Need to align Data strategy with business strategy

Creating a Data Strategy

Big data revolution has sparked a trend among organizations to collect all possible data. Cheap storage and processing capabilities offered by hyperscale's of today have ignited this trend further, which has resulted abundance of data. Everyone is capturing and storing this data in multiple ways and “Data Lake” is one such place. This data is used by business, data scientists and operational staff to gather insight.

Photo by Claudio Schwarz on Unsplash

Question is, however, “Is this data serving its purpose ?”. How to utilize this data, what purpose this data will server, will this data be used internally, externally ? Will this data provide monetary benefits, and if yes, what is the target audience, How this data will be delivered, will it be a batch approach, real time delivery ?

Too many questions. At the core, Is the data being treated as an asset instead of cost? and Are the technology investments aligned to the business priorities?. What is the data strategy ?

Every organization is not at the same maturity level for its data strategy and in implemented processes. Therefore, begore embarking on the journey of creating a data strategy, It is important to first assess the maturity of the organization, how are they using their data currently? What are the pain points? Where is it, in the path to be a data-driven organization? and more importantly, what are the business objectives and how data will be leveraged to meet this objectives ?

This hbr blog suggests to have two types of data strategy, offensive and defensive.

Offensive: An offensive strategy focuses on growth, increase revenue and customer satisfaction.

Defensive : A defensive strategy would focus on ensuring regulatory compliance and security of the infrastructure from potential threats, fraud detection and reduce risks.

Organization data strategy can be created with “Top down” or “Bottom up” approaches and should consider many aspects while being formulated. Some of the tenets are listed below:

  1. Align with Organization’s Business Strategy
  2. Data governance: consider people, process , policy and culture. Data Governance, Security, Enterprise Business Glossary, Data Quality
  3. Presenting data in the form via, Master data management, Data warehousing, Business intelligence, Big data analytics services, Data quality, Data architecture & modeling. Self services and and queryable — Secure data sharing, data visualizations
  4. Data Collection: Data assets harvesting , Data asset planning & inventory, Data Integration, Metadata management.
  5. Data estate assessment: Databases, Big data, unstructured data, semi-structured data, Documents & Content management.
  6. Change Management
  7. Stakeholder Management
  8. Technology Choice
  9. Build Capability
  10. Develop a data-driven culture
  11. And finally prioritize, budget & execute

Data is term as oil for modern day organizations and is a valuable asset. This asset is available to be harvested and monetize. Without a proper business and aligned data strategy and technology choices, this asset may not provide desirable benefits.



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Arun Singh

Arun Singh

Work as Enterprise Data Architect, Cloud Data Architect and focuses on building data architectures on cloud platforms.