How to prevent problems with Big Data Analytics?

Over 90% of global data was created in the last two years, which proves the huge demand for big data analytics systems. Despite this fact, only 27% of entrepreneurs consider their big data practices a successful initiative. 

So what conclusions can be drawn from these statistics? First and foremost, there’s a significant gap between a theoretical understanding of big data concepts and putting the ideas into practice. The root of these problems also lies in a lack of in-depth understanding of what big data is and how it works. We’ve prepared for you a thoughtful overview of several tricky issues related to big data. Without a shadow of a doubt, the future contains more and more data, which means more challenges as well. That’s why businesses are to stay aware of pressing issues they will have to deal with. 

What should you know about Big Data Analytics systems?

To start with, big data comprises data sets, which have enhanced ability to capture, process, and manage information with low latency. This technology has three distinctive features:

• high volume;
• high velocity;
• high variety.

Nowadays, big data is in demand because it allows industries to work with different information from numerous devices, sensors, networks, log files, video/audio. And usually, a multitude of real-time data is generated at a large scale. Another reason why sources have become more complex is Artificial Intelligence and the Internet of Things integration. Whether you use dig data only or combine it with traditional data, you obtain faster decision-making and improved business intelligence. What is more, Big Data analytics development is a powerful tool for flexible data processing, modeling, and predicting future outcomes. 

The list of industries using big data regularly is highly varied:

• Banking and Securities;
• Healthcare;
• Communications and Media;
• Entertainment;
• Education;
• Manufacturing and Natural Resources;
• Insurance.

Big data analytics provides for the use of analytic techs while working with diverse data sets. Such sets bring together unstructured, semi-structured, and structured information from various sources. The size of data can be different starting from terabytes to zettabytes.  As for advanced analytics techniques, the most widespread are data mining, text analytics, data visualization, predictive analytics, machine learning, and statistics. 

Top tricky issues, concerning big data

1. Insufficient understanding of what big data actually is 

Big data adoption projects may come to naught if enterprises don't have an acceptance of the concept. To avoid spending plenty of time and resources on unnecessary functions, top management should accept analytics software for business first. Workshops and training will help to achieve an understanding of data down the company's ladder. It's critical to teach your employees how to modify the traditional processes for the sake of adoption. 

2. A plethora of big data techs can be confusing

The bunch of cutting-edge techniques brings companies not only benefits but also challenges. Do you need to store information in HBase or Cassandra? Whether Spark is an appropriate option in your case? Finding the correct answers is another significant task, isn’t it? We advise you to seek professional help and contact experts for big data consulting. You will build up a comprehensive strategy by joining efforts, consequently, select the perfect technology stack for your business analytics solution.

3. Considerable expenses on big data adoption

Even if your company opts for a cloud-based system, you will need to pay for cloud services and hire administrators and developers. You will also be responsible for the needed frameworks’ maintenance. Another area of concern is future expansions, so take them into account as well. You should prevent big data from getting out of hand. Certainly, the model of cooperation with IT experts will depend on your enterprise’s needs. For example, the cloud is perfect for those who strive for flexibility. While the on-premises option will be appealing for industries with harsh security requirements. But either way, taking advantage of optimized algorithms and creating a roadmap will help to save a lot of money during big data analytics platform development. 

      4. Data may duplicate itself and contain contradictions

Please remember that big data isn’t 100% accurate. It’s just a fact. Even so, you should keep up controlling your data’s quality and reliability. We suggest you start by creating a proper BD model. And those are things that you can do shortly after this step:

• matching different records and merging them;
• comparing data to the single point of truth (as an example, you can look at variants of addresses and compare them with ones you found in the postal database.

      5. Dangerous security holes of big data

Oftentimes, big data adoption teams tend to solve security issues during later stages. As a result, data analytics for businesses is becoming vulnerable. So what should you do to prevent data security from getting cast aside? Focus on ensuring security at the stage of needs analysis, before designing the architecture. Getting along with security issues from the very beginning enables developers to design efficient IT solutions for businesses. 

      6. The complexity of upscaling 

Nobody is hiding the fact that the nature of big data lies in its incredible ability to grow. The real challenge is associated with the complexity of the scaling process. It’s critical to keep your company’s performance effective without exceeding the available budget and resources. Make sure your architecture is decent. While creating big data algorithms bear in mind the upcoming upscaling. Plus, always plan changes related to maintenance and IT support. And finally, conducting systematic performance audits can become your secret weapon against the system’s weak spots. If you want to address such issues timely, audits are indispensable. 

We are skilled enough to perform reliable  big data analytics development 

PNN Soft has been delivering programming products for 20 years, and we hone our skills to put our ideas into the newest solutions and services. In this process, special attention is paid to security and IT support both during and after development. 

We are focused on achieving an in-depth understanding of individual companies’ features and needs. That is why our clients prefer long-term cooperation.

PNN Soft gives priority to Agile, Scrum, and RAD methodologies to interact with clients effectively, satisfy customers’ needs and obtain more flexibility. Our Agile teams of experts include software developers, GUI designers, testers, technical writers, and managers. 

If you want to contact big data experts at PNN Soft, fill in the form below.