#big data projects

For btech CSE students in Chennai, the pursuit of excellence in the field of big data begins with practical experience. At Tru Projects, we're here to facilitate that journey and make it seamless. Our platform offers a diverse array of mini big data projects meticulously designed to cater to the needs of engineering students in Chennai. Whether you're seeking live projects or exploring mini big data projects to fulfill your academic requirements, we've got your aspirations covered.

Our Distinct Offerings for btech CSE Mini Big Data Projects in Chennai:

Innovative Mini Big Data Projects: Tru Projects takes pride in offering an extensive selection of innovative mini big data projects. Our project collection spans various domains within big data, ensuring that you find a project that aligns perfectly with your interests and career aspirations.

Live Projects for Real-World Exposure: If your goal is to gain hands-on experience and insights into the world of big data, our live CSE mini big data projects offer a unique opportunity. Collaborate with industry professionals and immerse yourself in the intricacies of the tech industry right here in Chennai.

Tailored for Engineering Students: Our mini big data projects are thoughtfully crafted cater specifically to btech CSE engineering students. They are designed to seamlessly align with your academic requirements while enhancing your skill set in the realm of big data.

Comprehensive Engineering Mini Big Data Projects: Tru Projects offers an expansive variety of mini big data projects that cater to the diverse interests of engineering students. Whether you're passionate about data analytics, machine learning, data management, or any other big data domain, our projects cover it all.

Academic Excellence with IEEE Standards: We're committed to maintaining the highest academic standards. Our mini big data projects adhere rigorously to IEEE standards, ensuring that your project is not only innovative but also academically robust.

Mentorship and Guidance: Recognizing the importance of guidance, our platform provides experienced professionals who are dedicated to mentoring and guiding you at every step of your big data project journey, from project selection to execution and documentation.

How Tru Projects Operates for btech CSE Mini Big Data live Projects in Chennai:

Project Selection: Dive into our extensive catalog of mini big data projects and select the one that aligns perfectly with your academic goals and passions within the realm of big data.

Guidance and Support: Once you've made your project selection, our team of experts will offer comprehensive guidance on project execution, documentation, and presentation, ensuring your success.

Real-World Exposure: If you're eager for hands-on experience and insights into the tech industry, consider our live big data projects where you'll work closely with industry experts.

Adherence to IEEE Standards: Your project's credibility and value are paramount. Rest assured that your big data project will strictly adhere to IEEE standards, enhancing its credibility in the academic community and industry.

Thorough Documentation: We recognize the importance of comprehensive project documentation. Our team will assist you in creating well-structured project reports and presentations, reflecting the depth and quality of your big data project.

Tru Projects isn't just a platform; it's your gateway to success in the world of btech CSE with a specialization in big data. Our mini big data projects nurture innovation, foster learning, and equip you with the skills necessary for your academic and professional journey.

Know Some Interesting Facts on Big Data Projects...

Big Data Project Ideas & Support by ElysiumPro...

There is no question that artificial intelligence (AI) is presenting huge opportunities for companies to automate business processes. However, as you prepare to insert machine learning applications into your business processes, I recommend that you not fantasize about how a computer that can win at Go or poker can surely help you win in the marketplace. A better reference point will be your experience implementing your enterprise resource planning (ERP) system or another enterprise system. Yes, effective ERP implementations enhanced the competitiveness of many companies, but many other companies found the experience more of a nightmare. The promised opportunity never came to fruition.

Why am I raining on the AI parade? Because, as with enterprise systems, AI inserted into businesses drives value by improving processes through automation. But eventually, the outputs of most automated processes require people to do something. As most managers have learned the hard way, computers can process data just fine, but that processing isn’t worth much if people are feeding them bad data in the first place or don’t know what to do with information or analysis once it’s provided.

With my fellow researchers, Cynthia Beath, Monideepa Tarafdar, and Kate Moloney, I’ve been studying how companies insert value-adding AI algorithms into their processes. As other researchers and managers have also observed, we are finding that most machine learning applications augment, rather than replace, human efforts. In doing so, they demand changes in what people are doing. And in the case of AI — even more than was true with ERP systems — those changes eliminate many nonspecialized tasks and create skilled tasks that require good judgment and domain expertise.

For example, fraud detection applications may reduce the time that people spend looking for anomalies, but increase requirements for deciding what to do about those anomalies. An AI application might allow financial analysts to spend less time extracting data on financial performance, but it adds value only if someone spends more time considering the implications of that performance. With the help of AI applications, customer service staff can spend fewer hours resolving routine problems, but they are more likely to improve operations if at least some of that saved time is reallocated to better understanding the problems customers are experiencing with the company’s most recent offerings.

Many leaders think that they will generate value from AI by recruiting more data scientists. Of course, there’s a shortage of data scientists — and some of them are more attracted to the challenge of building an application that wins at poker than solving a business need. Others will be inspired to find a cure for cancer or to mitigate global warming. So financial services and insurance companies attempting to uncover fraud and technology companies hoping to improve customer satisfaction will be fighting over the remaining talent.

But recruiting data scientists is not your biggest challenge. Data scientists can develop useful algorithms, but domain experts are needed to help train the machine to recognize important patterns and understand new data. Domain experts include top analysts, contract managers, salespeople, recruiters, and other specialists who are not only experts at their jobs but who are acutely aware of how they deliver excellence. That may involve just a few key people for a given application, but they’d better be good. And we still haven’t gotten to the really hard part!

Ultimately, you need people who can use probabilistic output to guide actions that make your company more effective. Probabilistic outputs are no problem when, say, an application such as Salesforce.com Inc.’s AI tool, Einstein, indicates that one lead has a 95% chance of converting into a sale, while another has a 60% chance. The salesperson knows what to do with that information. But what’s the next step when a recruiter learns from an AI application that a job candidate has a 50% likelihood of being a good fit for a particular opening?

When a machine learning application is helping a lawyer identify potentially relevant legal precedents, or helping a vendor management team ensure compliance with a contract, helping a banker decide whether a particular customer qualifies for a loan, the machine is taking over mundane tasks. Machines can surely learn to develop spreadsheets and search large databases for relevant information. But to generate competitive advantage from machine learning applications, you’ll need to upgrade your employees’ skills. You’ll also need to redesign their accountabilities, so that they are empowered and motivated to deploy machines when they believe that doing so will enhance outcomes. In short, you will need to build an entire workforce of intelligence-consuming, action-oriented superstars.

There are, of course, examples of AI algorithms fully automating a process rather than augmenting human efforts. Google DeepMind might automatically adjust temperature settings in a data center. Similarly, IBM Watson can trigger automated alerts to insurance customers in an area likely to be hit by a hailstorm. But these are exceptions. More often, machine learning applications are helping people accomplish something. Like people, machine has natural limits, which tend to leave parts of the tasks — the parts that don’t fit the algorithms well — to people. When a machine detects fraud or predicts customer or employee churn with 90% accuracy, people must address the other 10% — and that will be the toughest 10%. The machine will assuredly take care of the easy cases.

Addressing the toughest instances is particularly challenging because AI algorithms can produce indecipherable results. When a machine learning algorithm decides who gets a loan and who doesn’t, forget about trying to advise a client about how to qualify. Machine intelligence is not a substitute for human intelligence, because, as organizations, we need to be able to understand why we’re doing what we’re doing.

None of the issues associated with using AI to augment your employees’ skills are insurmountable. Great companies are already empowering their people with better information produced by smart machines. Those machines sift through far more data, and do it much faster, than people can. They also discover complex relationships that can be exposed only with massive amounts of data and a large pool of contrasting outcomes. Companies are succeeding with AI by partnering smart machines with smart people who are learning how to take advantage of what those machines can do. In short, AI implementation success depends on your ability to hire and develop problem-solvers, equip them with data (and potentially AI), and then empower them to actually solve problems. Note that addressing skill requirements this way may well require major changes to your existing hiring and development practices.

C-suite executives: What is the impact of big data in your organization? If you suggest I speak to the CIO, you might be surprised when you take a closer look.

Big data is at a tipping point across many corporate disciplines, from marketing to customer support. Corporate leaders across every part of their businesses are using it to innovate and gain new insights into their existing business.

The impact of big data is spreading rapidly throughout many companies and becoming a part of their DNA. The danger, however, is that some senior managers may be unaware of its effect.

SILOED DATA CAN CAUSE PROBLEMS

Big data deployments are commonly localized. When organizations first begin experimenting with big data, they often do so with small proof-of-concept projects. Sometimes the senior management may be aware of these, but in many cases they may be run as “skunk works” projects conducted autonomously to test a team’s assumptions.

This creates several problems for senior executives. The first is that they don’t have a unified view of the organization’s big data initiatives, which tends to keep big data from becoming a strategic focus. The biggest value of data will come when connecting the dots across an entire organization. For instance, a single view of a customer does not live in marketing or in sales—it spans the entire organization. It’s hard to encourage joined-up thinking and pursue a bigger goal when different people in the organization are fostering a piecemeal approach to big data projects.

Another problem is a lack of governance. Data can be a powerful tool, but it can also be destructive to your business if misused. If ad hoc projects are started without adequate oversight, they can lead to the inappropriate use of personal information.

Without a strategic approach to big data, executives risk developing pockets of noncompliance as well-intentioned middle managers experiment with sensitive data. All it takes is one slip, and it could leave chief executives trying to explain themselves while the company makes negative headlines.

TIME TO COMBINE BIG DATA PROJECTS

By pulling these projects together into a holistic strategy, business leaders can take control and help write the company’s big data narrative. This offers several advantages. First, it gives business leaders more control over data privacy and security. By imposing best practices in an enterprise-wide policy, executives reduce the risk of rogue managers creating security gaps in the corporate infrastructure. In a world where data provenance and protection is more important than ever, this point cannot be overstated.

Gaining control over data governance will make the compliance department happy. Instead of having to find and audit a universe of undocumented big data projects, the compliance team will already be aware of them, and will be assured that the project managers drew from a data governance playbook when creating them. This should make auditing far easier and reduce the risk of noncompliance.

The other benefit of creating a governing strategy for big data projects is that the whole can become more than the sum of its individual parts. While few companies expect to pile all their information into a single, huge data ocean, an end-to-end view of these projects can nevertheless introduce opportunities for efficiency and data aggregation. Multiple projects may share information in a single data lake.

Managing data in this way can increase the variety of information available to a single big data project. For example, an ad hoc project might only draw on the sales database, limiting its results. By making unstructured data from other sources available to that project, a company could generate more accurate, insightful results.

These partnerships require a strategic vision—which, in turn, increases the impact of big data at the corporate level. A comprehensive, top-down big data strategy creates space for the executive team to forge a vision of what it wants big data to achieve.

MAKING BIG DATA A PROGRAM, NOT A PROJECT

How can corporate leaders turn things around and get to this point? The first step lies in developing a mature big data strategy. Business leaders must treat big data as a program, not a project, with the highest level of executive buy-in.

Then you should give the program the attention it deserves by turning to experts to design big data architectures around open architectures: It’s unlikely that your big data program will support just one technology or put data in only one location.

Once you have a program with well-defined compliance practices and technical specifications, your organization can document its existing siloed projects, assessing each one to see if it can be incorporated into the new framework.

Even projects that cannot be integrated will still be valuable. Architects can learn from most deployments, understanding what worked and what didn’t. They can then factor those insights into a redesign, as they build the project from scratch with new, enterprise-approved parameters.

It’s a fact of business today that organizations focused on everything from exploration all the way to production need to rely on Big Data to run as efficiently as possible. According to a December 2016 report by McKinsey & Company, Big Data’s potential keeps growing, and companies must integrate an effective analytics strategy to their corporate vision to make better and faster decisions.

Even when just focusing on one part of your company’s operation, such as your company’s fleet of vehicles, data can be overwhelming. In a given year, a fleet of just 300 vehicles can generate up to 1,200 transactional data points from maintenance alone with another 30,000 from fuel transactions and a massive 15 million from telematics.

The trick is to not get lost in the numbers.

In the fleet industry as well as the energy industry, more companies are entering the Big Data game in an effort to yield valuable insights about how their operation flows and what they can change to make it more efficient.

Here are four smart steps for creating a tactical plan to harness the power of Big Data.

Define clear business objectives.

As your company prepares to begin collecting and analyzing data, you must first define clear business objectives. What are your goals and what parts of your operation should you monitor to help you achieve those goals? Focusing on collecting the right data will allow you to make better and faster decisions that align with your strategic vision. Also consider the quality of the data. Many companies struggle with their ability to achieve their goals due to poor data quality.

Set realistic, achievable goals.

From the outset, make sure you set achievable goals. Organizations see great potential in the data and want to realize the full benefit of their investment. Ultimately, success hinges on the ability to evolve your Big Data strategy and not bite off more than you can chew when you are just getting started.

Generate clear and specific reasons why higher costs are occurring.

Why is fuel spend increasing? Why are maintenance costs higher in one division than another? Why should drivers stick to the assigned preventive maintenance schedule?

As Big Data has evolved, fleets have shifted from trying to find meaning in an ocean of information to vehicle-focused data. The next generation of fleet management software is successfully deploying expertly designed statistical analyses that go beyond Big Data reports and are able to generate clear and specific reasons why higher costs are occurring. Today’s software makes it easier than ever to identify the outliers in terms of cost and speed up the ability to take corrective action.

Determine what will likely happen next.

Even more impactful than its ability to provide accurate insight, technology can make data genuinely impactful through predictive analysis—the ability to determine what will likely happen next based on the past.

Predictive analysis solutions are empowering companies to do deep dives into their data easily, analyzing current maintenance data along with the vehicle’s history to determine how likely a vehicle is to fail. This opportunity to take the analysis a step further allows companies to more accurately predict and manage future costs. Companies are starting to use predictive data to inform which vehicles they purchase and when to replace older vehicles before maintenance costs grow.