What are the Core Strategies for Successful Big Data Analysis Breakthroughs?

What are the Core Strategies for Successful Big Data Analysis Breakthroughs?
August 24 2016

Big data has been around for quite a while now creating buzz and effect in transformation of companies, industries and how they handle their growth strategy. It’s summation or drawing conclusion from various streams or sources of data from businesses and industries like, merchandising, textiles, supermarkets and departmental stores, financial services, security oriented firms, performance data and statistics collecting entities, auto industry to information technology etc every branch of business seems interested in the details that unravel the fundamental implications of big data.

However, at every stage, drawing the fine realities and perfect implications of big data flows with a set of core strategies that help harmonize and bring meaningfulness to sets of data. What is important to remember is that big data is a purely scientific data analytical tool that unravels a slew of challenges in industry or business management and critical decision making. Thus, whenever running any strategy or initiative that involves or has big data at its core, it is vital to align business or industry objectives with leadership, management and approach to big data science application.

Major and Core Strategies that Can Inspire Big Data Analysis Success

  1. Maintain Equity between Qualitative and Quantitative Data Analysis:

    qualitative data analysis explores data in terms of exploratory data analysis. Qualitative data research looks at the underlying themes in vast sets of big data. The data can be structured or unstructured and with breakthrough success, it reveals concepts and patterns in big data that were once perceived as nonexistent. Quantitative big data analysis develops and tests hypotheses once data themes are developed. Quantitative big data analysis also justifies which qualitative data research nurtures and develops. Quantitative data analysis is much more rigorous and critical than its more exploratory qualitative data analysis to the extent of polarization. In a mixed qualitative data analysis and quantitative data analysis it is commendable to maintain mixed-method research for proper structures in big data analysis.

  2. Ensure Big Data Science Product Development

    Generally, there are three layers of fine corporate strategy; competitive, distinctive and breakthrough. Employing a breakthrough strategy indicates intentions of going higher quality-wise with forthcoming products or services. And should the industry is a prediction business and the current best competitive product in the market has about 70% accuracy, the breakthrough strategy should involve products with higher or closer to 95% accuracy. This means the best faster and most reliable prediction algorithm that necessitates better and bright big data, analysts and scientists.

  3. Realistic Achievable Goals on Big Data Research

    The difference between an over-ambitiousness and a realistic assessment of expectations is the in-depth strength of commitment and intellect of data analysts. Realistic research and development must back up plans. At times, research and development are variable as there is no guarantee of even a breakthrough.

And so for businesses and industries to attain successful big data strategy and display competitive or distinctive qualities; a management-centric, quantitative big data approach emphasizing proper planning, control as well as accuracy is the most appropriate route. However, to attain transformation to higher breakthrough qualities; a leadership-centric, qualitative big data approach emphasizing exploration and change leadership is the most advantageous option.

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