You know you have an obligation to conduct a thorough internal investigation and report any significant findings to the regulator. But you also know that the sheer volume and diversity of data that you’ll need to comb through will present challenges if you use in-house resources to do it.
By Paul Delbridge
Insurers have been investigating the deployment of machine learning techniques in the pricing arena, seeking to exploit the speed at which models can be built and refreshed compared to the use of more traditional generalised linear modelling techniques.
Machine learning techniques offer significant advantages over these traditional models, including the availability of various types of non-linear models which can lead to a wide range of new insights. However, these new models are more difficult to explain to both brokers and to customers (especially when these more sophisticated models suggest significant changes compared to the expiring prices), and there is a degree of resistance to what might be viewed as a black box technology by management and marketing teams.
By Kavitha Chandrasekaran
Our phones have become an essential part of our lives – and that means that every day, we leave a data trail behind us. From emails and social media messages, to pictures and videos, we leave a digital footprint wherever we go.
Mostly, this is entirely intentional; it’s rare to find someone who owns a phone that doesn’t have any saved messages and images. But when something goes wrong, information is everything – which is why, in the world of forensic investigations, mobile data can be a valuable goldmine.
‘Mobile forensics’ is becoming an important part of the work of our Forensics Data and Analytics teams – we are increasingly asked to collect and analyse mobile data during investigations. It’s possible to collect an amazing array of information from the average smartphone, from SMS messages and emails, to calendar events, internet history and bookmarks and even your social media history. It’s even possible to recreate deleted messages, social media, pictures and documents from a phone.
By Paul Blase
Information has become a primary form of capital - to businesses that know how to monetize their data, that is. Organisations that embrace the art of the possible in data monetization stand to create new capital from data sources and ultimately transform their business models. In doing so, however, they must maintain a sharp focus on data privacy.
Capturing and monetizing data will require intelligent, connected systems and processes that help ensure that data and privacy are managed with the same rigour as traditional assets. The good news is that, as the volume of data has multiplied, the technologies for capturing, analysing and storing information have become more powerful - and less costly.
By Grant Waterfall and Jay Cline
The private sector’s rush to collect and monetize consumer data has led many companies to create vast information stockpiles without careful planning. That trend is continuing as developers of the Internet of Things produce countless devices without basic security and privacy features. For many companies, unfortunately, emerging risks tied to data usage have been an afterthought.
By David Geere Pricing is of key importance for any business. Setting the right price for your products and services can play a big part in your success or failure (even if you’re a not-for-profit) and it’s incredibly sensitive. Research suggests that a small increase in price can bring a...
By Cathy Wilks A recent survey predicted that by the end of 2016, fewer than 25% of technology jobs in developed countries will be held by women, slightly down on the proportion for 2015. The study suggested a number of reasons for the low take-up of technology jobs by women,...
By Tom Middleton In the fourth vlog in our series from the 'Our Lives in Data' exhibition at the Science Museum, we've experimented with the data mirror which is one of the most fun elements of the exhibition. Tom Middleton, from our Risk Assurance team, talks about the different technologies...
If you read the first part of this series you’ll undoubtedly have been on the lookout for Data Governance banana skins and with any luck, will have avoided a few slips!
In this blog I’ll talk about the silver bullet that will (apparently) resolve all data-related issues in one go – the Enterprise-wide Data Governance initiative. This subject deserves all the airtime it can get – it’s where most programs fail before they even begin.
Every time you use a search engine, land on a website, buy a product or download an app, you generate and share personal data via a sophisticated and sometimes covert combination of tracking tools like cookies, beacons and e-tags.
This expansion in personal data collection is creating enormous visibility into individual lives, preferences and behaviors, a trend that will escalate as technology continues to evolve with breakthroughs such as wearable devices, autonomous automobiles and the Internet of Things.
The proliferation of data also represents a potential treasure trove of opportunity for companies seeking new sources of revenue and competitive advantages. In fact, 64% of respondents to PwC’s 20th Annual Global CEO Survey
By Triin Sober
Bots have been getting a bad rep in the news recently. You may have seen articles on ticket touts using botnets to buy up tickets to concerts and events only to sell them on to fans at extortionate prices or smart devices connected to the internet of things being used in cyber attacks. But do we really know what bots are and can they be put to a better use?
By Maaike Platenburg
We hear a whole lot about big data these days. For many people, “big data” means a flood of data, but what exactly is it? According to UN Global Pulse, information can be defined as “big data” when the data volume can no longer be managed with normal database tools. Big data is typically characterised by the 3Vs: volume of data, variety of types of data and the velocity at which the data is processed. Big data comes in different ways and can be divided in structured data and unstructured data.
By Rav Hayer and James Drury-Smith
The General Data Protection Regulation (GDPR) will put individuals in control of their personal data, empowering them to choose how (and whether) businesses use their data. Where personal data is not treated correctly, individuals will have increased rights to legal recourse and can, in some instances, claim compensation. Regulators across the EU will have unprecedented power to enforce the legislation and impose hefty fines in instances of non-compliance.
By Jon Cooke
Data lakes, as I said in my previous blog, are the latest buzz word in analytics. While I warned about jumping into the world of data lakes without thinking carefully about what you want to achieve, I do believe that these data depositories are the long-term future. They have a clear advantage over data warehousing because they offer a non-relational way of looking at your data
By Ben Whittingham and David Doyle
Transport generates multiple sources of data, and it’s often being used real time.
In the third vlog in our series from the 'Our Lives in Data' exhibition at the Science Museum, we've focused on the transport section which displays the inside an Oyster Card reader and a 3D model of Bond Street station.
By Kiran Gill
Data Governance banana skins - n sudden, unexpected and sometimes creates embarrassing Data Governance situation, often causing difficulty in realising long-term governance success.
Data Governance banana skins can be easily side-stepped or comprehensively planned for if you know what to look out for in advance.
By Jon Cooke
It’s a feature of developing technology that buzz words tend to appear all of a sudden – everyone is talking about it. At the moment it’s ‘data lakes’, a method of storing Big Data – for which Apache Hadoop is the best-known platform.
By Paul Delbridge
In early November, a leading UK personal lines insurer announced the launch of an app that would use Facebook data to “better understand first time drivers and more accurately predict risk”. This was intended to award discounted premium rates to safer drivers, who would be identified on the basis of specific personality profiles. Shortly afterwards, Facebook announced that this contravened its privacy policies, triggering a change of plan for the insurer.
By Cathy Wilks and Matt Gosnell
Data visualisation is effective when we can trust the data we’re looking at. It has to be easily understood, and it has to be easy to read and decipher. It enables businesses to make quicker decisions and helps them to communicate with others in a relatable way.
By Robert Guidi
Picture the scene: the movie executives, producers and the director sit as the audience in a horse-shoe. At the centre, a humanoid robot “pitches” an idea for a feature film. Within seconds one of the producers rejects it as “too fluffy” and the others agree. Rejected. The robot thinks again, and produces another plot on the spot, modified to take the feedback into account. By the end of the one hour meeting, the director has three new scripts in development; the Artificial Intelligence (AI) engine driving the robot has already produced the 120 page drafts.
By Adrian Hughes
Many people talk about Management Information (MI) as if it’s a standard, uniform product. What’s easy to forget is that one organisation’s MI looks very different from another’s, even if they’re in the same sector. It’s rather like comparing hairstyles – it’s subtly different for each of us and how we choose to cut, colour and style it is uniquely personal (although I can only dream of those days sadly…).
By Phil Mennie
Data is everywhere. And social media is just one of the ways we’re creating massive sets of data every day, every minute, every second. From posting a status to clicking ‘like’ – just how much are we sharing about our lives on Facebook? And how much do we want companies to know about us?
By Michel Abbink
Financial Services organisations make predictions all the time, in all parts of their business. Guessing what the future might hold and aligning decisions with these estimates, is implicitly what most organisations do.
Organisations often take ad-hoc approaches to such predictions, leaving experts and professionals free to apply their judgement. Research has shown that this can lead to a hidden cost of inconsistent and sometimes suboptimal decision making.
By Nigel Wilson
Since Icarus and his dad looked at the birds in the sky to inspire their escape from Crete, humans have attempted to mimic the natural world. Two thousand years later, we have engineered ways to swim, fly and travel faster than nature, but are we finally ready to build a machine that can think faster than anything in nature?
By Adrian Hughes
Britain and America, the saying goes, are two countries divided by a common language. There have been numerous occasions during my working life in data analytics when a similar thought has crossed my mind. We have extraordinary power to collect, collate and analyse data and yet we often fail to ask ourselves the most basic of questions: Does this word mean the same thing to everyone?