Are CEOs and CIOs Unlocking the Value of Their Data?

Are CEOs and CIOs Unlocking the Value of their Data?

Three out of four companies think they are, but the outcomes suggest otherwise.  

Introduction

Data is valuable only when leaders and other decision-makers use it regularly to make impactful decisions. Technical teams and the CEOs and CIOs to whom they answer can easily become distracted from this bedrock truth. Companies should first identify the specific business outcomes from unlocking the value of their data when considering how to incorporate machine learning (ML) and artificial intelligence (AI), new architectures like custom data lakes or lakehouses, fully managed platforms like Snowflake or Databricks, quality initiatives like MDM solutions, or operational excellence goals like SLA improvements. The cost of getting IT infrastructure investment wrong outweighs the time it takes to get it right. 

This white paper helps CEOs and CIOs understand if they are unlocking the value company’s data. Also, it describes Dynamo’s approach to helping them make infrastructure investments that derive the fullest possible value out of their data to go on defense to achieve outcomes like reducing human error and eliminating bottlenecks and time delays, thereby cutting expenses and increasing profit margins; and simultaneously help companies go on offense to seize growth opportunities by increasing revenue or market share. 

Dynamo helps CEOs and CIOs unlock the value of their data by using our three-step process of discovering business problems, focusing on what is absolutely needed to advance each company’s strategic plan, and working our way backward from there.  This ensures we understand our client’s business objectives, build trust, and avoid unnecessary overinvestment in strategic IT infrastructure. This is a clear differentiator from our competitors, who may have a technical solution looking for a business problem.

Problem Statement

Solving this problem right the first time is more important than ever as investments in data continue to accelerate.i Companies invest in their IT infrastructure to solve measurable problems by making data readily accessible to all employees and stakeholders and by making data actionable by educating stakeholders on making data-driven decisions. Stated differently, this is how Dynamo defines Data Democratization.

Data democratization conveys clear benefits to organizations that adopt it, and the drawbacks of failing to achieve democratization are just as stark. Despite this, only a quarter of companies use data to inform decision-making supporting their offense and defense strategies. The root of this problem is not technical—it has little to do with a company’s choice of architecture, ETL tools, analytical tools, or its adoption of ML/AI to support its operations. Cultural factors, people, and processes account for over 90% of failures to achieve expected ROI on infrastructure investments, while less than 10% are due to technical limitations.ii In other words, strategic technological investments will not likely achieve the desired outcomes unless they directly address actionable decision-making at all levels of the business. 

When we listen to our clients discuss their challenges and plans to overcome them, we often hear statements like “If I can just ______, I’m certain our business teams will use it to accomplish their goals.” These statements are made in earnest and reflect the sincere intentions of our clients to help their businesses. They also characterize a trap that 90% of companies fall into, equating mere access to data with democratization and its practice for solving actual business outcomes. 

Dynamo addresses our clients’ need for broader, more effective use of data by first focusing intently on what is absolutely needed to solve a specific business outcome, then working our way back toward an IT investment strategy. This approach maintains a tight focus on the practical impact of IT investments, reducing the risk of failure and putting our clients firmly on a path toward joining companies and unlocking its value.  

This can be daunting but solving for a specific business outcome and building on its success creates a flywheel effect for more impactful decision-making.  For example, weekly and monthly business reviews become more relevant and generate useful insights. Information duplicated on multiple dashboards, spreadsheets, and other frequently consulted analysis sources finds a single, more efficiently administrated home. Monthly sales reports give way to weekly or daily ones, facilitating more responsive, more effective decision-making. When working with and analyzing data, the frequency and repetition build more confident, capable, and mature data-driven organizations.

Dynamo’s Approach to Optimizing IT Infrastructure Investment

We believe that the key to getting full value from your information infrastructure is to build information maturity within your organization, allowing people to spend less time on the mechanics of it and more time on the analytics that results in actionable insights and decisive, profitable action. This needn’t involve a massive new project, additional overhead, or rounds of training for your employees. With the right partner to help guide you, data optimization can be a quick, lightweight process. Dynamo specializes in tailored initiatives that address our client’s core business concerns without adding unnecessary overhead. We are specialists in this field, and our quick, nimble approach to data optimization has helped many of our clients pilot lightweight solutions that lead to measurable bottom-line results. 

Dynamo’s first step is to identify the core business concerns of each decision-maker. We consider the problem of suboptimal data use in the broadest possible context by discussing it thoroughly with each client. Then, we narrow our findings into a single pain point that can be resolved quickly and efficiently. Our second step is to explore possible solutions. Again, we go broad on possible solutions before paring down our options using rapid experimentation to find the most promising way forward. Our third step is to refine that solution and document its constituent elements. We consider various possible solution designs and implementations, then build, release, learn/test, and iterate.  

After this three-step process, a measurable business outcome is implemented, and their data is incrementally democratized. Each decision-maker has the information they need, and they receive it in a way that fits naturally into their day-to-day workflow, regardless of their role. This three-step process begins with a practical focus on business outcomes. It expands to create more data-driven organizations, directly addressing why three out of four companies fail with their strategic IT investments.  

Dynamo observes three common patterns that CEOs and CIOs must consider when going broad with possible solutions before narrowing them into effective, profitable implementations.

What is your company’s level of data maturity?

Data alone does not make a data-driven business culture. Even the most sophisticated and responsive infrastructure will fail to return value on your investment if your decision-makers lack the skills needed to read, interpret or work with the data they receive. A 2020 Qlik/Accenture survey revealed that only one out of five workers were fully confident in their data skills, defined as the ability to read, understand, question, and work with data and associated tools. Even though employees have increasingly more data at their disposal, three-quarters expressed feeling overwhelmed or unhappy when working with it. Further compounding this problem, CEOs and CIOs overestimate their workforce's capabilities and readiness to work with data. Three out of four executive leaders believe that their employees can work proficiently with it, and four out of five believe their employees have access to the tools they need to be productive.  

This disparity leads directly to the failure of expensive IT initiatives, specifically because organization-wide democratization tends to be overlooked during the solution and implementation steps. Providing the right data at the right time is only the first step toward achieving business outcomes: decision-makers must also be able to effectively and efficiently use it.  

Do your decision-makers trust the data?

Democratization is less useful if decision-makers do not trust the data. Companies with mature platforms often launch Master Data Management (MDM) programs to overcome organization-wide issues with data trust. They establish single sources of authority across multiple teams for critical information, such as describing customers and revenue and clearly documenting important factors like lineage, instances of duplication, and policies governing access, retention, and security.  However, most of these initiatives fail for these reasons iii:

Implementing MDM too quickly (implementation as a “Big Bang” event) 

Lack of C-level sponsorship 

Absence of a mature data governance framework  

Lack of business context experts assigned to the MDM initiative  

Data validation issues 

Incomplete adaptation of MDM output to established core business processes 

Dynamo believes that MDM projects fail because MDM is a fundamentally weak model for improving trust. While the MDM model appears at first to address crucial issues surrounding data distribution and use, it seeks to fix problems at the tail end of business processes when the more effective and efficient approach is to solve problems at their roots. 

 When decision-makers do not trust the information, they typically have a point. CEOs and CIOs should listen. Rather than investing in expensive, time-consuming MDM initiatives, we advise companies to examine why and how low-data quality is being introduced into their ecosystems and to correct that fundamental problem at its source. This is just one example of how Dynamo considers business problems in their full context before experimenting with possible solutions. 

Do your decision-makers have timely and relevant data?

A common motivation for improving democratization is to reliably provide the information employees need to generate insights into what happened a minute ago, an hour ago, last week, month, last quarter, and last year. These insights allow employees to plan more dependably and productively for the future; some companies refer to this process as operational excellence. 

Companies typically rely on service-level agreements (SLAs) to confirm that data is fresh, timely, and readily available to decision-makers. SLAs traditionally include performance measures such as ETL completion time, query and API response time, up-time, and various standards of data quality. These are critical measurements, and technology teams pay close attention to them because they are traditionally used to indicate how effective the team is.  As an unintended consequence of this intense focus on technical criteria, companies tend to measure the success of strategic IT investments in terms of system speed and efficiency, often at the expense of fully understanding the impact on critical business decisions and outcomes. 

For example, technology teams too often characterize data-related problems in terms that have little to do with their business purpose. “My sales ETL job exceeds the three-hour SLA by two hours 90% of the time,” or “The business team discovered five data-quality problems before the technical team could identify and fix them.” When issues are framed this narrowly, strategic IT investments too often follow suit, failing to consider end users’ needs and ability to make more actionable decisions. A given investment may solve a technical issue while exacerbating the practical business problems it was meant to resolve. 

An effective discovery process identifies how employees use data in their established workflows, establishing guidelines for the currency, timeliness, and availability of business-related data. When companies invest with this deeper understanding, they refrain from over-investing in specifications and technical infrastructure that do not directly support improved business outcomes. 

Once the discovery process is completed, the business problem can be reframed in more profitable terms—for example, “The Sales team uses customer data to organize outreach campaigns at the beginning of the day; for each hour it’s late, we lose $50,000 in revenue.” This fully informed statement allows the technical team to calculate ROI regarding the real business problems solved by IT infrastructure investments. 

Conclusion

There is nothing new about gathering data and using it to make critical business decisions.  CEOs and CIOs know this is hard to consistently achieve and are still willing to invest in IT solutions to make incremental progress, often without incremental outcomes. This is the space our competitors operate and thrive in.  However, Dynamo is differentiated from our competition because we take an outcome approach by leaning into our product management experience and specialized knowledge of data infrastructure and analytics that can help you benefit from the full value of the information your company stores by developing responsive, lightweight approaches to IT infrastructure investment that consistently delivers incremental business outcomes.