Impact of Good Insights in Business

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Data may be the new oil but a good insight is the fuel that powers business efforts. And drives decisions using data.

Unlike oil, data flows freely in large quantities within organizations but tapping into quality data to draw meaningful insights is the challenge faced by many. The vast inflow of data from multiple sources becomes overwhelming, making it practically impossible for senior managers/executives to analyze it and find contextual reasoning. To make key decisions swiftly every day they tend to fall back on their experience and intuition and remain closed to the approach of using data advantageously to power decisions. Due to this hindrance, typically very few senior executives and teams use insights in making critical decisions involved in the day-to-day functioning of their divisions or companies.
 

Why do Senior Managers not use Data and thereby Insights?

Let the numbers do some talking -
63% of executives are aware of analytics but lack infrastructure, are still working in silos, or are expanding ad-hoc analytics capabilities beyond silos.
67% of executives surveyed (who are senior managers or higher) are not comfortable accessing or using data from their tools and resources and even at companies with strong data-driven cultures, 37% of the survey respondents expressed discomfort.
Less than 37% of executives believe their companies are insight-driven or their analytic functions provide competitive advantage or positive ROI.
All of this points to the fact that a large proportion of major company decisions are not backed by data or insights but by the manager’s intuition. A very small percentage of executives (around 10%) use some kind of decision-support system to aid functions.
 

What prevents Senior Managers from using Insights in Decision Making?

Moving the mic along, senior executives of multi-billion-dollar enterprises had this to say about insights they received -
“What they give me is very generic to the function and not relevant to my role or business priorities.”
National Head Analytics, Food & Beverage Brand
“The results were not useful or even simple enough for a non-data expert to derive value to make decisions.”
Operations Head, Credit Card Division of a Leading Private Bank
“I mostly receive tactics passed off as strategy. Nothing to help business users conclude what should be prioritized.”
Head Insights & Analytics, Medical & Healthcare Player
“When I question the results, there is no provision to get more granular insights by drilling down to the root cause of the problem. Also, the insights are not consistent with each other.”
Strategy Head, Infrastructure, Real Estate and Power Transmission Group
“Just described problems instead of directing towards recommendations.”
Analytics Head - IT, FMCG Enterprise
“Most of the time it states the obvious and usually provides trivial output with low impact and no linkages to key success factors.”
Head of Business Operations, Biotech & Pharma Company
Sound familiar? These testimonials indicate that what is being passed off as “insights” by functional teams to senior managers are not meaningful, useful, valuable, or even credible inputs for decision-makers. It is in complete contrast to insights expected from analytical processes.
 

Masquerading as Insights

 

Let’s look at some examples that masquerade as insights -

  • The top 2 categories contribute to more than 75%.
  • 55% of our customers wear some kind of sports shoes when they visit our store.
  • Our product is the highest-rated in the industry.
  • Net revenue declined over 2.7% YOY in the same-store sales.
  • Our customers trust our product the most compared to all other products within the segment.
  • For Store 1, the overall sales value for Brand A is $3.61K.
  • In terms of Brand B, Store 10 rose 15 places from 16th to 1st after a growth of 262.7%. This growth can be attributed to the growth of 265.95% in Regular and 253.78% in Promo categories.
  • Market research and independent field tests combined show Brand Y outperforms Brand Z. This is an upward trend.
It is a given that these are findings from data. But what do these statements do besides stating observations “in sight”. These cannot be classified as insights because senior managers are unable to use these to arrive at conclusions while making executive decisions and there is nothing to indicate what the next steps should be or denote recommended action in either of them.

When analysts produce results such as these it becomes fairly obvious that they do not understand the functional needs and business requirements of senior leaders and other end-users of analytical reports.
 

What makes for a Good Insight?

Here is a qualitative evaluation of what is expected from a good insight.
Requisites of a Good Insight
 
1. New Inputs
An insight must provide information that is not known, not obvious, and most definitely not trivial. It should act as a new input that could be counter-intuitive or a warning, trend, pattern, or opportunity. Something that requires further investigation and advanced analysis. New inputs and findings that are least expected are most critical to identify at an early stage to change course and correct at the earliest.
2. Useful & Actionable
An insight must synthesize and not summarize the data. It must provide answers to the penultimate question “So what?” What is the implication of the analysis? What is the data telling us? And why should the end-user care? It must be useful in its action-ability. In its ability to effect change. With insights, business managers should be able to think ahead and not just “put out fires” for problems that arise but find ways in which to prevent them.
3. Contextual & Relevant
An insight must incorporate business context to be relevant for the user and her/his intent. It must focus on understanding and optimizing the business domain - industry in which the company operates and the functional team receiving analytical aid. This criterion ensures the insight is meaningful, useful, and valuable to all three types of users - power users, business users, and end-users.
4. Measurable
An insight must be measurable based on critical success factors namely, business objectives, KPIs, data numbers, and impact. Financial implications (ROI, revenue, margin, expenses, risks) should be capable of measurement (increase or decrease) with the ability to question the validity of the analyses. An insight must consistently support monitoring and tracking performance measurement, a key priority for every manager and executive.
5. Significant
An insight should be linked to the business priority and provide significant inputs related to Market Intelligence and Competitive Advantage. It must alert the user to awareness and intelligence that places the company in a better competitive situation and which has positive financial implications connected to ROI, contribution margin, and risk assessment.
6. Stable & Reliable
An insight must be based on facts (data) in entirety and derived using models built with business rules to make it stable and reliable. A robust insight helps separate the signal from the noise.
 
A good insight checks all these boxes to be impactful for any user and justify the investment of resources allocated in Data & Analytics endeavors.

If you are looking to improve the quality of your insights and better understand the value derived from BI and Analytics investments, do reach out to us.
 

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WRITTEN BY

Srishti Mittal

Srishti Mittal leads Solutions at vPhrase and partners with top executives to deploy analytics that solve complex business problems and help gain competitive advantage with data-driven decisions. In addition to client work, her deep analytics expertise is applied across sectors to build a range of use cases, and create a unique analytical engine powered by built-in domain context to drive cutting edge management practices. Besides Analytics, her areas of expertise include Growth Strategy, Customer Strategy, Commercial Strategy, Sales and Marketing Strategy. Over the last 14 years, she has worked extensively with CXOs of Fortune 100 companies (Emerging Markets). She closely tracks innovations at the intersection of advanced analytics, artificial intelligence, machine learning, design thinking, and behavioral decision making. Through her writing, she wants to help business and technology leaders harness the power of analytics to derive value from data.

Srishti Mittal leads Solutions at vPhrase and partners with top executives to deploy analytics that solve complex business problems and help gain competitive advantage with data-driven decisions. In addition to client work, her deep analytics expertise is applied across sectors to build a range of use cases, and create a unique analytical engine powered by built-in domain context to drive cutting edge management practices. Besides Analytics, her areas of expertise include Growth Strategy, Customer Strategy, Commercial Strategy, Sales and Marketing Strategy. Over the last 14 years, she has worked extensively with CXOs of Fortune 100 companies (Emerging Markets). She closely tracks innovations at the intersection of advanced analytics, artificial intelligence, machine learning, design thinking, and behavioral decision making. Through her writing, she wants to help business and technology leaders harness the power of analytics to derive value from data.