What Challenges Do Big Data Specialists Face

As the number of Internet users grew throughout the last decade, Google was challenged with how to store so much user data on its traditional servers. With thousands of search queries raised every second, the retrieval process was consuming hundreds of megabytes and billions of CPU cycles. Google needed an extensive, distributed, highly fault-tolerant file system to store and process the queries. Next up, let us look at a Big Data case study, understand it’s nuances and then look at some of the challenges of Big Data. To get a handle on challenges of big data, you need to know what the word “Big Data” means. One of the big data problems that many companies run into is that their current staff have never worked with big data before, and this is not the type of skill set you build overnight.

Thirdly, have the necessary plans in place for the maintenance and support of your system to help you address all data growth changes accordingly. Fourthly, encourage systematic performance audits of your system to identify weak spots and address them in good time. For starters, make big data analytics sure that your big data solution’s architecture is decent, as this will save you a lot of problems. Secondly, remember to design the algorithms of your big data with future upscaling needs in mind. It may sound easy and cheaper, but it will not be cost-effective in the long run.

Finding and Fixing Data Quality Issues

To see to big data acceptance even more, the implementation and use of the new big data solution need to be monitored and controlled. However, top management should not overdo with control because it may have an adverse effect. We build on the IT domain expertise and industry knowledge to design sustainable technology solutions. Within

• As such, it is not unusual to experience inconsistencies even in data with similar value variables, and making adjustments is quite challenging.
• For that reason, many enterprises decide to
  outsource their big data analytics efforts to dedicated
  service providers.
• Offer professional development opportunities that pay employees to go through data science education programs.
• Poor data quality may introduce inaccuracies and biases into the decision-making processes that can jeopardize the success of an organization.
• Employees do not even know what Big Data is and how it should be stored.
• An AI Solutions Consultant with more than 10 years of experience in business consulting for the software development industry.

technical requirements, architecture design, and the project implementation

Unlocking Insights

plan, you can move to big data solution development. The recent report from EY revealed that 53%

of senior executives consider data and analytics technologies as the top area

Solution: Unstructured Data Analytics Tools

of investment for the next two years.

On the other hand, data tiering permits the organization to store the data into different storage tiers. The storage tier chosen should depend upon the size and the importance of the data. Some companies are choosing tools for Big Data like NoSQL, Hadoop, and other modern technologies. The compression is deployed for reducing the volume of bits in data resulting in a reduction of its size. Deduplication refers to the process of eliminating unwanted or duplicated data from data- sets. Solution- In order to tackle the above problem, seminars and workshops should be organized at companies for all the employees.
A big challenge faced by the companies in the Big Data analytics is mending this wide gap in an effective manner. Scrubbing data of identifying factors like race, gender, and sexuality will also help remove bias-prone information from the equation. Before analyzing big data, it must be run through automated cleansing tools that check for and correct duplicates, anomalies, missing information, and other errors.
The firm stated that physical and manual labor skills are on the wane, but the need for soft skills like critical thinking, problem-solving, and creativity is becoming increasingly important. Additionally, the demand for workers who understand how to program, repair, and apply these new solutions is increasing. Another survey from AtScale found that a lack of Big Data expertise was the top challenge. A Syncsort survey got even more specific; respondents said that the biggest challenge when creating a data lake was a lack of skilled employees. You want to create a centralized asset management system that unifies all data across all connected systems.
The challenges of data governance are complex and require a blend of policies and technology. Organizations typically form an internal group tasked with writing governance policies and procedures. They also invest in data management tools with sophisticated capabilities for data cleansing, integration, quality assurance, and integrity management. Proficient big data

skills and technical expertise are one of the biggest challenges in implementing big data analytics
for enterprises.
If the option exists to schedule a demo, take advantage of it because it will give you a view of how the big data solution will work specifically for your business. One way to fix long response times from your system is to ensure that data is being stored efficiently by performing data re-engineering. Or, look for a more optimised data system that’s scalable for your growing data needs.
Make sure internal stakeholders and potential vendors understand the broader business goals you hope to achieve. Data scientists and IT teams must work with their C-suite, sales, and marketing colleagues to develop a systematic process for finding, integrating, and interpreting insights. In another report, this time from the Journal of Big Data, researchers reported on a whole range of issues related to Big Data’s inherent uncertainty alone. Rather than focusing on outside hires, foster data talent from within existing workforces. Offer professional development opportunities that pay employees to go through data science education programs.

A surefire way to overcome real-time big data issues is to deploy an automation solution that utilises artificial intelligence (AI) to process, analyse, and structure data in real-time. Along with the collection of data, data quality will also depend on how you store the data. It must be made accessible in order to be analysed (this is where automation solutions come into play). The demand for instant data access, regardless of whether by mobile applications or back-end machine learning frameworks implies data management systems must be lithe. Handling large datasets requires robust storage solutions and high-speed processing capabilities. Traditional systems may falter under the sheer load of big data, leading to performance bottlenecks.