Teradata’s Art of Analytics project is helping people make an emotional connection with the datasets that rule their world.

In her day-to-day work, Yasmeen Ahmad tackles immensely complex datasets, deploying an arsenal of approaches and methodologies that would sound intimidating to most lay people.

Not many people are able to make sense from complex datasets
Share on X

From predictive modelling to text analytics, time series analysis to development of attribution strategies, few people can easily wrap their heads around what such terms mean, and even fewer are capable of drawing actionable insights from them – which is why data scientists such as herself are always in such high demand.

Ahmad worked in the life sciences industry before pivoting to commercial work, where she is now Director of Think Big Analytics, the consulting branch of IT service management company Teradata.

Visualisation is a core component of any data science and analytical project
Share on X

The best way help people see the meaning of datasets is to paint them a picture
Share on X

Over many years helping clients across a variety of industries to make sense of their data, however, she realised that the best way help them see meaning in those datasets was to literally paint them a picture.

“Visualisation is a core component of any data science and analytical project,” she explains. “It is almost always used at the beginning to understand the datasets you are working with, and can help to quickly identify anomalies, outliers and strong correlations in the data.”

As far as data is concerned, she says, a picture really is worth a thousand words, as visualisation helps to add meaning on top of data that is much easier to assimilate for humans than descriptive words or single numbers and values alone.

Visualisation helps to add meaning on top of data that is much easier to assimilate
Share on X

The visualisations supported storytelling around a project
Share on X

Her team would therefore routinely include such visuals when they presented their key results to clients, and found that even people who might not be well versed in data science or technology could still connect with them. The visualisations supported storytelling around a project, engaging business stakeholders to understand connections, relationships and associations in the data.

“As more investment goes into data platforms and analytical technologies, the artwork helps to provide a face to this investment. We had business leaders commenting on how beautiful the visualisations were. Colour, shape and layout were all dimensions that were used to convey meaning. The choice of how a visualisation was formed is actually a creative process – like creating a piece of art.”

From there, she explains, it was a short leap to the idea behind the Art of Analytics project, which brings together a range of those visualisation pieces from their previous projects.

Art of Analytics brings together a range of visualisation from previous projects.
Share on X

Ahmad believes one of the main strengths of the project is its ability to bring data to life for lay audiences, creating a connection between data insights and observers and bridging the technical gap.

“The visualisations push the human to look beyond individual numbers and values, to thinking about data as a series of connections to be explored. They make it particularly easy to see associations, connections, pathways etc. The art is providing form to the complex fields of big data and data science – making them accessible to a wider audience.

Visualisations push the human to think about data as a series of connections to be explored
Share on X

Yet the usefulness of data visualisation is not limited to non-technical people. According to Ahmad, it is also a key component of a data scientist’s toolkit:

“During my life sciences research – I was working in a field where everything was abstracted. The data I analysed often came from human cell samples that could only be seen under microscopes. Hence, collecting data about these samples, analysing it to create insight and then relating the insight back to reality was somewhat difficult. Visualisation was key to help portray not only the insights, but also how they linked to human cells and biology in general.”

Art of Analytics was also an opportunity for Ahmad to bring together those creative and scientific sides. Data science, she says, is actually a highly creative discipline that combines technical know-how with lateral thinking and the ability to tease out stories from complex datasets.

Art and science need to come together to solve the world’s most complex problems
Share on X

Data science is actually a highly creative discipline
Share on X

“I believe that art and science need to come together to help to solve the world’s most complex problems, and the best data scientists are not only great at statistics, maths and analytical subjects, but are also creative problem solvers who can translate their work into meaningful messaging that connects with their business and commercial audiences.”

This is an on-going project, and there are plans to create new data representations as they work with new datasets they haven’t encountered before.

The emotional connection that people establish with the work highlights the power of data
Share on X

Ahmad is also keen to explore how other media such as animation, video and perhaps even VR could help add other dimensions to that work. By creating a video, she says, it would be possible to create another level of emotional connection with audiences, by representing how those relationships have evolved over time.

“We have had senior executives request copies of the Art of Analytics to hang in their office. The emotional connection that people can establish with the work highlights the power of data.”

This article was originally published on The Next Web

Art exhibit shows off the beauty of data https://t.co/Mq8uOvFCGH

— TNW Contributors (@TNWcontributors) November 20, 2017

Alice Bonasio is a VR Consultant and Tech Trends’ Editor in Chief. She also regularly writes for Fast Company, Ars Technica, Quartz, Wired and others. Connect with her on LinkedIn and follow @alicebonasio and @techtrends_tech on Twitter.

The post Big Data is Beautiful appeared first on Tech Trends.

To leverage Big Data and build an effective Artificial Intelligence infrastructure, enterprises must embrace collaboration.  

Using data smartly can give your business a crucial competitive advantage
Share on X

In a digital economy, the rule of thumb tends to be that the smarter your use of data and technology, the more of a competitive edge your business has. A recent report by Teradata, based on over 260 interviews conducted by research firm Vanson Bourne with senior IT and business decision makers, found that there is widespread enthusiasm for adoption of AI, with 80 percent of enterprises reporting that they were already investing in the technology in some capacity, and over 30% planning to expand their investment in the are over the next 36 months.

“C-level executives – namely CIOs and CTOs, maintain they are committed to AI in their enterprise, because of the expected ROI over the next 10 years.” The report therefore concludes that executives will accept those challenges as the long-term benefits clearly outweigh near-term pain. In fact their analysis showed that over a five-year forecast, organisations effectively expected to double their money when investing in AI: For every $1 spent on AI technologies, organisations predicted a return on investment of $1.23 over three years, $1.99 in five years, and $2.87 over a ten-year period.

The Economist – in a phrase that has since become rather cliché – put forward the idea that “data is the new oil,” and World Economic Forum has now designated Big Data as a new kind of economic asset, just like currency or gold.

A study by the MIT Center for Digital Business confirms that data-driven businesses do indeed have the edge. It surveyed 330 leading U.S. Businesses and found that companies that focused strongly on data-driven decision-making had an average of four percentage points higher productivity and six percentage points higher profits.

C-level executives are committed to implementing AI in their enterprise
Share on X

Yet as these results indicate, it would perhaps be more accurate to say that data is in fact the new oxygen. Whereas it is still true that businesses that best leverage data and AI will gain a significant competitive advantage, it is probably fair to say that those that fail to make their organisations data-centric will eventually not be able to survive at all in the digital age.

Data is in fact the new oxygen
Share on X

While most people agree on the essential role that Artificial Intelligence and Data play in their organisation’s success, there are significant challenges. The overwhelming majority of business leaders surveyed in the Teradata report anticipated major barriers for adoption within their organisation, with roadblocks ranging from an inadequate IT infrastructure to shortage of in-house talent.

The majority of business leaders surveyed predicted significant roadblocks to AI adoption
Share on X

In their book The Sentient Enterprise, The evolution of Business Decision Making – launched at the Teradata Partners’s Conference last month – Oliver Ratzesberger (Teradata’s Chief Product Officer) and Mohanbir Sawhney from the McCormick foundation talk about how agility is key to getting the enterprise to the sentient point where it can analyse data and make decisions in real time.

The crux of the problem enterprises face lies not in the difficulty of gathering data, but in extracting insights and then turning these into actionable processes. The reality is that we live in a time of data overload, and companies can easily find themselves trapped in reactive mode, spending most of their time sifting through mountains of data and making decisions only when problems emerge, rather than anticipating them.

The problem enterprises face is in extracting actionable insights from data
Share on X

To tackle these problems, enterprises need access to key talent and infrastructure so as to enable the leveraging of Artificial Intelligence and Big Data. Increasingly, this is not being done “in house” but rather in partnerships with dedicated providers. These go beyond the traditional SaaS and becomes much more of a “Platform as a Service” model that incorporates complex customization and consultancy services.

All banks need a scalable, advanced analytics platform
Share on X

Teradata’s Think Big Analytics team, for example, worked with Danske Bank to create a fraud-detection platform that uses machine leaning to analyse tens of thousands of latent features, scoring millions of online banking transactions in real-time to provide actionable insight regarding true, and false, fraudulent activity. Together, they built a framework within the bank’s existing infrastructure, crating advanced machine learning models to detect fraud within millions of transactions per year, and in peak times, many hundreds of thousands per minute:

“Using AI, we’ve already reduced false positives by 50% and as such have been able to reallocate half the fraud detection unit to higher value responsibilities,” explains Nadeem Gulzar, Head of Advanced Analytics, Danske Bank. “There is evidence that criminals are becoming savvier by the day; employing sophisticated machine learning techniques to attack, so it’s critical to use advanced techniques, such as machine learning to catch them.”

Using AI, Teradata and Danske Bank reduced false positives by 50%
Share on X

“All banks need a scalable, advanced analytics platform, as well as a roadmap and strategy for digitalization to bring data science into the organization.” says Mads Ingwar, Client Services Director at Think Big Analytics. “For online transactions, credit cards and mobile payments, banks need a real-time solution – the platform we developed with Danske Bank scores transactions in less than 300 milliseconds. It means that when customers are standing in the supermarket buying groceries, the system can provide immediately actionable insight. This type of solution is one we’ll begin to see throughout organizations in the financial services industry,” he concludes.

Fintech company Verifi, also collaborates with banks and merchants to connect the multi-layered data-streams and combat fraud. The work the company does with is based on optimising data transmissions using APIs (Application Programming Interfaces). This is much more efficient because whereas legacy systems rely on processing large numbers of files sent in bulk, APIs can process data in real-time.

Verifi collaborates with banks and merchants to connect multi-layered data-streams
Share on X

The Verifi system collates APIs from the merchant shopping cart, customer relationship management (CRM) system, shipping data system, and others, and provides the merchant with better information to handle charges disputed by consumers, allowing them to often resolve the issue directly rather than have the bank issue a chargeback. Merchants benefit as they’re able to control the message to the consumer, and banks are happy because the sale remains and they reduce their operating costs. It is the sort of technology enabled, data-driven system that is a true win-win.

Friendly fraud and chargebacks cost $130 million in Q1 2017 in the U.S.
Share on X

One of the problems that such systems help tackle is so-called “friendly fraud,” a term used to describe a situation when a customer experiences “buyer’s remorse” and “tries it on” by putting in a claim directly with their bank or card issuer for a refund, when the sale did in fact legitimately occur. Julie Conroy, Research Director of the Aite Group says that in the U.S. friendly fraud and chargebacks would likely near $130 million, in Q1 2017 alone.

“Since many such claims are relatively low-value, banks will often issue the refund rather than dedicating the resources needed to investigate and dispute each transaction,” explains Matthew Katz, Verifi’s CEO Their research in fact showed that up to 86% of cardholders bypass the merchant and contact their issuing bank directly to dispute or question a charge on their bill. “When you add all of these claims up, it amounts to a huge problem for the industry, with billions of dollars in costs being passed onto merchants, and – ultimately – to the consumer,” he says.

Friendly fraud amounts to a serious problem, confirms a report from Javelin Research estimates that the increase in card-not-present (CNP) fraud and chargeback management now consume between 13% and 20% of operational budget for such retailers.

Even with such resources dedicated to fighting fraud, however, not having the right information on hand in real time can mean that businesses are not only vulnerable to fraud, but also risk losing customers and reputational goodwill by challenging legitimate customer claims.

“The key functionality we enable is to connect all the parties to the transaction with the right information at the right time,” says Katz. “Our Order Insight platform provides robust transaction data on each sale, like the merchant’s name and contact information, date of purchase, device name used in the order process and many more important details, directly to the consumer via their bank’s mobile or online interfaces. In most cases, having access to this data can resolve a dispute and avoid a phone call to the issuer altogether, saving the merchant money and keeping customers happy.”

The report by Javelin Research concludes that partnering with third-party providers like Verifi makes sound business sense, in that it frees up merchants to focus on their core business. “It is imperative that merchants stay customer-friendly in the face of growing fraud pressure, yet the amount they have to put into building, managing, and maintaining in-house solutions has significant negative impact on their ability to scale their business over time. The projected growth of CNP fraud will introduce a new level of burden for digital merchants, which they will be unable to combat from a resource and scalability perspective. This will increase the pressure and negative impacts on them because they will be hit by fraud that they can’t handle. Their inability to scale quickly enough may result in measures that will hinder customer experience.”

The key advantage that these technologies – when properly leveraged – bring to enterprise is their ability to streamline and break silos
Share on X

The key advantage that these technologies – when properly leveraged – bring to enterprise is their ability to streamline and break silos. Deeper integration and more collaborative approaches to technology can help deliver robust information to the right parties at the right time, ultimately enhancing the experience for customers and users, improving efficiency across the board, and providing higher returns for companies and investors. The pieces of the puzzle are all there, it seems, but stakeholders must all work collaboratively to bring them together.

This article was first published on ERP in News

How Collaboration is key to leveraging AI and Big Data https://t.co/UGfUT6KjYl via @erpinnews@alicebonasio #tw17 #erpinnews #eintechnews

— ERPINNEWS (@erpinnews) November 15, 2017

Alice Bonasio is a VR Consultant and Tech Trends’ Editor in Chief. She also regularly writes for Fast Company, Ars Technica, Quartz, Wired and others. Connect with her on LinkedIn and follow @alicebonasio and @techtrends_tech on Twitter.

The post Working Together to Build a Big Data Future appeared first on Tech Trends.

We’re off to Los Angeles from the 22^nd to the 26^th October to join the brightest minds in data analytics for the Teradata Partners Conference 2017.

Tech Trends continues its relentless globe-trotting by adding yet another top tech event notch to its proverbial bedpost: The Teradata Partners Conference in LA.

Tech Trends will be at the #TDPARTNERS17 conference from the 22nd to the 26th October
Share on X

OK, I’m not going to lie. Any chance to escape the rain for a spell of California sunshine is always welcome, yet for anybody who’s into technology, data is actually a pretty sexy subject.

Data is actually a very sexy subject
Share on X

Think of any cool tech, from VR to the Internet of Things, EdTech, green energy, mobility, finance, and yes, even sex. Big Data, AI and Machine Learning will be the backbone of how these pan out.

Machine Learning, AI and Big Data affect every cool technology out there
Share on X

Described as the premier customer-led global data conference, the conference aims to bring together experts to help companies in all sectors drive more value from data.

At PARTNERS, you will meet and learn from change-agents and practitioners—those marching to their own tune. You will see tomorrow not as another day, but where The Edge of Next is.

Illustrious keynote speakers include Jason Silva from National Geographic’s Brain Games, Emmy-winning tech expert Katie Linendoll and the legendary director of MIT’s Media Lab Joi Ito. I know that I for one am very excited about hearing Mr Ito’s thoughts on what lies just over the horizon for tech.

MIT Media Lab Director Joi Ito is among this year's keynote speakers
Share on X

Check out some of the highlights from last year’s conference below:

If you’re lucky enough to be heading over there, reach out and say hello on Twitter @alicebonasio or @techtrends_tech , and you can also follow the latest from the conference on #TDPARTNERS17

To find out how to leverage VR/AR/MR in your enterprise, Tech Trends offers bespoke Virtual Reality Consultancy support

Alice Bonasio is a VR Consultant and Tech Trends’ Editor in Chief. She also regularly writes for Fast Company, Ars Technica, Quartz, Wired and others. Connect with her on LinkedIn and follow @alicebonasio and @techtrends_tech on Twitter.

The post Tech Trends at #TDPARTNERS17 appeared first on Tech Trends.