From Information to Actionable Insights

In this installment, we discuss the often-overlooked difference between performance insights and actionable insights. The latter must become a primary focus of executives everywhere to proactively navigate a recessionary environment and the uncertainty that comes with it.

Let's jump in.

The data economy is increasingly embraced worldwide, across every industry. According to the?World Economic Forum ?(WEF), by 2025 it is estimated that 463 exabytes of data will be created each day worldwide – the equivalent of 200 million DVDs of data created per day [WEF, 2019]. Faced with overwhelming amounts of data, organizations across the world are looking for ways to derive actionable insights from this information.

But why do?data-driven insights ?matter in business? Fundamentally, these insights help companies gain better visibility into their operations and make faster decisions to improve business results. Research by McKinsey found that insight-driven companies report superior growth and EBITDA (earnings before interest, taxes, depreciation, and amortization)?to the tune of 15 to 25 percent above the broader market [McKinsey, 2022].

So what exactly is an insight? According to the Cambridge dictionary, an insight is the ability to have a clear, deep, and sometimes sudden understanding of a complicated problem or situation [Cambridge, 2022]. In terms of data analytics, insights involve bringing unknown elements (such as relationships, patterns, categorizations, and predictions) that influence decisions to light.

Such insights can be classified as hindsight (understanding a past situation), near-sight (interpreting a current situation) and foresight (predicting a future situation) [Southekal, 2020]. However, from the perspective of generating tangible value from data and analytics, there are two main types of insights:

1. Performance Insights.?Performance insights provide visibility into the success of a given aspect of the business (a product line, functional area, strategic initiative, etc.). Examples are the top five products by sales volume, top three customers by MRR (monthly recurring revenue), and so on. Efficiently conveying this information to decision makers requires effective data storytelling (stay tuned for our next installment on this topic), ensuring that insights are delivered in context when and where they are needed.
2. Actionable Insights.?Actionable insights are based on performance insights, but take things a step further to shed light on the results of a decision. Actionable insights?involve three elements: (a) the decision (b) a commitment to consume resources like time, money, or labor associated with the decision and (c) the business?impact and consequences of the decision. Examples of actionable insights are: new product A offers the highest ROI, customer X can be given a credit to improve retention, and repairing our Northeast fleet now will prevent lost revenue and replacement costs.

So, what can enterprises do to get tangible value from insights? In data and analytics, the so-called “last mile of analytics” is considered the missing piece between the analytical insights and actual business results. In fact, according to?McKinsey, 90%?of organizations that are significantly outperforming peers are?devoting more than half of their analytics budgets?to bridging the?last mile of analytics gap [Mckinsey2, 2018].??

In other words, business leaders have to focus on actionable insights – or specifically focus on converting performance insights into actionable insights. Below are three key steps?companies can take to?implement actionable insights.

1. Derive performance insights based on business objectives, questions, KPIs (key performance indicators), and data.?These insights can be derived using a combination of descriptive, predictive, and prescriptive analytics techniques and models which provide visibility into past, current, and future states.

Given that data is a critical component in this step, the data related to the question and KPI should come from the proper source, typically the relevant transactional SoR (system of record). In this regard, the?semantic layer ?can be leveraged for standardized data definitions, providing common meaning, and granting rapid access to data to derive faster insights [Southekal, 2022].

To ensure that these performance insights are reliable, it’s important to factor in different stakeholder perspectives, time frames, and avoid framing bias. Framing bias refers to the manner in which the question is framed and can be addressed by reframing the problem in at least three different ways. Basically, having the right data means mapping the data source subjects and understanding the data attributes (i.e., features) in order to align data source content to the question / answer being sought. The relationship between business objectives, questions, KPIs, semantic data, and models to derive insights is as shown below.

2. Once the performance insights are derived, we formulate the decision problem.?A typical decision problem has four key elements: objectives, alternatives, outcomes, and payoffs.

1. The?objectives, which are based on performance insights, are the things the business plans to achieve from the decision.
2. The?alternatives?are potential actions or strategies considered based on different performance criteria such as profit margin, cost, time, quality, service, and more. It is recommended to keep the number of alternatives to three, using the Pugh Matrix or Decision Matrix to narrow down the available alternatives.
3. The?outcomes, which are usually probabilistic, are the resulting situations that arise by pursuing the selected alternatives.
4. The payoffs or?benefits?are the values placed on the outcomes associated with each alternative. The payoff values are a combination of tangible and intangible benefits.

These four elements of the decision problem should help the business select the best or optimal alternative to implement the decision using the appropriate decision science techniques.

3. Once the decision to implement an alternative is made, the next step is to identify the resources needed to execute the decision.?The resources could be time, skills, budget, equipment, and even data. The key step in executing the decision is to manage change with the right lines of accountability. This means having an accountable leader who is close to the objective and KPIs being tracked for business performance. For example, if the KPI is on “days payable outstanding (DPO)” to improve the cash conversion cycle (CCC), it is advisable to have the accounts payable (AP) manager track and improve the DPO KPI. Basically, identifying a leader to hold ultimate accountability over the decision will help in mobilizing the necessary support, including the desired resources and culture, to convert the insights and decisions into action.

The effectiveness of a decision can be validated with appropriate feedback mechanisms given that drifts in models and data are bound to happen as the business?evolves and adapts to change. The best way to manage model and data drift is by continuously measuring the performance of data and insights using the right KPIs [Southekal, 2021] and feedback mechanisms.?The insight value chain and its components discussed above are shown in the figure below.

Today, actionable insights are at the heart of every business decision around increasing revenue, reducing expenses, and managing risk. The purpose of insights is not just to know; it is to make informed decisions and ultimately improve business performance in a measurable way. The three steps described above can help organizations formulate the right business and data strategy for turning data into performance insights and then actionable insights for improved business results and performance.

References

• Cambridge,?https://dictionary.cambridge.org/dictionary/english/insight , 2022
• McKinsey1, “Insights to impact: Creating and sustaining data-driven commercial growth”,?https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/insights-to-impact-creating-and-sustaining-data-driven-commercial-growth , January, 2022
• McKinsey2, “Breaking away: The secrets to scaling analytics”,?https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/breaking-away-the-secrets-to-scaling-analytics , May, 2018
• Southekal, Prashanth, “Analytics Best Practices”, Technics Publications, April 2020
• Southekal, Prashanth, “Model Drift in Data Analytics: What Is It? So What? Now What?”, Forbes, Sept 2021
• Southekal, Prashanth, “Demystifying the Semantic Layer for Smarter, Faster AI and BI”, Demystifying the Semantic Layer – White Paper AtScale, Mar 2022
• WEF, “How much data is generated each day? “,?https://www.weforum.org/agenda/2019/04/how-much-data-is-generated-each-day-cf4bddf29f/ , Apr, 2019

About the Authors

Prashanth Southekal is the managing principal of DBP Institute (www.dbp-institute.com), a data and analytics consulting, research, and education firm. He is a consultant, author, and professor. He has worked and consulted for over 80 organizations including P&G, GE, Shell, Apple, and SAP. He is the inventor of DEAR Model,?a systematic and structured approach for data-driven decision making.?Dr. Southekal is the author of two books – “Data for Business Performance” and “Analytics Best Practices” – and writes regularly on data, analytics, and machine learning in Forbes, FP&A Trends, and CFO University. Apart from his consulting pursuits, he has trained over 3,000 professionals worldwide in Data and Analytics. Dr. Southekal is also an Adjunct Professor of Data and Analytics at IE Business School (Madrid, Spain) and CDO Magazine included him in the top 75 global academic data leaders of 2022.?He holds a Ph.D. from ESC Lille (FR) and an MBA from Kellogg School of Management (U.S.). He lives in Calgary, Canada with his wife, two children, and a high-energy Goldendoodle dog. Outside work, he loves juggling and cricket.

Chris Andrassy ?is an entrepreneur focused on transforming data into sustainable business value on a global scale. He began his career at PwC in New York City, supporting the digital transformation of mature organizations struggling to innovate in a hyper-competitive world. After experiencing the shortcomings of traditional analytics practices, he decided to begin a new chapter alongside colleagues and industry veterans. His departure from New York marked the inception of?Astral Insights , a Raleigh-based decision intelligence firm helping mid-market and enterprise clients transform data into profit. Chris is also an investor focused on innovative technologies including synthetic biology, sustainable energy, and artificial intelligence. Outside of work, he is an avid musician, skier, traveler, and fitness enthusiast.