We live in a world, which is increasingly driven by data in the sense that with time data communication around the globe has grown to be very voluminous. This large volume of data that businesses capture on a day-to-day basis-whether structured or unstructured has the potential to be mined for useful information in a trend called Big Data.
In simple terms, big data can be characterized by data volume, velocity, variety of data type and value. Great volumes of data are collected from different sources such as social media, and this has become easier with the growth of technology. There are a wide variety of types of data, from structured which include data arrays, files, and records to unstructured which can be textual or non-textual. Big data contains unlimited commercial value as its technology and services value rises each year.
Challenges of Big Data
The fact that Big Data is all about huge data means that there are many challenges that those analysts and clients face each day. The following are some of the challenges of having big data implementation within an organization or company.
Security and Privacy
Security is the process of preventing data loss or attacks from malicious malware and get protection from hackers. Execute the big data using SSL certificate to provide secure communication between web applications and web servers. Regulations have to be set so as to limit users who access information and how often check-ups for malware are to be done. Data loss can also be a challenge especially with storage facilities that are not up to the right standards. Frequent backup and redundancy mechanisms need to be done to prevent this.
Privacy aspect can be individual, or company based. The biggest privacy issue is seen mostly on social media- for example in Facebook. An individual might participate in questionnaires, but they do not know where the data will be used. Another example is that of a scenario where a person shares pictures with personal accounts but the privacy in terms of those who view it is not guaranteed. Almost everything that is uploaded to media sites can be accessed maliciously by anyone in public domain.
Speed
Big Data is stored in databases and EMC storages, which can handle large data in Terabytes. These storage areas need high speed to efficiently import and export data. With growing volumes of a data that constantly need to be stored, speed can be a challenge. Backups must be done every now and then and with slow speeds, the process takes long resulting in traffic that leads to slow services.
Analysis
It requires much time to analyze data as the sets of data being received increases. Companies have difficulty in identifying the right data and determining how best to use it. Data is collected from different sources such as social media, which has to be analyzed to have a better understanding of users in general and what they want. Data analysts have to pay attention to what the clients want and how they use the information provided to them.
Access and Connectivity
This can be an obstacle especially where companies are not connected to data points, and they do not have the right platforms to access and manage their data. Those that export their data to databases located abroad have challenges accessing their data when the network is down or if there is any maintenance taking place. Data from different sectors can be stored on the same platform and access to the sensitive materials is not restricted to any sector. Policies have to be set to restrict the users that can access certain information to protect sensitive information. Also, companies may be reluctant to share their clients’ information and any data related to them to avoid competition and to generally protect the information they hold.
Cost
When choosing hardware and software, the cost of purchase has to be put under the microscope. Big data storage requires high-end equipment that guarantees high-quality data processing performance. The investment and operation cost are as high as hiring professional technical personnel to maintain servers and databases.
Data Storage
Large amounts of Big Data accumulate each year, and this has brought a challenge in the storage and processing industry. Cloud storage comes in handy by providing users high levels in the realm of scalability, availability, and reliability. Cloud storage/computing is simply defined as the process of applying a network of remote servers lay on the web to store, manage and process data. This is mostly used by sites such as Google and Yahoo which have many users to offer services such as the creation of accounts and online storage to their users. Here, clients can upload their data from images to videos and they can also receive and send emails.
Evolving Technology Landscapes
The other challenge is finding the right talent capable of working with high-level technology and interpreting the data to find meaningful business insight. This can be a hurdle in creating the right IT architecture that can adapt to changes in data technology landscape in an efficient manner. The voluminous amount of data is too large and complex for humans to successfully extract information. This needs technologies such as Hadoop that offer new ways to extract and transform big data into meaningful information. This is bound to grow even tougher in future.
Big data is escalating with the constant development of new technologies and applications. Companies are now more data driven and thanks to the access of large volumes of data, business leaders are able to make data-driven decisions. Data analysis helps companies make better decisions and identify new opportunities. It is now all dependent on how data experts can alleviate the trend of its challenges in a bid to make big data bigger in the coming years.
