Data-Driven Business Decision

March 11, 2021

“You kids are so obsessed with social media. You always measure your worth in likes and followers on Instagram. These numbers mean nothing. In our teens, our world was much different. We didn’t tally our worth with numbers.”  That was my uncle chiding my teen cousin a few months ago.

Little did he knew that just about week later I will be signing him up for the same social media accounts for his much-beloved pet project of an Automobile workshop.

Little did we knew that 3 months later, the same man who chided us on our life online would spend a huge chunk of his leisure time obsessing over the user engagement analytics those platforms offer for business accounts. Alas, life is a blend of ironies.

My uncle was using the data & reviews these platforms offer to aid his business decisions. In my world, we call it DDDM, an acronym for Data-Driven Decision Making.

I am sure there are many people like my uncle who use these tools without realizing there is a whole science behind this. The tools they are using now were corporate exclusive just 8 years ago.  Technology progresses fast and couple it with the adoption rate that we have in a land like India, these miracles are bound to happen.

5 years ago, the number of mobile phone internet users in the world are 2.2 Billion. We are up by 1.3 billion now and are projected to hit another half a billion in the next 18 months. With the emergence of smartphones humanity in the last 10 years has generated more data than it ever did since the inception of consumer computing.

                  Data has become the gold & the petroleum of 21st century.

So, why did I bring these numbers up? It has something to do with DDDM.

In layman’s terms, Data-Driven Decision Making is a discipline of collecting data collecting, processing, and analyzing data to help one make informed business decisions.  Every business has data and measurable goals we call “Key Performance Indicators (KPIs)” in our good old corporate lingo. DDDM helps businesses identify patterns in their data and weigh them against the KPIs which will enable them to formulate strategies and mend their existing ones.

The quality of the analysis always depends on the quality and context of the data. Increased accuracy brings better insights.

Just a decade ago this was a very comprehensive task that needed an abundance of effort from the analysts, but a combination of factors like the emergence of business intelligence tools, growth in optical fiber and wireless technologies, and accessibility of computing power to the masses changed the spear off DDDM resulting in a new field of study that combines computing, statistics & data analytics called “DATA SCIENCE”.

         If Data is the Petrol of 21st century, Data science is the power drill.

The fundaments of DDDM and its importance:

DDDM deals with 2 types of approaches. The Quantitative Analysis & the Qualitative Analysis.

Simply put, Qualitative analysis deals with the data that can’t or doesn’t need to be aggregated, i.e. the intangible & inexact information such as culture, case studies, sentiments, the robustness of R&D. Any observation that can give a direction to the decision-maker or the analyst.

Whereas Quantitative analysis deals with the Quant/Math part i.e. the statistics, numerical pattern identification, etc.

So, Qualitative analysis incorporates observation while Quantitative analysis focuses on measurement/aggregations.

If you ask me why DDDM must be a part of any business. I would state the following:

  • The modern digital-driven business scape exists in a constant state of flux. DDDM helps stabilize the chaos.
  •  DDDM limits the risk arising from a human error like cognitive bias & Optimism bias.
  • DDDM gives a direction to decision making by curbing “Cognitive Inertia”, the inability to adapt to new environments.
  • DDDM acts as an antidote to groupthink. Everybody wants to fit it, Data does not.

Is your business handling data the “right way” in making the right decisions? The DOs and the DONTs:

The DOs:

1) Gather, nurture & harvest your business data.

Consider the data as the building blocks for your analysis. Organize the data methodically and work on eliminating redundancies at the database level.

2) Right questions lead to right data.

Always formulate a set of objectives to set the approach for the analysis. Having a set of questions that would point you at the right kind of data to look for makes a world of difference. Focusing on the right set of data with the right kind of analysis in mind saves a lot of time in analysis.

3) Define your biases. Be aware of them.

We all have our biases. The decision-makers must constantly work on being aware of them and restricting them. The analyst must always be unprejudiced and open-minded. Discussing & brainstorming with colleagues having contrasting stances will help open new perspectives.

4) Define your KPIs.

With the help of your DDDM report define and set your KPIs which will give you an insight into the health of your system. Monitor them religiously watch out for fluctuations. Tracking the KPIs helps in making sure that the incoming data is reflecting the goals & strategies of your business.

5) Analyze, Develop insights & Revisit.

Analyze and documents your findings. Recognize the patterns and identify the root cause should a problem arise. Work on finding solutions and iterate the process. There shall always be new situations/problems given the volatile nature of the modern business environment.

6) Visualize.

It’s obvious that a good dashboard goes a long way in help keeping an eye on the performance fluctuations of your business. It does not need to be anything extravagant coded by the best of your IT team (If you have one). It just needs to be a simple dashboard that shows you the current performance values, near-future projections, and how the current values line with your previous projections.

7) Adapt, Evolve, Document.

If the data contradicts your mission always be ready to adapt your strategy guided by actionable insights. Data collected with a precision never lies.

The DONTs:

1) Never stop improving the quality of your data.

The quality of your analysis can only be as good as the quality of the data you collect. Make it a point to periodically monitor the quality of your data and keep in mind that there is always a place for quality enhancement.

2) Reliance on gut feeling.

It is human nature to put more trust in one’s experience, the more one has it the harder it becomes not to trust it. But keep in mind that the current market conditions might not follow the same patterns as the ones you’ve been in before. New challenges require new tools.

3) Never let your biases impact the direction of your analysis.

Again, it is human nature to base your actions on prior experiences. Remind that it is just a fight or flight response triggered by your Amygdala. Never let it impact the analysis or let you cook up data to suit your stance. Integrate your experience with the data and come to a logical conclusion.

Some anecdotes from recent history:

  • Kodak Inventing the digital camera & missing the digital revolution.

Engineer Steve Sasson invented the digital camera under Kodak’s funding way back in 1974. Sasson & the team were to keep confidential as it would affect their film sales. In 1981, Vince Barabba Kodak’s head of market intelligence conducted a study and concluded that the digital photography market will become mainstream in the next 10 years. Despite the study, the senior management hesitated to move their operations to accommodate digital. History now remembers Kodak as a has been.

  • An end to Nokia’s domination.

By 2004 Nokia, the world’s largest phone manufacturer headed by Olli-Pekka Kallasvuo was already the biggest shareholder of Symbian OS. A study conducted by their market research team in 2003 predicted the emergence of smartphones in the next 5 years. Nokia focused on gaming & business smartphones instead of improving Symbian OS. The introduction of the iPhone in 2007 and Android OS in 2008 marked an end to Nokia’s domination plummeting their market share from 49.4% in 2007 to 3% in 2013

  • Motorola changing industries.

Motorola used Data-driven decision-making throughout their existence which enabled them to transition from battery manufacturing to radio manufacturing and from diversifying their operations to Television manufacture, Semiconductors, and Satellite communications. They used data analytics to venture into new markets and gain the first mover’s advantage in those markets.

  • 1997 apple cleans its lineup and saves itself from bankruptcy.

In 1997 Apple was trading $3.56 a share and was close to bankruptcy. Steve Jobs returned as CEO and commissioned a report on their sales. They were able to cut their offering from 20+ products to just 5 products and posted a profit first time in ages when their stock price hiked from $3.56 in 1997 to $34 apiece in 1998. DDDM allowed them to clean up their supply chain and play on their strengths making them the most valued company 20 years later.

In the current times, one just cannot say numbers don’t matter. Incorporating the right data-driven procedures & disciplines might make or break a business.