5 Challenges in Big Data Analytics to Watch Out For

Although data collection and analysis have been around for decades, in recent years big data analytics has taken the business world by storm. However, it does come with certain limitations. In this article, we will talk about the challenges in big data analytics companies are going to face in the near future.

As the name suggests, big data is huge in terms of volume and business complexity. It comes in various formats, such as structured data, semi-structured data and unstructured data, and from a wide array of data sources. Big data analytics is useful for quick, actionable insight. Since big data analysis is based upon various parameters and dimensions, it does come with certain challenges, including:

• Handling a large volume of data in a limited time
• Cleaning data and formatting it in order to get the desired meaningful output
• Representing the data in a visual format
• Making the application scalable
• Selecting proper technology/tools for analysis

Handling an Enormous Volume of Data in Less Time

Handling a large volume of data in a limited time is a significant challenge, given the fact that over 2.5 quintillion bytes of data are created on a daily basis. On top that, we can’t even name all of the various sources from which the data is being created — the data sources can be sensors, social media, transaction-based data, cellular data or any other of a myriad of sources.

In order to make critical business decisions effectively, we need to have a strong IT infrastructure which should be capable of reading the data faster and delivering real-time insights. So, we see that the challenge is how to extract the insight of the data from an enormous volume in a cost- and time-effective manner.

If we talk about handling complex data, the first big data tool which comes to mind is Apache Hadoop. In Hadoop we have MapReduce, which has the ability to split the application into smaller fragments. Every single fragment is then executed on a single node inside a cluster. Hadoop has many handy features and is widely used, but we can’t ignore the fact that organizations need a concrete solution which should be able to handle an array of both structured and unstructured data while allowing minimal downtime. On top of these, Hadoop has some additional challenges, including:

• Challenges related to data management
• Challenges related to job scheduling
• Challenges related to resource sharing
• Challenges related to cluster management

IBM InfoSphere BigInsights, which is built based on top of Hadoop, has the ability to meet these critical business requirements. At the same time it also has the ability to maintain compatibility.

Cleaning and Formatting Data to Get Meaningful Output

Data cleaning is an integral part of data analysis. In fact, it is a more time-consuming task to clean the data than to perform any statistical analysis on it. While doing a statistical data analysis, data has to pass through the following five steps:

Figure 1: Data cleaning and analysis steps

In the above figure we can see an overview of data analysis stages. Each of the boxes represents one stage through which the data passes. The first three steps fall under the data-cleaning mechanism, while the last two are part of data analysis.

• Raw data — This is the data as it comes in. In this state there could be three potential problems:
  • The data may not have the appropriate headers.
  • The data may have incorrect data types.
  • The data may contain unknown or unwanted character encoding.
• Technically correct data — Once the raw data is modified to get rid of the above listed discrepancies, it is said to be "technically correct data."
• Consistent data — In this stage, data is ready to be exposed to any sort of statistical analysis, and can be used as a starting point for analysis.
• Statistical results and output — After getting statistical results, they can be stored for reuse. These results can also be formatted so that they can be used for publishing various kinds of reports.

Visual Representation of Data

Representing the data in a well-structured format which is readable and understandable to the audience is vitally important. Handling the unstructured data and then representing it in a visual format can be a challenging job which organizations implementing big data are going to face in the near future. To cater to this need, different types of graphs or tables can be used to represent the data.

Application Should be Scalable

Given the increasing volume of data day by day, the biggest challenge organizations are going to face is the scalability factor. In order to have a scalable application, we foresee the following challenges while collecting the data:

• Data services are deployed on multiple technological stacks:
  • Apache/PHP for the front end
  • Use of programming languages (like Java or Scala) to interact with the database or the front end

As there are multiple layers (consisting of different technology stacks) between the database and the front end, traversal of data takes time. So when the application tries to scale up, performance goes down. As a solution, the architecture and the technology stack should be designed properly to avoid performance issues and increase scalability.

There should be minimal latency in the production data services. When an application scales up, the response time to each request is one of the major issues. As the volume of data increases, the latency problem has to be handled properly by implementing best practices in the data service area.

Selection of Appropriate Tools or Technology for Data Analysis

Regardless of the approach we take to collect and store the data, if we don’t have an appropriate tool for analysis, it is of no use to have these things in place. We need to take extra care while selecting tools for data analysis. Once we finalize the tools, we can't easily switch to another. Therefore, while selecting tools for analysis, we should consider the following:

• Volume of data
• Volume of transaction
• Legacy data management and applications

Conclusion

The challenges mentioned here can be easily predicted, but who knows what other, unforeseen challenges may lie ahead? When working with big data, it's a good idea to anticipate challenges and try to plan for any issues that may arise.