Data Scientists Spend Most of Their Time Cleaning Data
Data scientists spend 60% of their time on cleaning and organizing data. Collecting data sets comes second at 19% of their time, meaning data scientists spend around 80% of their time on preparing and managing data for analysis. 76% of data scientists view data preparation as the least enjoyable part of their work 57% of data scientists regard cleaning and organizing data as the least enjoyable part of their work and 19% say this about collecting data sets. These findings are yet another confirmation of a very widely known and lamented fact of the data scientist’s work experience. In 2009, data scientist Mike Driscoll popularized the term “data munging,” describing the “painful process of cleaning, parsing, and proofing one’s data” as one of the three sexy skills of data geeks. In 2013, Josh Wills (then director of Data Science at Cloudera, now Director of Data Engineering at Slack) told Technology Review “I’m a data janitor.
Are bots set to be your next banker?
The global race for banks to be digital first is on, but it is early days still. Leading banks are in the process of learning how to take a mobile-first approach and re-imagine their customer experiences, from opening up a current account to buying a home or taking out a small business loan. While many have begun migrating their customers from the branch or call centre to their digital channels, it’s critical to take a country-specific view and carefully consider the cultural differences and preferences before deciding on the pace of change. The UAE could serve as an ideal test market, research from Google on the matter recently ranked the country as no. 1 in global smartphone penetration, with 73.8 per cent of consumers carrying smartphones. The UAE’s strong retail sector based upon it’s a growing middle class, surging consumer confidence in technology and increasing domestic consumption means there is major potential for digitisation.
Susanne Tarkowski on How Smart Contracts Can Add Value to Your Business
Smart contracts are applications ‘living on’ the Blockchain, and therefore can’t be censored. Simple, immutable and autonomous applications, basically. As Primavera de Filippi eloquently phrased it during her talk at OuiShare Paris 2016 “Smart Contracts are neither smart nor contracts...”. But ironically, smart contracts are however ideally suited to be… well… contracts! In essence, your contract, whether it’s a marriage contract or a freelance gig contract, becomes a self-executing application. The contract goes from being a static agreement to a living application. That’s tremendously exciting. What really turned me onto it was reading the “Lex Cryptographia” blog post by Justin Ranvier back in 2013, that’s when I realized the Blockchain, and smart contract technology more specifically, could be used to replace the government in its core function: security and dispute resolution.
What’s the Difference Between Consumer and Industrial IoT?
In theory, an IoT system should be expandable, allowing dynamic changes to its operation and include devices not provided by a single vendor. Consumer, commercial, and industrial IoT share attributes and are typically built on the same hardware and software platforms. That’s why IoT discussions tend to get murky, especially when delving into the details. For instance, smartphone and tablet apps tend to provide one way of querying and controlling devices. Windows and iOS PCs, on the other hand, generally run the heavier user interfaces, often providing management tools that would be cumbersome on the smaller, portable devices. The IoT devices and software basically differ in areas such as ruggedness and expected lifetimes, as well as who has access to data and how that data is made available to various parties.
‘Security Fatigue’ Can Cause Computer Users to Feel Hopeless and Act Recklessly
Security fatigue is defined in the study as a weariness or reluctance to deal with computer security. As one of the study’s research subjects said about computer security, “I don’t pay any attention to those things anymore…People get weary from being bombarded by ‘watch out for this or watch out for that.’” “The finding that the general public is suffering from security fatigue is important because it has implications in the workplace and in people’s everyday life,” cognitive psychologist and co-author Brian Stanton said. “It is critical because so many people bank online, and since health care and other valuable information is being moved to the internet.” “If people can’t use security, they are not going to, and then we and our nation won’t be secure,” Stanton said.
The role of the CIO in a digital age
The CIO is now expected to be an expert in user experience, security, customer centricity, journeys and behaviours. They are storytellers, evangelists and advocates of communicating the art of the possible, they are the voice of the customer and their role is far more commercial than reducing costs. Indeed, modern ‘IT’ functions also generate revenue as well as drive margin. They are ‘schizophrenic chameleons’…on the one hand grounded in strong engineering and process orientation skills and are champions of best practice and on the other they are risk takers, innovators, change agents and disrupters of traditional working models. In addition, they have been further challenged by the rise of the chief digital officer – who tend to be very customer focussed and have come about because of executive perception of legacy IT and a misunderstanding about what ‘being Digital’ really is…. in reality the best CIOs could equally be CDOs.
Transitioning into 'Machine Learnable' Data Management
The MDM to MLDM transition can have various business benefits. Some of them are: quicker procurement times due to closer & quicker material matches; increased market (regional/zonal/geo) penetration due to the much closer & faster accuracy of customer profiles, demographics and segmentation; automation of mundane stewardship activities which will lead to better business resource utilizations. The faster shelf replenishment of the higher revenue products for retailers, improving the e-commerce efficiency for e-tailers and maybe handling & procuring the right parts of a prototype from a vast set of suppliers to beat the competition on a launch. The benefits can be endless. All of this is made possible, as the system can now look at reducing the number of iterations that it takes to arrive at the right threshold based on business priorities and inputs.
We Don’t Need No Stinking IoT! Or Do We?
While countless headlines play up the potential security snafus of the Internet of Things, there is barely a mention of the its potential to improve the security of everything from schools to urban areas. Connected cameras or gunshot detection systems could instantly notify police that there is a sniper in, say, a school. The possibilities extend much further beyond connecting household gadgets and security. Many technology pundits believe we are on the cusp of a new industrial revolution, where devices can warn shop floor owners of potential problems before they occur while bolstering efficiency. In the near future, machines could even transact business with one another. But an average person is likely to only have a vague idea of the power of IoT technology.
How Location-Based Marketing Will Evolve in 2017
Location-based advertising and marketing technology has seen tremendous growth and improvement in 2016. Thanks to innovations in location intelligence, marketers can now leverage real-time data to better target consumers based on where they go, effectively measure how digital ads drive foot traffic into stores, and even connect the consumer journey from ad exposure to store visit to purchase data. Location intelligence is a massive industry. It allows consumer obsession with mobile devices to create significant amounts of data and insights that drive critical decision-making for a wide range of businesses. But since the space is still nascent, marketers should expect evolution in the year ahead. Here are five predictions for location intelligence in 2017:
How data science turns big data into ROI
Big data is the business buzz word of our era, bandied around at conferences and in the press as the universal panacea. However, data on its own is not the answer. As an unprocessed asset data is a cost centre, not a source of profit. Where the ROI lies is in what you do with the data and how you leverage it to drive business decisions, and the answer to that lies in data science. Long the preserve of academics and rocket scientists, data science is now front and centre of business strategy and is one of the fastest growing areas of technology. Using advanced statistical techniques to extract value from data can be transformational for businesses, boosting existing revenue streams, creating entirely new sources of revenue and identifying areas of inefficiency and waste.
Quote for the day:
"You think you can win on talent alone? Gentlemen, you don't have enough talent to win on talent alone." -- Herb Brooks, Miracle
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