In today’s digital economy, every organization is a data organization. Virtually every organization is also using cloud, and often, multiple clouds, to host databases, applications and AI-driven products and services in a distributed manner. They run their product development and business operations in this manner, creating a highly distributed data infrastructure covering data sourcing, integration, access, security, privacy, and compliance.
Such distributed environments create data challenges. Since data is traveling from on-premises to the cloud, and between clouds, it is traveling a long distance from its source and is undergoing various transformations along with way. This creates additional complexities associated with data lineage, security, privacy, compliance, quality and governance. Not to mention, it’s creating information silos as well, if not done properly.
While organizations are collecting and storing data across on-premises and cloud environments, different teams interpret it in their own way, creating data trust issues for AI and ML-driven use cases, and even for centralized BI and reporting.
As part of this digital economy, data’s importance is only increasing. As organizations become savvier through rapid adoption of AI, ML, advanced real time decisioning techniques at scale, using modern cloud technologies and algorithms, data becomes the key to their competitive advantage. This also means that how well an organization collects, ingests, integrates, and processes data in a governed and secured way at scale is of paramount importance.
Getting data right is the key. And this requires a methodical and disciplined engineering approach – a call to action to implement an effective and actionable enterprise data strategy that works for your organization.
Your organization’s data strategy is the data blueprint that helps you define how best to ingest, integrate, process, manage, analyze, and act upon business data at scale in a highly distributed, multi-cloud environment. It’s also the ability to make informed business decisions based on your data while keeping it safe, secure, and compliant. Data strategy refers to the tools, processes and rules that help you manage the lifecycle of data from creation all the way through to archiving.
An effective data strategy helps by ensuring that data is managed and used as an asset. It provides a common set of goals and objectives across projects to ensure data is used both effectively and efficiently.
If implemented correctly, your data strategy blueprint will enable your organization to accomplish:
To summarize, your data strategy and how effective you are at executing it, is key to the success of your overall digital transformation strategy. Getting data strategy right is an opportunity to set out how you want to use data, clarify your top data priorities, and plan for delivering your goals. It's the most effective way to drill down to your core business data needs and create an achievable plan that aligns with your strategic business initiatives.
An effective and actionable data strategy puts a business in a strong position to solve challenges such as:
To conclude, a business without a data strategy is poorly positioned to operate efficiently and profitably or to grow successfully. Businesses thrive only when they adopt a systematic approach to collecting, storing, analyzing, and managing their data. That requires a data strategy that serves the entire organization.
In Part 2 of this series, we will discuss the HOW of an effective data strategy, covering foundational components, best practices, and key takeaways.
Is this something that interests you? Want to learn more? Let’s connect to discuss how we can help as we have a ton of experience in this space. Please feel free to send us a note at firstname.lastname@example.org and we can find some time to have a conversation.