Why Most Companies Are Still Struggling to Use Their Data Effectively

Most companies are data-rich but insight-poor. Discover why businesses struggle to leverage data effectively and learn strategies to achieve better results.

Data is referred to as the new oil of the 21st century. Unlike oil, data is an abundant resource, and the challenge is not in sourcing it but in processing it to value. Today, most companies are insight-poor but data-rich. On the same token, they are yet to master the ability to leverage data to make better business decisions.

Yet, a relatively barefaced shocking statistic — only about 16% of organizations were classified as truly data-driven — is made all the clearer by a survey by SD Times. Hence, where are companies going wrong? So why is this road to being insights-driven so rocky? This article looks at the major obstacles that still prevent companies from making the best use of data, then offers ways to overcome them.

Key Challenges Hindering Effective Data Usage

Data Silos and Poor Data Quality

The average enterprise today uses around 400 sources, with 20% of large organizations utilizing over 1,000 sources. From operational systems like ERP and CRM to unstructured data in emails, documents and social media, data lives in disparate silos across the organization. Pulling insights by connecting these islands of data is still a persistent struggle for most companies.

But it’s not just about integrating data; the quality of data itself poses a huge headache. Critical issues like incomplete data, duplication errors, outdated information, etc., make getting a single source of truth next to impossible. Unless the underlying data foundation is robust, any insights drawn risk being misleading or inaccurate.

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Lack of Analytical Talent and Data Literacy

Making sense of data requires specialized analytical skills that are still in short supply. During the 2024–2034 period, it’s estimated that there will be a shortage of 350,000 data and analytics professionals in the US alone. Most companies simply don’t have the analytical horsepower to harness the power of data.

But it’s not just about having data scientists. Insights can be used only when organizations nurture data literacy across teams. However, research by Qlik shows that currently only 24% of employees can read, work with, analyze, and argue with data. Most employees are unsure of how to interpret data and apply it effectively in decisions.

Insights Not Tied to Business Outcomes

Many analytics programs focus heavily on modeling data accurately but fail to link insights back to business priorities and outcomes. While predictive models might be mathematically sound, they offer little value if they can’t demonstrably improve a business process or metric.

According to a leading analyst firm such as Gartner, more than 80% of analytics insights will result in failure to deliver business outcomes by 2022. Data teams don’t have a chance to impact a business if they don’t speak to businesses until it’s time to measure.

Strategies to Start Using Data More Effectively

The challenges may seem daunting, but with the right strategy, companies can steady their analytics efforts to deliver real business value. Here are six proven approaches:

1. Invest in Data Pipelines and Governance

This means that data quality resolution must start with establishing a robust data pipeline for collecting, validating, transforming, and publishing data with a modern data integration platform. By this, you have a buildable, scalable, and reliable data foundation.

Similarly, key stakeholders are actually implementing data governance policies and their roles of duty. This brings responsibility towards ensuring high data quality and solving problems before they magnify into the fire of problems disrupting downstream analytics.

2. Build In-House Analytical Capabilities

While outsourcing helps alleviate immediate analytical gaps, building in-house capabilities is vital for long-term sustainability. Leading companies invest heavily in upskilling employees through formal data literacy programs. They also nurture analytical talent through training programs, job rotations, and growth opportunities to retain skilled resources.

3. Democratize Insights to More Employees

Proving this, the key to improving data literacy through the organization is to make insights easy to discover and understand by employees. Today, data catalogs, embedded BI, and no-code analytics give employees the ability to find, understand, and work with data without advanced analytical skills. It democratizes data, breaking down silos, and allows much more value to be extracted from analytics.

4. Practice Outcome-Focused Analytics

Data teams should always start by identifying key business outcomes and defining metrics to track them before modeling data and generating insights. This prevents analysis paralysis through tangential insights that offer no real business value. Maintaining continuous dialogue between data scientists, analysts, and business teams is vital to linking analytics tightly to business results.

5. Continuously Experiment and Scale

Analytics is an iterative process. Companies should adopt a startup mindset – test analytical proofs of concept, quickly experiment, and scale ones that demonstrate tangible impact. Fail fast, learn fast. Embedding continuous experimentation accelerates learning and focuses analytical efforts on use cases that deliver maximum business value.

6. Augment Human Intelligence through AI

Sophisticated AI techniques can help amplify limited human analytical capabilities. Automating aspects of data preparation, insight discovery, and predictive modeling through AutoML, data robots, and virtual analysts allows human analysts to focus on high-value work. Blending human creativity and machine intelligence unlocks smarter, faster, data-driven decisions.

7. Build a Data-Driven Culture

Using data effectively is not just about having the right technology and talent. Companies must nurture a data-driven culture where decisions based on facts and insights are valued at all levels.

Leaders should role model data-based decision-making and incentivize teams to back recommendations with data. Hiring managers should evaluate candidates on analytical skills. Data literacy training should be made widely available. Platforms for open data sharing and collaboration should be implemented.

Small cultural nudges can drive behavior change across the organization. For example, making real-time metrics visible through dashboards makes problems transparent quickly. Establishing centralized analytics CoEs and data scientists embedded in business units accelerates idea flow.

With a data-pervasive culture, organizations can institutionalize data-driven decision-making as the norm rather than the exception.

8. Choose the Right Data and Analytics Tools

The technology landscape today is exploding with sophisticated data and analytics solutions. While choice is great, too many tools can backfire without a coherent strategy. Based on use cases, companies should choose technology platforms that can scale from data management to predictive insights seamlessly.

Equally critical is ensuring a cohesive user experience across tools. If users need to learn new UIs for every new tool, adoption will stay low. With modern open and cloud-based platforms, it’s possible to democratize insights directly to business users via interfaces they already use daily, like MS Office.

9. Maintain Continuous Planning with Agile Analytics

In today’s dynamic environment, analytics must keep pace with changing business priorities. But 85% of data and analytics projects fail to deliver their expected value, largely due to rigid planning approaches.

Leading companies are adopting agile analytics techniques to introduce flexibility. Priorities are defined and adjusted continuously through weekly sprints and daily standups between data teams and business stakeholders. Deliverables are released in minimally viable increments focused on business impact.

This nimble approach accelerates time-to-insight across ever-evolving business needs while maintaining alignment to value. As the famous quote goes, “It’s not the daily increase but daily decrease. Hack away at the unessential.”

The Road Ahead

In an era where velocity, variety, and complexity are growing exponentially, making use of insights to beat competition will become harder. And yet, by enhancing the fundamentals of data, analytical modeling talent, and business linkage, it is possible to formalize these analytics efforts and make organizations insight-driven. Bumps will certainly be along the way, but the view is sure worth it. It is now time to accelerate.

Conclusion

Independent of massive technology investments, most companies can not leverage data effectively as a result of issues related to data quality, talent, business alignment, and the inability to scale. However, the fault lies not in our data but in our approach. When companies have robust data pipelines and democratized analytical capabilities that are closely linked to business outcomes and further enhanced by AI, they can successfully navigate the journey towards becoming an insights-driven organization. However, the fundamentals are keeping up with the data, upskilling analytical talent, and maintaining constant business alignment for creating impacts.