Five Principles of Data Analytics

Data analytics is the use of qualitative and quantitative techniques and processes to increase productivity for leveraging business gains for enterprises. It scrutinizes the collected data to analyses and identifies patterns to create unique solutions and software to address business challenges.

Data Analytics help businesses identify patterns and linkages in data, which are useful in taking their business forward in terms of growth and profitability. Data Analytics is often referred to as Data Science. Effective use of Data Analytics depends on four key components. These components are business data, hardware platform, analytics software and managers with a good understanding of what is possible using analytics. Even though data, hardware, and software are available in plenty, there is a real shortage of managers who understand the potential of Data Science.

This article outlines five principles of Data Analytics, which will help top management to understand what is required to make the best use of Data Analytics.

1. Develop company-wide Data Science culture 

Data Science unlocks insights. Appreciating and understanding how value is derived from the analytical insights should be part of your organization’s culture. The entire organisation should be educated and made aware of this culture. Conscious efforts should be made to ensure that all decisions are better informed and transform the organization to an insight driven one.

2. Correctly identify and frame your problem.

Proper analysis of data starts with identifying a problem or decision required and prepares to solve it. In data analytics, this is called framing of the problem. A faulty framing of the problem will lead to an incorrect solution. It is critical to frame the problem correctly.  The decision to go ahead with any kind of analysis is driven by manager’s hunch or intuition. The analysis will help in testing your hunch against the outcome.

3. Make data-driven insights available as a service

There is increasing demand for information and insights from the business executives at all levels. This demand is increasing day-by-day. The organization must make these insights available to executives when & where they need it, on-demand. Creating such a powerful platform that can deliver these in-sights on demand is crucial.

4. Understand the implications and difference between small & big data

The term “big data” is used very loosely these days by executives without knowing the real difference and implications of big-data. Small data is nothing but traditional transactional data for a company which is small, structured and can be accommodated easily on one server. In contrast, big-data is large, unstructured and requires huge processing power. Big data originates mostly from outside through social media interaction of customers, their call center conversations, etc. Executives must know the difference and know how to make the best use of it.

5. Enable your infrastructure to handle flooding, security, and privacy of data

Today businesses generate large chunks of data every day. Big Data infrastructure must have the ability to handle this influx of data. However, insights from un-reliable data are worse than no insights at all. The organization must ensure data reliability. Equally important is security and privacy of data. If these aspects are not handled properly, it can leave a company exposed and can even lead to legal issues.

There are different types of analytics starting from descriptive analytics such as reports or dashboards to more advanced predictive and prescriptive analytics. Business executives must adapt to these advanced techniques which are far more valuable than the descriptive variety.

Knowing and understanding these fundamental principles would help organization shape their data science strategy and make the best use of this remarkable resource.