How to Address Common Big Data Pain Points

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SreeramIn this special guest feature, Sreeram Sreenivasan of Ubiq takes a look at some common pain points for big data adoption that enterprises need to confront. Sreeram is the Founder of Ubiq, a new business intelligence & reporting application. Before Ubiq, he has worked at ZS Associates, a global Consulting Firm, where he helped Fortune 500 companies in various BI, data analysis & strategic consulting projects.

Big data comes in various forms – structured, semi-structured and unstructured – from a variety of data sources. From this constantly growing mountain of data, organizations need to extract actionable insights quickly. This poses a huge challenge to organizations, in terms of business and volume complexity. Here are the common Big Data pain points faced by organizations:

Handling Large Volume of Data in Less Time

Nearly 2.5 quintillion bytes of data is created daily, from various sensors, mobiles, social media and transaction based products & services. For rapid decision-making, organizations need a robust & resilient IT infrastructure capable of reading data quickly and providing insights in real-time. Apache Hadoop is a solution that comes to mind, while dealing with large & complex data. Its Map Reduce process breaks an application into smaller fragments, each of which is executed on a single node in a cluster. There are various software packages such as Cloudera, Hortonworks & IBM InfoSphere which integrate with Hadoop and handle parallel processing issues such as scheduling, cluster management, resource & data sharing.

Cleaning Data to Make it Suitable for Analysis

To get the most out of your data, and to get it quickly, it is necessary that the data is cleansed before analysis and formatted to be suitable for various kinds of statistical analysis. The process of data cleaning and analysis includes the following steps:

  1. Data Cleansing: The initial data, as received by your organization, is known as Raw Data. Many times, it may have incorrect data headers, data types, even unknown characters or encoding. Once you have modified the raw data to remove these issues, it can be considered suitable for statistical analysis. The data will now have correct headers, format and encoding. Also, it needs to be examined for consistency (such as missing data) across data sets. If data is missing for a few of the entities in some data sets, it will distort the result of data analysis.
  1. Using Statistical output: After statistical analysis, the results can be stored and reused. They can also be formatted and used to publish different kinds of reports. Various extract, transform, and load (ETL) tools are available for performing the data cleaning, analysis & reporting. Only then the clean data can be used as inputs to the processing systems.

Visualizing Data to Get Meaningful Information

Representing Big Data in a visual format that can be easily understood, is a challenge that organizations are going to face, as their need to analyze unstructured data grows over time. Visualizing data enables you to quickly transform data into meaningful information, spot trends & outliers. Data Visualizations such as graphs and charts can be used to represent data. However, it’s important to remember that different types of visualizations are suitable for different types of data. For example, categorical data are best represented by bar charts or line graphs. Continuous data are best represented with line charts or histograms. One of the ways to simplify this process is to summarize your data before visualizing it. You can even visualize a sample of your data to get general direction of trends, before trying it on the full load of Big Data.

Selecting the Right Tool for Data Analysis

If you don’t have the right tool to analyze your data, then it doesn’t matter how you collect, clean and store your data. While evaluating the different tools for analysis, it’s important to consider the following:

  • Volume of data
  • Number of Transactions
  • Legacy data systems

The legacy data should be formatted to suite the input requirements of your data analysis tool else it won’t be analyze your data properly.

Deployment in Production

Many times, big data applications don’t work out when they are deployed in production, as it involves integrating your new application with the existing production system. Many enterprise applications, data analytics tools and dashboard solutions like to directly query your production data. The key is to select a tool that can handle these queries efficiently with minimum configuration.

Addressing these pain points at the beginning of any big data initiative cam enable organizations to gain meaningful insights and efficiently deliver value & ROI from their data.


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