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Data-Driven vs. Data Centricity: How to Build a Data-Centric Framework

Many organizations claim to be data-driven or data-centric, using the terms loosely and interchangeably. But the two are not the same and have particular applications.

As we talked about in our last article, being data-driven is a mindset that involves making strategic decisions based on data and insights.

In one way, data centricity is a mindset, but really, it’s architecture.

Data-centric organizations see data as a fixed, immovable asset

Technologies and systems are built around the data they maintain and amassed over time. It’s something that is tightly, thoughtfully managed, and data security is of the utmost importance.

For organizations to be truly data-centric, you have to start with a holistic data framework.

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Common components of a data-centric framework:

Business goals and data strategy
Data science and architecture
Data governance
Reporting and visualization

Business goals and data strategy

The crucial first step to establishing your data framework is to understand and align to your organization’s overall business objectives, which will help your organization use and interpret data across the enterprise, and use it to make business decisions.

For example, how is the cost of your data infrastructure tied back into business operations? Are there opportunities to conduct a cost-benefit analysis to determine how your data infrastructure aligns with your business goals?

To answer these questions, look to data scientists and architects to provide the infrastructure to measure success and create the landscape for measuring the data strategy.

For a refresher on establishing a data strategy, check out our last article on adopting a data-driven strategy.

Read article

Data science and architecture

Once the business goals and data strategy are set, you need the technology to support those functions and see how those technologies can produce the necessary data to measure success.

Data science involves looking at all systems that can produce the needed data to measure against organizational goals. It allows those extracting the data to evaluate performance but also for predictive analytics to forecast performance and even anticipate trends (or data modeling).

Data science can help:

Discover which models can help your company further streamline and quantify your business strategy.
Determine which KPIs can measure success among certain audiences in different markets.
Create models that show how your current strategy is likely to perform or how small changes can have a massive impact on revenue.

Though without the systems and tools, data science is impossible, so formal data architecture is essential.

Data architecture is the process of identifying and designing technologies that can effectively manage data and enable modeling. Like data scientists, data architects will use business goals as the “north star.” They work to ensure the right systems are in place to produce the information that aligns with a company’s goals.

While these two disciplines are often separate, they complement each other. Because of that, it’s becoming more common for the data science and architecture teams to have crossover duties.

Learn more about data science and architecture

Reporting and visualization

With the models and infrastructure in place, now it’s time to present your data in a digestible way. As organizations get started on their data journey, they often see data as a smattering of indiscernible numbers.

Companies would need to understand the different ways to present data in a visual format. They need to determine which reporting tools and functionalities are available. Is the data presented in the correct format? Should the data visualization be interactive?

It’s fair to say that most stakeholders with an organization are not data scientists or statisticians, so looking at data points on a spreadsheet is not always helpful.

Data visualization is like presenting stats with a paintbrush

It plots existing data and models and presents them in a more easy-to-understand format through charts, graphs, maps and other illustrative formats.

To make viewing data more relevant for different audiences, scientists and architects will create custom dashboards based on the organization’s role and level of access.

See how data visualization helped a global MedTech company increase visibility and efficiency across the organization.

Read case study

Data governance

For data-centric organizations, legal and regulatory guidance is involved in ensuring data is secure and accessible – especially if your organization is accountable to European Union General Data Protection Regulation (GDPR) standards.

A suite of technologies and a strategy for managing data can turn into the Wild West without oversight. That’s where a data governance framework comes in; it’s a set of formalized rules and processes for how data is collected, stored, used, and for how it’s disposed of.

Data governance is essential for technology companies and large organizations that maintain vast amounts of data over several years. Businesses would want to establish a data governance framework that outlines which type of data enters their systems and at which stage throughout the data lifecycle process.

Here are some common components of data governance documentation:

Data org chart – Documentation that indicates which individuals and teams own which data and how much access they have.
Technology documentation – A list of all technology platforms used for managing data and how each is used.
Data lifecycle policies – Determines how long data is stored, where it is stored, and the process for removing data once it reaches the end of its lifecycle.
Meta instructions – Meta-information includes descriptions within a site, app or platform to help make information findable through search and tagging. Having a consistent format and semantics guidelines for updating meta information makes looking for information easier.
Security protocol – Potentially one of the most important tools in the framework, security policies determine how data is stored and protected. It should also include protocols for data breaches if they occur.

Data can help organizations grow more quickly and systematically when accurate and properly managed. That’s why 92% of companies who invest in data see a return on the investment.

When data is your most powerful resource, it’s time to adopt a data-centric approach.