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Digital Skills

Around the world, across the industries, C-suite executives are concerned about the widening digital talent gap in their organization.

AI, big data, cryptocurrency, cyber-security – with so many technologies creating buzz at once, it is becoming increasingly difficult for organizations to determine which skill set they need to invest in.

Here are top 10 essential digital skills that you must have on-board to succeed in 2019, whether you are an organization aiming at transformation or a service provider delivering next-gen services to clients.

Data Analysis

digtial skills 2019 data analysis data science data analytics

With today’s advanced analytics tools, companies now have the means to analyze heaps of untapped data they have about their customers and organization. But they also need expert data analysts and scientists who can efficiently use these analytics tools to do all sort of analysis work (descriptive, diagnostic, predictive, and prescriptive) on that data, interpret it, and come up with crucial insights.

Besides analysis, the data science team should be especially skilled in the visualization part in order to showcase data and create reports that are easily understandable for the management and can assist them in making decisions.

Data Engineering

digital skills 2019 data engineering

Data engineering involves building tools and infrastructure that data analysts/scientists use. While data science focuses on analytics, data engineering is more about data consolidation and warehousing. It is essentially software engineering whose primary purpose is to keep data clean and flowing and deploy data insights at scale.

On the tech side, SQL, Java, Python, Hadoop, and Linux are the hottest data engineering skills currently. In fact, according to a recent study by Stitch Data, the demand for data engineers exceeds the demand of data scientists.

Mobile Expertise

digital skills 2019 mobile development expertise

No business needs a reminder that it needs to adopt a mobile-first approach in current dynamics – be it customer apps, content, or internal communication. The mobile computational environment in itself is evolving constantly. So, it is important that developers and marketers stay abreast with the latest mobile trends and be proactive in delivering customers an optimized and state-of-the-art mobile experience.

And in 2019, mobile expertise must not be limited to smartphones or tablets – there is a whole new generation of mobile devices hitting mainstream adoption such as wearables, IoTs, and more.

UX Design

digital skills 2019 UX design

UX design may sound nothing new, but with user’s attention span constantly declining across platforms, focusing on it has become all the more important. In current dynamics, UX isn’t just about visually appealing UI and tried-and-tested navigation. It has become more of a creative-meets-analytical type of role, where every decision is backed by data rather than just guts.

Today’s UX designers also need to think in terms of multi-platform since modern customers’ buying journey span over multiple platforms. So, it is important to deliver a consistent digital customer experience across multiple platforms to ensure a smooth purchase experience.

Machine Learning

digital skills 2019 machine learning

Machine learning is unquestionably one of the hottest digital skills in demand today. From voice assistants to data analysis and self-driving cars, there are tons of use cases of this futuristic tech across industries. In fact, all the other digital skills listed here has or may have some use of machine learning for better efficiency.

However, the AI/machine learning ecosystem is quite vast and is mostly exclusive to research currently. Only the supervised learning part of it has corporate applications as of now. So, it is crucial to know the current state of machine learning in the corporate world, how your business can leverage it, only then invest in acquiring the required skilled resources.

Blockchain

digital skills 2019 blockchain cryptocurrency

Thanks to the Bitcoin buzz, the tech world is now aware of blockchain (even if many still don’t understand it). Blockchain (or distributed ledger) has given rise to decentralized applications, which are inherently more secure and transparent. Apart from its widespread use cases in the finance sector (cryptocurrency), today, the tech community has found a number of other use cases for blockchain such as crowdfunding, file storage, identity management, digital voting, and more.

Building blockchain-based applications requires skills such as networking engineering, cryptography computing, database designing and programming languages (C++, Java, Python, Solidify, etc.).

Related: How AI is driving the next phase of growth in Fintech

AR/VR

digital skills 2019 augmented virtual reality AR VR

AR/VR is already transforming the gaming and entertainment industries and is also gaining wide adoption in media, marketing, advertising, health care, and manufacturing. Businesses of retail, travel, and many other industries have already begun to provide AR capabilities in their apps.

Besides these, AR/VR has opened a whole new world of possibilities how people will consume content in the near future. Currently, video is the most popular mode of content consumption. But as AR/VR based interactive content become easier to create and easier to access, it will naturally surpass video.

Cybersecurity

digital skills 2019 cybersecurity network security

Data breaches are the biggest threats of the digital age. And when they happen, they often result in long term financial loses for a company. And as the security measures develop and evolve, so do the threats. So, network security or cybersecurity is undoubtedly one of the most important digital skills to have on-board in today’s business environment.

In fact, according to a recent ESG report, cybersecurity has topped the list of the problematic shortage of skills in organizations globally. And over the past few years, the concern has only grown (from 42% in 2015-16 to 53% in 2018-19).

Cloud Computing

digital skills 2019 cloud computing edge computing multi-cloud

Cloud adoption continues to grow. According to LogicMonitor’s Cloud Vision 2020 survey, 83% of enterprise workload will be in the cloud by 2020. To accommodate cloud adoption, migration, and upgrade, organizations need network engineers, cloud architects, developers, and system administrators with relevant cloud computing skills.

However, today the cloud is not the same decade old cloud. From multi-cloud to edge computing, it has evolved a lot and organizations need to keep the latest trend in check and regularly upgrade their cloud strategy to make the most out of it.

Social Selling

digital skills 2019 social selling

Social media has matured over the past decade. It is no longer exclusive to connecting friends and communities. Serious business happens on it every day. Engagement on social media is far better than traditional mediums. For instance, LinkedIn’s InMail open rates are 300% higher than email. And given the nature of the platform, the business world has moved away from hard selling to value-based selling, where mutual trust and relationship with clients/customers is of the highest priority.

So, in current dynamics, having a marketing team with expert social selling skills is must for continuous growth of the organization.

Concluding Remarks

The skill gap is the most prominent threat that looms over the business world today. And multiple reports warn that it will get worse in the near future. It is imperative for C-suite executives to react to this threat and come up with ways to handle the widening digital talent gap in their organization.

And in case we missed mentioning any digital skills that you think are crucial in the current digital age, let us know in the comment section below.

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Technology


Recent advances in computer vision are rapidly taking us towards a future we have long imagined.

A future where you don’t have to feed in or tell computers anything for a range of simple tasks – domestic and industrial. Computers have a vision of their own and can automatically initiate the task you want them to do. [Ex: FaceID by Apple]

Thanks to the advances in deep learning and abundance of visual data available today, computer vision market is one of the fastest growing innovative tech markets today – with a multitude of applications sprouting across multiple industries.

computer vision market revenue

Adopting computer vision is a smart move forward in current dynamics. But don’t just dive in without doing your homework.

To help you with that, here are top computer vision trends that are currently driving value in the sector.

Deep Learning

Deep learning algorithms have multiple advantages over traditional machine learning algorithms. First, they effectively reduce the need for frequent human intervention and thorough domain knowledge in training a model. Second, their workflow allows superior accuracy. And third, the more data you give them, the better results they produce, which is not typical with ML algorithms.

deep learning vs machine learning

How is it relevant in regards to computer vision — Apart from doing a full picture recognition based on extracted features, they can also identify light and dark pixels, categorize lines and shapes to produce far more accurate results, far more efficiently.

For example, recently, Trantor team did a deep learning project for a leading electric appliance company to automate the extraction of data from electrical blueprints. Our solution provided 95% accuracy and reduced the operation cost for the task by 60%.

Another advantage with deep learning is that the more data (images/videos) you feed in, the better the results get. Many retailers are using these features to map customers’ interaction with products in-store so they can provide them a more personalized shopping experience.

Transfer Learning

Transfer learning has gained a lot of popularity in the recent past. Thanks to its extensive applications in the field of computer vision.

If you are not familiar, it essentially includes using data from one model to train a similar model. The data is fed in the form of layers. These layers are simply properties or constraints related to a specific task.

transfer learning from one ML to another

For instance, if you have a trained ML model A that identifies animals’ pictures, you can use it to train a model D that identifies dogs’ pictures. Or if you want to go a bit complex, then you can use the model D to train a model C that identifies cats’ pictures. In terms of data layers, training D would require adding a few additional layers to A, whereas, training C would require eliminating some dog-specific layers and adding some cat-specific layers to D.

Transfer learning makes things much easier for developers, as you don’t have to work from scratch to train an ML model. And for developers’ convenience, there are thousands of open source ML models, which they can customize to train new models with applications in a number of industries such as retail, healthcare, automobile, transportation, and so on.

Recommended Read: State of Machine Learning (AI) – Notes from DataHack Summit 2018

Point Cloud

Point cloud is a set of data points in 3D space. Simply put, every point on the surface of an object has 3-dimensional coordinates (X, Y, Z), which is referred as point cloud.

point cloud computer vision

Point cloud is a 3D machine vision-based technology, which provides an accurate representation of where an object is in the space. For this reason, it has multiple applications that involve object identification or object movement tracking.

Some of these applications are – monitoring of physical assets, scanning construction sites, scanning landscapes, mapping utility infrastructure, etc. These applications can come quite handy in solving real-world problems like urban planning, repair works, natural disaster management, and so on.

Final Remarks

Visual content provides a firehose of information about the state of the world. And with today’s data technologies, we are able to observe, record, and act upon this information effectively. As a result, the world is changing rapidly, and for any organization, it is crucial to keep up the pace to stay state-of-the-art for itself and its client.

While we have discussed only major developments here, you shouldn’t limit your focus to these alone. We also encourage you to explore areas like mixed reality, edge computing, and semantic segmentation, which are gaining popularity across industries.

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AI

Why are machines so smart?

Because we make them so.

But they can be only as smart as we make them to be.

In today’s AI ecosystem, there is pressure on everyone to make their machine learning algorithms as good as human intelligence. And the only way to achieve it is to have a good amount of quality labeled data to train those algorithms.

Except that the data doesn’t come easy.

Every organization entering into machine learning related services faces this challenge today. And to overcome it, they must have the know-how of different data labeling tools that can help in building quality training data sets and build them efficiently.

Here is a list of the best data labeling tools based on what type of data you are labeling.

Image & Video Labeling Tools

Annotorious

It’s a free, easy to use, MIT-licensed annotation tool for labeling of images on a website. It’s free for commercial use as well. Integrating it with your website only requires adding 2-3 lines of code. You can also explore many of its features in the demos.

annotorious image video data labeling tool 2019

Annotorious’ most noticeable features include:

  • Image annotation with bounding boxes
  • Process maps and high-resolution zoomable images
  • Annotorious can be modified with plugins to suit a particular project’s need
  • Annotorious Community; where developers can find how they can modify it to extend its capabilities
  • Annotorious Selector Pack plugin (to be launched), which will include features like custom shape labels, freehand, point, and Fancy box selection

LabelMe

LabelMe is an open source online data labeling tool. With simple signup, it allows users to label images and share their annotation publically, which is primarily used for a range of computer vision based applications and research.

LabelMe image video data labeling tool 2019

Some of LabelMe’s key features include:

  • LabelMe also offers its mobile app for image labeling & annotation
  • Image collection, storage, and labeling
  • Training object detectors in real-time
  • Simple and intuitive UI
  • LabelMe offers MATLAB Toolbox that allows users to download and interact with the images and annotations in the LabelMe database

Labelbox

Labelbox is one of the most versatile labeling tools available today. Its comprehensive features enable organizations to easily adapt and train their machine learning models. Its pricing varies based on the amount of data and the sophistication of the model you are training.

labelbox image video data labeling tool 2019

Key features include:

  • Labelbox supports Polygon, Rectangle, Line, and Point segmentation, as well as pixel-wise annotation
  • You can create bounding boxes and polygons directly on the tiled imagery (zoomable maps)
  • Ideal to work with a big team of labelers as it serves up images to be labeled asynchronously, i.e., no two labelers label the same image
  • Assured security as the source data is either stored in-house or on a private cloud
  • Labelbox allows you to maintain quality standards by keeping track of labeling task performance

Sloth

Sloth is a versatile annotation tool for various data labeling tasks related to computer vision research. It’s free and is one of the most popular tools for facial recognition, therefore, is widely used for surveillance and user identification related applications.

sloth image data labeling tool 2019 facial recognition

Most notable of Sloth’s features include:

  • It allows an unlimited number of labels per image or video frame – leading to more detailed file processing
  • It supports various image selection tools – points, rectangles, and polygons
  • Developers consider Sloth as a framework and set of standard components that can be configured to build a label tool specifically tailored to one’s needs

Audio Labeling Tools

Praat

Praat is a free audio labeling tool under the Creative Commons (CC BY SA) license, meaning, any derivative works must also come under creative commons license.

praat audio data labeling tool 2019

Praat’s primary features include:

  • Spectral analysis, pitch analysis, format analysis, and intensity analysis of audio files
  • It can also identify jitter, shimmer, voice breaks, cochleagram, and excitation pattern
  • You can work with sound files of up to 3 hours (2GB)
  • It allows you to mark time points in the audio file and annotate these events with text labels in a lightweight and portable TextGrid file
  • Users can work with sound and text files at the same time when text annotations are linked with an audio file

Aubio

Aubio is another free and open source annotation tool for audio data labeling. The tool is designed to extract annotations from audio signals. Aubio is written in C and is known to run on most modern architectures and platforms.

aubio audio data labeling tool 2019

Aubio offers the following key features:

  • Digital filters, phase vocoder, onset detection, pitch tracking, beat and tempo tracking, mel frequency cepstrum coefficients (MFCC), transient / steady-state separation
  • You can segment a sound file before each of its attacks, performing pitch detection, tapping the beat and producing midi streams from live audio
  • There’s a dedicated function library to execute above-mentioned functions in real-time applications
  • Users can also use these functions offline via sound editors or software samplers

Speechalyzar

Speechalyzar is an audio data labeling tool specifically designed for the daily work of a ‘speech worker’. It can process large speech data sets with respect to transcription, labeling, and annotation. Its main application is the processing of training data for speech recognition and classification models.

speechalyzar audio data labeling tool 2019

Speechalyzar’s main features include:

  • You can implement it as a client-server based framework in Java and interfaces software for speech recognition, synthesis, speech classification and quality evaluation
  • Speechalyzar also allows you to perform benchmarking tests on speech-to-text, text-to-speech and speech classification software systems
  • Ideal for manual processing of large speech datasets

EchoML by Azure and Soundscape are some other audio data labeling tools with rich visualization capabilities that you can also explore.

Suggested: Leverage the power of data with visualization

Text Labeling Tools

Rasa NLU

Rasa NLU is an open-source NLP tool for intent classification and entity extraction. It is primarily used to annotate text for chatbots but can be used for a variety of applications. For instance, recently Trantor used Rasa NLU to train a machine learning model to detect harassment and abuse in email communication within an organization.

Rasa NLU text data labeling tool 2019

Some of the advantages with Rasa NLU are:

  • Users can tag multiple words in a single sentence to their respected class or assign the same word in multiple classes
  • You can customize and train its language model as per domain-specific needs and get higher accuracy
  • Rasa NLU’s open source library runs on premise to keep users’ data safe and secure

Stanford CoreNLP

Stanford CoreNLP is a free, integrated NLP toolkit that provides a set of human language technology tools, which allow users to accomplish various text data pre-processing and analysis tasks.

Stanford CoreNLP text data labeling tool 2019

Here are some advantages with Stanford CoreNLP:

  • It offers a broad range of grammatical analysis tools (base forms of words, parts of speech, names, normalize dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases and syntactic dependencies)
  • CoreNLP is a fast, robust annotator for arbitrary texts, widely used in production
  • It offers a modern, regularly updated package, with the overall highest quality text analytics
  • Support for a number of major (human) languages
  • It offers APIs for most major modern programming languages and can run as a simple web service

Bella

Bella is a text annotation tool that helps data scientists manage, label, and evaluate natural language datasets. It is designed to save time spent in measuring and learning data, which involves collecting, inspecting, training, and testing data.

Bella text data labeling tool 2019
Image: A Bella project file for labeling a social media post (Source: Github)

Some plus points of using Bella:

  • It offers an intuitive GUI, which allows users to label and tag data through convenient keyboard shortcuts and swipe, and visualize metrics, confusion matrices, and more
  • Bella also offers database backend to easily manage labeled data
  • Bella is a preferred tool for sentiment analysis, text categorization, entity linking and POS tagging

Tagtog

Tagtog is a versatile text labeling tool that offers manual as well as automated annotation. It’s an AI startup with an impressive client base including AWS, Siemens, and a number of data science research institutions.

tagtog text data labeling tool 2019

Some of the best things about Tagtog are:

  • Users don’t require coding knowledge or data engineering concepts to use Tagtog
  • Tagtog offers inbuilt ML model to automate text annotation and also provides hassle free deployment and maintenance of manually trained models
  • Tagtog annotation tool allows multiple users to collaborate to a single project

Conclusion

There are numerous other data labeling tools in the market, apart from the ones listed above. And as with any other tool for any other purpose, the key is not to know a lot of tools but to know which tool will work best for a given project and to understand how to leverage it best.

And as for which approach you should adopt for labeling – in-house or outsource – that also depends on the project requirements. If you have time and resources, you can do it in-house. If priority is to cater customers with AI driven solutions as quickly as possible, then it is suggested to outsource your projects to a professional firm.

The machine learning market is brewing up and companies are in a rush to get ahead of each other. So, in current dynamics, spending a little to take the advantage of the early bird can make a big difference in the long run.

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