Big data is a term used to refer to large datasets that are difficult to process using traditional database management tools. The challenges faced by big data are mainly related to storage, processing and extracting insights from the data.
The two major challenges faced by big data are storage and processing. Storage is a challenge because there is an exponential increase in the amount of unstructured and structured data generated every day. Processing is also a challenge because the volume of data being processed increases with every second, which makes it difficult for companies to keep up with the processing capabilities required for their business needs.
Businesses all around the globe are becoming increasingly reliant on data to operate their day-to-day operations and make educated business decisions. With so much data being created, it has become challenging to manage big data throughout the enterprise, which may be dispersed over different geolocations and using tens of business lines applications.
Implementing best practices for big data and following latest trends can help organization overcoming the challenges associated with it and increase your big data management project’s success while enjoying all the benefits rewarded.
We are all aware that data is the new oil for any organization. Unlocking its worth may result in tremendous opportunities for companies, which is why it is vital to have well-defined big data plans in place in order to face the most difficult challenges that big data implies.
In this post, we will look at the most common big data challenges, pain points, and how to solve them.
Top 11 Big Data Challenges
The eleven most common big data challenges are:
1- Sheer volume of data
Every day, it is estimated that 2.5 quintillion bytes of data are created, and guess what? The majority of this data is generated by various types of enterprises. As a result, the organization now faces new challenges in terms of obtaining, maintaining, and generating value from data.
The more data is collected, the more monitoring and validation would be required.
By effectively managing them, they may gain a better understanding of consumer behavior and market trends, allowing them to make more forceful decisions, enhance processes, improve their marketing campaigns, and optimize products and services.
Typically, when there is a large volume of data, challenges such as data categorization, raw data processing, data accuracy, and so on arise.
The trick is to comprehend the data itself. How is it created? What is it used for? What are its applications? How accurate is the data? Is a single point in time value more essential than a trend value over time?
2- Data silos
This is one of the most significant challenges that businesses confront. Large organizations may wind up with tens of business solutions, each with its own data repository, such as databases, CRM, ERP, and so on.
Having this much data storage poses a significant barrier that must be addressed appropriately in order to evaluate and handle it.
When data is kept in separate siloed systems, it is difficult to identify and consolidate in a universal data platform to speed up data-driven choices.
Creating a single source of truth for your data by removing data silos and linking data from consumers, products, and suppliers should be an organization’s top focus.
3- Data quality
Data quality is one of the most critical big data problems confronting many companies today. Most businesses utilize a database to update information, however maintaining data quality becomes difficult while processing or recording information.
At the end of the day, you must get rid of unnecessary data while retaining high-quality and accurate data that your business will most likely require to function.
Always keep in mind that not all data is created equal!
Data saved in your systems, like any other resource, may be out of date, incorrect, or malfunctioning. Making judgments based on this sort of data might result in your firm losing a lot of money every year.
As a result, it is critical to have adequate data quality monitoring standards in place to guarantee that choices are based on high-quality and reliable data.
4- Lack of processes and systems
When big data is gathered from many sources, inconsistency in the data is unavoidable. Inadequate big data processes and systems contribute to inaccurate data. As a result of the insufficient amount of data, the data is of poor quality and does not fulfill the criteria.
5- Data integration
This is one of the most common big data problems and pain points.
The ultimate purpose of having quality ready data is to have it available for further analysis and processing by other business intelligence tools in order to deliver it to senior management for more informed decision making.
The ability to effortlessly integrate this data with the many tools available will simplify your life and help you speed up the processing step.
6- Lack of talent
In order for a company to compete in today’s world, they need people who know how to use big data and analytics tools, as well as people who understand how to design systems that will support these tools and processes in the future.
Training entry-level personnel will be costly for a business that works with new technology, and they must do an excellent job of retaining these people after they have mastered this skill set.
Organizations are increasingly depending on automation, which includes cognitive technologies such as machine learning and artificial intelligence, to produce data-driven insights.
7- Security concerns
While migrating to the cloud will benefit organizations in so many areas, the fear of storing sensitive information in the cloud has led many organizations to keep their data on-site where it can be better protected from hackers or other security threats such as natural disasters, power outages, etc…
8- Data governance
Data governance is in charge of establishing rules and regulations for an organization’s information state. A data governance framework is comparable to a constitution in that it aids in the implementation of policies, rules, and laws for data-related procedures.
In order to comply with industry-specific or federal norms and regulations, an organization-wide data governance plan should be in place.
Read more about the difference between data management and data governance.
9- Data automation
Collecting, collecting, and categorizing this data is a massive effort that cannot be completed manually. This is where data automation comes into play.
Without requiring much human participation, the procedure streamlines the whole cycle from collection to analysis. However, necessary knowledge is still required, otherwise, the entire endeavor would be futile.
10- Data analysis
Even if the data is of high quality, it has little relevance in its raw form. The advent of technology is assisting in the analysis of large volumes of data, but many obstacles remain, such as operating the tool without mistakes, extracting data methodically, and so on.
There are several complex tools available to assist your company in importing data and temporarily manipulating it in order to evaluate it depending on specified parameters.
11- Going from unstructured to structured Data
According to existing studies, more than 80% of data collected is unstructured, which is a major problem.
The ability to convert unstructured data into structured data is the real thing. When data is first acquired, it is typically dispersed and unstructured. To derive value from such data, businesses must mine through it and analyze it before it can be used.
Avoid common challenges that businesses face by looking at these big data pain points and how to overcome them.