Business enterprises view Big Data as a valuable asset imperative to their business growth. And it’s no surprise that more companies are opening up to the idea of using data analytics to extract valuable business insights.

At the same time, data volumes and complexity continue to grow. Overloaded with data, companies are struggling to implement effective analytics solutions that can sustain over a longer duration. Successful implementation of data analytics initiatives requires synchronizing human resources, processes, and analytics skills.

Bill Szybillo of VAI says, “one of the greatest challenges around Big Data projects comes down to successfully applying the insights captured.” Without effective data management, Big Data projects can exceed their implementation costs and duration.

To that end, what are the major challenges organizations face to keep their analytics initiative going? Let’s discuss them along with plausible solutions.

5 Challenges Facing Data Analytics

To sustain their Big Data analytics initiatives, companies first need to address each of the following 5 challenges:

1. High Data Volume and Complexity

Big Data itself means large data volumes generated from multiple systems and platforms. Business enterprises face the challenge of consolidating data (from extremely large datasets) into a single and unified Big Data architecture.

Scalable cloud-hosted servers can handle the massive influx of data. However, organizations still face the challenge of storing the data on Big Data platforms. They
need a complete understanding of the implications of data analytics before planning for the right cloud infrastructure.

Additionally, organizations need to invest time explaining the benefits of Big Data analytics to their business stakeholders and management team.

2. Technology Issues

With more Big Data, companies can extract more valuable insights. However, the challenge lies in selecting the right technology tool or platform to perform this task.

Data management teams also need to deal with the complexity of technology tools that often overlap in their capabilities. Besides, companies have a range of NoSQL tools and frameworks to choose from.

As Big Data analytics tools serve different purposes, choosing the wrong technology tool can be both a costly and time-consuming affair.

3. Skills Shortage

Finding and retaining competent talent stands as one of the biggest challenges. An S&P Global report found that cloud architects and data scientists are among the top skills in demand. Besides, there is a growing demand for application developers skilled in NoSQL and Hadoop technologies.

Big Data initiatives often fail due to unrealistic expectations or incorrect estimations right from the project beginning to the end. Therefore, data-dependent organizations need the right work culture to attract and retain good talent. It’s noteworthy that effective training programs help prepare data professionals to address future challenges.

4. Poor Data Quality

As the number of data sources increases, organizations find it challenging to filter and extract high-quality data from multiple data sources. Applications driven by data analytics and Artificial Intelligence (AI) produce inaccurate results when fed with poor-quality data.

Data quality issues are more challenging when data management & analytics teams work with different data types. Data quality is easily impacted by duplicate data entries and typo errors. Hence, organizations need an effective solution to manage data quality.

5. Scalability-Related Problems

As data volumes and complexity multiply, organizations face the challenge of scaling up their data processing capabilities. As explained by Travis Rehl of CloudCheckr, organizations often “start from one data model and expand out – but quickly realize the model doesn’t fit [their] new data points.”

With the right data structure, generic data lakes make it simpler to efficiently reuse data at lower costs.

Next, let us discuss how Ellicium’s Big Data Managed Services can sustain any data analytics program.

How Ellicium’s Big Data Managed Services Can Help with Sustained Analytics

Organizations must constantly monitor their data growth and infrastructure to achieve their Big Data objectives. Ellicium’s Big Data managed services help organizations to streamline and sustain their data operations.

Here is how Ellicium’s services help in sustaining data analytics:

  1. 24/7 customer support for cloud platforms and Hadoop clusters.
  2. Continuous data performance monitoring using Ganglia and Grafana technologies.
  3. Resolving data management and server issues in Apache Spark, Yarn, Hive, and HBase technologies.
  4. Effective incident management and troubleshooting of reported problems.
  5. Periodic reporting related to data availability.
  6. Efficient data backup and disaster recovery plan

Here are some of our customer success stories:

Our Hadoop-based managed services solution enabled a bank to streamline its analytics operations and optimize costs by 80%. They also improved their service delivery by 80% and reduced time spent on non-core business tasks by 50%.

A financial services company optimized its Big Data infrastructure using our managed services. Based on Hadoop cluster technology, our solution provided a cost-effective model that is managed by their professional team.


To deliver business impact, data-centric organizations need to sustain their analytics initiative until completion. This is easier said than done. Organizations need to address multiple challenges related to data volumes, complexity, scalability, and skill shortage.

At Ellicium Solutions, we enable our customers to focus on their business while we take care of their data management through our managed services. With our services, organizations can achieve continuous improvement in data availability and stay updated on the latest technology trends.

We can help you in your data analytics journey. Start your cloud journey with us today.