Data Adoption Challenges to Watch Out for in 2022

Data Adoption Challenges

Big data offers a roller coaster of emerging technologies, advanced methodologies, and never-ending waves of data. As we excavate and explore the scope of big data in 2022, it is equally important to watch out for specific data adoption challenges and the ways to overcome them with the role of big data professional.

It is expected that over 150 Zettabytes of data would require analysis by the end of 2025, with an annual compound growth rate of 61%, which is incredibly huge. With more data required to process, naturally the organizations come across various big data adoption challenges which need to be addressed on priority.

The article focuses on the data adoption challenges you can expect with the changing big data trends and the role of data science leader in fine-tuning them.

What is Big Data Adoption?

Big data adoption is a procedure with which businesses discover innovative ways to improve productivity and forecast the risk to ensure customer satisfaction efficiently. It is estimated that the big data industry will grab a 77 billion USD worth value by 2023. 

Big data adoption has the power to let innovation modify the infrastructure of an organization. It covers information processing techniques, technologies, and strategies to enrich the decision-making process.

Challenges of Adopting Big Data in 2022

Big data is an enormous pool that grows every millisecond in gigantic amounts, and organizations are on their way to leveraging advanced big data analytics and tools to excavate the data mines. However, what are those huge data roadblocks that hold back firms from extracting relevant and useful insights from millions of data they collect diligently?

Let’s deep dive into these challenges.

1. Data Privacy and Compliance

When we discuss big data privacy, we need to realize the sources of data and the method they were used. The discussions happen based on how to exploit or utilize the data and avoid the sensitive topic of how to conserve or protect the data In the exposed digital network. It is a matter of struggle to balance between the two aspects like:

  •  The value that end-users receive
  • Maintaining and managing the level of protection and data privacy

Organizations have to mitigate the issues if they require to portray the digital business practices as reliable and credible sources to the customers or prospects.

2. Legacy Systems integration with Big Data Technology

While business platforms make a transition or an upgrade from one to the other, then the valuable data held by the legacy systems are prone to lose. The legacy systems of many organizations need conversion since newer technologies are introduced now and then. However, integrating the new with the old is always a cumbersome task, where many customers get scared about losing their significant data, and won’t accept their integration.

3. Lack of Big Data Skills

It is not hard to find skilled big data professionals, since companies look for smarter experts. However, most of the big data professionals lack expertise, and remain new for longer, without serving the organizations needs. Since the majority of the solutions are operating based on tools like Hadoop. Hence, big data developers find it difficult to gain an edge over the competition.

4. Cost of big data tools

The big data projects face high costs to set up the infrastructure that supports the gathering and analysis of millions of data points every day. When it lacks a business case and the data sources need integration, this is also an option that holds back organizations. Rodney Gedda, the Telsyte analyst responded that it could be costly to build the infrastructure for business projects that focus on generating Business Intelligence and Analytics from the huge volume of data.

According to Tomer Shiran, the founder of Dremio, newer technologies such as data lake houses can gain more attention in 2022, since they can offer the organizations better than the old data warehouse models, to address the cost concerns.

These challenges faced by the firms hinder their capabilities of exploring and implementing the data science capabilities.

Role of Data Scientist to overcome these challenges

The challenges listed above can create an undesirable impact on the growth of the organization and decision-making capabilities. However, a skilled big data professional can pave the way to overcome these challenges and find feasible solutions for data processing. A certified data scientist can help organizations in overcoming the aforementioned challenges and trigger business decisions.

Data scientists work together with the business stakeholders to achieve their goals and understand how the data can be utilized to achieve the goals. They build data modeling processes, develop algorithms, and predictive models for data extraction as per the business requirements, and assist businesses to analyze the huge volume of data and share relevant insights to the team.

Here are the ways data scientists add value to the big data adoption of any firm:

1. Risk mitigation and fraud detection-

Data scientists are taught to understand and detect the data that is unique in any way. They build various methodologies to make predictive fraud detection using statistical and other data science tools and ensure making timely responses while recognizing any unusual data.

2. Delivering relevant and feasible solutions-

With data scientists, organizations can find when their products sell the best. The data science professionals deliver feasible solutions from the big data-driven solutions, thereby helping the company to sell their products at the right time. This also helps companies to develop new products to satisfy the requirements of companies.

3. Integration of legacy systems with Big Data-

The legacy systems possess considerable significance and behold the business logic of any organization. The firms cannot eliminate or replace the business logic, being the assets of the organization. With experience and immense data science skills, data scientists can expedite the integration of these invaluable assets with big data without any losses in the existing business systems.

Conclusion

Why are data science certifications crucial to overcome the challenges in data adoption? Data science is continuously evolving in this dynamic landscape, and data scientists with relevant data science certifications use ideal ways to implement relevant business decisions. Since organizations demand advanced data science skills, data science certifications can give you a competitive advantage when compared to other industry professionals. With data science certifications, you learn complex skills which can be rarely sourced from the job market and give you more expertise than potential employers look for. A qualified data science leader can address the big data adoption challenges, offer feasible and perfect solutions while managing the data for business development.

By Master James

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