Why is Analytics Essential?

What exactly is Analytics?

Analytics is an encompassing and multidimensional field that uses mathematics, statistics, predictive modeling and machine-learning techniques to find meaningful patterns and knowledge in recorded data.

Today, we add powerful computers to the mix for storing increasing amounts of data and running sophisticated software algorithms – producing the fast insights needed to make fact-based decisions. By putting the science of numbers, data and analytical discovery to work, we can find out if what we think or believe is really true. And produce answers to questions we never thought to ask. That’s how powerful analytics is.

Why is analytics essential?

Instinct does not reap fruitful results each and every time and in order to be sure about the consequences of a particular decision we need to analyse the past and the present data in order to take decisions which would be more accurate and goal oriented.

“Fact-based decisions have become our competitive strength. Whether or not to utilize analytics was no longer an option”.

-Dan Ingle

Popular methods for analysis 

Descriptive Statistics: 

It’s one of the oldest statistical methods that have been around. Mean, Median, Mode, Measures of  Central Tendencies, Measures of Dispersion are some of the methods of descriptive statistics. These are the models that will help you understand what happened and why. There are still plenty of descriptive analytics in use today – everything from how many clicks a page receives to how many units are produced vs. how many are sold.

Predictive Analytics:

Predictive analytics has surged in popularity. The desire to predict customer behavior has been a main driver. Increased computing power with the ability to run hundreds or thousands of models quickly – and widespread adoption of predictive techniques like support vector machines, neural networks and random forests – are bringing predictive analysis to the forefront of many organizations. These models use past data and predictive algorithms to help you determine the probability of what will happen next.

Prescriptive analytics:

Prescriptive analytics is the newest kid on the block. Knowing what will happen and knowing what to do are two different things. Prescriptive analytics answers the question of what to do by providing information on optimal decisions based on the predicted future scenarios. The key to prescriptive analytics is being able to use big data, contextual data and lots of computing power to produce answers in real time.

Tools used for Analytics:

SAS: SAS is a software suite that can mine, alter, manage and retrieve data from a variety of sources and perform statistical analysis on it.

WPS: WPS can use programs written in the language of SAS without the need for translating them into any other language

Tableau:  It produces a family of interactive data visualization products focused on business intelligence

Qlikview: Qlikview is a business intelligence software from Qlik.

BigML : BigML is a Corvallis, Oregon based startup with a SaaS-based machine learning platform that allows everyday business users to create actionable predictive models within minutes.

The above mentioned are paid tools. There are free/open source tools like R, Google Analytics and Python, Spotfire which can be used by beginner’s to exploit the possibilities of Data Science and Analytics.

Benefits of Analytics:

1. Proactivity & Anticipating Needs:

Organisations are increasingly under competitive pressure to not only acquire customers but also understand their customers’ needs to be able to optimise customer experience and develop longstanding relationships. By sharing their data and allowing relaxed privacy in its use, customers expect companies to know them, form relevant interactions, and provide a seamless experience across all touch points.

2. Mitigating Risk & Fraud:

Security and fraud analytics aims to protect all physical, financial and intellectual assets from misuse by internal and external threats. Efficient data and analytics capabilities will deliver optimum levels of fraud prevention and overall organisational security: deterrence requires mechanisms that allow companies to quickly detect potentially fraudulent activity and anticipate future activity, as well as identifying and tracking perpetrators.

3. Delivering Relevant Products:

Products are the life-blood of any organisation and often the largest investment companies make. The product management team’s role is to recognise trends that drive strategic roadmap for innovation, new features, and services.

4. Personalisation & Service:

Companies are still struggling with structured data, and need to be extremely responsive to cope with the volatility created by customers engaging via digital technologies today. 

5. Optimizing & Improving the Customer Experience

 Poor management of operations can and will lead to a myriad of costly issues, including a significant risk of damaging the customer experience, and ultimately brand loyalty. Applying analytics for designing, controlling the process and optimizing business operations in the production of goods or services ensures efficiency and effectiveness to fulfill customer expectations and achieve operational excellence.

Future of Analytics:

The reliance on data driven decision making will continue to grow. Just like the widespread usage of metrics and reports today, companies will start expecting to see some predictive analytics insights as part of regular dashboards.

As analytics becomes more and more prevalent in the corporate consciousness, a basic awareness and understanding of analytical techniques will become a required skill for career growth at the middle to senior management tiers, irrespective of industry and function. There will also be an increased demand for some super specialized roles. These will require intensive expertise with programming and technology to support the actual analytics implementation.

In the next decade we will witness technological advances that will play an increasingly important role in the ability of companies to mine data for real time insights and actions in the context of the rapid pace of data produced and the variety of data that is being captured.

 Author : Keyur Dhavle

Connect with me @Keyur Dhavle (LinkedIn)