Modern complex organizations generate and store more and more data each day. As our access to data expands exponentially, detailing every nook and cranny of the org and its ecosystem, so does our exposure to data jargon.
It leaves a leader to wonder — what is a data centric organization anyway?
“Well, the data is in the center, obviously…” you might say to yourself. But in the center of what exactly?
“In the center of decisions,” one might suppose. “So, we just need to fast track data to those central to the organization — that’d be the decision-making leadership team, of course — and as everyone generates heaps of data daily it can simply be pushed into a handy BI dashboard so the leadership team can tap it as needed when they issue their next strategic directive. We’ll just have the ops guy handle that bit and we’ll be set by next week. This will be easy.”
Just like that, you’ve set yourself up for data failure.
The 4 Levels of Data Maturity
Organizations fall into 4 levels of data maturity, which we will cover in the sections ahead:
- The Intuition Based Organization
- The Data Informed Organization
- The Data Driven Organization
- The Data Centric Organization
Within each level, how management uses data, the role of the Data Team, and the pitfalls the organization may encounter differ. If the above story sounds familiar, read on and consider first what a data centric organization is not.
Let’s start at level 1.
The Intuition Based Organization
The first level of data maturity is the intuition based organization.
At this undeveloped stage, management makes decisions based on gut feel. Data is not in the picture for the team. Leaders in this type of organization may be hyper-focused on the creation of their vision — often to the exclusion of everything else.
The intuition based organization lacks a feedback cycle. It becomes easy to forget that other people will be using your product, and fails to collect feedback to check assumptions — a dangerous state to be in. What may feel intuitive to you may not be intuitive to others.
The culture will be political and intimidating. With no data to inform decision making, it can be easy to mistake confidence for competence. The loudest voice will win debates, whether or not it’s the best idea in the room.
This data maturity level is common for brand new orgs. It may be tempting to pass it off as expected “startup” behavior, but we disagree. It’s important to move out of this stage as quickly as possible, or risk spending weeks or months building a product that is useable yet useless.
For established orgs, you risk losing any trust and reputation you have built with your customers. One day they’ll be gone for a new service that has collected the data and shows their needs have been heard.
The Data Informed Organization
The second level is the data informed organization.
Data informed organizations collect and store small amounts of data as is convenient. Data is neither irrelevant, nor is it a priority. Management knows where to find it when they need it, and they access it ad hoc. It’s a resource to be mined when the moment requires.
While you may avoid the occasional pitfall with ad hoc data analysis, this is still a very risky level of data maturity. Impromptu data analysis does not produce scientific rigor or empirical decision making.
And yet, decisions will be made with a high level of confidence.
Ultimately, in the data informed organization, data is used as a tool to confirm bias rather than to produce reasoned decisions. Further, management may unwittingly encourage false confidence as an accepted behavior, or even a cultural value, to the rest of the team. As a result, politics will circle around access to gated data stores. Those with access will tap data as desired, delivering “truths” to those without.
That isn’t to suggest the data informed organization will never hit upon valuable insights — they will.
But making decisions based on ad hoc analysis is akin to timing the stock market. Sometimes you’ll win. Over the long term you’ll lose.
Organizations succeed only when successful bets outweigh unsuccessful bets. This cannot be achieved through ad hoc analysis and requires a consistent and rigorous process.
In the end, results for the data informed org may not stray far from the intuition based org. Decline will perhaps occur more slowly, maybe over a few years rather than weeks or months, before you’ll no longer be able to win against more clever competitors.
The Data Driven Organization
The third level is the data driven organization.
At this level, data is recognized as a highly valuable asset. As a result, the organization employs an IT or Data Team dedicated to maintaining applications which collect, store, and present data from each area of the company. Strategy revolves around these applications, enabling management to mine for insights and trends proactively. Data is used with intention.
This info transparency, intentional inspection, and the resulting adaptation produces an iterative process with real scientific rigor.
While no outcome can ever be guaranteed, in the data driven organization we can avoid making decisions based on cognitive biases and feel assured we’ve done everything possible towards making successful bets. Now we’re getting somewhere!
So, what’s the catch? If being data driven is so great, why aren’t more organizations doing it?
As organizations scale — discovering fresh opportunities for data collection, adding more staff and new departments entirely, implementing emerging applications and technologies, etc — data lives in silos that become more and more difficult to access and integrate. It becomes necessary to implement a middleware application so management is able to mine the data more effectively.
If you’ve tried to achieve this level of data integration in your org, you already know the problem: It’s expensive.
Over time the organization struggles to change and loses its agility. Even small changes to systems this complex can be prohibitively expensive to budgets and time.
The data itself takes a backseat, as decisions revolve around applications and the continuous integration needed to keep them functional. Data is no longer the most important thing — applications are.
Is there a better way?
The Data Centric Organization
The final level of data maturity is the data centric organization.
Here, data culture is the primary focus. All levels of the organization take ownership of the data. Everyone is trained on tools and techniques for harvesting and understanding data, enabling those closest to the work to make the best decisions possible.
Strategy no longer centers around applications. Rather, applications access data from the shared model as needed, truly putting data at the heart of the business model.
Optimizing for quality is key. With bad data comes bad decisions. This means data is clean, timely, and consistently available across the organization, equally accessible by all.
But if everyone has free access to the organization’s data, what is the role of management? And what of the Data Team?
Becoming a data centric organization is a paradigm shift for many leaders. Often companies who believe they are data centric are actually data informed or data driven. Others may recognize the costly challenge of application-centricity, but still struggle to move to a data centric architecture.
Their barrier is in seeing the needed organizational changes as a technical obstacle, rather than seeing it as mindset and inertia obstacles.
To Become Data Centric, Start From The Top
To become data centric, the role of management must shift from transactional to transformational leadership.
Management must do more than “manage”. They must become true leaders and champions of the essential role that data plays — in understanding customers, making sound decisions, the value of empiricism and experimentation.
In the data centric organization:
- Management will hire and empower a Data Team which can architect and maintain a centralized, org-wide, open data resource.
- Management will arm all individuals with equal opportunity tools, training, and resources for interacting with the data.
- Management will promote a culture of experimentation, reinforcing the value of using data to test hypotheses with empiricism.
- Management will build business processes which serve the team and establish the organization as a data hub.
To become data centric, a shift in the status quo is required. This may sound intimidating, but the reality is simple: Those who put data at the heart of their organization are the ones who will innovate and pull ahead of the competition.
As modern organizations collect exponentially more data each day and increase their competency with data tools and analysis techniques, intuition based and data informed organizations will no longer be able to compete in the market.
Data driven organizations may seem to come out ahead initially, but will fail to scale as the cost of application-centric integration becomes unmanageable.
In the end, data centric organizations emerge as the winners by building a culture which can support an innovative team and long term data ambitions at scale.
Are you a data centric organization?