‘Big data’ may not pop up in news feeds as often as it did five or ten years ago, but not because it’s become any less important. On the contrary; today, just about everyone knows that gathering data, analysing it for new insights, and then applying those insights are all vital processes for any business that can afford them.

There are many upfront costs associated with big data, whether you’re acquiring and organising data, managing its storage, or hiring data scientists. Thankfully, for small- and mid-size firms, the rise of  Big Data as a Service (or BDaaS) has made those processes available to more companies than ever before.

As major industry players have gained more experience in managing and analysing large quantities of data, they’ve begun to offer BDaaS to smaller firms. For those businesses, being able to access what you need on a pay-as-you go model is attractive.

How ‘New’ is Big Data?

Big data sets aren’t new, and neither is the desire to analyse them. Back in the 1960s and 1970s, the first data centres were already getting up and running, and the development of the relational database was underway. By the early 2000s, it was quickly becoming clear that users of websites like Facebook and other online services were generating much more data than ever before; plus, that data was clearly valuable.

Hadoop, an open-source framework designed specifically for large data sets helped big data to take off starting around 2005. Spark, another such framework, has gained popularity more recently. In the more than 15 years since, the amount of data coming in has only increased, and the potential for what can be done with it is increasing as well.

The Three (or More) Vs of Big Data

One of the concepts that has come to dominate discussions of big data is the so-called ‘three Vs’: volume, velocity and variety. Though the idea is commonplace now, it comes from just before big data took off, thanks to the emergence of technologies like Hadoop.

Analyst Doug Laney, beginning work in the year 2000 and publishing a note in early 2001, articulated a concept that has shaped how researchers understand big data ever since. Looking at trends in e-commerce in 2000-1, Laney wrote about what he called ‘the three Vs’:

1. Volume: Even twenty-odd years ago, Laney noticed that businesses were beginning to gather more and more data, and that they were ‘reluctant to discard it’ as they had begun to understand its potential value. That trend has only increased as storage has become cheaper
2. Velocity: The other immediate issue in e-commerce at the time was the increasing speed at which all this new data was coming in. This hasn’t changed, and things have only sped up in the decades since
3. Variety: Another emerging issue was the number of kinds of data that businesses were beginning to collect. Since then, companies have developed preferred practices for reconciling the issues created by ‘incompatible data formats, non-aligned data structures, and inconsistent data semantics’ that Laney described

Since then, researchers have proposed many other Vs. Two that have gained particular currency are:

1. Value: Companies are able to glean value from their data, constantly going over it as it comes in to help them with product development, or to find ways to be more efficient.
2. Veracity: For the value to be real, the data itself must be reliable. If you can’t believe what you’re analysing, then there isn’t much use in spending resources on doing so.

Big Data Today

Today, the challenge posed by the three Vs remains, as volume, velocity and variety continue to increase:

The amount of data collected today dwarfs that which was collected twenty years ago, and it varies greatly from company to company. Some firms might be looking at terabytes of data, while others might have many petabytes to store and analyse.

The data is being collected faster than ever before, as more smart devices deliver data in real time or at speeds close to it. While data was often of mixed types in the past, it used to be (mostly) structured. Now, un- and semi-structured data types, ranging from audio and video files to twitter feeds all require different processing approaches. BDaaS helps to make solutions to these problems available, and affordable, to more and more companies.

What Can Big Data Do For Your Business?

Big data is powerful. Even small changes made by businesses and governments in response to insights garnered from big data can make a big difference in people’s lives.

To inspire your BDaaS brainstorming, here are a few ways in which organisations have used big data that may help with your needs:

• Developing products: Using customer data, companies have built predictive models that can help them to understand and anticipate customer demand. This could help you to ensure that any products in development are more likely to succeed
• Maintenance: If your company has a large fleet, there’s an opportunity to use the data that you already have to predict which vehicles will need maintenance and when. Preventing potential equipment breakdowns can save you time and money in obvious ways
• Improving the customer’s experience: Finding and retaining customers isn’t getting any easier, which makes understanding every aspect of the customer experience crucial. With big data, you can gain access to a wealth of information about your customers, and then spend resources in a targeted way to perfect their experience
• Preventing fraud: Today’s fraudsters are sophisticated, and your approach to taking them on must be likewise. Using big data, you can identify patterns that might indicate fraud, and adapt to the strategies used by those seeking to take advantage
• Machine learning: Big data is one of the key components powering machine learning. The models that enable it are only possible because of the tools developed to manage, analyse, and utilise big data sets
• Efficiency: This might be both the simplest and most important application of big data for your business. Whether you’re analysing production data or customer feedback, improving your operational efficiency will have huge an impact on your bottom line

What is Big Data as a Service (BDaaS)?

As you probably know, there is a whole range of resource intensive technology solutions available on the “as a Service” model. BDaaS itself isn’t new either—it was being called the ‘next big thing’ five years ago, but its growth runs parallel to the increasing importance of big data.

Today, companies need access to data services regardless of their size, and BDaaS makes it possible for smaller businesses to access a range of services while paying only for what they use. In other words, while you might not have the money to build a data centre and hire data scientists, you could probably still use data storage and analysis. This is where a BDaaS provider can help you.

One service that some BDaaS providers offer is access to proprietary data sets. Depending on your industry, this could be especially helpful if you don’t have the time or resources to gather and analyse a given set of data yourself.

If you’re considering a BDaaS provider, you’ll find plenty of options offering you a Hadoop-based solution, or another framework, along with varying suites of analytical tools and other add-ons. Depending on what you need, you can pick and choose among the options that make the most sense for you. Here are some things to consider when choosing which BDaaS provider is best for your business:

What are others in your industry using? If your provider has experience working with clients like you, they’ll likely be better able to meet your needs and answer your questions.

Does it meet your organisation’s specific needs? If you’ve got a simple data set, you might be able to do much of what you need on your own by hiring one or two people. If, on the other hand, you’ve got a large and unstructured dataset, and transferring it to a third-party doesn’t violate any company policies, a BDaaS provider could be the right call.

What kind of feedback and analysis tools does it provide? You should look for tools and services that allow you to see what’s happening in real time, or as close to it as possible. Otherwise, you may lose some of the benefits of BDaaS that you’re hoping for.

What level of service do you need? While most services can mix and match, make sure that you’ve got a good idea of what you need before you choose a provider. If you have experience analysing data and you simply need storage, maybe a self-service plan is right for you. If, on the other hand, this is the first time that you’ll be doing data collection and analysis, you might want a managed plan.

What Does The Future Hold For Big Data And BDaaS?

Big data and BDaaS aren’t going anywhere, and according to many working in the sector, the field is actually in its early stages. Writing in Forbes, Bernard Marr put forward four potential trends in BDaaS, and big data in general, for the rest of 2021.

More And More Sophisticated Automation Due To AI

As artificial intelligence increases in complexity, due in no small part to machine learning made possible by big data, new and more complex forms of automation become possible. Machines that are able to learn for themselves by analysing data sets can automate all kinds of processes that previously took time and resources away from your employees.

For example, you could use AI to look at customer data that could work to predict how likely a new customer is to become a regular one, based not only on how much they’ve spent, but on their web browsing and demographic data. Plus, it can learn and adjust its predictions based on whether or not those predictions are accurate.

New Ways To Look At And Understand Data

Finding new ways to interpret and analyse data is always a focus in data science. Marr calls data visualisation the ‘final mile’ of analytics, and points to the emphasis on human judgment. The problem is that people miss valuable insights all the time. Recent breakthroughs in natural language processing and visualisation techniques like extended reality (XR) a blanket term for virtual and augmented reality (VR and AR) will, according to Marr, make for new and better ways to both understand and look at data.

New Emphasis on Hybrid Cloud Computing

One of the benefits of BDaaS is that it can be adapted to your specific needs. Cloud computing has played a major role in the development of big data in general, and BDaaS specifically. Marr sees a shift coming in 2021 towards hybrid cloud computing, where some infrastructure is on-site, and other aspects are handled by third-party service providers. Privacy concerns aren’t going to decrease in the future, so data storage solutions that guarantee more control over data, for companies and consumers alike, may well grow in popularity.

More Emphasis on ‘DataOps’

Borrowing from the idea of ‘DevOps’ in software development, Marr predicts more emphasis in the coming year on employees whose jobs focus on BDaaS tools. The increasing popularity of these services makes it more and more important that you can get access to what you’re paying for, and that it’s working as expected.

The post What is Big Data As A Service (BDaaS)? appeared first on Artificial Intelligence.

Today, the role of artificial intelligence in an enterprise has become so important that it has touched every facet of business. Its role will become more critical in the years to come.

For the purposes of this article, let us define A.I. as follows. Human intelligence is learnt from experience. Machines so far have been primarily used to follow instructions, i.e. programmed, hence machines have provided automation based on rules.

A.I. is not programmed to follow rules, it is like human intelligence, learns from “experience”, i.e. data.

A.I. application in businesses today can be divided into 5 key areas:

1. A.I. in data cleansing and streamlining.

2. A.I. in BI—i.e. AI to replace business analysts in preliminary analysis on dashboards.

3. A.I. in cognitive intelligence such as voice recognition, video analytics, face recognition.

4. A.I. in natural interaction – chat bots, NLP, Natural-Language Generation (NLG).

5. A.I. in expert systems—learning from myriad data sets and crystallizing an insight or action. This can be applied in classification of future unknowns e.g. fraud prevention, preventive maintenance. This can be applied also in forecasting quantities e.g. demand forecasting, supply shortage prediction. And can be applied in real time dynamic operations e.g. self-driving cars, dynamic digital marketing.

A.I. in data cleansing

There are A.I. applications today that can weed out errors in master data. In fact, a business was able to reduce their data errors in incoming data by 94% through A.I. application in data error correction. Humans used to correct such errors before and the correction was based on knowledge about the product, data on similar products and so on. If a product is a liquid and the units of measure are missing, the human planner used to correct that gap looking at the product being replaced by this new product or other liquid products in the same price range and so on, or the image of the product itself. AI now does the same and has been seen to be able to correct 94% of data errors without human help.

A.I. in BI

We know when reports and dashboards are delivered, junior analysts typically do a pre-analysis to ascertain exceptions, and explanations for those exceptions. These analysts circle these exceptions and comment on the components contributing to the exception. They also may mark something as high priority if an urgent action is needed. This saves the decision maker’s time and presents them with distilled information. Today, A.I. is able to do this – augmenting junior analysts using anomaly detection and auto-drill down, pattern recognition and clustering.

A.I. in cognitive intelligence

The usual examples people think of when talking of AI applications is in cognitive intelligence. As an example, in this pandemic, cognitive systems are tracking masks wearing compliance in closed spaces, distancing norms compliance in factories and warehouses. Cognitive intelligence is also used in less known forms, there is a recent MIT report on an AI model that can detect Covid-19 infection from your cough, being incorporated into an FDA approved cell phone app.

A.I. in natural interaction

Chat bots are here to stay. If we haven’t interacted with one yet, we are probably not clicking on that chat button on most online shopping sites. It is interesting that A.I. learning is not being achieved in most chat bots from chat histories of those businesses alone, but using innovative data sources such as Q&A dialogues in published interviews, consumer panel discussions, even published plays, and so on.

The way personalisation is achieved is even more interesting. If A.I. was simply replacing a human agent the chat would begin with “how may I help you?”. However, A.I. is much more than that. There are call intent prediction models which learn from the behavior of customers who had a similar interaction, purchase history and profile. Gleaning from data, what did such cohorts mostly call about when they did call? So, the conversation starts with “Are you calling about your oximeter ordered yesterday?”

A.I. in expert systems

This is the area impacted most post pandemic. For example, demand forecasting of CPG products using traditional techniques became impossible. However, applying A.I. made a difference.

For example, loyalty card identifiers in transaction data helped A.I. learn that there are some products which saw an upswing in sales that would continue (because the same shoppers were re-purchasing high quantities). Products like soap, sanitizers, home cleaners, etc. were identified for continued upswing from such analysis. Other items that did see an upswing but were unlikely to sustain (because the same shopper was not repurchasing high quantities) were items like paper towels, pet food, baby food, etc. This kind of ability in the first two months of dynamic demand helped many firms to plan better for the supply and allocation across locations.

With digital interactions rising, application of A.I. for a cookie less world has led to Federated learning of cohorts (FLOCs). FLOCs use models learning from customer cohort behavior through distributed data, without transferring data to a central server, thus protecting privacy.

What has been true of times of upheaval, applies to this one as well. It is a time of opportunity for those who leverage available tools including A.I. to transform their business to gain competitive advantage. Others who delay, cannot afford to miss this revolution. They will be late and may struggle, if they sustain at all.

The post The critical role of A.I. in an enterprise today appeared first on Artificial Intelligence.

Software architecture, Java and machine learning are amongst the top programming skills ranked by software engineers as important to their role in 2021, according to new research from Degreed. 

Degreed global user data from February 2020 – February 2021 uncovered the skills ranked as most important in a role, for software engineers, product managers, and business analysts. The data highlights the transferability of skills in certain technology and product management roles both internally and externally. 

Data was analysed from millions of active users in the U.S. UK, LATAM, Germany, France, the Netherlands, India, APAC, and Australia, and New Zealand – in companies that include Capgemini, Visa, Ford, Mastercard, and Unilever.

For software engineers, the top 10 skills they rank as vital to doing their work well are Java, Python, Programming, Software Architecture, Machine Learning, Software Engineering, Artificial Intelligence, Software Testing, SQL, and Linux.

Programming languages are most popular across all software engineering roles regardless of sector. The order of the top 10 changes in different sectors but the skills listed remain the same. In finance, for example, more focus is placed on Java, while in media/telecoms the priority is Python.

The top 10 skills most frequently cited as important by product managers were:

• Product Management
• Product Strategy
• Leadership
• Product Planning
• New Product Development
• Product Lifecycle Management
• Project Management
• Product Marketing
• Strategic Partnerships
• Go-to-market Strategy

Across multiple industries, the top 10 list remains consistent (particularly within finance, manufacturing, and media/telecom).

The top 10 skills ranked by business analysts are:

• Business Analysis
• Microsoft Excel
• Data Analytics/Analysis
• Project Management
• Business Intelligence
• Agile
• Project Planning
• Agile Development
• Reporting
• Software Testing

“What’s compelling here is that the top 10 lists for each role do not change significantly as we look at what skills workers find crucial to their work, no matter their sector,” says David Kuntz, head of data science at Degreed.

“A product manager in finance will broadly require the same skill set as a product manager in manufacturing. This means that the market for talented product managers, software engineers, and business analysts is not limited to an employers own sector or to a single department,” he says. 

“Employers should look further afield when struggling to find the right talent as many of the skills within roles are transferable across industries and business functions,” Kuntz says.

“They can look broadly for candidates, both internally and externally. This is particularly relevant for in-demand roles such as software engineering, where there is a well-known talent shortage.”

Kuntz says it is vital to regularly look at the skills people say that they need to effectively do their work, as this ties into their productivity and quality of work. 

“Especially as organisations look towards recovery post-pandemic,” he says.

The research found more than four in ten workers say that a lack of confidence in their skills means tasks take longer to complete and 22% say that their work is of a lower quality. 

“Spotting skills gaps early will help employers proactively upskill their workers to prevent this,” Kuntz adds.

The post Java, machine learning, AI amongst most important programming skills – report appeared first on Artificial Intelligence.

The term “digital transformation” has become so ubiquitous that it can mean almost any change from a manual process to an electronic one. But why do we have to think of change in terms of digital transformation? Few would argue that replacing an inefficient manual task with automation is a “transformation.” However, I think of change in terms of innovation, in terms of altering how we do something or the way we behave—in terms of disrupting an ecosystem. Innovation isn’t just automating processes that already exist, but rather applying technology to solve a problem in a different way.

In my view, one of the best ways organizations can approach a given problem space is by leveraging the myriad of data they collect every day. Data analytics comes to mind, of course — crunching a sea of data to find correlations and insights we can use to make a process better. How then do we decide what to do with those insights? You develop and train machine learning (ML) models to make more accurate, unbiased decisions based on the available data. Then you apply artificial intelligence (AI) to suggest the best way to act on those decisions to improve the chance of a successful outcome.

Using AI/ML for innovating the customer experience

One of the most visible targets of transformation initiatives is to improve the customer experience. The internet has removed geographical distance as a barrier between you and your competitors, so a company’s online presence is more important than ever. That’s why everyone is rushing to provide ever more-engaging online experiences, to hold a prospective customer’s interest.

Numerous companies provide website plugins to track a visitor’s clicks and actions, analyze them to intuit intention, and determine, for example, what content, advertisement, or offer to display next. Going beyond that, today’s most successful e-commerce sites also use AI/ML to personalize each shopper’s experience, like the order and presentation that will most likely result in another click or a purchase.

AI can anticipate with near certainty—based on past and present action, search patterns, profiles, external demographics and more—what a customer wants to see now and will do next. If successful, your website visitors will come to feel at-home, excited, and perhaps even brand loyalists. They’ll buy more and return more often.

But digital innovation shouldn’t stop with customer experience

There is nothing wrong with applying analytics, AI, and ML to create a more innovative and engaging customer experience. Not doing so can put you behind your competition. It’s all about building customer loyalty and boosting revenue.  

No matter how important customer experience is, however, it is a mistake to believe it is the only operational area that can (and should) be transformed using technologies like these. After all, today’s enterprise amasses data about more than just customers and orders. Your company, product, and delivery must broadly innovate — and all these happen on the backend. The efficiency of your internal operations — your support team, supply chain, production, inventory, quality control, human resources, and so on — can all benefit from applying AI and ML technologies. Consider just a few of many possible examples.

• Motivating a remote workforce – With so many teams working remotely, first-hand observation of employee engagement is next to impossible today. AI can analyze which applications employees use most, possibly even judging their levels of efficiency or frustration. Organizations can understand how happy, motivated, and engaged teams are so they can maintain or increase efficiency and productivity.
• Refining a business model and marketing – Beyond mere numbers, AI can analyze which products in your online portfolio work best and for which shoppers. Yes, this can help you shape the online customer experience. But it also lets you adapt which products you choose to keep or eliminate from your lineup (your business model) and adapt your offers based on observed customers’ choices or preferences (your marketing strategy).
• Protecting intellectual property – Organizations can even protect their patents, intellectual property, and product uniqueness by using AI, ML rules, and image recognition to smartly crawl the web to identify look-alike products and would-be theft.

The possibilities for internal process improvement across the enterprise are endless.

AI/ML isn’t just for large technology companies

In short, companies should apply AI/ML innovation to their operational processes as much as they do to the customer experience. Artificial intelligence isn’t just for technology companies, nor is it for analyzing and solving only technology problems. Organizations can use it to better understand their customers. They can use it to automate inefficient internal processes. They can leverage it for improving online security, boosting employee engagement, and reducing theft and risk.

Why aren’t more companies using AI? Frankly, they do not know how to start. They know they need to use it, but they don’t know where to “plug it in” to their systems first. Or they think they have to hire a team of AI engineers and build their solution from scratch.

Today AI is available for any company to use and benefit from, even smaller companies, without the need for a team of experts. There are many commercial apps and solutions in the marketplace that readily adapt to an organization’s existing processes. Some are even SaaS- and cloud-based solutions, meaning they do not require a big infrastructure investment to get started. The important thing to know is that any company can start small and scale up their AI solution in their own time — but getting started is the only way to stay competitive.

The post A better customer experience is important, but it’s just one way AI and machine learning can transform the enterprise appeared first on Artificial Intelligence.

DRDO has invited applications for a short term certificate online course on artificial intelligence and machine learning to be conducted by DIAT

DRDO through its autonomous organization Defence Institute of Advanced Technology (DIAT) is offering a short term certificate online course on artificial intelligence and machine learning for which interested graduates from any domain can apply. The 12-week long online course will consist of a mix between fundamentals and advanced topics of various domains of artificial intelligence and machine learning. However, applicants are required to have the basic knowledge of algorithms, databases, data structures, modular mathematics, statistics, and probability theory.

The course will be conducted online for two hours a day and five days a week. Applicants will be selected based on their performance in an online test, which will be conducted on 21 February 2021. Registration for the online test, and taking the test is mandatory for prospective students of the course. Once an applicant is selected for the course, based on their performance in the test, they would be required to pay a fee of Rs 15,000 including GST.

What Will Be Taught in the DRDO DIAT Online Course on Artificial Intelligence?

As mentioned before, students will get to learn about fundamentals and advanced topics of various domains of artificial intelligence and machine learning such as:

• Forensic and incident response.
• System programming.
• Reverse engineering and malware analysis.
• Basic and advanced vulnerability analysis, exploit mitigation and penetration testing.
• Tools and techniques for Cyber Security professionals.

Syllabus for the Entrance Test for Admission into the DRDO DIAT Course

Some of the topics based on which questions will be set in the online entrance test are as follows:

• Fundamentals of OS.
• Networking: OSI, TCP/IP, socket programming, win32 socket APIs, server messaging block, application and ports, TLS/SSL including TLS1.3, Firewalls, UTM, routing protocols, routers – core/edge routers, ASN, IPv4/v6.
• System Software: basic knowledge of assembly – x86 instruction set, addressing modes, registers, Main memory space.
• Knowledge of programming language.
• Data structures.

How to Apply for DRDO DIAT Online Course on Artificial Intelligence?

Interested students need to first register for the entrance test, and online applications for the same will be accepted from 29 January to 15 February 2021. The online test will be conducted on 21 February, and the results will be announced on 22 February 2021. Qualified candidates will need to pay the course fee before 26 February 2021. Those who wish to know more about the online course are advised to go through the official notification available on the DRDO website.

The post DRDO Invites Applications for a Certificate Online Course on Artificial Intelligence: Check Important Details appeared first on Artificial Intelligence.