Big Data Analysis: Effective tips to success

Big data analysis
Big Data

Can data especially big data be considered as the new gold? Considering the pace at which data is evolving all across the globe, there is little question. Big data contains huge information and we can extract them by performing big data analysis. Consider the following: 

  • Netflix saves $1 billion per year on customer retention only by utilizing big data.
  • Being the highest shareholder of the search engines market, Google faces 1.2 trillion searches every year, with more than 40,000 search queries every second!
  • Additionally, among all the google searches. 15% of those are new and are never typed before, leading to the fact that a new set of data is generated by Google continuously regularly. The main agenda is to convert data into information and then convert that information into insights. 

Why need Proper Big Data Analysis Strategy?

Organizations were storing tons of their data into their databases without knowing what to do with that data until big data analysis became a completely developed idea. Poor data quality can cost businesses from $9.7 billion to 14.2 million every year. Moreover, poor data quality can surely lead to wrong business strategies or poor decision-making. This also results in low productivity and sabotages the relationship between customers and the organization, causing the organization to lose its reputation in the market.  

To deter this problem, here is a list of five things an enterprise must acquire in order to turn their big data into a big success:

Strong Leadership Driving Big Data Analysis Initiatives  

The most important factor for nurturing data-driven decision-making culture is proper leadership. Organizations must have well-defined leadership roles for big data analytics to boost the successful implementation of big data initiatives. Necessary stewardship is crucial for organizations for making big data analytics an integral part of regular business operations. 

Leadership-driven big data initiatives assist organizations in making their big data commercially viable. Unfortunately, only 34% of the organizations have appointed a chief data officer to handle the implementation of big data initiatives. A pioneer in the utilization of big data in the United States’s banking industry, Bank of America, specified a Chief Data Officer (CDO) who is responsible for all the data management standards and policies, simplification of It tools and infrastructures that are required for the implementation, and setting up the big data platform of the bank. 

Invest in Appropriate Skills Before Technology

Having the right skills are crucial even before the technology has been implemented: 

  • Utilize disparate open-source software for the integration and analysis of both structured and unstructured data. 
  • Framing and asking appropriate business questions with a crystal-clean line of sight such as how the insights will be utilized, and 
  • Bringing the appropriate statistical tools to bear on data for performing predictive analytics and generating forward-looking insights. 

All of the above-mentioned skills can be proactively developed for both hiring and training. It is essential to search for those senior leaders within the organization who not only believe in the power of big data but are also willing to take risks and perform experimentation. Such leaders play a vital role in driving swift acquisitions and the success of data applications. 

Perform Experimentation With Big Data Pilots

Start with the identification of the most critical problems of the business and how big data serves as the solution to that problem. After the identification of the problem, bring numerous aspects of big data into the laboratory where these pilots can be run before making any major investment in the technology.  Such pilot programs provide an enormous collection of big data tools and expertise that prove value effectively for the organization without making any hefty investments in IT costs or talent. By working with such pilots, implementation of these efforts at a grassroots level can be done with minimal investments in the technology. 

Search For a Needle in an Unstructured Hay 

The thing that always remains on the top of the mind of businesses is unstructured and semistructured data – information contained in documents, spreadsheets, and similar non-traditional data sources. According to Gartner, data of organizations will evolve by 800% in the upcoming five years and 80% of that data will be unstructured. There are three crucial principles associated with unstructured data. 

  • Having the appropriate technology is essential for storing and analyzing unstructured data. 
  • Prioritiing such unstructured data that is rich in information value and sentiments. 
  • Extracting relevant signals must be done from the insights and must be combined with structured data for boosting business predictions and insights.

Incorporate Operational Analytics Engines

 One potential advantage that can be attained by using big data is the capability of tailoring experiences to customers based on their most up-to-the-minute behavior. Businesses can no longer extract the data of last month, analyze that data offline for two months, and act upon the analysis three months later for making big data a competitive benefit.

Take, as an example, loyal customers who enter promotional codes at the time of checkout but discover that their discount is not applied to result in a poor customer experience.

Businesses need to shift their mindset of traditional offline analytics to tech-powered analytic engines that empower businesses with real-time and near-time decision-making, acquiring a measured test and learn approach. This can be achieved by making 20% of the organization’s decisions with tech-powered analytical engines and then gradually increasing the percentage of decisions processed in this way over time as comfort grows about the process. 

Final Thoughts 

In this tech-oriented world and digitally powered economy, big data analytics plays a vital role in the proper navigation of the market and to come up with appropriate predictions as well as decisions. Organizations must never ignore understanding patterns and deterring flows. especially as enterprises deal with different types of data each day, in different sizes, shapes, and forms. The market of big data analytics is growing dramatically and will reach up to $62.10 billion by the year 2025. Considering that progression, 97.2% of the organizations are already investing in artificial intelligence as well as big data. Hence organizations must acquire appropriate measures and keep in mind all the crucial above-mentioned tips for turning their big data into big success to stay competitive in this ever-changing world.

6 thoughts on “Big Data Analysis: Effective tips to success

  1. Hairstyles

    Hey! Do you know if they make any plugins to help with SEO? I’m trying to get my blog to rank for some targeted keywords but I’m not seeing very good success. If you know of any please share. Kudos!

  2. thedataxp

    Nice Tips! As a professional Data Analyst working in the field for 9 years, I think, A smart data analyst can analyze data and explain why it is significant to their boss and firm. Their reports should reflect this knowledge and be tailored to the needs of individuals who will read them. A competent data analyst is self-driven and proactive and performs what has to be done without being instructed.
    thedataxp recently posted…Self-discipline and taking action is key to a happy, balanced mindMy Profile

Leave a Reply

Your email address will not be published. Required fields are marked *

CommentLuv badge