Improving Organizational Agility With Data-Driven Intelligence

Introduction

With the rise of generative AI, organizations are paying more attention to their data and analytics capacity to fuel this new technology. Business leaders are keenly aware that these technologies, if applied smartly, can fundamentally transform business operations and improve workers' productivity by up to 40%.¹ 

However, the AI prize remains elusive for many. In a recent survey of data and analytics (D&A) leaders, 69% of them are still struggling to deliver measurable ROI from their investments.² What prevents organizations from effectively transforming their biggest commodity, data, into a strategic advantage? 

For long-time industry practitioners like myself, the reality is all too familiar, and I can think of four specific examples.

Why organizations face a long road before enjoying data-driven benefits

First, organizations have collected data for years – they’ve tucked it away in databases, data lakes, data swamps, BI reports, dashboards, ERP systems, spreadsheets, and many other places. Few can find the relevant data scattered across their organization to then feed data-driven initiatives. It’s not the analysts' or data scientists’ fault; it’s because organizations hoard data for many years and haven’t had the discipline to keep track of its location. It’s analogous to a basement or attic that has never been cleaned out – who knows what hidden treasures (or scary pests!) await?

Second, if D&A professionals manage to stumble upon a few golden data sources, they often question their integrity. Is it up-to-date and trustworthy? What do the columns even mean? Again, time is wasted trying to understand the data’s context, quality, and usefulness. Do you need to combine data across a few different systems? Forget about it – in many organizations, that is an impossible task because the columns often have different definitions and formats. Most data scientists I know consider themselves data janitors – they spend most of their time cleaning the data and very little on the analytics.

Third, many organizations need more organizational skills and leadership to scope projects appropriately. Left to their own devices, show me a data scientist who wouldn’t like to use cutting-edge algorithms. They’re new, and a great resume builder. However, just because you have a hammer doesn’t mean everything is a nail. Organizations need to do a better job of aligning projects to business objectives.

Finally, many companies are worried about intellectual property (IP) rights, personally identifiable information (PII) data leakage, and compliance with regulations like GDPR, CCPA, HIPAA, and the upcoming AI regulations. However, many organizations merely pay lip service to compliance and do not invest in technology to ensure it. And for savvy organizations that want to be proactive and establish AI governance, they often don’t know how to catalog and enforce policies across the organization.

These problems are not new, but they can be mitigated and reduced. How can leaders transform their approach to harness the full potential of data in an era where it is the new currency? The path to transformation is not easy, nor will it happen overnight. It involves a combination of factors, including data literacy, business literacy, and data maturity. To help build this organizational muscle and capacity, CIOs, CTOs, and CDOs should consider increasing their data intelligence investments.

Let’s explore how data intelligence platforms and organizational success offer data-driven hope to CIOs, CTOs, and CDOs who must navigate the complexities of the modern data stack and hybrid ecosystems.

Background and Context

What exactly is data intelligence?

Initially coined by IDC, data intelligence is a system to deliver trustworthy data. It includes metadata, which is intelligence about data. “Data intelligence helps organizations answer six fundamental questions about data.”³ These questions are:

1. Who is using what data? 

2. Where is data, and where did it come from (lineage and provenance)? 

3. When is data being accessed, and when was it last updated? 

4. Why do we have data? 

5. Why do we need to keep (or discard) data? 

6. How is data being used, or – how should data be used? 

7. Relationships – what relationships are inherent within data and with data consumers?

Now that we understand data intelligence, let’s define some key concepts.

What is data literacy?

Data literacy is the ability to read, understand, create, and communicate data as information. It involves a set of competencies necessary for working with data, including reading graphs and charts, drawing conclusions from data, and understanding the meaning behind data. Data literacy is distinct from statistical literacy, which is more about the ability to read and interpret summary statistics in everyday media.

Data literacy is increasingly important as data becomes more integral to business, government, and society. It enables individuals at all levels of an organization to ask the right questions about data, build knowledge, make decisions, and communicate meaning to others. It is a critical skill that empowers employees to drive the business forward and is a crucial component of a thriving data culture within an organization.

Data literacy is a fascinating topic. If you want a great resource on the subject, check out Jordan Morrow’s book Be Data Literate: The Data Literacy Skills Everyone Needs To Succeed.⁴

^{What is business literacy?}

Business literacy is the knowledge required to make meaningful and effective decisions in a business context. Business literacy is understanding financial, accounting, marketing, and operational functions, and the ability to speak and read the language of business. This includes understanding areas such as financial principles, market dynamics, organizational strategies, and having the ability to interpret and use business data to inform related decisions.

What is a data culture?

A data culture refers to the collective behaviors and beliefs of people who value, practice, and encourage the use of data to improve decision-making. It is characterized by weaving data into an organization's operations, mindset, and identity, equipping everyone with the insights they need to make data-informed decisions. However, data culture is a paradox. Businesses that have a robust data culture see higher ROI. But a data culture is not something you can buy – only by investing in data, technology, and people can you achieve a strong culture rooted in data.

What is data culture maturity?

Data culture maturity can be considered an organization's progression in developing its capabilities to effectively manage and apply data. It involves enhancing data accessibility, trustworthiness, and reliance on data for decision-making across all levels of the organization. 

The "Alation Data Culture Maturity Model" categorizes organizations into maturity stages based on their data management practices and culture, from initial awareness and fragmented practices to advanced stages where data-driven decision-making is embedded across the organization.

Strategic Integration of Data Intelligence

Enhancing data literacy across the organization

To become more data-driven, organizations need to invest in their people, creating learning and development opportunities promoting data and business literacy. Of course, technology has a role to play as well. With modern cloud platforms, companies can now personalize experiences for different types of users. Personalized, contextualized, and intuitive interfaces make it easier for everyone in an organization to understand and use data. When people find software easy to navigate, they are more likely to use it to find information and make decisions. This is important because it helps employees at all levels become more comfortable with data, leading to a more mature data culture.

A data intelligence platform built for business and IT users is essential. Alation’s modern user interface, with Google-like search, ratings, and comments, makes finding and interacting with data-related assets as natural as shopping on Amazon. These features are straightforward for users as they search for and interpret data without specialized training. By making data access simple, all employees, regardless of their technical skills, can find and use data relevant to their work and interact with it from their business applications like Slack, Microsoft Teams, and Excel.

With more accessible data, employees can make better decisions faster. Instead of spending time trying to find data or understand complex reports, they can quickly gather insights and apply them. This leads to more informed decisions in less time, improving the organization's agility and effectiveness.

Vattenfall, a major European energy company, has actively developed a strong data culture centered around organizational data literacy. “Before Alation, we saw a lot of silos and individual documentation — and the risks and inefficiencies associated with that,” says Sebastian Kaus, data governance leader at Vattenfall. “After implementing Alation, we see more collaboration, and we see more discussions. For the enterprise, Alation is the single source of truth.” Developers can launch new projects within weeks instead of months because approximately 300 data users each month can now easily find, understand, and collaborate around the trusted data they find in Alation.

Suggested Action

An easy way to enhance data literacy is by implementing personalized, contextualized training sessions across the company to enhance data literacy. This is not a one-size fits most, however, as it should be specific to different organizational roles.

Cultivating business literacy through data insights

Effectively using data goes beyond understanding the numbers; it's about applying these insights to make data-driven decisions and meet business objectives. Thus, the earlier emphasis was on data and business literacy. Data science offers analytical tools, but integrating these analyses into business process workflows creates real value.

Modern analytics and AI platforms include analytics and reporting tools tailored for business users. These tools help generate insights directly relevant to business aims, such as customizable dashboards focusing on key performance indicators (KPIs) for specific strategic priorities.

Maximizing the benefits of data insights requires promoting collaboration across various teams within an organization – I call this a Tiger Team. Combining different areas of expertise can ensure that data-driven strategies are effectively integrated into the organization’s operations. This collaborative effort leads to more cohesive and impactful business decisions, based on relevant, actionable data.

Accelerating data maturity for competitive advantage

Data intelligence platforms play a crucial role in helping organizations progress through various stages of data maturity. From initial data collection and understanding to integrating analytics into decision-making processes, these platforms provide the tools and frameworks to find, understand, trust, govern, and measure your data’s impact. As organizations evolve, the ability to manage and use data more effectively becomes a critical factor in maintaining a competitive edge.

Data is a key driver of innovation and can significantly help organizations stand out in crowded markets. Businesses can identify innovation opportunities using data platforms to analyze trends, customer behaviors, and market dynamics. This includes developing new products, enhancing customer experiences, or optimizing operations to improve efficiency and reduce costs.

Suggested Action

Create a detailed roadmap for achieving data maturity by outlining specific steps, milestones, and key performance indicators (KPIs). This roadmap should leverage the capabilities of data platforms to address current gaps in data management and use, setting clear targets for improvement. Regular reviews and updates to the roadmap will keep the organization on track to data maturity goals and adapt to new challenges and opportunities. To create a roadmap, take the Data Culture Maturity Assessment.

Conclusion

Data intelligence platforms enable organizations to improve their data literacy, understand business contexts more deeply, and move towards greater data culture maturity. They provide the framework and insights necessary for making informed decisions, fostering a culture of data-driven strategic thinking across the organization.

Reflecting on the initial question about how leaders can fully leverage their data, it's clear that investing in data intelligence platforms provides the foundation for transforming data into actionable insights and driving strategic decisions and actions.

For organizations looking to navigate the complexities of today's data landscape, the next step is to dive deeper into what these data platforms can offer. Engaging with a demo, discussing potential applications with our team, or integrating the platform into your data strategy can be pivotal steps toward realizing the benefits of data-driven intelligence.

^{1. Somers, Meredith. 2023. “How Generative AI Can Boost Highly Skilled Workers’ Productivity | MIT Sloan.” Mitsloan.mit.edu. October 17, 2023. https://mitsloan.mit.edu/ideas-made-to-matter/how-generative-ai-can-boost-highly-skilled-workers-productivity.}

^{2. Duncan, Alan D., et al. “CDAO Agenda 2023: Presence, Persistence and Performance.” Gartner.com, 13 Mar. 2023, https://www.gartner.com/document/4171099.}

^{3. https://blogs.idc.com/2019/11/25/defining-data-intelligence-intelligence-about-data-not-from-data}

^{4. https://www.amazon.com/Be-Data-Literate-Literacy-Everyone/dp/1789668034}