The term data-centric refers to an organization using data as the primary and permanent asset for decision-making. As a data-centric enterprise, employees lean on data to validate hypothesis and inform transformational efforts. Data-centric organizations leverage data to inform, assess and temper their assumptions to guide decision-making.
Operating as a Data-Centric Enterprise
There’s a wealth of information out there today — so much so that it can quickly become overwhelming and difficult to wrangle. To help organizations make sense of what’s available, and leverage that intelligence to make data-centric decisions, organizations use strategy, science, and structure to democratize data, making them understandable and actionable.
Data-centric insights are music to any executive’s ears. That’s because these insights offer validation that the organization is heading in the right direction. Yet data alone is too complex to use exclusively for decision-making. Tapping into data sets to inform strategies, algorithms, and key business decisions still requires a human touch. That pairing of science and human intelligence empowers organizations to surge forward.
Simplifying the Complex by Becoming More Data-Centric
The term “data-driven” has become synonymous with how organizations leverage data and analytics. But, today’s businesses that seek to run an intelligent operation aren’t striving to let the data make the end decision. Instead, these organizations strive to leverage data to inform and validate ideas, concepts and strategies.
Becoming Data-Centric to Inform and Validate Decision-Making
It’s not enough for an organization to gather data. As data gets wrangled from various departments, it can then be woven together to tell a deeper story and inform important decisions throughout the organization.
Become More Predictive
Taking a predictive stance empowers organizations to stay one step ahead of their customers. Using data to inform past behavioral trends among their consumer base allows the business to maintain that predictive posture with less risk. In turn, these companies can use micro-moment insights to examines what customers need, where they can deliver, and what’s missing from the overarching experience. Analyzing data points and pulling out patterns allows the key decision makers to predict future behaviors better.
Execute on New Paths Faster
Intelligent operations allows key decision-makers to gather evidence that their human instinct is on the right track and their hunch is correct.
In doing so, organizations can run more intelligently and pick up momentum faster than they would by relying on gut feeling alone. This lower risk means the organization can iterate and innovate faster than ever. In addition, the data can be used to execute on more event-driven paths faster, adjusting to meet the customer where they’re at instead of sending them down a singular predictable journey.
Gather Unbiased Insights
One of the hardest parts about relying on the human brain to make decisions is that humans are inherently biased. By becoming more data-centric, organizations can streamline decision-making and eliminate bias simultaneously. Moreover, aligning the company culture with one that’s data-centric helps to weed out further the temptation to make decisions based on gut feelings or past experiences. Instead, it offers an unbiased outsider assessment of what the market and your team needs.
Break Down Silo Walls
When employees have data at their disposal in a format that’s easy to understand, they’re more likely to see the bigger picture about what’s happening in the organization. Rather than make assumptions based solely on what they see inside their silo walls, data can be democratized and disseminated throughout the organization to keep the guardrails up when making key decisions. By tapping into the same insights and data points, teams work together in unity rather than working towards unique goals.
Customer Experience (CX) Terms
- 360 Degree View of the Customer
- Barlow Bands
- Behavioral Triggers
- Bow Tie Funnel
- Business Impact Analysis
- Cognitive Computing
- Content Mapping
- Conversational User Guidance
- Customer Data Profile
- Customer Experience (CX)
- Customer Friction
- Customer Insights Map
- Customer Journey
- Customer Journey Mapping
- Customized Ratios
- CX Intelligence
- CX Led Growth
- CX Metrics
- Data as a Product (DaaP)
- Data as a Service
- Data Culture
- Data Engineering
- Data Fabric
- Data Governance
- Data Looping
- Data Mining
- Data Modeling
- Data Monetization
- Data Swamp
- Data Visualization
- Data Warehouse
- Data-Centric
- Descriptive Analytics
- Diagnostic Analytics
- Digital Asset Management
- Digital Transformation
- Dirty Data In Dirty Data Out
- Embedded Intelligence
- Employee Data Profile
- Employee Experience (EX)
- EX to CX Data Mapping
- EX to CX Mapping