Article Snapshot

You may have already heard the statement, “data is the new oil”. In today’s business environment data represents such a transformational and valuable resource in the same way as oil during the previous century. Like oil, if “crude” data can be extracted, refined, and piped to where it can impact decisions in real time, its value will soar. And if data can be properly shared across an entire ecosystem and made accessible in the places where analytics is most useful, then it will become a true game changer. But still, data is an immensely, untapped asset within most businesses. So then, what do organisations need to do to leverage data and engage in data-driven strategic planning? The answer lies in the effective practice of Data Analytics.

Defining Data Analytics

Data Analytics comprises the capability to collect and store data, curate data, perform operations based on data and visualise the output in a manner that enables strategic decision-making. In the same way that oil requires significant infrastructure investments to be sourced, stored and processed, data puts the same requirements on Chief Information Officers (CIO). The first step along the analytics path for an organisation would be deciding how to store and collate data securely and address how the data will be processed and shared across the organisational value chain. This may initially call for Data Warehousing Best Practice such as:

  • Change management and transformation initiatives.
  • Organisation-wide stakeholder engagement to define requirements.
  • Modelling the requirements and architecture and validating Extract Transform and Load (ETL) of data methods.

“Tapping” Data for Business Intelligence

One of the biggest challenges that anyone dealing with data faces is not merely storing or finding data. It’s finding the right data and being able to use it efficiently in order to serve customers better. As with oil, data is only valuable when it’s refined. Amazon, Google, Facebook, LinkedIn and Uber are just a handful of companies exploiting data analytics to derive competitive advantages. However, companies from startups to established businesses can derive advantages through:

  • Recommendation engines to up-sell/cross-sell products/services which can lead to incremental revenue.
  • Blending company data with external data. Eg. Correlations between car rentals and a major sporting event in a particular location at a certain time of year could be used to target marketing activity.
  • Guiding stock inventory and arranging of items on a supermarket shelf. Eg.  If there is a demand for hot dog buns sausages and sauce would also be in demand etc..

A further step is performing Business Analytics where the data (with or without blending with external data) is used to forecast future operating conditions. This allows for true strategic decision-making and becomes a powerful executive tool.

Finding the "Right" Data

As an independent consultant, I help companies find the right data and use data analytics help them inform business strategy. In a recent project with TRIP, a clinical article manager and curator company, I was required to inform on the connections between articles in their data warehouse and a user's interaction with them  and to develop algorithms that could recommend to an individual what other articles they would like based on criteria such as their profession, location etc. Quite simply, imagine you’re an individual user of a clinical article website, do you want to see other articles that are similar to the one you are reading? Do you want to know what others users with the same interests as you read? Almost certainly, yes! Data Analytics to Recommend Articles To enable TRIP to make data-based recommendations and ultimately show a user related articles, an algorithm that used association rules that explored the relationships between articles on a per session basis needed to be developed. The purpose was to set the stage for providing users with recommendations based on their initial article of interest and their particular user characteristic. So, if we know a user’s activity on TRIP we can start to understand them and then –– recommend new articles that should be of interest. Each user will then be classified as being interested in articles within a cluster based on attributes such as their first choice of article and user attributes (profession, country etc.) using a method where a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility is created. Increasing Customer Value By refining the data, processing it in real-time and delivering outputs that are truly useful to its customer, TRIP has been able to increase its value proposition and develop a tangible competitive edge. Equally, organisations that can take advantage of data as the valuable new resource it is, will be in the best position to become leaders in their industries. If you feel that your business would benefit from a discussion with Sanjeev please contact him by posting a project on the Expert360 platform.

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