Is Your Data Worth the Costs?

TL;DR: Data, when used strategically, can be a powerful tool for businesses to achieve their goals. However, it is important to approach data with a clear plan and to avoid the hidden costs of data hoarding. By prioritizing relevant data, evaluating its impact on business outcomes, and continuously refining data-driven strategies, businesses can unlock the true potential of data to drive growth, improve efficiency, and achieve sustainable success.

In today's data-driven world, businesses are increasingly relying on data to make informed decisions. But how can we ensure that we are using data effectively? In this article, we'll explore three key steps for evaluating the effectiveness of data: setting fixed company KPIs, evaluating the necessity of the data, and assessing the impact of the data. We'll also provide real-world examples of how companies have used data to achieve success.

In the past few months, I have been fascinated by curry dishes, or better Gram Masala spice mix, it was first introduced more than 4,000 years ago in the middle of Asia, and the mix helped to evolve the taste we all know today in the west as curry, there are Pakistani, British, Indian, Thai, Japanise… curries. For the past few weeks, I tried to create my own Gram Masala mix, I realized that some of the premade mixes at the supermarket do not always bring the flavors that I like and do not always fit my dish, but rather leave it bland. And then this article came to my mind

Just as a chef carefully blends spices to create a flavorful curry dish, businesses must carefully select and evaluate their data to extract meaningful insights. Just as some premade spice mixes don't always deliver the desired flavor, inaccurate or incomplete data can lead to poor decision-making. Similarly, irrelevant data is like adding a spice that doesn't belong in the dish, creating a disjointed and unpleasant experience.

Just as a chef ensures the quality and freshness of their spices, businesses must ensure the quality and reliability of their data. This means carefully collecting data from trustworthy sources, verifying its accuracy, and ensuring its relevance to the business goals. By taking these steps, businesses can ensure that they are using data effectively to make informed decisions that drive success.

The hidden costs of data

In the culinary world, a poorly blended spice mix is akin to data that fails to deliver meaningful insights. Just as an inexperienced chef can ruin a dish by haphazardly combining spices or over-spicing, businesses can squander valuable resources by failing to extract actionable intelligence from their data. Before delving into the potential rewards of data-driven decision-making, it's crucial to acknowledge the hidden costs that can hinder progress and even lead to financial losses.

A recent study by Gartner reveals the staggering figure of $1.25 trillion spent on data annually, yet only 40% of this data is effectively utilized. This stark contrast paints a disturbing picture of wasted investments and missed opportunities.

The hidden costs of data extend beyond the initial acquisition and storage. Data cleansing, the process of ensuring data quality and accuracy, can be a significant expense, as can the tools required to analyze and interpret vast datasets. Furthermore, the human capital dedicated to managing and extracting insights from data is a valuable asset that demands fair compensation.

Just as a chef meticulously selects and blends spices to achieve a harmonious flavor profile, businesses must carefully curate the data they collect and analyze. Irrelevant or inaccurate data can be likened to adding a discordant spice that disrupts the overall taste of the dish.

To ensure that data investments yield a positive return, businesses must adopt a data-driven approach that prioritizes focus, quality, relevance, and effective utilization. By carefully selecting key performance indicators (KPIs) that align with their strategic goals, businesses can transform data into a powerful tool for informed decision-making.

Here are my three steps to make data costs lower, and increase the focus and relevancy of the data in use:

Step 1: Set fixed company KPIs

Just as a chef carefully blends spices to create a flavorful curry, businesses must carefully select and define KPIs to extract meaningful insights from their data. These KPIs serve as the compass that guides decision-making, ensuring that businesses are aligned with their strategic goals and making informed choices that drive growth and success.

Pragmatic Approach: Essential Questions for Small Businesses

For smaller businesses with limited resources, a pragmatic approach to KPI selection is often the most effective. This approach focuses on identifying three core questions that can effectively assess a business's performance for example:

1. Average Cost Per Install/Session (ACPI/ACPS): This metric measures the cost of acquiring a new user and their average engagement level. A high ACPS with low engagement indicates that marketing efforts may be inefficient.
2. Average Session Duration: This metric provides insights into user engagement and interest in the product or service. A long average session duration suggests users find value in the platform, while a short duration indicates potential issues or lack of interest.
3. User Completion of Key Actions: This metric measures whether users are achieving the desired goals, such as making purchases, signing up for a service, or completing a specific task. A low completion rate indicates areas for improvement in user experience or product design.

Brainstorming Approach for Larger Organizations

In my book, “Data is Like a Plate of Hummus” I talk a lot about this method and how to leverage it for the organization's benefits. As businesses grow and complexity increases, a more comprehensive approach to KPI selection is necessary. The brainstorming approach involves gathering a team of stakeholders from various departments, including marketing, sales, customer support, and product development. The team brainstorms a list of questions that can be answered with data, categorizes them into three tiers (Must-have, Nice-to-have, and Non-essential), and then selects the top three Must-have questions to translate into actionable KPIs. Some examples of Must-have KPIs include:

1. Monthly Active Users (MAUs): This metric measures the number of unique users engaging with the product or service within a specific month. (It is a joke, I hate this KPI, please never pick it as the must-have:🦖)
2. Average Revenue Per User (ARPU): This metric indicates the average revenue generated from each user.
3. Churn Rate: This metric measures the percentage of users who stop engaging with the product or service within a specific period.

Creating a Data Dashboard for Continuous Monitoring

Once the core KPIs have been identified, it's crucial to create a simple and user-friendly data dashboard that displays these KPIs in real time. This dashboard serves as a central hub for tracking progress, identifying trends, and making data-driven decisions.

Strategic Method

For organizations with sophisticated data capabilities, a strategic approach to KPI selection is recommended. This approach involves assembling a team of data analysts and domain experts to collaborate on the following steps:

1. Clearly define business goals: Articulate the overall strategic objectives of the business, such as increasing revenue, reducing costs, or enhancing customer satisfaction.

2. Identify data sources: Analyze existing data sources, including website analytics, CRM systems, and marketing automation platforms, to identify relevant data sets.

3. Explore relationships: Delve into correlations and patterns between different data points to uncover insights that can guide KPI selection.

4. Prioritize KPIs: Evaluate the potential impact of each data set on the business goals and prioritize KPIs with the strongest influence.

5. Develop a data strategy: Formulate a comprehensive data strategy that outlines the plan for collecting, analyzing, and utilizing data to achieve business objectives.

   
   

Distinguishing KPIs from Metrics

It's important to clearly distinguish between KPIs and metrics. KPIs are specific metrics that are directly aligned with the business's strategic goals and have clear targets or goals. Metrics, on the other hand, are broader measures of business activity that provide context for KPIs. For example, Average Order Value (AOV) is a KPI for an e-commerce business, while Number of Orders, Customer Lifetime Value (CLV), and Product Margins are metrics that can provide context for AOV.

Leveraging Data for Growth and Cost Reduction

Data is a powerful tool that can be harnessed to drive revenue growth and cost reduction. Here are some examples of how businesses can utilize data for these purposes:

Revenue Growth:

• Targeted advertising: Identify and target potential customers with relevant advertising campaigns.
• Product recommendations: Personalize product recommendations to increase customer engagement and conversion rates.
• Customer segmentation: Segment customers

Key takeaways from Step 1:

• KPIs are essential for data-driven decision-making.
• There are two main approaches to KPI selection: pragmatic and brainstorming.
• KPIs should be specific, measurable, achievable, relevant, and time-bound (SMART).
• Data dashboards are crucial for monitoring KPIs in real time.

Step 2: Evaluate the necessity of the data

Businesses must critically evaluate the data they collect to ensure its relevance and impact on decision-making. This step involves systematically assessing the value and necessity of each data point to identify the most actionable and impactful information.

Unveiling the Hidden Costs of Data Hoarding

Data hoarding, the practice of collecting and storing vast amounts of data without a clear purpose, is a common misconception of data-driven decision-making. While data is undoubtedly valuable, amassing vast quantities without proper evaluation can lead to several hidden costs:

• Data Validation and Quality: The more data collected, the greater the effort required to validate its accuracy, consistency, and completeness. This can be a time-consuming and resource-intensive process that can hinder data utilization.
• Data Storage and Processing: Storing and processing large datasets requires significant infrastructure and computational power, which can be costly and may not be justified for data that lacks clear value.
• Data Interpretation and Actionability: The sheer volume of data can make it overwhelming and difficult to extract meaningful insights. Without clear goals and prioritization, data can become a burden rather than a catalyst for improvement.

To learn more you can read my article about data hoarding

Prioritize Actionable Data for Informed Decisions

Instead of blindly collecting data, businesses should prioritize data that directly aligns with their strategic goals and can inform actionable decisions. This involves a critical evaluation of each data point to assess its:

• Relevance to business objectives: Does the data directly relate to key performance indicators (KPIs) and contribute to achieving business goals?
• Actionability: Can the data be used to identify trends, make informed decisions, and drive improvement? and in what iteration can you react to what you see

Eliminating Redundant and Unnecessary Data

By systematically evaluating data against these criteria, businesses can identify and eliminate redundant, unnecessary, or irrelevant data points. This decluttering process not only reduces data storage costs but also frees up valuable resources for analyzing and acting upon the most impactful information.

Example: Evaluating the Necessity of Monthly Active Users (MAUs)

Consider the KPI of Monthly Active Users (MAUs), often used to measure user engagement. While MAUs can provide general insights into user activity, their true value depends on how they drive decisions.

• Reaction to declining MAUs: If a drop in MAUs prompts targeted marketing campaigns or product improvements, then the KPI is relevant and actionable.
• Lack of actionable implications: If a decline in MAUs does not lead to any specific actions, then the KPI is less valuable and may be replaced with more actionable metrics.

Key takeaways from the optimized text:

• Data hoarding can lead to hidden costs and hinder data utilization.
• Prioritize data that is relevant to business objectives, and actionable.
• Eliminate redundant and unnecessary data to reduce storage costs and focus on impactful information.
• Evaluate the necessity of each KPI based on its impact on decisions and actions.
• Utilize data for informed decision-making, not just collection

Step 3: What is the impact of the data

Just as a skilled chef meticulously evaluates the flavor and aroma of their creations, businesses must critically assess the impact of their data-driven insights to ensure they are driving meaningful change and achieving desired outcomes. This step involves evaluating how data informs decisions, drives improvement, and contributes to the overall success of the organization either by saving money or increasing growth.

A study by Forbes Insights revealed that companies that effectively use data analytics achieve an average revenue growth of 10% and a cost reduction of 15%. These figures highlight the immense potential of data to transform businesses.

Unveiling the True Value of Data Insights

While data can provide valuable insights, it is crucial to assess whether those insights are effectively translating into positive outcomes. Simply having data is not enough; it's about understanding how it impacts the business and leveraging it to make informed decisions that drive growth and success.

Data-driven insights can be categorized into two distinct types: offensive and defensive KPIs. Offensive KPIs focus on enhancing performance and improving the product or service, while defensive KPIs aim to optimize costs and reduce expenses.

Offensive KPIs: Fueling Growth and Innovation

Offensive KPIs provide insights into areas where the business can improve its performance, such as increasing user engagement, enhancing conversion rates, or boosting customer satisfaction. By tracking these metrics, businesses can identify opportunities to refine their products, optimize their marketing strategies, and enhance overall customer experience, leading to increased revenue and market share.

Such as:

• Increased user engagement: KPIs like session duration, bounce rate, and active users measure how engaged users are with the product or service.
• Enhanced conversion rates: KPIs like conversion rate, average order value, and customer lifetime value measure how effectively the business is converting visitors into paying customers.
• Boosted customer satisfaction: KPIs like Net Promoter Score (NPS), customer satisfaction surveys, and churn rate measure the overall satisfaction and loyalty of customers.

Defensive KPIs: Streamlining Operations and Reducing Costs

Defensive KPIs, on the other hand, focus on reducing costs and improving operational efficiency. These metrics can help businesses identify areas where they can save money, such as optimizing supply chains, streamlining processes, or renegotiating vendor contracts. By reducing costs and improving efficiency, businesses can boost profitability and enhance their overall financial position.

Such as:

• Cost per acquisition (CPA): KPIs like CPA and acquisition costs measure the cost of acquiring new customers.
• Average cost per order (ACoS): KPIs like ACoS and marketing expense to sales (MES) measure the cost of running marketing campaigns.
• Supply chain efficiency: KPIs like inventory turnover ratio, order fulfillment time, and delivery cost per order measure the efficiency of the supply chain.

By effectively utilizing both offensive and defensive KPIs, businesses can achieve a holistic approach to data-driven decision-making. By focusing on both performance enhancement and cost optimization, businesses can create a sustainable path to growth and profitability.

To effectively evaluate the impact of data, businesses should establish clear cause-and-effect relationships between data insights and tangible results. This involves tracking how changes in data metrics correspond to changes in business outcomes, such as revenue growth, cost reduction, or customer satisfaction.

Measuring the Impact of Data on Key Performance Indicators (KPIs)

To effectively evaluate the impact of data, businesses should establish clear cause-and-effect relationships between data insights and tangible results. This involves tracking how changes in data metrics correspond to changes in business outcomes, such as revenue growth, cost reduction, or customer satisfaction.

By tracking these metrics over time, businesses can assess the effectiveness of their data-driven decisions and make adjustments as needed.

Case Study: Analyzing Marketing Budget Allocation

Consider a company that increases its marketing budget by 30% but fails to observe a corresponding increase in conversions. By analyzing data, the company discovers that the additional marketing efforts are attracting a higher proportion of "zombie users" who are not engaging with the product or making purchases.

Pivoting to Product-Level Optimization

Armed with this data-driven insight, the company reevaluates its marketing strategy, focusing more on product optimization and user experience to attract high-value customers. This pivot allows the company to maximize its marketing spend and achieve better ROI.

Real-World Example: Data-Driven Product Iteration

A software company introduced a new feature that was expected to increase user engagement and conversion rates. However, upon analyzing usage data, the company discovered that users were quickly abandoning the feature due to usability issues and a lack of compelling value.

Product Improvement Based on Data Insights

Based on these data-driven insights, the company redesigned the feature to address user pain points and enhance its perceived value. The revamped feature was met with positive user feedback and resulted in a significant increase in engagement and conversion rates.

Conclusion: Data for Action, transformative force

Assessing the impact of data-driven insights is essential for ensuring that data is not merely collecting dust but actively driving business transformation. By evaluating how data informs decisions, drives improvement, and contributes to organizational goals, businesses can harness the power of data to achieve sustainable success.

Key takeaways from the optimized text:

• Data insights must translate into tangible business outcomes to be truly valuable.
• Establish clear cause-and-effect relationships between data and KPIs to measure impact.
• Continuously evaluate the effectiveness of data-driven decisions and adapt strategies accordingly.
• Utilize data to identify and address user needs, improve user experience, and enhance product value.
• Data is a transformative force; it's a tool for driving continuous improvement and achieving strategic goals.

Checklist

In today's data-driven world, businesses must be strategic about how they collect, analyze, and utilize data. By adopting a data-driven mindset and following the three steps outlined in this article, businesses can transform data into a powerful force for growth and success.

• [ ]undefinedHave clear business goals and objectives: Are you clear about what you want to achieve with data-driven decision-making?
• [ ]undefinedHave a defined data strategy: Have you established a plan for collecting, analyzing, and using data effectively?
• [ ]undefinedAre you prioritizing relevant data: Do you focus on data that is directly related to your business objectives and can inform actionable decisions?
• [ ]undefinedHave you eliminated redundant data: Are you tracking data that is actually valuable and avoiding unnecessary data collection?
• [ ]undefinedHave you established causal relationships between data and KPIs: Do you understand how changes in data metrics affect your business outcomes?
• [ ]undefinedAre you consistently evaluating data-driven decisions: Do you regularly review the effectiveness of data-driven decisions and make adjustments as needed?
• [ ]undefinedAre you using data to improve customer experience and product value: Are you using data to understand user needs and enhance the product or service?