Charles Babbage, the 19th century mathematician and inventor considered by some to be the father of the computer, was an early adherent to what we now call data centricity and data-centric organizations. “Errors using inadequate data,” he once said, “are much less than those using no data at all.”
If he were around today, Babbage would appreciate how data has become the lifeblood of 21st century enterprise, essential to business, government and the military. If an application crashes, you can build or buy a new one. But lose your data, and you’re not only blind and deaf, but wandering like a deer into oncoming traffic.
Data-driven decision-making has been around for decades, but even good data can yield bad decisions if it is poorly understood, badly analyzed or misinterpreted. That can happen easily when data is collected and analyzed in a silo disconnected from the rest of an organization.
That’s why there’s been growing realization that organizations that want to stay competitive must evolve past a culture that is data driven toward one that is data centric. The differences between the two are subtle, but they are nonetheless considerable.
Data driven vs. data centric
In the first chapter of his 2015 book, “Creating a Data-Driven Organization,” data scientist Carl Anderson writes, “Data-drivenness is about building tools, abilities, and, most crucially, a culture that acts on data.”
Having worked as a data scientist at WW, Warby Parker and now at Indigo, a company that supports sustainable agriculture, Anderson describes a data-driven culture as producing “forward-looking analyses” that “involve answering the ‘why’ questions — or more generally ‘w-questions’: who, what, when, why and where — making recommendations and predictions and telling a story around the findings.”
So that sounds pretty data centric, doesn’t it? But contrast it with how David McCombs describes data centricity in The Data Administration Newsletter:
“Data-centric refers to an architecture where data is the primary and permanent asset, and applications come and go. In the data-centric architecture, the data model precedes the implementation of any given application and will be around and valid long after it is gone.”
“Data-centric culture” and “data-driven culture,” in other words, are neither the same nor mutually exclusive. Rather, data centricity becomes the foundation on which sound, accurate data-driven decisions are made.
At SDV INTERNATIONAL, we act on the belief that data-centric organizations will be the leaders of the future. Data-driven decision-making capabilities are a necessity for organizations to build a competitive advantage in the near-term and will be a standard requirement for organizations to survive over the long-term. A data-centric company can better align its business strategy and organizational goals with the interests of its stakeholders by using information generated from its operations. Data architecture that is integrated and accessible at all levels can yield more accurate, faster and more agile data-driven decisions.
This article will take a holistic view of data centricity, examining the need for it and the data strategy, data governance and data infrastructure involved, along with obstacles and opportunities.
WHAT DOES IT MEAN TO BE DATA CENTRIC?
Being data centric means an organization builds a model for the way data will be used, then makes the data conform to the model. The applications that analyze and manipulate the data may evolve, but the data remains the permanent asset.
Most organizations are adept at collecting information from a variety of data sources and end up with huge datasets that are difficult to use. These organizations struggle to spot useful insights and trends. As Jay Baer, a marketing and customer service expert, says, “We are surrounded by data, but starved for insights.”
To use their data, organizations buy or build application systems, each of which may have its own data model. They are difficult to change and hard to integrate with other application systems. The organization has data, yes, but to what end?
In contrast, let's look at the characteristics of data-centric organizations.
DATA IS CONSIDERED A PERMANENT ASSET
It might not be listed as an asset on the plus side of the balance sheet, but the status of data as an asset will be obvious in how the organization functions. Everyone in the entire organization — from the executives to the data scientists to the IT department to those who run the day-to-day business — has a mutual understanding about the value of data. They treat it as an organizational asset that, just like fixed assets, has value, depreciates over time and needs protection.
DATA QUALITY IS PARAMOUNT
The organization strives to collect data that is accurate, relevant, complete, up-to-date and consistent. All aspects of business decisions and business operations, including regulatory compliance and customer satisfaction, depend on data.
DATA IS DISSEMINATED UNIFORMLY
All data consumers can access data through the same system to retrieve information associated with a specific subject, regardless of where they are located, geographically or within the company. In short, the “s” word – silo – is not a thing.
Eliminating data silos improves data discovery and consumption and enables smarter decisions. Migrating an organization’s data to the cloud, as opposed to storing it in legacy data centers, can give teams in any location fast and easy access to critical information. In addition, data users can contribute their own knowledge, enriching the data through their use of it.
Related reading: Why IT modernization is mission critical
INVESTMENTS ARE MADE IN DATA PROFESSIONALS
Unless there are personnel to effectively manage data, it can make an organization even less data-centric. No matter what they’re called – chief data officers (CDO), data evangelists, data architects — an organization needs people to see the big picture and understand all the ways that employees trained in data literacy can use data to achieve strategic goals and improve the business and operations. Data-centric organizations — and those that aspire to be data-centric — hire data professionals who are open-minded about what stories the data is telling and have the trust of decision-makers.
THERE IS A UNIFIED GOVERNANCE MODEL
To be able to trust the data and become authentically data centric, an organization needs processes and standards for it. Data governance comprises roles, policies, processes, standards, and metrics to ensure data is used effectively and efficiently. It establishes the processes and responsibilities that provide data quality and data security across a business or organization. Today’s regulatory climate is also a huge driver for data governance initiatives.
OBJECTIVES OF A DATA-CENTRIC ORGANIZATION
Over the decades, the U.S. military has driven innovations that made their way into the private sector, from jeeps and superglue to the internet itself, and it is on the leading edge of data centricity.
The U.S. Department of Defense (DoD) declared in a 2020 report that "data is a strategic asset that must be operationalized in order to provide a lethal and effective Joint Force that, combined with our network of allies and partners, sustains American influence and advances shared security and prosperity."
DoD weapons platforms, connected devices, sensors, training facilities, test ranges and business systems generate enormous volumes of data. All retain and share their data so it can be put to broader use.
In February 2022, Secretary of the Army Christine Wormuth made becoming more data centric her second goal for the Army to enable success on the battlefield.
"Data is the ammunition,” said David Spirk, former DoD chief data officer. “It is increasingly central to warfighter advantage on and off the battlefield. This strategy is our first step to making that ammo persistently available to the men and women of the DoD, regardless of echelon or geographic location."
Data life-cycle management is just as vital as ammunition management to the Army’s operational needs. To that end, the DoD has set out goals and objectives that, taken together, will let them achieve data-centricity. As a partner with DoD, SDV INTERNATIONAL subscribes to these objectives and has helped several DoD and federal agencies implement them.
Business leaders would do well to take note — these objectives are applicable to their own organizations.
DoD DATA STRATEGY
The objectives set out by DoD come together to form a sort-of word: VAULTIS.
Visible. Authorized data consumers can discover the existence of data that is of particular interest or value. Data managers are “responsible and obligated to make their data visible to authorized users by identifying, registering and exposing data in a way that makes it easily discoverable across the enterprise.”
Accessible. Data consumers can obtain the data they need when they need it. Data can even be pushed automatically to interested and authorized users. Of course, security controls are required to ensure data access follows the laws and policies.
Understandable. Data consumers can find descriptions of data to recognize the content, context and applicability. “Without proper context, interpretation and analysis of the data could be flawed, resulting in potentially fatal outcomes,” the DoD writes.
Linked. Data consumers can exploit complementary datasets through innate relationships. DoD will implement globally unique identifiers so “data can be easily discovered, linked, retrieved and referenced,” and also deploy common metadata standards.
Trustworthy. Proper tagging, maintenance and data quality management will give data consumers confidence in the soundness of the data they’re using to make decisions.
Interoperable. “Achieving semantic as well as syntactic interoperability using common data formats and machine-to-machine communications accelerates advanced algorithm development and provides a strategic advantage,” DoD writes.
Secure. Data consumers know that data is protected from unauthorized use and manipulation.
Related reading: Zero trust: How to reach maximum security
HOW TO BECOME DATA CENTRIC
You might think that becoming a data-centric organization starts with the data. That’s important, but it’s not the first step. If an organization is new to data centricity, it could benefit from working with a knowledgeable and experienced consulting firm like SDV INTERNATIONAL that can help with the nuances of this complicated process.
The commitment to data-centricity — the acknowledgement of data as a permanent asset — must come from the very highest levels of the organization and be sustained for the long run. What follows is a simplified overview of the process of becoming data centric.
STEP 1: DEFINE THE “WHY”
Data from myriad sources must be continuously gathered, cleaned, analyzed, and then put to use. And that’s where we start: with how the data will be used. In other words, with a data strategy.
Why does your organization need to become data centric? For the military, as we saw earlier, it’s about creating “a lethal and effective Joint Force that, combined with our network of allies and partners, sustains American influence and advances shared security and prosperity."
Is the goal to improve sales and customer service? Is it to become more efficient operationally? Is it to gain more insight into marketing and financial decisions? Is it to ensure regulatory compliance? (Or is it all of the above?)
STEP 2: DEFINE THE “WHO”
The next question is: What roles are needed to execute the data strategy, and do we have the right people? Many organizations skip this step. This is not the time to give the IT department another job to do. They may excel at data processing, but putting data at the heart of an organization is a far different skillset.
Becoming data-centric requires not only a high level of data literacy, but data leadership that drives the philosophy of data centricity through the organization. This is why many organizations are hiring chief data officers to sit at the executive table with the president and other decision makers.
STEP 3: DEFINE THE “WHAT”
Now it’s time to inventory the “what” — all the data sets that exist within the organization, along with their hierarchies, structures and principal users. It’s also important to document the processes and transportation systems along which the data travels and how much of it lives in silos.
Another aspect of “what” relates to the business processes the data supports and the employees that use it. Going back to the data strategy, an organization doesn’t pursue data centricity for its own sake but to achieve business and strategic goals. All along the way, it is essential to keep a business view of the data assets with definitions that make sense to business users.
STEP 4: DEFINE THE “HOW”
This is where we get to architecture. Data-centric architecture requires up-to-date data and a single source of truth (SSOT). The SSOT involves creating a single data model for all personnel and information systems. This eliminates information silos and multiple instances of data. As data is created or changed, it is made available to all connected information systems and authorized data consumers.
This is also where processes are documented and continuously updated so the right data is collected and handled in a way that ensures accuracy and security. If one doesn’t already exist, a quality management system (QMS) would be a big help.
Related reading: Quality management systems: Standardization for better outcomes
STEP 5: MAKE IT SUSTAINABLE
People come and go in organizations. Standards for best practices and governance need to rely on documentation and not on individuals.
Data governance requires a policy framework designed by stakeholders to outline how the organization will manage data. An actionable plan must define tools and technology and assign responsibility to data stakeholders. Leaders can set the goals and framework for data initiatives. The organization also must document its commitment to ongoing assessments of policies and its plans for achieving business objectives.
COMMON OBSTACLES TO OVERCOME
The five steps outlined above represent a best-case scenario. In business, as in life, most things are easier said than done. Here are some of the potential roadblocks.
Dirty data. At a 2002 Information Quality Conference, a telecom company was revealed to have recovered over $100 million in “scrap and rework” costs, a bank $60 million, and a government agency $28.8 million, all because their data wasn’t up to snuff.
Data silos. Data has more of a chance of being dirty if it’s living in silos in legacy IT systems and managed by different parts of the organization, even different individuals.
Data inconsistency. When there are silos, it’s not uncommon for the same data request to elicit different results. In other words, there is no “single source of truth.”
Unstructured data. IBM estimates that up to 85% of the data within an organization may be unstructured, living in Word documents, Excel files, videos, PDFs and other sources.
Application-centric IT departments. Whether the department is called data processing, information management or information technology, it tends to focus on applications rather than data management and delivery.
Poor relationships between business and IT. How often have you heard business leaders say that IT just doesn’t understand the business? And IT leaders say the business people don’t understand technology?
Lack of centralized data governance. This could be the result of turf battles or just a lack of understanding of how important it is to have consistent policies around the gathering, securing and usage of data.
DATA AT THE HEART OF THINGS
Organizations that do not keep up with data tech advances will lag behind their peers, sailing wind-powered ships when they could be flying jets. SDV INTERNATIONAL helps federal agencies, state governments and other organizations improve their data capabilities and help them become data centric.
To give one example of many, SDV INTERNATIONAL integrated data for the U.S. Navy from numerous information systems at its medical treatment facilities around the world to improve payment processing for medical treatments.
SDV INTERNATIONAL’S strategic benefits to customers include: expanding cloud migration; implementing zero trust architecture; enabling secure, rapid software development; accelerating data-driven decisions; enhancing cloud operations; developing the cloud workforce; and providing cost transparency and accountability.
Learn how we can serve your organization by visiting sdvinternational.com, or contacting us at 800-738-0669 or info@SDVInternational.com.