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Do You Have An Enterprise Data Strategy?

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This time of year, I like to talk about updating the data strategy for the new year to include new business goals and new technology and in doing so I sometimes forget that there are still many companies that do not have a data strategy to update.


What is a Data Strategy and why do I need one?

Most organizations have multiple data management initiatives underway including master data management, data governance, data migration, data modernization, OLTP operational data cleanup, data ingestion, and data quality, etc., most efforts are focused on point solutions that address specific applications, projects, or business unit needs. The data strategy establishes a plan for aligning these activities across the enterprise in such a way that they complement and build on one another to deliver greater benefits and value. Key topics of this data strategy include:

  • What data should be ingested, acquired, managed, exported, archived, and deleted?
  • How should data be structured? (warehouse vs. Data Lake, lakehouse)
  • How and where should data be stored and managed? (on-premises or cloud)
  • Where should data be integrated and cleansed?
  • How should data ne governed, protected, and shared?

Addressing these questions and others will ensure that your organization gets the most value from data and that your organization has a plan to prioritize data requests and an adaptable strategy to handle unanticipated needs in the future. Simply put, the data strategy is an organization’s roadmap for using data to achieve business goals. The Data Strategy is a roadmap that ensures all the activities surrounding data management from collection to collaboration, work together effectively and efficiently to be as useful as possible and easy to govern. With a data management strategy in place, organizations can avoid some of these common data challenges:

  • Incompatible, duplicate, or missing data from undocumented or inconsistently documented sources.
  • Siloed projects that use the same data, yet duplicate the efforts and costs associated with that data.
  • Data activities that consume time and resources but do not contribute to overall business objectives.

A data management strategy is the strong foundation needed for consistent business insights, constant cost savings, new revenue generation, and accelerated business growth.

Components of the Data Strategy

6 key components define the data strategy. These components work together as building blocks to comprehensively support data management across the entire organization: identify, collect, integrate, secure, apply, and govern.

I could talk about each of these components in detail, but for the purposes of this blog and brevity, let me say that organizations need to decide what data of all the data inside and outside the organization they want to collect. The organization then must collect the data in a Data Lake. Once the raw data is in the Data Lake it needs to be integrated into easily usable, denormalized, wide tables. The data then will be secured using a data category security scheme and then the data must be made available for data workers to use, to create business insights, and to make better business decisions. Finally, after your data is in place and being used it should be continuously governed.

If you do not have a data strategy, I strongly recommend that you create one and if you need help, just give me a call, I will be happy to help you!

Perficient’s Cloud Data Expertise

The world’s leading brands choose to partner with us because we are large enough to scale major cloud projects, yet nimble enough to provide focused expertise in specific areas of your business. Our cloud, data, and analytics team can assist with your entire data and analytics lifecycle, from data strategy to implementation. We will help you make sense of your data and show you how to use it to solve complex business problems. We will assess your current data and analytics issues and develop a strategy for your long-term goals.

Download the guide, Becoming a Data-Driven Organization with Google Cloud Platform, to learn more about Dr. Chuck’s GCP data strategy


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Chuck Brooks

Dr. Chuck is a Senior Data Strategist / Solution Architect. He is a technology leader and visionary in big data, data lakes, analytics, and data science. Over a career that spans more than 40 years, Dr. Chuck has developed many large data repositories based on advancing data technologies. Dr. Chuck has helped many companies become data-driven and develop comprehensive data strategies. The cloud is the modern ecosystem for data and data lakes. Dr. Chuck’s expertise lies in the Google Cloud Platform, Advanced Analytics, Big Data, SQL and NoSQL Databases, Cloud Data Management Engines, and Business Management Development technologies such as SQL, Python, Data Studio, Qlik, PowerBI, Talend, R, Data Robot, and more. The following sales enablement and data strategy results from 40 years of Dr. Chuck’s career in the data space. For more information or to engage Dr. Chuck in an engagement, contact him at

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