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How VC Firms Are Using Machine Learning to Make Robust Investment Decisions

Posted on March 12, 2018December 17, 2018 by Dexlab

Venture capital companies find it hard to pool in interesting investment options – the task is laborious and travel-intensive. But, thanks to machine learning and predictive analytics – they have now started to transform the entire procedure of how an investor builds up a portfolio altogether.

Considering the power of AI’s utility in determining the most fabulous startup investments, InReach Ventures co-founder Roberto Bonanzinga has decided to invest $7 million on respective software that deploys machine learning to identify significant European startups to invest capital into. Following its footsteps, several other VC firms have started doing this, already just to thrive in.

Rightfully so, AI is an incredible tool that is capable enough to filter out all the unnecessary noise and pull up VCs with potential candidates for sound investment. This makes it easier for entrepreneurs to hit the optimal level of funding and appeal to strong VCs.

AI: An Investment Ally

According to a Social Science Research Network Study, there lies an inherent risk with investing on newbie entrepreneurs, and just only 18% tastes success on their feats. Brand new business owners are ambiguous, they need some scrutiny before investment – for that, AI framework is armed with the required tools and information – it can internalize data to easily derive at conclusions and fasten a success rate to a company on the basis of past industry performance, revenue growth, profit ratios and market size.

As a result, entrepreneurs can tweak their pitches and alter company profiles to better tally with AI, and this how they can start:

Get Deeper

Who doesn’t dream of owning a company that’s a market leader?! However, raising such adequate amount of capital becomes the real challenge. The challenge intensifies when budding entrepreneurs need to attract funds.

For such minority-fronted startups, Alice, a formidable AI platform uses data to decide which businesses are worth funding. Entrepreneurs should implement AI platforms, like Alice to take a deeper look into the key metrics to get a larger picture how their startups are staking up to their tailing rivals who received funding and how well they are functioning.

Tracking Investor Trends Helps

Age-old methods of tracking investment trends are things from the past, because AI and machine learning is changing the entire ball-game. A Berlin-based VC firm Fly Venture plans to target European startups in the seed stage and pre-Series A startups and finally closed its first fund at $41 million. It aims to use machine learning to generate deal flow. This type of technology helps entrepreneurs meet the right investors at right time. After keeping a close eye on the market, it’s about time to utilize the AI-sought information to make sure your company is line with what investors are seeking in a veritable startup partner. This will bear more fruits and less frustration.

Never stop evolving

The best thing about AI is that it never stops improving. Constantly, machine learning is on the move – it analyzes information 24/7 so that entrepreneurs gain access to non-stop updates to tweak their businesses, while pitching for investors.

In a nutshell, to have better insights and cleaner access to data, entrepreneurs need to harness the relentless power of AI. The technology isn’t eating away our jobs, instead its bringing a new change in the data-inspired environment. And if you are already working with it, you’ll understand how it’s reshaping and guiding venture capital to startups that AI finds worthwhile.

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How Machine Learning Coupled With Data Science Improves Retail Scenario (Part II)

Posted on March 9, 2018May 23, 2020 by Dexlab

This blog is a continuation of the previous blog that talked about how data science is improving retail – through cutting edge machine learning training models. For sure, retail and ecommerce churns out a humongous amount of data, but with no proper tool of analysis, the vast pool of data lies unutilized, unearthed and you end up knowing what you already know.

In this blog, we will delve into a few common uses of data science in retail – which demands absolute attention, before we start automating procedures.

Product Recommendations

In a traditional shop setup, retailers would have consultants who would understand customer requirements and use their own judgments to help them find a suitable product. In an online scenario, this would be based on past performance and revenue generation and the sole aim would be to pitch the highest selling product. Not only historical purchases and recent online activity, but consumer’s social media and online sharing would give more idea about their interests, preferences and designer they like.

Sometimes, we also come across ghost clients – clients about whom we have no information, in fact, we don’t even know from where they are browsing. In this case, your recommendations would be based on intuition and might not be 100% accurate. The deal becomes trickier here. On the other hand, there are clients about whom who know everything – and thus tailor our offerings.

Product Assortment

No wonder, we have to keep products to satisfy our niche customers, but on a wider scale, we have to introspect what stuffs to keep in stock. A proper analysis of our product demands and the kinds of products our clients swear by, we can ascertain what items to restock again and again. Also, we can take a cue or inspiration from our vying competitors, as they are a good source of information for a perfect assortment of products you want to include. A full account of their inventory will enlighten you about a few blind spots you had, and devise how to correct them before it’s too late.

Pricing

The people will pay whatever rates the market supports. The price of the product is still subject to change, depending on the country of origin, taste and preferences and market scenario. But these are more of a supply side changes, so what about the demand side? Interested customers are keen to buy products even at varying prices, but the products should be truly good enough. The scale also plays an important role in deciding prices. The best pricing decisions take into account data regarding weather, day of purchase, several economic factors, location and more.

Customized branding/marketing

It is mostly curated for large retailers. For example, how about some doing some routine advertising – it applies to both digital and offline branding, though much easier for digital. A monthly newsletter carrying all the needful information about discounts, new product launch and promotions always will keep your customers’ updated about everything that’s going around the company. But, make sure they have some sort of personal touch – personalized marketing helps!

Summary

While the sky is the limit for data science, the blog above sheds light on the benefits of data science and the true impact of having trust on data. After all, it is of no use to keep data and not take advantage of it!

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How Machine Learning Coupled With Data Science Improves Retail Scenario (Part 1)

Posted on March 8, 2018May 23, 2020 by Dexlab

The mammoth growth in ecommerce signifies an entire paradigm shift in retail sector. Figures say, ecommerce accounts for $2 trillion dollars in sales and more. Though traversing through both the offline and online market seems a rather challenging task, but when we finally concentrate on each customer and their purchasing manner, it feels easier to break up the analysis into a few different paths.

In this blog, we will take into account a few interesting ways, in which data science increases your sales, online and offline, alike. But before that, understand whom are you selling your products? Hoarding information about your clients is crucial, and of course there are many ways to do this. Amazon is one of the biggest examples of this. They predict future purchases of customers, based on the past behavior. Companies lose valuable customers if they don’t look at the data with a wider scope and search for insights. But Amazon is definitely not one of them, and their technique is clearly working for them with over $2 billion profits made last year.

The Mechanism Behind

At Amazon, products are shipped even before customers have ordered them. This means, when the products are shipped, there’s no one to receive them. But, does it really matter! The main logic behind such steps is that once the products are taken out of the warehouse and transported to a particular area, they can easily be marketed to other dealers at discounted rates or kept inside the final hub. This is more like a logistic marvel than an ecommerce miracle – but it definitely makes us believe in the concept of forward thinking to lead the change.

The working principle in here is the most innovative concept of machine learning that helps in predicting future client behavior pattern. It works on data to train a formidable model. Training is a notable process of pouring data into the model so that it can employ statistical weights to automatically identify future purchase trends. For example, Mr. A purchases a new item every two or three weeks, so it’s expected that he will make a purchase within that time limit. For this, we don’t have to use data, but just divide it into train and test data. However, this is a very simple example – in reality these trends are juxtaposed with other millions of clients to differentiate clients into numerous cohorts that overlap and vary. Machine learning techniques are used in a plethora of different use cases, like product recommendations, churn predictions, logistics planning and automatic personalized marketing. We will discuss deeply about them in our next blog section.

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Make Flexibility Your Bae

While working on data science, it is important to focus on flexibility – the whole structure of data warehouse will start changing once you start trying something new. At times it may seem to be amusing, but on the long run, you will come across several significant insights.

With all these on point, scoring high on retail is no more a distant dream. Data science and machine learning methods have made everything so easy, and so manageable. To give a robust push to your career in data science, take up data science online training from DexLab Analytics. Apart from data science, they also offer excellent Machine Learning Certification for all data-hungry candidates – go take a look at their course structure.

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Getting Started with Machine Learning: Crack the Code

Posted on February 20, 2018December 22, 2018 by Dexlab

Machine Learning has taken us all to the tipping point from where the entire ballgame of technology and the way we interact with the digital world has started changing and the surge is expected to continue over the next decade. Increasingly, the decisions of the future are going to be made by machines, and we can’t seem to be more excited!!

It’s time to adopt Machine Learning

According to McKinsey reports, AI adoption in the tech sector is at its nascent stage, with few firms implementing it on a large scale. The companies that are yet to deploy it are still in two minds whether they should expect return on investments or not.

Nevertheless, skilled data scientists better be start speeding up the process of implementation of these emerging technologies if they want to stay right on edge ahead of their tailing rivals. Machine Learning is the new in-thing that must be embarked on RN.

And for that, here goes the following tips that will help you ride towards AI success:

Inspect the areas where data science fits into

Leverage data science and Machine Learning within an organization to trigger better optimization and smoother implementation. Imbed data science and machine learning into every department, like HR, marketing, sales and finance. Also, try pairing data scientists with software engineers to build agile models on machine learning, that’s the best way to scale across company operations better.

Treat data as money

Today, data acts as the fuel for an organization. But it can also be treated as money, and diligent data consultants need to manage, protect and obsess over it. Data is powerful but in order to derive the best out of it, it needs to be played well in the hands of experts. And those hands are of data specialists who values data like money.

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Stop hunting down purple squirrels

No wonder, data scientists are individuals with an exceptionally high aptitude in math and statistics; they are skilled in evaluating insights in data. They don’t necessarily have to be software engineers who only know how to write algorithms and curate tech products. Data scientists are much more than that.

Companies often seek unicorn-like aspirants who are ninja software engineers, ace statisticians and master of industry domain, but the sad part is that they look for all these 3 character traits in a single job candidate, which needs to be changed.

Keep an eye on ‘derived data’

If you are thinking of sharing your algorithms with any other person then the chances are high that they will see your data. But companies that are keen on protecting its data should refrain from such activities. Data for informatics companies is like a new currency – they need to be well-guarded and treasured for life!

Educate about the perks of AI

AI is a blessing, for all you tech nerds and gizmo jerks. And accomplished data professionals should look for ways to promote AI and influence friends and co-workers to embrace this new king-some technology. After all, successful machine learning implementation may become the key to your company’s future growth, provided you treat it in the right manner.

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IoT is Advancing but without Enough Security: What You Need to Do

Posted on February 13, 2018May 23, 2020 by Dexlab

IoT is eminent. And a wide number of nerdy IT specialists are scared as hell.

A recent survey pointed that 78% of IT decision makers accepted that they are skeptical if their business lose crucial data enabled by IoT devices. Amongst them, 72% even said the speed at which IoT adoption is gaining ground is worrisome because they are yet to evolve necessary security arrangements.

As the saying goes, there’s no smoke without fire – the recent WannaCry Ransomware is the best example to point out security issues related to IoT– it infringed and crippled Bank of China’s ATM networks and washing machine networks. In another instance, a company that looked after much of the Internet’s domain name structure was brought down by using somewhat 100,000 “malicious endpoints” from IoT devices. All owing to security lapses in the adoption of IoT infrastructure.

The notion is that IoT is still largely under-secured and poses larger than life security threats and risks as companies are trusting suave IoT devices for some way or the other, as a result jeopardizing their own operational, profitability and safety decisions.

AI is the Answer!!

AI can come to the rescue, whenever security of IoT is the concerning matter. Proponents defend saying machine learning can easily identify usage patterns and send signals to the system, whenever abnormalities are recorded or even occur. Reason: all the IoT devices are limited in function, so it becomes easier to recognize irregularities.

Again, a combination of everything is never a bad idea. Undoubtedly, AI plays a prominent role in uplifting IoT security, but a comprehensive IoT solution would include an amalgamation of everything, like AI, government regulation and standards.

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Though the ever-so-increasing tech industry is capable of devising an effective solution, but the real deal in here is to perform everything on a breakneck timetable. At present, in between IoT security and IoT adoption, the latter is winning.

Here’s a set of suggestions that helps in latching on with the IoT without compromising on the security part:

Implement an integrated approach – Anytime, more is better. The companies that are relying on IoT should seek to integrate management solutions and welcome a powerful IoT framework to boost smooth data movement and good connectivity that pulls in data into a robust analytical environment, which is albeit more sophisticated and makes room for flawless behavioral analysis, which is automated – “by integrating those components, you can be more confident that what you’ve got from a feed in an IoT environment is more statistically valid,” Chris Moyer, CTO and VP-cybersecurity at DXC said.
Choose the perfect IoT devices – Formidable ecosystem and having a series of companions that shows no inhibitions in the manner they share information stems out to be the right IoT devices.
Look forward to Edge Devices and IoT Gateways – To counter the lack of security measures, top of the line companies are using Edge Devices and IoT Gateways to bind more impregnable layers of protection between insecure equipments and the internet.
Go create standards – From a macro level perspective, you should ensure a 360-degree IoT security for the next few years and that is only possible if you start setting standards in your business as well as in tech from now on.

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AI-Smart Assistants: A New Tech Revolution in the Make

Posted on February 12, 2018May 23, 2020 by Dexlab

2018 has begun. And this year is going to witness a mega revolution in the field of technology – the rise of AI-powered digital assistants. Striking improvements in key technologies, like natural language processing and voice recognition are making smart assistants more productive, helping us use electronic devices just by interacting with them.

Smart voice assistants are going mainstream. From Apple’s Siri to Google’s Assistant to Samsung’s Bixby, superior digital assistants are on a quest to make our lives easier, while taking us a step closer to a world where each one of us will have our own personal, 24/7 –all-ears AI assistants to fulfill our every wish and command.

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The Future of Technology is Here: 3 Tech Categories That Will Rule in 2018

Posted on January 29, 2018May 23, 2020 by Dexlab

No organization can survive without – technology. It has seeped into our lives for good, and is further predicted to expand its horizons across multiple sectors in the next couple of years. The forecasters has even predicted that 4% more would be spent on purchasing software, technology and hardware by government bodies and corporations, as well.

Now, this kind of growth and development will push the global tech industry to hit the $3 trillion mark. But amidst all this startling stats and discoveries, what still remains under the clouds is the fact how to decide which technology to invest in – or whether you should invest at all.

Communication

Communication technology is crucial. It is more important for startups, where employees work remotely, as highlighted by a leading daily. In fact communication platforms offer a gamut of benefits, right from increased productivity to form superior teams and ability to nurture better company cultures. Furthermore, these technologies in the end help entrepreneurs monitor budget closely, which is enough to point out why strong communication is fruitful.

Cutting edge messaging services like HipChat and Slack ensures better communication networking for employees working outside the office. On the other hand, fantastic tools like Basecamp streamlines operations irrespective of the location of staffs, while LinkedIn and Ripple ensures better connectivity on a more personal level.

Artificial Intelligence

AI is a must-have technology tool for every organization coming up this year and ahead, because this field of science is expanding very rapidly. A lot of new discoveries and innovations are taking place around AI, and you need to be ahead of the curve. As more and more data gets churned out, deep-learning techniques starts to gain momentum, which tends to evolve the pattern of data analysis.

On this note, Google Home and Amazon Alexa launched their own virtual assistant – though the ability to interact with devices was in existence for quite some time now, these masterminds gave a push to this technology a little more.

Similarly, it’s been revealed in a McKinsey report that around 45% of work activities are going to be automated, thanks to the power of AI.

Blockchain Technology

Much more than just performing financial transactions, blockchain technology is a bright beacon of hope for all tech freaks. Though this technology was in active phase, it’s enjoying heyday now. Market Reports Hub predicts that this market will exceed $2 billion by 2021, and that definitely calls for a celebration!

The main objective of blockchain is to nurture and enhance the trust between enterprises. Though in the beginning, startups and entrepreneurs may feel a little bit intimidated to implement this new-age, ultra-famous tool of technology in their organization but later on they can’t thank more! Right from navigating through sales, finances, marketing, communication and creativity, Blockchain is a sure-fire way to make this New Year a booming success.

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The More, The Better: Why Focus on More Than One Coding Language for Better Developer Jobs?

Posted on January 22, 2018May 23, 2020 by Dexlab

Attention all you developers: Be versatile, be ahead of the curve.

Excelling over just one programming language won’t do any good, and will restrict your career pathway. On closer inspection, you’d find that the top 25 Forbes listed companies never rely on a single coding language for their products and services – instead they prefer on applying at least 4 different languages to always be on edge.

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5G Mobile Innovation: 3 Key Takeaways That Every Business Leader Should Know About

Posted on January 19, 2018May 23, 2020 by Dexlab

5G stands for fifth generation wireless connectivity based on the IEEE 802.11ac standard of broadband technology, though an official standard is yet to be fixed. It comes with a lot of promises – better bandwidth, faster speed and lower latency that affects (positively) customers and businesses, as well.

Although it isn’t expected to roll out until 2020, a large number of companies have already started prepping up to adopt and incorporate 5G mobile connectivity into their business scopes and operations. As the biggest shift in technology is looming right ahead, business leaders around the world are leaving no stone unturned to fathom the rich impact of 5G on several next-gen techs, like self-driving cars and cloud computing.

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