Data Warehouse: Keys to Success

What does success look like for a data warehouse?

A centralized data warehouse can serve the needs of numerous business units simultaneously. Business users can then leverage a single enterprise data model that serves the needs of multiple business divisions, which will deliver a tremendous amount of value to the organization. 

In today’s business environment, turning data into information means having the ability to drive business outcomes. Having relevant information can transform how an organization operates as it moves toward becoming a data-driven business.

 

Success requires a more sophisticated thought process 

Organizations must start to become more sophisticated with their data strategies as they gather and aggregate disparate data to create useful information. Many companies have thought about or implemented data warehouses, but simply having a data warehouse is not enough to solve the complex data challenges of many organizations.

 

Success depends on asking the right questions of the right people

Organizations must address the following questions:

  • What business problems are we trying to solve? 
  • What benefits can we expect to achieve with a single version of the truth by building an enterprise data model? 
  •  How are we increasing the organization’s capabilities?  
  •  Are we capturing and measuring the details through key performance indicators? 
  • How do we plan to collect, integrate, manage and visualize the data?

There’s certainly a lot to think about when planning an enterprise data warehouse approach and strategy.

 

Successful data warehouse implementations are built leveraging a proven best practices approach, strategy and methodology

The key to success is taking a best practices approach by leveraging a proven methodology to deliver information to the enterprise. This raises yet more questions:

  • Which approach is suitable for your organization and business challenges? 
  • Have you built an effective data warehouse strategy that enables business users to access vast amounts of data in an easy-to-use format? 
  •  What is the proposed framework and methodology you plan to leverage to standardize the development of your data warehouse system? 

 

Success starts with effective requirements gathering

Effective requirements gathering is essential to maximize capabilities and deliver business value early and often. I personally use a proven technique that I’ve refined over the years. The process of future state envisioning along with business modeling enables us to determine what information is needed to meet the business requirements. An enterprise data warehouse implementation is a business exercise, and the requirements are paramount to the success of the project.

 

Success can only be achieved when all data challenges are accounted for

Data challenges must be accounted for to ensure the orangi. Solving for these technical challenges requires strong solutions architects, data architects and data modelers to work closely together to come up with a well-organized data warehouse.

 

I enjoy helping organizations achieve success

It’s always exciting to watch an organization transform data into a strategic asset and harness its power as a competitive advantage. As the organization shifts its thinking to become more data-centric, it’s enjoyable to watch the transformation as the organization develops a data-driven culture.

Increasing the analytics capabilities of an organization by leveraging a best practices approach to align business and technical leaders enables success, thanks to the endless benefits of a data-driven enterprise. This only happens with a successful implementation that transforms data into usable information that business leaders can use to plan actions. 

Written by Mike Sargo
Mike Sargo is Chief Data and Analytics Officer and Co-Founder of Data Ideology with over 18 years of experience leading, architecting, implementing, and delivering enterprise analytics, business intelligence, and enterprise data management solutions.

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