Current Issues and Challenges in Big Data Analytics

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. When there are no native integrations, many businesses choose an iPaaS tool to integrate their software stack is the most comprehensive and cost-effective solution. Examples of these tools include Zapier, Tray.io, and Make, which specialize in trigger-action and one-way data pushes between apps. After auditing your current processes, you will hopefully have a much better idea of what works for your organization and what doesn’t when it comes to data management.

What challenges do big data specialists face

Fortunately, organisations can mitigate most of these challenges by their outsourcing data storage requirements. With the right solution, businesses can reap the benefits of secure and accessible data systems at a reduced cost. It is imperative for the data scientists to communicate effectively with business executives who may not understand the complexities and the technical jargon of their work.

However, communication can prove challenging, especially in hybrid work. While effective communication can help solve the big, complex problems facing IT departments today, poor communication can have the opposite effect. While technical skills remain in demand, soft skills — we call them Power Skills at Skillsoft — have an elevated importance in today’s workplace. Skills like these make a big impact in team dynamics, especially when fusing teams or working cross-functionally. When leaders lack the skills to grow and nurture their teams, it can cause mutinous friction that leads to disjointed workflows, poor relationships, and attrition. Almost one-quarter of IT professionals quit their jobs because of management.

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For a data science team to function effectively, tasks need to be distributed among individuals pertaining to data visualization, data preparation, model building and so on. Many analysts are still focused on descriptive analytics that explain what already happened. They would like to move toward more forecasting and informed scenario-building, but aren’t sure how. At one time, predictive and prescriptive models needed to be built by data scientists, but that’s no longer the case.

Many companies mistakenly believe that their big data can be used effectively as it is. However, in practice, before using the colossal amounts of unstructured data coming in different formats and from different sources, it needs to be checked, formatted, and, if necessary, cleaned up. Clearing data takes a long time, and only after that can it be used within software algorithms. For example, data processing by big data analytics a specific algorithm can take only minutes, while its preliminary cleaning can take weeks. Organizations need to first raise awareness about big data and its benefits among the employees. Afterward, they need to provide training programs and support to help them learn the basic knowledge of big data technologies and how to utilize the big data tools to grasp valuable insights and achieve their work efficiency.

What challenges do big data specialists face

Big data now is no longer a strange concept in today’s business world. On the contrary, its growth and popularity in multiple industries exceeded any imagination and prediction when the global big data industry was reported to reach $274.3 billion in 2022. Plus, big data technologies are highly expected to fuel the next wave of business digital transformation and open up new opportunities for various industries to thrive in the future. According to Statista, the global market of big data is promised to expand in the upcoming years, and perhaps it will hit a record of $68 billion by 2025. Despite the rapid rise in big data adoption and the beneficial applications it brings, many organizations are still struggling to find ways to take full advantage of it.

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To enable that, plan for an infrastructure that allows for incremental changes. Perhaps most importantly, enterprises need to figure out how and why big data matters to their business in the first place. Naturally, you can’t always give out promotions and raises to increase employee morale. See more about learning consumption trends by downloading the Lean Into Learning Report. It covers in-depth 2022 learning data, trends, and the state of upskilling. When change happens at work — and in IT, there is always change — it can set some on edge, especially when their manager doesn’t communicate what’s happening.

What challenges do big data specialists face

There is certainly a large amount of noise at the moment regarding Big Data around what it can do, its challenges, and how it could change the world for a better place. When its ERP system became outdated, Pandora chose S/4HANA Cloud for its business process transformation. Combining different types of simulation models with predictive analytics enables organizations to forecast events and improve the… A generic data lake with the appropriate data structure can make it easier to reuse data efficiently and cost effectively. For example, Parquet files often provide a better performance-to-cost ratio than CSV dumps within a data lake. Once you have a sense of the data that’s being collected, it becomes easier to narrow in on insights by making small adjustments, he said.

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It includes all aspects of managing data from its inception to disposal. Data governance is essential to ensure that the data is of high quality and is used in a consistent and compliant manner. Lack of governance can lead to chaos and confusion and can result in bad decision-making. One of the common issues with big data governance is that it is often underfunded and under-resourced. Many organizations do not have a dedicated team to manage and govern their data.

  • Research shows that, as of 2021,humans generated a total of 79 zettabytes of data.
  • Analytics and machine learning processes that depend on big data to run also depend on clean, accurate data to generate valid insights and predictions.
  • Data scientists spend nearly 80% of their time cleaning and preparing data to improve its quality – i.e., make it accurate and consistent, before utilizing it for analysis.
  • Hardware, software or staff, and data storage, therefore, becomes a pay-as-you-go operating expense.
  • Failure to comply could result in organisations being fined up to 4% of annual turnover or €20 million depending which is higher.
  • What this means is that your teams aren’t all looking at the same data, but instead only have access to a limited snippet that doesn’t tell the whole story.

The profile was drafted by representatives of technology companies, government agencies, and universities using a formal evaluation process known as DACUM. The results were then reviewed and validated by big data professionals representing more than 15 industry sectors, from criminal justice to marketing to the sciences. He has been doing IT consulting in the data and analytics space for large CPG and BFSI companies for more than a decade.

Hardware, software or staff, and data storage, therefore, becomes a pay-as-you-go operating expense. In essence, big data is a term that describes data that is huge in volume and growing exponentially. It refers to the large volumes of structured and unstructured data that inundates organisations.

of data analysts must depend on others within their organization to perform at least some steps in the analytics process.

This will help build better insights and enhance decision-making capabilities. With the increased load of content and the complex formats available on the platform, they needed a stack that could handle the storage and retrieval of the data. They used the MEAN stack, and with a relational database model, they could in fact manage the data. To avoid all these big data problems, we strongly recommend that you analyze your solution and identify the above problems if any. Or you can shift the responsibility for planning, implementation, and further support of big data systems to us—a company that has successfully implemented numerous big data solutions. In addition to looking for talent “on the side,” you will likely be puzzled by the issue of training your employees.

What challenges do big data specialists face

Data management teams have a wide range of big data technologies to choose from, and the various tools often overlap in terms of their capabilities. By a long shot, team communication is the single most important skill for IT leaders, according to 66% of survey respondents. Interpersonal communication (15%), emotional intelligence (6%), business (5%) and technical skills (4%) follow. Big Data technologies are evolving with the exponential rise in data availability.

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Data governance issues become harder to address as big data applications grow across more systems. This problem is compounded as new cloud architectures enable enterprises to capture and store all the data they collect in its unaggregated form. Protected information fields can accidentally creep into a variety of applications. It’s also important to establish a culture for attracting and retaining the right talent. Vojtech Kurka, CTO at customer data platform vendor Meiro, said he started off imagining that he could solve every data problem with a few SQL and Python scripts in the right place. Over time, he realized he could get a lot further by hiring the right people and promoting a safe company culture that keeps people happy and motivated.

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This means you’re stressed about missing deadlines and making excuses to the boss. To answer more complex questions, you likely have to join multiple sources of data. It can be tough when there are a million different data sources that you have to bring together from different file types and locations like SQL databases, CSV, XML, AWS, Excel formats, and more. While each source represents a piece of the data puzzle, the manual processes you currently use to bring all this data together are highly inefficient. Like any new discipline or specialty, there is a large shortage of genuinely skilled individuals in Big Data. There are many people who will pass themselves off as data scientists, data miners, or Big Data specialists but care needs to be taken when employing people to ensure they have the skills and experiences required.

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This saves you the time and resources of bringing new contractors’ services up-to-date. And of course, make sure that manuals on how to use big data solutions are always available to each of your employees. As digital technology advances, companies’ business goals and the needs of their customers also change. From the point of view of challenges in big data analytics, this suggests that they must be up to date, which means that some of them, which were relevant yesterday, may already be outdated. In addition, the COVID-19 pandemic, which has significantly changed the habitual patterns of users, aggravates the problem of relevance.

That has improved the accuracy of the business insights generated by analyzing the data. We have already mentioned above how difficult it’s for companies to provide centralized management. At the same time, incorrect integration also has negative consequences.

Jones Lang LaSalle leveraged Alteryx to develop the analytics skills of its workforce to increase productivity, improve quality, and enhance employee engagement in the wake of the pandemic. Many organizations struggle to manage their vast collection of AWS accounts, but Control Tower can help. Another strategy is to work with HR to identify and address any gaps in existing big data talent, said Pablo Listingart, founder and owner of ComIT, a charity that provides free IT training. Team communication can help clear up issues of misaligned expectations , department priorities and challenges, and more. When the IT Skills and Salary Report was released in fall 2022, almost 60% of IT leaders reported budget increases for their departments — a positive sign for those who need to hire staff, train employees and more. Naturally, workload and resources can prove challenging — more on that later — but for some organizations, there are few other choices in today’s current climate.