The Biggest Data Analytics Challenges in 2021 and Beyond
Big data and analytics is full of potential benefits, but also full of challenges. We explore the major pitfalls that most organisations face.
Storing, managing, and analysing large amounts of data is a problem even for large business enterprises. To tackle these challenges, organisations are looking at options like big data analytics tools and data lakes that can help reduce the time and effort involved in deriving business value from “big data”.
New data and analytics methods are developing at such a rapid pace that it is difficult for enterprises to keep up. You might be surprised to know that users and devices create over 2.5 quintillion bytes of data daily.
Storing, managing, and analysing this voluminous amount of data is a challenge even for large business enterprises. their data.Additionally, organisations are still facing major challenges when it comes to implementing their big data strategies. In fact, IDG Data and Analytics Research indicates that 90 percent of enterprises surveyed reported running into problems related to their data analytic projects. To tackle these challenges, organisations are evaluating design patterns such as data lakes, and technology solutions to simplify and streamline the work involved in extracting value from their data.
That said, let’s look at the common challenges faced in this sector.
Managing Larger Data Volumes, Faster
It is hardly surprising that data is growing at a fast pace with every passing day. Handling such enormous volumes of data is in itself a significant challenge. In IDC’s Digital Universe report, it is estimated that the amount of data stored in the world’s IT systems is doubling every two years.
Much of that data is unstructured, which means that it does not reside in a traditional database. Documents, emails, aerial & satellite imagery, video and other unstructured data types can be difficult to search and analyse. Critical business decisions need to be taken effectively, but there is a need to have a strong infrastructure in place which can process the data faster and deliver insights closer to real-time.
To deal with data growth, enterprises are turning to big data analytics platforms to help them manage data storage issues and process many different types of data to find the data insights their enterprises need.
Data Privacy and Security
Security is also a major concern for enterprises with big data stores. After all, companies that deal with enormous amounts of data tend to be attractive targets for hackers and must deal with persistent security threats. Increasing regulatory compliance requirements together with greater public awareness of data privacy concerns add further weight to the importance of managing data securely.
As new business operations grow, some organisations cannot maintain regular maintenance checks due to the generation of huge amounts of data. Moreover, during data collection, privacy laws differ from one geographic location to another. These apply differently based on the data type and intended use.
It is essential to keep data security at the top of your requirements for data analytic solutions, to design your solutions with security in mind from the ground up, and to choose technology vendors and service providers who take data security as seriously as you do.
Converting Big Data into Meaningful Insights
Of course, enterprises do not want to only store their big data – they want to utilise that data to achieve their business goals. Organisations must produce relevant insights from their data, and decision makers across the organisation must have access to good quality information when they need it. This “democratisation” of data, and delivery of self-service reporting & analytic capabilities are big challenges faced by organisations that are looking to become more data-driven.
To achieve that speed, some companies are turning to a new generation of analytics tools that rapidly reduce the time it takes to generate reports and insights. This calls for a proper framework and data sources, that provide critical analysis and needed insights to ensure everything falls within the scope.
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Costly Data Handling Processes
Big data management, right from the adoption stage to launch, requires investment. While big data offers big business gains, there are additional costs associated with the development, setup, configuration, and maintenance of the frameworks needed which presents barriers that organisations may struggle with.
Effective planning based on business needs and strategising will help eliminate unnecessary spending. Incorporating data lakes can also help provide cheap data storage opportunities for the data that you do not plan to analyse at the moment.
The most unique feature of data is its ability to grow. The only problem is that analytics can be hard to scale for an organisation as data growth increases. Similarly, gathering information and creating reports becomes increasingly complex.
Having a system that can scale to meet organisational growth is vital to ensure there is improved performance within the system. The modern architecture of your data system can help support and handle any changes related to data growth in a cost-effective manner.
There is no doubt that the excitement about data and analytics is not likely to go away any time soon. From gaining a competitive edge and anticipating future business demands, the actual possibilities with data analytics are endless. Tracking and monitoring today’s world of data is difficult. Data practitioners ought to plan and prepare for the future.
A modern data analytics platform can provide your business with meaningful insights that drive action and results. With our analytic accelerators and frameworks, we not only reduce data gathering and analysis time, but we also help you discover hidden trends and patterns to help customers focus on their data insights and make better decisions.
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Blog sources include socialmediatoday.com, idg.com, and emc.com.
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