Big Data Challenges and Solutions to Overcome Them
Are you dealing with some of the major big data challenges, preventing you from leveraging the varied advantages it offers? Know the challenges and their solutions here.
While we cannot deny that big data has become a game changer for several businesses and industries, it poses some serious challenges that cannot be overlooked. Most companies are investing in big data technologies and becoming data-driven organizations. However, they report that implementing big data solutions comes with some challenges.
Let us identify those challenges and look for ways to solve them.
Big Data Challenges and Solutions
Here are some common challenges of big data, along with solutions.
Huge Amounts of Data Handling
As the name suggests, big data is very big. According to Forbes, more than 2.5 quintillion bytes of data are created every day globally. 90% of data in the world has been created in the last two years. The amount of data is also increasing daily, whereas the storage capacity is usually inadequate. This is a big cause of worry for the business leaders. As per Dell, 43% of the leaders say that their infrastructure is insufficient to match the growing data demands. Without proper architecture and computing power, businesses cannot handle data growth.
To solve this issue, the companies that have not yet migrated their IT infrastructure to the cloud are doing so as soon as possible. Cloud storage solutions are the best for companies that require more storage. Using other storage technologies can also address the volume challenges like on-premises hosting, hybrid approach, etc., according to your business goals and needs. While on-premises is not immediately scalable, cloud solutions offer superior flexibility in tackling increasing data volume.
Multiple Data Sources
One of the main challenges faced by businesses is that there are diverse data sources. An organization’s analytics data comes from various sources like websites, social media sharing, CRM software, emails, databases, files, and more. This data might be structurally the same but requires integration and data reconciliation for generating reports and meaningful insights.
To manage this challenge, business leaders have been widely using data integration software and tools. Many businesses intelligence software can map different data sources and combine them to form a common structure.
Multiple Data Formats
Most of the gathered data is unstructured or semi-structured. Finding how to bring heterogeneous data to a single format to match your business intelligence and tools like analytics, visualization, predictions, etc., is challenging. The data processing technologies and tools can reformat unstructured data and offer insights. To manage multiple formats, big data professionals combine various tools and extract the required information.
Bad Data Quality
You need clean and relevant data to generate valid insights and predictions. You will not get the results if the data is of poor quality, corrupted, outdated, incomplete, etc. With an increase in the source, type, and quantity of data, it has become challenging to determine whether the data is good quality and will help you get accurate insights. The solution for this is data governance applications. These data science techniques can help you store, handle, filter, sort, and secure the data. It also helps to authenticate data sources and clean corrupt and incomplete data.
Data Security
Companies handling confidential and sensitive data, like financial institutions, healthcare, etc., find it challenging to maintain the security of the data. They find it threatening that their competitors can use the data to their advantage and sweep away a bigger market share. Personal customer information can create the risk of identity theft. The businesses handling sensitive data are often the target of hackers. To solve this problem, business leaders must invest in the best security practices and techniques.
While some companies hire a consultant, some have an in-house team for data encryption, adding identity and access authorization control, implementing endpoint protection software, and performing real-time monitoring to prevent cyberattacks.
Data Cost
Managing data projects and infrastructure is a costly affair. Limited IT budget makes data capitalization and big data implementation difficult. Big data requires you to invest upfront costs and create supreme infrastructure to handle growing data. Identify cost-saving opportunities like detecting duplicate data, tiering the data and optimizing the management cost, being careful with data archiving, etc.
Lack of Data Talent
One of the biggest data problems companies face today is working with untrained personnel who do not have the knowledge, skills, and experience to handle big data. The organizations wish to hire data specialists who excel in big data career and have data science certifications. Only these specialists can manage volumes of data and leverage the same for business growth.
Wrapping up
Most organizations use big data to support their business initiatives and make wise decisions. Business leaders are wary of these challenges and implementing correct solutions to leverage the potential of big data!
