How to build data analytics skills that accelerate business

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Picture a future in which business analysts combine the power of data with new-age techniques to help companies stay connected with their customers every step of the way—knowing where they are, what they want and how they can find it.

Business analysts are the change catalysts, solving critical business challenges for companies and helping create new experiences for their customers.

That future is closer than you think.

You now have an unprecedented opportunity to push the accelerator and play a much larger role in your company. Focusing on industry insights alone is not enough. Now, you need to build excellent data analytics skills to read and use the wealth of data your company possesses.

This will help reimagine the business and build brand power—with innovative, personalized ways of connecting with customers.

Data full throttle

Companies thriving in the digital economy are embracing new big data technologies and advances in data science to mine insights from the massive amounts of data they have access to and enrich customer experience.

In other words, assessing data quality, identifying meaningful information from all the noise and eventually applying the right algorithm mix to come to a possible set of relevant solutions.

In return, it’s helping them improve engagement and customer satisfaction.

Therefore, you need to evolve your competencies from an “inside-out” to an “outside-in” approach where customer value and experience dictate the transformation strategy of your company.

Take the OSEMN route

To get started, you should be clear about the goals of your organization and the business problems it wants to solve and then use data to define solutions that will help your organization achieve its goals. And, structured approaches such as OSEMN—Obtain, Scrub, Explore, Model and Interpret—can be highly instrumental in data crunching and extracting actionable insights.

As mentioned earlier, customers know what they want. Once you know exactly what they want and when they want it, you can easily apply the OSEMN approach to come up with solutions that delight customers and boost business outcomes.

A structured approach such as OSEMN covers every step of a data science project lifecycle.

Step 1: Obtain data.

The first step is to identify the right set of data required for business problem analysis. Across industries, websites and social media offer detailed information about any place under the sun and offer valuable customer insights.

Stage 2: Scrub the data clean.

Not all the data you gather is useful, especially when you are trying to offer tailored solutions to customers. The second step helps you organize and tidy up the data by removing irrelevant information and replacing it with what’s missing. Here, you can also standardize the format across all the data collected.

Stage 3: Explore data.

In this phase, you must try to understand the patterns in the data. Several types of visualization and statistical testing are used to support the findings and derive hidden meanings from the data.

Stage 4: Model data.

Models are general rules, in a statistical sense. While modeling data, you must reduce the dimensionality of your data set. Not all your features or values are essential to predicting your model. What you need to do is to select the relevant ones that contribute to the prediction of results.

Stage 5: Interpret data.

The most crucial step is to translate the outcome of models into definite actionable solutions. You should be able to deliver results that provide the most useful answers to business challenges.

All industries leave a long trail of data—customer preferences, inquiries and additional services purchased, among others. It’s time you seize this golden opportunity to be come a master of data analytics and enable your company to elevate customer experience like never before.

Are you a data science expert or an industry specialist? We’re growing our team. Find your fit with Accenture.

For a deeper look at how business analysts can learn, adapt and deliver value in the thriving travel industry, read the IIBA Data Science Age – Hyper-personalizing Travel Experience whitepaper, developed in collaboration with Accenture.

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