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Overcoming the Biggest Challenges in Enterprise Big Data Implementation
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Overcoming the Biggest Challenges in Enterprise Big Data Implementation

The right way of adapting and implementing big data is genuine debate. Foreseeing a data-driven future will be essential for market leaders as data quantities and sources are growing at exponential rates. Pressure on IT departments to make the switch to big data platforms has been amplified as the digital transformation has hastened the obsolescence of traditional enterprise data management structures. The big data-enabled architecture has been established, but so far, it has not yielded any tangible benefits for some enterprises. In this article, we'll discuss six factors that prevent enterprise big data implementation from providing business value and widespread adoption.

Enterprise Big Data Implementation Challenges and Solutions

Enterprise big data implementation might be challenging for many companies because of a lack of forethought. Here we'll go through some of the more intractable problems that might arise from Big Data use, as well as some potential solutions.

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  1. Lack of Big Data Understanding

    A lot of times, businesses don't even have fundamental understanding of an enterprise big data implementation, including how it works, potential benefits, required infrastructure, etc. The success of an initiative to adopt big data could be jeopardized without thorough preparation. When businesses invest in technologies they don't understand, they risk squandering valuable time and money. And if employees don't see the value in adopting big data and/or don't want to alter their current workflows, they may push back and slow up the company's growth.

    Enterprise big data implementation is challenging because it can cause radical shifts in how a company operates. IT teams will need to host multiple workshops, seminars, and training programs to guarantee that big data is understood and accepted by all employees. Strong leadership that understands how to maximize Big Data's potential will result in better decision-making. While it's important for higher-ups to maintain oversight, excessive control can actually hinder progress and stifle innovation.
  2. Lack of clarity in choosing Big Data tools

    Selecting the right Big Data tool can be a daunting task, with so many options available in the market. While Hadoop MapReduce has long been a popular choice for distributed computing, the emergence of Apache Spark has introduced new possibilities for faster and more streamlined data processing. Is HBase or Cassandra preferable for storing data? Finding the right information might be difficult. It is essential to have a clear understanding of the specific requirements and objectives of the organization before making a decision. A well-informed decision can lead to improved data processing, analysis, and insights, ultimately resulting in better business outcomes.

    The best course of action, if you are new to the big data sector, would be to try to find professional assistance. For big data consulting, you might use a vendor or employ a specialist. Together, you can come up with a plan for each situation, from which you can select the appropriate technological stack.
  3. Huge financial outlay

    The costs associated with adopting a big data strategy are substantial. If you choose an on-premises option, you'll need to factor in the price of utilities, space, servers, software, and more. The required frameworks may be free to use, but creating, launching, and keeping up with brand-new applications will still cost money. If you choose a cloud-based big data solution, you'll still have to spend money on people and cloud services, the creation of your big data solution, and the installation and upkeep of any necessary infrastructure. To prevent big data growth from spiraling out of control and costing a fortune, both scenarios require planning for potential upgrades.

    Specific technology requirements and business objectives of your firm will determine how your company's finances will be spent. For instance, businesses that value adaptability can take advantage of cloud computing. While businesses that need the highest level of security typically opt for an on-premises solution. It is possible to save money with hybrid solutions, in which some data is stored and processed on the cloud and some is kept on-premises. The correct use of data lakes and algorithm enhancements can also reduce costs.
  4. Difficulties with data growth

    One of the most significant challenges of Big Data is how to properly store these large data collections. Companies' data centers and databases are bursting at the seams with an ever-increasing volume of information. The exponential growth in these data sets over time makes them extremely difficult to handle. The majority of the information is not organized and originates from different documents, videos, audios, text files, and other media. So, finding it in the database has been challenging. This may present major obstacle in terms of Big Data analytics, and it is crucial that they be addressed without delay so as not to impede the business's growth.

    Data growth is exponential, but it can be managed with the help of converged and hyper-converged infrastructures, as well as software-defined storage. With compression, tiering, and deduplication; storage costs and space needs can be reduced even further. Businesses employ a variety of tools, including Big Data Analytics software, Hadoop and NoSQL data repositories, Spark, AI, ML, BI software, etc., to address this challenge.
  5. Big data security concerns

    When it comes to Big Data, security can be a major obstacle, especially for businesses that deal with sensitive company information or have access to a lot of user data. Cybercriminals and hackers are always on the lookout for vulnerable data to exploit. Most businesses assume their data storage facilities are secure because they have implemented adequate security measures. When it comes to protecting their data, few companies take the extra steps particular to Big Data, such as identity and access authority, data encryption, data segregation, etc. Businesses are typically more involved in tasks like data storage and analysis. Unfortunately, data security is often overlooked. This is not a good idea, as compromised data can quickly snowball into a major headache. The consequences of a breach can be devastating, both in terms of financial losses and damage to the organization's reputation. Protecting valuable information can save an organization million.

    Putting security first is a preventative measure against potential big data security concerns. Getting along with big data security from the very start will ensure that you are well-prepared to handle any challenges that may arise in the future. Businesses are hiring cybersecurity experts to protect their data. Data encryption, data segregation, identity and access control, endpoint security implementation, and real-time security monitoring are all additional measures that can be taken to secure the system.
  6. Inadequate Data Governance Structure

    Data governance refers to the organizational framework for handling large amounts of data. It covers everything from creating data to clearing it. For data to be of good quality and used consistently and compliantly, data governance is required. Anarchy, confusion, and poor decision-making are all possible outcomes of inadequate leadership. Big data governance typically runs into problems due to a lack of resources and funding. Unfortunately, many businesses lack a specialized data management and governance group. Because of this, they have a hard time adapting to the rapidly shifting environment of big data.

    Organizations must invest in effective data governance practices and technologies to address this difficulty. They must also establish transparent protocols for handling information. They must also make sure that all stakeholders are familiar with and following the data governance policies, and that sufficient resources have been allocated for data governance.

Read why IT governance in general fail so often

Conclusion

When it comes to gaining useful insights from massive amounts of data, Big Data is one of the most effective methods. Using those learnings in your business will definitely work wonders for your marketing and business planning. Once you know the major obstacles to implementing Big Data and how to overcome them, you'll be able to do it.

Qentelli offers the industry's leading Big Data solutions and can help you in enterprise big data implementation to monitor and control your business. For more information on how we can tailor our Big Data Solutions to your unique business needs and how they can integrate with the rest of your company's infrastructure and architecture. Send us a note at [email protected] and let's get started on your journey to success!