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Effective artificial intelligence requires a healthy diet of data

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Effective artificial intelligence requires a healthy diet of data

In the current technology landscape, nothing elicits quite as much curiosity and excitement as artificial intelligence (AI). And we are only beginning to see the potential benefits of AI applications within the enterprise.

The growth of AI in the enterprise, however, has been hampered because data scientists too often have limited access to the relevant data they need to build effective AI models. These data specialists are frequently forced rely solely on a few known sources, like existing data warehouses, rather than being able to tap into all the real-time, real-life data they need. In addition, many companies have great difficulty efficiently and affordably determining the business context and quality of massive amounts of data instantly. Given these difficulties, it’s easy to understand some of the historical barriers to AI acceleration and adoption.

At the end of the day, data only becomes useful for AI—or for any other purpose—when you understand it. Specifically, this means understanding its context and relevance. Only then can you use it confidently and securely to train AI models. The only way to achieve this is with a foundation of “intelligent data.”

Over the years, we’ve moved beyond the collection and aggregation of data to drive specific business applications (data 1.0), and organizations have been able to create well-defined processes that allow anyone to access data as its volume, variety and velocity continue to explode (data 2.0). But this simply isn’t enough. We’ve now reached a point where intelligent data is needed to truly power enterprise-wide transformation (data 3.0).

As an example, consider the challenges a company would face trying to redefine its traditional relationship with its customer base. Let’s say that you’re a company that makes razor blades and the goal is to sell them by subscription rather than over the counter. Guiding such a disruptive change requires input from a multitude of data sources (databases, data warehouses, applications, big data systems, IoT, social media and more); a variety of data types (structured, semi-structured and unstructured) and a variety of locations (on-premises, cloud, hybrid, and big data). Or if you are a heavy equipment manufacturing company that needs to ensure that you can process all the data from the shop floor and robots on a real-time basis to predict any downtime while maintaining your scheduled maintenance to avoid operational downtime that can cost millions of dollars in lost revenue.

The data lake is becoming the repository of choice for the vast collection of disparate data required for transformative efforts like this. But without intelligent data, these lakes are of little value. Gartner estimates that, through 2018, a shocking 90 percent of data lakes will be useless because they are filled with raw data that few individuals have the skills to use. (“Metadata Is the Fish Getter in Data Lakes.”)

In contrast, with intelligent data, data scientists can conduct a Google-like search on words like “customer” and instantly discover all the potential sources of relevant data. Intelligent data saves an enormous amount of valuable time that data scientists would otherwise have to spend collecting, assembling and refining the data they need for their models. It also delivers the most reliable results.

So how do you ensure that your data is truly intelligent? By building an end-to-end data management platform that itself uses machine learning and AI capabilities, driven by extensive metadata to enhance the overall productivity of the platform. Metadata is the key that unlocks the value of data.

There are four distinct metadata categories to look at if you want to ensure that you’re delivering comprehensive, relevant and accurate data to implement AI:

  1. Technical metadata – includes database tables and column information as well as statistical information about the quality of the data.
  2. Business metadata – defines the business context of the data as well as the business processes in which it participates.
  3. Operational metadata – information about software systems and process execution, which, for example, will indicate data freshness.
  4. Usage metadata – information about user activity including data sets accessed, ratings and comments.

AI and machine learning applied on this collection of metadata not only help identify and recommend the right data. That data can also be automatically processed—without human intervention—to render it suitable for use in enterprise AI projects.

Digital transformation is forcing organizations to look at data differently; it’s a matter of becoming “the prey or the predator.” Today, there’s real-time, always-available access to data and tools that enable rapid analysis. This has propelled AI and machine learning and allowed the transition to a data-first approach. The AI renaissance is flourishing because of digitization, data explosion, and the transformative impact that AI has on the enterprise.

Obviously, there are countless data inputs that may shape the decisions of an AI application, so organizations need to sort through what is relevant and impactful and what is just noise. Before your organization adopts an AI driven approach to data management, consider the following questions:

  • What do you want to achieve from AI enabled technologies?
  • Do you have the right strategy around data to help drive AI driven decisions?
  • Do you have the right skill sets?

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Big Data

How big data has changed politics

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Data isn’t new, but we often forget that. In fact, even Sherlock Holmes recognized the power of data, as we can tell from one of his most famous quotes: “It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.”

This is especially relevant to us in an age of fake news and in which politicians are arguably being held more accountable than ever thanks to our newfound ability to process and understand data. Politicians are finding that it’s more and more difficult to “twist facts to suit theories” when thousands of wannabe Holmeses are taking to Reddit to debunk them.

Data is so important these days that it’s overtaken oil as the world’s most valuable resource, which of course means it’s a hot topic amongst politicians. Governmental organisations are learning to understand and to deal with data at regional, national and international levels, not because they want to but because they have to. Data is just that important.

Big data and machine learning

To understand how data has changed politics, you need to first understand what big data is and how its complex interplay with machine learning is a game changer. Big data is essentially just data at a massive scale, while machine learning is a subset of artificial intelligence which relies on teaching computers to “think” like human beings so that they can solve abstract problems.

Netflix’s recommendations system is a great example of big data and machine learning in action. Their algorithms are able to process the huge amounts of viewing data that they store on each of their users and then to crunch the numbers and to make super relevant recommendations. The machine learning algorithm learns as it goes, which means that the more data it has access to, the better it gets.

At first glance, it might seem as though this doesn’t relate back to politics, but the same idea applies no matter what the data itself is actually about. So for example, imagine if the mayor’s office had access to real-time traffic data which could be analysed by machine learning algorithms to provide suggestions in real time about when to close roads or to re-route traffic. We’re talking about an algorithm that has the potential to save lives.

The power of data

Data is knowledge and knowledge is power, which is one of the reasons why data has changed the way we think about politics. You just have to look at the Cambridge Analytica scandal to see how much of a difference data can make, especially when it comes to elections. It’s not even anything new. After all, Obama’s 2012 reelection campaign was largely successful because of its smart use of big data.

Data – or more specifically, the interpretation of it – can make or break a political campaign. But while it’s true that it can help people to be elected into office, it can also help them to do their jobs much more effectively and efficiently. We’ve already talked about data being used to improve traffic flows and to make roads safer. Now imagine that the same concept could be rolled out in every single area that it’s a government’s job to oversee and facilitate.

For example, data and its analysis can be used by healthcare heads to determine where best to allocate funds. It can be used by foreign ministers to simulate complex trade agreements or to predict the long-term effects of uncertain political situations such as the UK’s decision to leave the European Union. It can be used to identify potential terrorist threats or to give advance warnings of disease outbreaks or other phenomena using population data.

Arguing the point

When it comes to debates, which politicians tend to be pretty good at, one of the most powerful assets to have is a set of data that supports the point that you’re trying to make. The only problem is that while data doesn’t lie, people do. People also disagree on what exactly the data means, and there are often multiple different potential conclusions that could be drawn. There’s often not any one right answer.

That’s assuming that politicians even have access to the data in the first place. After all, one of the biggest debates of our time is the debate over privacy and what data companies should be able to store about us. You only have to look at the incoming General Data Protection Regulation (GDPR) to see how times are changing.

Politicians find themselves in the interesting position of having to define these new rules and regulations whilst simultaneously working within their constraints. There’s also the risk that we’ll end up with people who don’t really know what they’re talking about drafting legislation that could cripple the future of the internet before it really has time to settle in as a medium. After all, the World Wide Web is less than thirty years old. When you compare it to some of our other inventions as a species, it’s just a baby. A baby made up of millions of terabytes of data.

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Big Data

Nasscom checks into Guiyang for analytics, big data projects

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After striking a partnership with Chinese city of Dalian – a technology hub – last year, IT industry body Nasscomis going to strike a second partnership with the city of Guiyang on Monday to focus on collaboration in Big Data and Analytics.

As part of the partnership with the Guiyang Municipal government, agreements worth 25 million Yen between Chinese customers and Indian service providers are also going to be announced. The pilot projects, launched on the Sino Indian Digital Collaborative Opportunities Plaza (SIDCOP) platform, would be executed over the next year.

In the coming months, an IT Corridor within Guiyang HiTech city will be created to specifically promote Big Data and Analytics projects. The government of Guiyang is not only offering a host of policy benefits and incentives such as rentfree office space along with other tax concessions but will aid Indian service providers – who are members of Nasscom— in bagging government contracts.

Gagan Sabharwal, senior director, Global Trade Development, Nasscom, said the platform in Guiyang will promote co-create culture with the Chinese industry and advance mutual leadership in the global innovation ecosystem.

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Big Data

Why data science must stand at the forefront of customer acquisition

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Why data science must stand at the forefront of customer acquisition-min

Just how valuable can big data be for a business? Some analysts believe that simply increasing data accessibility by 10 percent can help the average Fortune 1000 company generate an additional $65 million in income.

Quality data can be even more valuable for new startups. When you have a limited marketing budget, you can’t afford to let your customer acquisition efforts go to waste — especially when in many industries, companies spend hundreds of dollars to acquire a single customer.

Unlocking the potential of data science and analytics will enable you to gain greater insights into your customers, allowing you to spend your marketing budget more efficiently. Here are some of the top reasons why data science should play a central role in your acquisition strategy.

Identifying signals of intent and creating predictive models

When it comes to making the most of your marketing budget, few insights are more valuable than discovering why a customer wants to buy a particular product or service. This intent is usually signaled through a wide range of resources, including Google searches, visiting shopping comparison sites or reading product reviews on your own website.

Big data helps identify when a particular user is engaging in these activities, indicating that they are more likely to become a paying customer with the right marketing push. Targeting the right person at the right time is usually a recipe for sales success. Over time, as data science determines the strongest signals of intent, your team will also be able to create predictive models that will allow you to consistently target those who are most likely to convert.

By focusing your customer acquisition efforts on the customers that are demonstrating signals of intent, you’ll be able to stretch your advertising dollars further and get a much greater return on investment. Analytics can even be used to predict future needs, allowing you to nudge current customers at the right time to encourage additional purchases.

Testing marketing strategies

Data doesn’t just help you identify those who are most likely to make a purchase; it can also help you fine-tune the strategies you use to guide potential customers through the buyer’s journey. A/B testing can be used to determine the effectiveness of everything involved in customer acquisition. From comparing email campaign copy to changing the location of your call-to-action button, these tests will enable your team to find ways to keep customers engaged until they make a purchase.

As an example of this, Google Analytics allows businesses to break down their e-commerce products based on product list performance. This data goes well beyond revealing how well a certain item sold. It can also reveal which items receive a lot of views, but few clicks, or even which items are most likely to be abandoned in a consumer shopping cart.

Identifying underperforming product lists can greatly improve your customer acquisition efforts. Data will help your team find the reasons why a particular product isn’t performing well, or help you decide to discontinue an unappealing product. In some cases, even something as simple as changing the display order can provide a boost in sales — and A/B testing will reveal the answers.

Improved segmentation yields improved targeting

Not all customers are created equal — while some may become lifelong devotees to your brand, others may only make a single purchase. A customer may not generate a profit from an initial purchase, but if their lifetime value outweighs the cost of acquisition, your company will be better poised for long-term success. Once again, proper use of data analytics can help you identify the right customers to target as part of your acquisition strategy.

The Lyric Opera of Chicago “used machine learning algorithms to take into account hundreds of dimensions at once” to better fine-tune their audience segmentation strategy. Even without predictive modeling, these algorithms examined a wide swath of data that described top opera-goers. Combining this data with lookalike modeling improved their conversion rate by 3.7 times with a high-value group.

Segmentation data will allow you to identify those individuals who deliver the highest average customer lifetime value, ensuring that those you reach with your customer acquisition efforts will deliver significant profits in the long run.

The potential of big data

Put simply, better data enables better decision-making. Leveraging the power of big data will do much more than help your marketing team create more effective advertising campaigns. It can help you adapt your web content, fine-tune your SEO efforts and even help you discover new potential customer groups, all by providing invaluable insights that you wouldn’t be able to discover on your own.

For companies that truly wish to maximize their potential for customer acquisition, it is clear that big data is the key to a successful future.

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