Practical Solutions for Common Data Challenges

Are you struggling with data integration, data quality, technology cost control, or navigating the ever-changing data technology landscape? If so, you’re not alone. Many organizations face these challenges, but there are practical solutions that can help you unlock the true value of your data. 

Over the past 10 years, we’ve seen major advancements in data management and analytics that many organizations are eager to leverage. However, these four common data challenges continue to vex our clients, despite significant technological advancements related to data.

  1. Joining data from multiple source systems to answer key business questions
  2. Building trust in data quality and integrity
  3. Controlling the spiraling costs of data platforms
  4. Navigating the rapidly evolving data technology landscape of changing tools

If your organization is struggling with any of these issues, read on to learn practical strategies to overcome them and unlock more value from your data.


Joining Data from Multiple Source Systems to Answer Business Questions

Joining data that comes from a variety of systems is complex. This is because architectural direction has changed over the years and tools to do analytics continue evolving and leapfrogging each other. For example, best practices used to be to transfer data into a data warehouse and layer BI tools over it for reporting and visualizations. However, many companies now realize that warehouses can’t do everything needed to experiment with predictive analytics and AI use cases now have tough decisions to make about whether to abandon the approach of moving all data to a warehouse. 

Solving these challenges is like completing a complex puzzle – there is no single piece that can do it all. Lakehouse architecture with open-source data storage formats like Delta or Iceberg represent important new puzzle pieces that can help create a complete and effective solution. With a Lakehouse architecture, it’s easier to consume data where it resides and remove the need to load to data warehouses. A Lakehouse architecture brings together the benefits of a data warehouse, with the flexibility and readiness for AI-driven use cases that data lakes promised, but didn’t always achieve. 

There is a vast ecosystem of tools that can work with a Lakehouse architecture and this also avoids the vendor lock-in that many are stuck with. Many aren’t familiar with all of the great benefits of a Lakehouse architecture. We can help you get started with your Lakehouse , and a great place to read about the concept is from the original article introducing Lakehouse.


Building Trust in Data Quality and Integrity

One of the key challenges organizations face when working with data is building trust in the quality and integrity of their data. Far too often, there is a disconnect between the analytics and reports generated from the data, and the real-world “feel” that customers and stakeholders have based on their anecdotal experiences. 

A major contributor to this trust gap is the proliferation of multiple versions of analytical reports and dashboards. As data movement and reporting processes become increasingly complex and tailored to individual use cases, it can be difficult to maintain a single, authoritative source of truth. This can lead to confusion and a lack of confidence in the data. 

To overcome these challenges, organizations need to establish a strong culture of data governance. This involved putting in place clear policies, processes, and responsibilities for managing data throughout its lifecycle. This includes ensuring data quality, maintaining data lineage, and enforcing data access and usage policies.


Understanding and Controlling the Spiraling Costs of Running Data Platforms

A key challenge organizations face is the spiraling costs of running and maintaining data platforms. In large enterprises, we sometimes see challenges of providing cost visibility to users and internal groups that are utilizing these platforms. There is often a disconnect between the finance teams who handle the budget and the individual business units who are driving the consumption of these data resources. 

To address this challenge, organizations need to first focus on improving cost visibility to ensure accountability is felt equally by the central technology and finance teams as well as individual users and their leadership. This involves making available more granular cost tracking data and putting alerts or cut-offs in place when users spend above the threshold. 

The next step is looking at the underlying architecture and its impact on costs. A key driver of costs for many is the approach of centralizing all data in on-premises or cloud-based data warehouses. This is often not the most cost-effective or flexible option. 

In contrast, a Lakehouse architecture, where data moves minimally and can be consumed where it resides, it a most cost-effective solution. This allows the use of open data storage formats stored on common cloud storage like AWS S3. Data can be automatically archived or purged based on usage, further reducing costs. 

Additionally, a Lakehouse architecture provides the flexibility to use the most efficient compute resources, such as options provided by Databricks, to consume the data. This results in a more future-proof and cost-effective data platform architecture. 

These suggestions can help organizations gain better control over their data platform spending and we are here to partner with you and create a business case for this investment. 


Navigating the Rapidly Evolving Data Technology Landscape of Changing Tools

One of the key challenges organizations face in the data space is the rapidly evolving technology landscape. Frequently, the complaint is that “we have too many tools and keep changing tools.” This is a particularly tough challenge for larger enterprises, where various lines of business find a tool they like and start using it, without a comprehensive assessment or comparison to what’s already in use across the organization. Decommissioning a tool and forcing user migration can be a significant challenge. 

Organizations need to balance the agility and innovation of new tools with the stability and governance of a centralized approach. To navigate this landscape effectively, organizations need to put in place clear policies and processes for evaluating, selecting, and managing data tools and platforms. This can help to ensure that the technology choice align with the organization’s overall data strategy and priorities, while also providing a mechanism for decommissioning tools that are no longer serving the business needs. 

We can help focus on building a solid foundational data infrastructure that gives the flexibility to pivot new tools and subset others as needed. 


Let’s Talk About Your Data Challenges

If any of these challenges sound familiar, we’d be happy to discuss how we can help. Our team of data experts has extensive experience in guiding organizations through these complex issues and helping them unlock the trust potential of their data. Contact us today to learn more about our services and how we can help you move up the “data maturity” curve, future-proof your data architecture, and prepare your organization to thrive in the age of AI and advanced analytics.