Data is everywhere. The amount of global information is growing at an exponential rate. The International Data Corporation forecasts that by 2025, the global datasphere will grow to around 160 zettabytes (that is a trillion gigabytes); over 10 times the amount of data generated in 2016. 

Traditionally, data was structured and neatly organised in databases. This all changed with the emergence of the internet and the adoption of distributed technologies like the Internet of Things. There is a proliferation of ‘unstructured data’ being generated through a multitude of digital interactions coupled with exponential growth in the number of devices recording and transmitting data. Most of what we now do can be translated into noughts and ones capable of being captured, stored, searched and, ultimately, analysed. 

The emerging velocity, variety and volume of data have given rise to digital concepts such as ‘big data’ and ‘data science’. But why is data so relevant to our lives? Information is only as useful as the intelligence we can extract from it. This entails effective, insightful data analytics coupled with a large amount of computing power to cope with the exponential increase in the volume of data associated with big data.

Several market studies have consistently shown that those that have adopted big data analytics gained a significant lead over the rest of the corporate world. Data analytics is not simply an informed historical view of your company; it is the combination of real-time data together with the ability to combine multiple data sets from various sources to provide new insights into business that have not yet been available to companies until now.

Practically, any individual or company that manufactures, grows and sells any good or service can use data analytics to make their manufacturing, sales and operations processes more efficient and their marketing more targeted and cost-effective. Data must be the driving factor behind business decisions as it provides unparalleled insights into the ins and outs of the company, its supply chain and end customers.

However, in a world that is now more than ever focused on protecting people’s data, how can companies responsibly and efficiently utilise their data through analytics while ensuring they remain compliant?

Data breaches have been shown to throw organisations into the global limelight for all the wrong reasons. Companies tread a fine line between maximising their data capabilities and ensuring compliance with global regulations. Despite this, data analytics and compliance must not be viewed as opposing forces but instead as complementary aspects that seek to maximise the overall capabilities of the company.

 For starters, data analytics empower proactive and ongoing compliance efforts. Rather than waiting for compliance issues to emerge through periodic testing or risk-based internal audit activities, companies can uncover potential problems before they fully hatch and then take the steps necessary to correct the issues before they come to regulators’ attention.

The unprecedented effects that COVID-19 has had on effectively all sectors and many countries have brought the importance of utilising data and analytics back to the forefront of business discussions. The economic implications currently bearing down on organisations means there is far less room for guesswork and businesses must transition to a data-driven approach for decision-making to ensure their business continuity going forward. 

The shift towards this data-driven approach will require several decisions to be taken. Organisations must revisit their data collection goals to recognise what data exists within their organisation. They should look to better understand what data analytics tools exist and analyse which tools to adopt as part of their operations.

Additionally, the setting up of a data strategy is pivotal for companies to ensure alignment across the entire organisation and determine essential factors such as data lineage to understand the origin of the data and data governance to align with their existing business goals. 

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Organisations continue to collect more data than ever before. While the IT department is often responsible for establishing how data is collected, it will be the task of a range of people from across the business to exploit this information. But tech analyst Gartner estimates 50% of organisations lack sufficient data literacy skills to make the most of the data they hold.

The analyst says the ability to “speak data” is becoming an integral aspect of most day-to-day jobs. So how can your organisation ensure its staff are confident to work with, analyse, and argue with data? Four tech chiefs provide their best-practice tips for boosting data literacy.

1. Find people who are passionate

Ian Cohen, chief product and information officer at ICS Group, recognises the importance of building data literacy in an organisation – but he also knows that you can’t force it.

“I do not believe that you can make people do things they don’t want to do,” he says. “So you can’t walk into an organisation on the Monday and say, ‘This week we are going to increase the data literacy of all our teams, and we’re going to run this programme and we’re going to send everyone on these courses’, because it doesn’t work.”

What you typically find, says Cohen, is that the people who are interested in something will gravitate towards it – and the people who aren’t, just move away. If you’re on a mission to increase data literacy, you’ve got to first find the people who care about data and you’ve got to create environments where it’s easy for them to do more of what they already love.

“It comes back to the development piece – it’s much easier to develop people’s strengths or get them to do more of what they’re already good at rather than trying to fix their so-called weaknesses,” he says. “So help people who are really passionate about something; indulge their passions and they’ll be more passionate.”

Cohen says this in turn will hopefully feed the curiosity of those who weren’t immediately drawn to it on day one.

“Find the people who are naturally enthused, make them more enthusiastic and turn that enthusiasm into a contagion by making it visible, making it accessible and demonstrating the simplicity and the ease of the outcome. And then people come along with you,” he says.

2. Build a community of like-minded people

Malcolm Lowe, head of IT at Transport for Greater Manchester (TfGM), says the best way to boost data literacy is to bring like-minded people together, both inside and outside the organisation.

“We’ve created a community of interested parties internally, so they’re all sharing hints and tips,” he says. “We also liaise with a lot of other organisations across Greater Manchester that are on this journey as well.”

As an example, Lowe says TfGM has got a good relationship with the Manchester Airports Group: “They’re on a similar journey, but they’re maybe two or three years ahead of us. So we’re able to learn from them as well.”

He also points to a lot of Manchester-focused groups. Lowe says his local area has a strong learning vibe when it comes to technology and digital, including educational events around getting the most out of Tableau, Power BI and data analytics in general. Those kinds of events, which took place in-person prior to lockdown, are now moving online – but the potential benefit is the same: education.

“So for me, boosting data literacy is all about building a community and networking. You can read documents and websites to your heart’s content. But if you want to improve your data literacy, then it’s important to find people who’ve got similar interests and goals and to then start learning from each other,” says Lowe.

3. Ensure your data strategy is articulated and understood

Wincanton CIO Richard Gifford says he fully understands the benefits of building data literacy, both for his internal business and its external customers. However, getting people to understand the nature of that journey is far from straightforward.

If you want to make the most of data, says Gifford, then you also need a comprehensive data strategy that explains how you will make the most of the information you hold.

“It’s not a quick process to take people on that journey and show them that there’s an opportunity here to sell back to our customers, as well as making our internal operations more efficient from taking decisions based on data,” he says.

Education programmes will help to build an awareness of the importance of the data strategy and, at the same time, will build your enterprise’s data literacy. 

“I talk about us being data-driven and, in an organisation like ours, we’ve got a huge amount of data,” he says. “If you build data literacy, you can then start to make propositions with that data, and that can be beneficial for customers. I think taking people on that journey on a simple level is good, and that gets them to understand the value of data.”

4. Use classification to help people learn the value of data

Simon Liste, chief information technology officer at the Pension Protection Fund, is another tech chief who says the best way to boost digital literacy is to concentrate on education and awareness – and that process needs to spread across the entire organisation.

“Somebody that works within the business probably knows what they need from the data,” he says. “So if the people in your organisation want to make the most of information, data and security shouldn’t just be owned by IT – it’s the responsibility of every individual.”

Liste says classification of data is crucial if people want to learn how to use data effectively. That classification process includes behavioural concerns, such as how data is going to be used, distributed and dissected. He also refers to technical considerations, such as how data is going to be secured, visualised and shared, both internally and beyond the enterprise firewall.

“The classification is all about making sure that people know what they want to do with that data and how,” he says. “I think that classification involves a collaboration between those that actually do your data analysis, which is a front-end business function, and those that run back-end functionality, such as developers and your architecture team that might be interrogating data. Data literacy, therefore, should be a focus for both IT and business people.”

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Today, with the increasing volume of data day by day, processing and analysis of that data have become significant. This is where data scientists come into the scenario, determining the right data sets and variables and deriving actionable insights from that. Data scientists are analytical data experts and their roles can vary organization to organization as per companies’ requirements. Since data is the new currency for all-sized businesses, the role of data scientists is continuously becoming essential, even some are turned to forge their own business becoming CEOs and putting heavy emphasis on data strategy.

According to a report from Telsyte, big data analytics is playing a significant role in empowering CEOs and boards to drive the innovation agenda. There is also a growing realization that the information age is rapidly transforming traditional decision-making. In the next few years, the report noted, Australian organisations will have at least one executive in their team, if not the CEO, specialising in big data.

Data Scientists-Turned-CEOs

There are a number of data scientists who became CEOs making data a core part of their strategy, operations, and decision-making process. Sebastian Thrun, who led the integration of big data into robotics, is the founder of edtech startup Udacity. He is also the founder of Google X, where he led projects including the Self-Driving Car, Google Glass, and more. Brad Peters, a data scientist-turned-CEO, who founded business intelligence startup Birst. Before starting his own business, Brad led analytics at Siebel System.

Jim Goodnight, the CEO of SAS, the world’s leading business analytics software vendor, has led the company since its inception in 1976. He earned his bachelor’s degree in applied mathematics and his master’s in statistics from North Carolina State University (NCSU). He also earned his doctorate in statistics at NCSU, where he was a faculty member from 1972 – 1976. Thomas Thurston is the founder and CEO of Growth Science, which uses data to predict if businesses will survive or fail. According to Thomas, a background in data science tends to help CEOs ask better questions and get better feedback, because it brings conversations down to a level of reality and practicality. Facts, data, and probabilities can have a way of removing the ego, politics, and hand-waving from a conversation.

Considering these leaders, this is clear that data scientists are moving into CEO roles and helming most successful data startups, instead of choosing a well-paid position at a large company.

Becoming Data-Driven CEO

A data-driven CEO leverage a various number of sources of data to make decisions with precision. CEOs leading successful data-driven companies must adopt data-driven skill sets, processes, and cultures. While businesses worldwide are trying to make more effective use of data, analytics, and AI, CEOs need to develop their own core skill sets around data analytics while at the same time adapting, enhancing, and training their teams. They should also focus on acquiring talent pool rich in data analytics expertise and ensure the right teams surround them.

Additionally, future CEOs will need to consider data to make decisions that influence every aspect of their businesses. In some cases, there will be a requirement to procure a Chief Data Scientist and a team of statisticians. Comprehensively, a data-driven CEO of tomorrow will leverage big data and analytics technologies to drive outcomes with precision and relevance across the entire business.

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RL One is a long/short strategy that generates returns through deep reinforcement learning, a category of machine learning that reacts and learns from its environment by determining which decision will result in the highest risk/reward trade-off. The reinforcement learning model predicts optimal long or short exposure to the S&P 500 Index on a market-close to market-close basis. This exposure could range from 100 per cent long to 100 per cent short. These predictions are then implemented with unleveraged long or short positions in the S&P 500 Index E-mini futures.

 As a next-generation quantitative investment manager, Rosetta Analytics uses proprietary advanced artificial intelligence models, such as deep learning and deep reinforcement learning, to create robust and scalable active investment strategies.
 
Rosetta’s existing deep-learning strategies – DL One and DL Two – were funded by a US institutional investor and have been live since 1 September, 2017. The deep-learning model driving DL One and DL Two generates a signal that offers a binary trading decision. DL One implements this signal as either 100 per cent long or short S&P 500 E-Mini futures, and DL Two implements this signal as 100 per cent long S&P 500 E-Mini futures or 100 per cent cash.
 
RL One takes Rosetta’s predictive capabilities to the next level by determining the optimal allocation of its trading signals, including the size of the trade and the extent to which it should be long or short across multiple asset classes. Rosetta has also successfully tested other multi-asset strategies, including a 22-stock long-only strategy and a US large cap-equities and US bonds long/short strategy.
 
During the day-to-day management of RL One, representations of S&P 500 Index stock-level returns and financial and macro-economic data – such as interest rates and spreads, commodity prices and currency pairs – act as inputs into the strategy’s reinforcement learning model. The result is a daily optimal allocation of capital between the S&P 500 and cash.
 
Leading the Rosetta Analytics investment team are co-founders Julia Bonafede, CFA, and Angelo Calvello, PhD. Bonafede is the former president of Wilshire Consulting who, at the time, managed an institutional consulting and OCIO firm with more than US$1 trillion in assets under advisement. Angelo has a proven track record, having co-founded Blue Diamond Asset Management AG and Impact Investment Partners AG. Earlier in his career, Angelo also held senior roles at Man Group and State Street Global Advisors.
 
Julia Bonafede, CFA, co-founder of Rosetta Analytics, says: “We believe investors shouldn’t compromise on earning consistent net-of-fee returns when actively allocating to risky assets. For too long, traditional active managers have consistently failed to provide promised returns to investors. Traditional quantitative models have been using the same quantitative methods to make investment decisions based on academic frameworks developed 50 years ago. It’s time for innovation and disruption. Traditional quantitative methods continue to produce homogeneous and suboptimal performance, whereas our next generation quantitative methods use powerful self-learning computational algorithms that can identify actionable insights in traditional and nontraditional data that are hidden from conventional investment processes. These insights provide a new and sustainable edge in investment decision-making.”
 
Angelo Calvello, PhD, co-founder of Rosetta Analytics, says: “We are excited to launch our RL One Strategy with its transformational and market-disrupting reinforcement learning model that reacts and learns from the environment to generate returns. Our approach has no preset notions and is continuously learning and adapting to market conditions. The successful live performance of our deep-learning strategies and the strength of the hypothetical performance of our reinforcement-learning prototype strategies demonstrates that deep learning and reinforcement learning can be used to find new commercially valuable insights undiscoverable by traditional quantitative methods.”

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Artificial intelligence (AI) and machine learning (ML) are all the rage in retail marketing, mainly because of new challenges caused by high-dimensional data sources that were largely unheard of a few years ago. While exceptionally powerful, AI and ML are not without their criticisms, which include solution complexity, believing they can solve all problems, and a lack of transparency and insight generation. 

So, how do you begin your brand’s journey and avoid some of the pitfalls and challenges that other retailers have faced? Here are five steps to getting the most out of AI and ML investments:

Step 1: Data Strategy

Retailers should begin their AI and ML journey by updating or creating a proper data strategy. This involves assessing current data strengths and weaknesses through a series of audits and stakeholder sessions to define how data is used today, how it will be used in the future, and assessing the foundational readiness for AI and ML from a data perspective. This includes looking at how the retailer collects, assembles, stores and utilizes data.

Step 2: Use Cases and Business Case for AI and ML

It is critically important to do the due diligence up front to define and quantify the use cases for which retailers will employ AI and ML. What is the brand trying to accomplish? Are AI and ML the right or best way to tackle the problem? Does the retailer have the right data in the right shape to feed the machine and fuel the use case? Will you be able to measure and quantify the impact of the effort? If personalization is the goal, do you have the content and assets and the adtech/martech required to act on the AI and ML outputs? By spending the right amount of quality time up front to define and quantify the retail uses cases, you’ll identify gaps you might have in data or infrastructure and have what you need to build a solid business.

Step 3:  Data Readiness for AI and ML

You literally cannot build and execute an effective AI and ML program without robust, clean, tuned data and a solid identity management solution. Data is the fuel that powers AI and ML, and retailers need to make sure they understand how much effort is involved in getting data ready. This might include doing a data assessment, creating a data strategy, and activating a data unification and enrichment project. 

Step 4:  Model Development

As with any model development, the first step is data preparation.  The next step is creating a training data source. From there retailers need to create a model, review the model’s predictive performance, and set a score threshold. As you would expect, an ML model is a mathematical model with a number of parameters that need to be learned from the data. By training the model with existing data, you can fit the model parameters. These parameters are usually fixed before the training process begins and are based on your retail use cases.

Step 5:  Model Deployment

The concept of deployment in data science refers to the application of a model for prediction using new data. Once the retail model is built and tuned, you are ready to use or deploy the model to generate predictions in real time. Deployment is the process of integrating your machine learning model into an existing production environment to make practical business decisions based on data. Even if the purpose of the model is to increase knowledge of the data, the knowledge gained will need to be organized and presented in a way that your team can understand and use.

Concluding Thoughts

At Acxiom, we help marketers and retailers navigate the complexities and evolution of data, privacy management, identity management, and technology solutions to help them improve their insights about their customers and deliver business impact and ROI. AI and ML are powerful tools that can be applied to the full spectrum of data management requirements to fuel desired CX and use cases, and deliver business impact at scale. We can help you develop strategies and solutions that unlock the power of AI and ML for real-time, people-based marketing and retail use cases. It’s what we do.

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Data is the new oil for businesses, and therefore data science has emerged to be arguably one of the most preferred jobs of this decade. Data science is a competitive weapon and businesses of all sizes have been scrambling to find top talents. Therefore, the demand for data scientists is way higher than its supply. The data science sector is flourishing to such an extent that our earlier jobs study revealed that there are currently more than 97,000 job openings for analytics and data science in India right now.

However, hiring data scientists isn’t the biggest hurdle; it’s, rather, keeping these individuals engaged in the job and retain them for the future. According to the study, only 13% of the respondents said that they were happy at their current workplace. Nowadays, it is believed that organisations’ biggest data science challenge isn’t about the new technology or data itself; instead, it is the people and ways to retain them. In this article, we are going to discuss strategies to retain your data science talent in a competitive market and the ways to develop data science job satisfaction.

Enhancing Work Practices

Creating employee satisfaction includes more than just free meals, office trips, and TT tables. Organisations need to keep their data scientists engaged and satisfied with their current job roles. It is very important for a data scientist to feel that the practices at work are just and fair. The key is to create an overall vision for the work they are doing and help them understand their value in the company’s mission.

Additionally, it is also important for data scientists to have a clear connection with the rest of the business to create transparency across the enterprise. A popular method at 33%, according to the study, is involving data scientists in decision making. Many data scientists even voted to see more hands-on meetings and more feedback from their peers. The industry is booming with something new to explore every day. Therefore, organisations should aim to create a power-packed learning environment, which will enhance the personal and professional growth of data scientists.

Improving On The Go With Upskilling

In today’s world of data, if an individual isn’t able to keep pace with new technologies and capabilities, they become redundant. Therefore, many organisations take initiatives in upskilling their employees and improving their skills during the work tenure. There could be immense scope for learning and improving, be it technology related, business domain related or soft skills related. Even in our survey, we found out that all of the data scientists wish to be upskilled in all ways possible.

To keep talented data scientists interested in their work, organisations should take active efforts to increase engagement — from hackathons to brainstorming sessions. Training, workshops, conferences, events, and mentorship and innovation contests are some other initiatives that organisations can take to improve their employability skills in the field. 28% of the data scientists also said that they would appreciate it if their employers tied up with noted institutes for customised certification programmes.

Internal cross-functional learning sessions along with external training sessions are also helpful for data scientists to learn new techniques on their job. Serving the right tools is another aspect that is extremely crucial for the organisation to take care of. Without the right tools, data-minded professionals may not be fully equipped to get the desired results from your data.

Providing Growth Opportunities For Career Advancement

Innovation has been the core of data science, and therefore data scientists always want to be a part of it and drive it. Data scientists are also driven by focus, and therefore they value the flexibility of time and the ability to dig deep into subjects. It is also crucial for organisations to be data literate and be thoughtful about the work given to their data scientists. Because if the data science team is dealing with every aspect of data and serves as the de facto interface, it will be very difficult for them to come up with any desired solution.

According to our survey, 15% of the respondents said that they felt trapped in their current employment due to the lack of career advancement opportunities. A data scientist should always be given high-value projects along with the tools, leeway and a perfect environment, which will keep them motivated and engaged, which, in turn, will enable them to achieve big things. The poor use of their talent can put the future of the business at risk. It is believed that organisations shouldn’t underestimate the importance of retaining data scientists as the role of AI, and ML is only going to increase as businesses look to gain a competitive edge.

Getting Data Strategy Right

To hire and retain data scientists, organisations need to know the basics of their data and how to handle it. A data scientist would always like to join a company that has a clear mission and strategy of processing data and capitalising on the same. Having a robust data strategy — in other words, what is your business trying to achieve — will provide a clear vision to data scientists about the problem and what is expected out of them. 

No data scientists would like to surf through terabytes of data only to find relevance for the business. In fact, stress has become one of the main reasons for data scientists to switch companies. In the aforementioned study, we found out 13% of the data scientists think that their work in their previous company was stressful and therefore they switched.

Having a data strategy will ensure that the data is managed well and can be used as an asset by the data science team. It will provide the team with a standard set of goals and objectives across projects to ensure that the data is used both effectively and efficiently. A defined data strategy will establish standard practices to manage and share data across the enterprise, which can be used to make informed decisions, understand customer trends, provide smarter services, improve internal operations, and create additional revenue.

Cross-Training Data Scientists

Along with strategising the data and communicating the role of data scientists, organisations also need to describe the purpose of data in their businesses. Data scientists can process and analyse the data gathered in a better way if they are aware of why the data is being gathered, and what are the aspects of its usage in the business. 

Organisations need to create a strong relationship between their data scientists and business executives by cross-training them with other business aspects. Cross-training data scientists with different business roles can give them a better clarity of the organisation and also help them grow in their career path towards more C-level positions — it works as a motivator for the talent. 

A few companies also take a step further and identify insiders who have analytical skillsets and train them in data science. Having such liberty will help data scientists to correctly fit data analytics into a narrative context for the whole company. It will also help them see the financial implication of their work.

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The explosion of Big data has resulted in many new opportunities for the organizations leading to a rapidly increasing demand for consumption at various levels. The big data applications are generating an enormous amount of data every day and creating scope for analysis of these datasets leading to better and smarter decisions. These decisions depend on meaningful insight and accurate predictions which leads to maximization of the quality of services and generating healthy profits. This storm of data in the form of text, picture, sound, and video (known as “ big data”) demands a better strategy, architecture and design frameworks to source and flow to multiple layers of treatment before it is consumed. The 3V’s i.e. high volume, high velocity, and variety need a specific architecture for specific use-cases.

When an organization defines a data strategy, apart from fundamentals like data vision, principles, metrics, measurements, short/long term objectives, it also considers data/analytics priorities, levels of data maturity, data governance and integration. This is very crucial for the organization’s success and a lot depends on its maturity. As the organization moves forward with the aim of satisfying the business needs, the data strategy needs to fulfill the requirements of all the business use-cases.

The use-cases differ from one another resulting in one architecture differing from another. In such scenarios, the big data demands a pattern which should serve as a master template for defining an architecture for any given use-case. Most of the architecture patterns are associated with data ingestion, quality, processing, storage, BI and analytics layer. Each of these layers has multiple options. For example, the integration layer has an event, API and other options. The selection of any of these options for each layer based on the use-case forms a pattern. Likewise, architecture has multiple patterns and each of them satisfies one of the use-cases.

The big data architecture patterns serve many purposes and provide a unique advantage to the organization. The pre-agreed and approved architecture offers multiple advantages as enumerated below;

1. Agreement between all the stakeholders of the organization

2. Better coordination between all the stakeholders within the organization especially between Data Strategy and IT

3. All the stakeholders provide their complete support for the implementation of the architecture

4. Minimal or no effort from all the stakeholders during any new architecture implementation

5. Faster implementation of new architecture

6. Early enablement of architecture will lead to the speedy implementation of the solution

The architecture pattern can be broadly classified as;

1. Source

2. Data Integration

3. Storage

4. Data Processing

5. Data Abstraction

6. Data Schema

Each layer has multiple architecture options along with technologies tagged to each of them. The source system or application broadly generates 3 types of data namely, structured, semi-structured and unstructured depending on the nature of the application. This data can be acquired in many ways using any of the methods like messaging, event, query, API or change data capture (CDC). The extraction of data could be either push or pull depending on which method of architecture pattern is used. Generally, API, CDC and messaging use push while query uses pull mechanism. The ingested data needs storage and this can be done on relational, distributed, Massively Parallel Processing (MPP) or NoSQL databases. In some patterns, the data resides in memory. The in-memory storage is useful when all the processing has to be done in memory without storing the data. The processing of data can be distributed, parallel or sequential. The data abstraction and schema define the output format and further redirect it to analytics, dashboards or downstream applications.

Once the architecture pattern is defined, it can be used for any new or modified use case as mentioned in the below illustration.

As an organization expands its business, it has to deal with a new set of applications and data. In this scenario, the organization’s existing data architecture supports only a structured dataset whereas the adoption of new applications generates semi-structured and unstructured data. In such scenarios, a well-defined architecture pattern, as part of the data strategy, can quickly absorb and adopt the new use case requirements. The above illustration depicts the end to end flow of the architecture that is required to bring the semi and unstructured data to support the business with the required analytics and predictive models.

Well, we have covered the architecture patterns with various options like Kappa, Lambda, polyglot, and IoT and included all the major patterns that are currently used. We will glance at other aspects of data strategy in the upcoming articles.

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You may have heard about deep learning and felt like it was an area of data science that is incredibly intimidating. How could you possibly get machines to learn like humans? And, an even scarier notion for some, why would we want machines to exhibit human-like behavior? Here, we look at 10 examples of how deep learning is used in practice that will help you visualize the potential.

What is deep learning?

Both machine and deep learning are subsets of artificial intelligence, but deep learning represents the next evolution of machine learning. In machine learning, algorithms created by human programmers are responsible for parsing and learning from the data. They make decisions based on what they learn from the data. Deep learning learns through an artificial neural network that acts very much like a human brain and allows the machine to analyze data in a structure very much as humans do. Deep learning machines don’t require a human programmer to tell them what to do with the data. This is made possible by the extraordinary amount of data we collect and consume—data is the fuel for deep-learning models. For more on what deep learning is please check out my previous post here.

10 ways deep learning is used in practice

1. 1.       Customer experience

Machine learning is already used by many businesses to enhance the customer experience. Just a couple of examples include online self-service solutions and to create reliable workflows. There are already deep-learning models being used for chatbots, and as deep learning continues to mature, we can expect this to be an area deep learning will be used for many businesses.

2.       Translations

Although automatic machine translation isn’t new, deep learning is helping enhance automatic translation of text by using stacked networks of neural networks and allowing translations from images.

3.       Adding color to black-and-white images and videos

What used to be a very time-consuming process where humans had to add color to black-and-white images and videos by hand can now be automatically done with deep-learning models.

4.       Language recognition

Deep learning machines are beginning to differentiate dialects of a language. A machine decides that someone is speaking English and then engages an AI that is learning to tell the differences between dialects. Once the dialect is determined, another AI will step in that specializes in that particular dialect. All of this happens without involvement from a human.

5.       Autonomous vehicles

There’s not just one AI model at work as an autonomous vehicle drives down the street. Some deep-learning models specialize in streets signs while others are trained to recognize pedestrians. As a car navigates down the road, it can be informed by up to millions of individual AI models that allow the car to act.

6.       Computer vision

Deep learning has delivered super-human accuracy for image classification, object detection, image restoration and image segmentation—even handwritten digits can be recognized. Deep learning using enormous neural networks is teaching machines to automate the tasks performed by human visual systems.

7.       Text generation

The machines learn the punctuation, grammar and style of a piece of text and can use the model it developed to automatically create entirely new textwith the proper spelling, grammar and style of the example text. Everything from Shakespeare to Wikipedia entries have been created.

8.       Image caption generation

Another impressive capability of deep learning is to identify an image and create a coherent caption with proper sentence structure for that image just like a human would write.

9.       News aggregator based on sentiment

When you want to filter out the negative coming to your world, advanced natural language processing and deep learning can help. News aggregators using this new technology can filter news based on sentiment, so you can create news streams that only cover the good news happening.

10.   Deep-learning robots

Deep-learning applications for robots are plentiful and powerful from an impressive deep-learning system that can teach a robot just by observing the actions of a human completing a task to a housekeeping robot that’s provided with input from several other AIs in order to take action. Just like how a human brain processes input from past experiences, current input from senses and any additional data that is provided, deep-learning models will help robots execute tasks based on the input of many different AI opinions.

The growth of deep-learning models is expected to accelerate and create even more innovative applications in the next few years.

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In retail, supply chain efficiency is essential. Inventory management, picking, packing and shipping are all time and resource-intensive processes which can have a dramatic impact on a business’s bottom line.

The problem is these are complex processes, particularly when it comes to large scale operations covering multiple outlets and territories. The fact they are often dependent on outside forces – suppliers, service providers and even weather – make getting it right even more difficult.

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