Donald Rumsfeld’s “known knows” is a famously quoted phrase from a response he gave in 2002. Since then, the world has changed significantly, transforming the way we think, act, and engage in an increasingly digital world. These changes are contributing to the world of business and technology, and are now driving organisations to become Data Driven Enterprises.
A Data Driven Enterprise can be defined as an Enterprise that is capable of validating what is known, confirming that which is unknown and discovering the truly new.
The need for a Data Driven Enterprise stems from a more competitive landscape which has emerged due to the rapid changes in technology. These have impacted the overall product life cycle, significantly reducing the amount of time available between conceptualising a new initiative and launching it. Today, companies need to deliver their best efforts within a very short period of time and gain as much as possible, as their competition is often able to adapt their products and services to nullify advantages quicker than ever before.
In order to remain ahead of the game, organisations need to;
- Understand and satisfy the needs of their customers.
- Understand the operational costs of satisfying those needs to qualify revenue opportunities.
- Analyse and optimise their supply and delivery channels.
- Understand their competition.
A Data Driven Enterprise helps build the systems needed to gather and analyse the data needed to understand such insights. It captures the activities with internal and external stakeholders which are performed via the enterprise infrastructure. It helps gather the knowledge that is required to improve understanding and strategy with solutions which are needed to stay ahead.
There is a huge value to utilising Big Data, but likewise there are several questions that needs to be clarified;
- How to build a Data Driven Enterprise?
- What are the various components of the organisation, one should look at?
- What are the technologies which is need to build expertise on?
- What changes needs to be done to the current infrastructure?
- How much it would cost?
- What would be the ROI and time to recover the cost while working within budgetary constraints?
It is my attempt in this blog (and the upcoming) to answer a few of those questions. I may not be able to answer each one nor I can say if there is a definitive answer for it. But it is always better to have a conversation that leads to answers, which I intend to initiate or continue depending upon each situation that is presented.
The Solution
Based on the experience gathered while working on multiple Big Data Initiatives, the answer lies in a four-phased approach described below. Since we work in a field of discovering patterns, I will also explain the pattern that I think we could use to deliver the Data Driven Enterprise.
- Discovering
- Defining and Planning
- Implementing
- Expanding
- Discovering
In this phase, the requirement is simply to identify existing challenges within the organisation.
- Defining and Planning
During this phase information is gathered from across the Enterprise about various data sources that can be tapped into to build the knowledge pool (A Data Hub, Data Lake etc.). These may include current systems in use as well as future plans to deliver new offerings. In this phase, an Enterprise wide infrastructure plan is built that would fulfil the needs of a Data Driven Enterprise.
- Implementation
The next phase relies on building the exact infrastructure which is required for Big Data as well as focusing on building a small Cluster. This will be a platform for building POC’s for the use cases which are identified in Discovery phase. This starts small, to build the hypothesis, solution, and then proving value.
Next, an automated pipeline will ingest, cleanse, process, analyse and visualise the information. This involves data scientists building various models alongside engineers that will be using these models to build data solutions. Finally, the business users validate the solution in order to help more effective decisions within their business units.
- Operational
Finally, once built, a solution can be scaled by focusing on propagating the knowledge and insights that were gathered throughout the Enterprise. Every stakeholder in the organisation can be empowered to use the knowledge gained from the data and apply it to their day-to-day decision making more effectively.
If you would like to find out more about how Big Data could help you make the most out of your current infrastructure while enabling you to open your digital horizons, do give us a call at +44 (0)203 475 7980 or email us at Salesforce@coforge.com
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Key Takeaways
- A Data Driven Enterprise validates what is known, uncovers what is unknown, and discovers new insights through systematic analysis.
- Rapid changes in technology have shortened product life cycles, demanding faster, more accurate datadriven decisions.
- To remain competitive, organizations must understand customer needs, operational costs, supply chains, and competitor strategies.
- Becoming datadriven requires well-designed systems for capturing, analyzing, and distributing enterprise data.
- A practical transformation approach involves four phases: Discovering, Defining & Planning, Implementing, and Expanding.
- Big Data initiatives must be aligned with organizational goals, infrastructure readiness, cost considerations, and expected ROI.
- Discovering current challenges
- Defining & Planning enterprise-wide data infrastructure
- Implementing Big Data systems and small clusters for POCs
- Expanding/Operationalizing insights across the enterprise
Why This Matters
Data is now a strategic differentiator. Enterprises that convert raw information into actionable knowledge outperform those who rely on intuition or legacy decision-making processes.
Building a Data Driven Enterprise enables faster innovation, increased efficiency, and more informed decisionmaking at every organizational level.
Frequently Asked Questions (FAQ)
Q1. What is a Data Driven Enterprise?
A Data Driven Enterprise is an organization that uses structured and unstructured data to validate known ideas, uncover unknown insights, and discover new opportunities to guide decisions.
Q2. Why is it important today?
Technology innovation cycles are faster than ever, requiring companies to quickly understand customer needs, optimize operations, and outperform competitors.
Q3. What challenges does a Data Driven Enterprise address?
It helps organizations understand customers, manage operational costs, analyze supply chains, interpret market trends, and gain competitive intelligence.
Q4. What is the fourphase approach mentioned in the blog?
The approach includes:
Q5. What questions should organizations ask before starting this journey?
What components to focus on?
What technologies are needed?
What infrastructure changes are required?
How much will it cost?
What is the expected ROI?
Glossary of Terms
Big Data
Extremely large datasets that require advanced technologies for processing and insight extraction.
Data Lake / Data Hub
Central repositories for storing raw enterprise data from multiple sources.
POC (Proof of Concept)
A small-scale demonstration used to validate hypotheses and test feasibility before broader rollout.
Enterprise Infrastructure Plan
A blueprint outlining how systems, data sources, and technologies will be integrated to support a Data Driven Enterprise.
Cluster
A group of connected machines used for distributed data storage or analytics processing.
Best Practices for Building a Data Driven Enterprise
- Begin with identifying real organizational challenges before choosing tools or technologies.
- Engage stakeholders across business, IT, operations, and leadership from the planning phase.
- Build an enterprisewide infrastructure plan that accounts for current systems and future growth.
- Start small with POCs to validate concepts before scaling to full production.
- Implement automated pipelines for ingestion, cleansing, analysis, and visualization.
- Empower business users with accessible insights to drive daily decision-making.
- Continuously measure ROI and refine data strategies based on value delivered.
Common Pitfalls & How to Avoid Them
Pitfall: Starting without clear business problems
Solution: Use the Discovery phase to thoroughly understand challenges and pain points.
Pitfall: Over-investing in technology before planning
Solution: Build an Enterprise-wide infrastructure plan first.
Pitfall: Trying to implement everything at once
Solution: Start with small clusters and focused POCs.
Pitfall: Poor data governance and validation
Solution: Automate ingestion, cleansing, processing, and modeling to ensure reliability.
Pitfall: Not operationalizing insights
Solution: Share findings broadly, train teams, and embed insights into daily workflows.
