Entrepreneurs Need to Make the Transition From Intuition to Evidence in Business Decision Making Through Analytics

Data has replaced oil as the world's most valuable resource, and analytical tools to evaluate it are improving every day

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The five most valuable listed companies in the world- Alphabet (parent company of Google), Amazon, Apple, Facebook, and Microsoft, have something in common. Fundamentally, they all deal with data, the most precious resource of the global economy today. As data volume increases to unimaginable numbers every passing second, the tools to make sense out of it all and bring some order in the chaos are also getting better.

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Most organizations have also bowed down to the massive might of data and accepted it as perhaps the biggest determinant of business success during the Industrial Revolution 4.0. Yet, it’s not easy to get business users to adopt analytics software and tools as part of their day-to-day work. What’s the problem?

 Skillset Schism Leading to Slow Adoption of BI Analytical Tools

The rapid transformation of global business environments has left both enterprises and employees reeling. While trying to keep up with the blistering pace of digitization, most employees have often found their existing skills to become obsolete, within a couple of years. The G2 Crowd’s The State of Software Happiness Report 2019 gives great insights into this scenario. Some of its key findings include-

·        51% of employees surveyed were not with existing software

·        24% even considered quitting because of company software, 12% actually quit

·        95% would be happier and more productive with better software.

This does not stop enterprises from generating massive amounts of data every day during operations. Yet, they often are compelled to seek the services of third-party service providers for transforming this data into something comprehensible and meaningful for various teams within the enterprise. Additionally, the adoption of analytics is hindered contingencies such as training costs, learning curve latency period and infrastructure refurbishment expenditure. Sometimes, the conventional mindset of owners, who are not receptive to change, acts as an obstacle to Business Intelligence and Analytical tools adoption.

What an Entrepreneur Should Look for in an Ideal Analytical Tool

Do Not Advise, Specify

Data solutions today need to move away from playing an advisory role which leaves most business owners, promoters and investors confused. Instead, the outcomes should be specific and the tools should be able to create a firm blueprint on how to go about achieving the objectives. Vague insights further motivate decision-makers to go by their instincts, which more often than not, are not good for a long-term.

However, the confidence to adopt such a specific stance can only occur if the groundwork has been laid correctly. Analytics tools must be built on sound analytical frameworks, predictive models, proper business context and relevant technology. Additionally, strong predictive models play an important role in understanding the target markets in their current and future state and identify long term opportunities.

Lastly, every such suggestion needs to be backed by strong informational evidence from relevant categories to the operation. For instance, a manufacturing firm would try to analyze the inventory inflow/outflow data, a list of credit extension activities per account, demand for products and services and scale of demand before arriving at a conclusion. It is also important for the client to know the rationale behind every recommendation made as it helps build credibility.

Easy Interface

The Lazy User Principle, which states that people will choose things that take less effort over the more complicated offering, is an adage that still stands strong in the user engagement domain. Minimum jargon, intuitive interface, clear listings, fast and seamless operations, and conciseness are qualities that should be present in every interface. The main goal before designing any interface is to keep it simple, and engaging. 

Analytical tools should be easy to use for the employees. It should have a non-intrusive presence, being there whenever it is needed. If possible, an opinion should be taken from end-users for design elements. After all, it is the end-users for which the interface is being built, and listening to their ideas to be incorporated increases the chances of adoption. The analytical tool should quickly become a part of an employees’ existing work structure, and enhance their work experience through efficient management of the existing workload. 

Relay Decision Making to End-users

The end-users determine the success of a tool, therefore their opinion needs to be incorporated as early as possible. Users need to be enabled with the power to share an opinion on various parameters, report glitches and suggest improvements. The tool must be prepared in a way to easily switch between various use cases, such as revenue growth vs market share valuation, and report actions as well as expected results easily to the user.

Enterprises need to do a bit of soft launching, perhaps within departments, before adopting a tool in an operational framework.  This can point out flaws in a simulated and controlled environment. It is also important to know the context, purpose and targeted objective from an analytical tool and not implement it arbitrarily. After all, the weapon is as good as the wielder, and these solutions can only prove to be worth the effort if they are guided by a strong vision and quantifiable goals. Once these goals are achieved, the organization’s culture gradually makes a transition towards being adoptive towards cutting-edge solutions that optimize productivity while reducing efforts and costs, the ultimate goal for any enterprise functioning in the current competitive business ecosystem. 

Anil Kaul

Written By

Anil has over 22 years of experience in advanced analytics, market research, and management consulting. He is very passionate about analytics and leveraging technology to improve business decision-making. Prior to founding Absolutdata, Anil worked at McKinsey & Co. and Personify. He is also on the board of Edutopia, an innovative start-up in the language learning space.

An in-demand writer and speaker, Anil has published articles in McKinsey Quarterly, Marketing Science, Journal of Marketing Research and International Journal of Research. He was recently listed among the ‘10 Most Influential Analytics Leaders in India’ by Analytics Magazine India and has been quoted as a “Game Changer” in Research World. Anil has spoken at many industry conferences and top business schools, including Dartmouth, Berkeley, Cornell, Yale, Columbia and New York University.

Anil holds a Ph.D. and a Master of Marketing degree, both from Cornell University.