Building a Data-Centric Company

In today’s tech-savvy world, data is being generated at an unprecedented rate both outside of organizations and within. We are all involved in the creating/storing and reading of data on a day-to-day basis. In business, we are constantly engaged in the attempt to organize and harmonize the information from a variety of independent sources, in order to get a clear view of the entire organization. However, because most of these data sources are operated in silos — each focused on specific business purposes — there is often little concern with how the data is used after it leaves the owning business unit. This can be a huge barrier to an organization looking to grow. The solution to successfully combating this is to foster and nurture a data centric company.

A data-centric company is an organization in which its people, processes and technologies are designed and implemented with the clear goal of generating and utilizing clean, relevant information — with the collaborative goal of furthering the business success of the organization.

The Information Evolution Model 

As a company grows, so too does the challenge to manage the data that the company relies on to make informed decisions. This ability to manage information is often described as the data maturity model, or as it’s explained in Information Revolution, the information evolution model.


The Five Evolutionary Stages of the Information Model

In order to achieve a data-centric culture at your company, it is critical to evaluate where on the spectrum of this model your company falls, as this will dictate what strategies you should employ. The five levels are explained below.

Operational Level

The operational level is characterized by individual data “ownership” and control, applied to tackle day-to-day functional issues. For instance, the “Operational Model” is characterized by information being stored on individual PCs as opposed to on networked servers. Information is duplicated, not shared between groups, or inconsistently extracted. Companies at the operational level often are hamstrung by the fact that key information is controlled by so-called “data Mavericks” who are often touted as heroes in the company as they are the only ones who “know the real truth” regarding a company’s specific information questions.

Consolidation Level

The consolidation level is where individual-level perspective is replaced by departmental- or functional-level standards, metrics and perspective.

Integration Level

The integration level expands consolidation into an enterprise-wide view.

Optimization Level

The optimization level closely aligns the organization with its markets and gains market leadership by applying predictive insights about customers, suppliers and business partners.

Innovation Level

The innovation level is where sustainable growth and most revenue potential is fueled by continuing creativity and renewal.

Four Critical Dimensions for Evolving Through the Information Evolution Model

Transforming your business into a more mature data-centric organization requires that you are prepared to invest in four key assets:  

  1. People
  2. Processes 
  3. Infrastructure 
  4. Culture


The key to creating and fostering a data-centric mentality throughout an organization starts with people. Everyone needs to understand the impact that incorrect information can have both on the organization as a whole and their specific job security (strategies based on incorrect information can be very costly to the company). Employees often focus on their day-to-day tasks without necessarily understanding how that can directly contribute to the direction and success of the organization. Implementing BI and explaining the way that the data flows throughout the company can help to change this mindset.   

At Hitachi Solutions, when implementing a business intelligence project, for example, we start by identifying the following stakeholders:

  • Primary stakeholders will usually include executive level involvement to initiate and monitor the success of the project
  • Secondary stakeholders are often analysts/managers that currently create reports/dashboards

However, this is where most project engagements stop. Often overlooked are the upstream employees who use the source systems and generate the data. If we identify and involve those employees in the project as additional stakeholders, they begin to understand both the importance of how their roles contribute to BI and the impact that they have on the success of other departments.

By establishing this tangible relationship between the upstream data generators and downstream consumers, we begin to establish an awareness of how work in one department impacts the success of other departments and in turn the organization. The ways we have successfully approached this issue have been through communication with the front line employees and their supervisors about their roles and responsibilities from a data perspective. Some of the questions that help to facilitate conversations include:

  • Who enters the data?   
  • What does the system allow them to do? (i.e., Customer ZIP code is not a mandatory field so we never fill it in)   
  • What business decisions are made based on this source system data?   
  • How will this data be combined with other disparate source systems?   
  • Does the source system even allow them to capture all the data they would like to?  

By asking the above questions we can get a better sense for how these source systems operate, which then allows us to redesign the way that data is generated and captured within the system. This can be as simple as ensuring that all customer data is filled out, not just name and address, giving a more complete and usable view of a customer.   

As part of engaging key employees to help improve processes we need to identify “guardians” or “champions” of each source system. These are often mangers or someone in a supervisory capacity. These “champions” are the gatekeepers of their individual source systems, meaning they know the system inside/out. They are the people entering data into that system and they understand the downstream effects of that data on the organization. And if they do not understand this downstream effect, this type of a program is an opportunity to educate and change the mentality to one that is more collaborative, a data-centric focus across the organization.  

When changes need to be made to the system, we now have an identified group of individuals responsible for validating and approving changes, as well as individuals who need to be consulted and made aware of pending impacts. This prevents breaks in the data flow that can cause major headaches for the consumers of the data. It also helps provide clear optics to trace data directly to the original source.  


The knowledge processes in a company that assist in accomplishing a business goal are usually formulated as policies, best practices or business standards on how information is used and validated.

Evaluating and redesigning processes means assessing processes by the quality, amount and completeness of the data they produce. This is best achieved by asking the right questions.   

  • What data does this process currently produce?   
  • In an ideal situation what data would this process produce?  
  • What processes do they follow to enter that data?   
  • Who executes on this process?   
  • If we change the process can the source system handle it or do we need to look into additional tools to support that? 

Through the answers to these questions and the questions we ask the key employees and stakeholders (see discussion above), we can identify whether or not the current processes meet both the day-to-day individual job responsibility needs as well as the larger organizational strategic data requirements. If not, it will be important to redesign the processes so they can fulfill both requirements. In certain situations, this may not be achievable with the current systems. For example, maybe the current system has no screen to enter customer address information. However, with the right product and functionality such as “master data management” in Microsoft Dynamics 365,  there is always a way to capture that “extra” data even if it’s not being generated in the source.   


Infrastructure is the hardware and software tools used to manage information. Generally speaking, the decision to apply technology as a means to help us manage our data is the right decision. Technology provides us with the ability to centralize, back up and secure data with ease. However, technology shows weakness when we are dealing with newer or more volatile data (e.g., data that requires manual intervention in order to achieve the necessary level of accuracy).

Often it is the data that is maintained in spreadsheets or communicated through emails, phone calls, pdfs or physical copies that is the most disputed. The reason for this is the capability to make rapid, although often poorly communicated, changes and improvements upon the data.

These are the areas where technology is the weakest. Because the process for capturing or maintaining this data is such a difficult and often ad hoc one, the inflexibility of data capture, storage or integrity management software presents more of a barrier to creating trust in the data than it provides assistance.

Take the all-too-common example of the analyst who spends a significant amount of time amalgamating information into a highly valuable dataset for his or her team. This exercise is often born of necessity or the laudable desire to create better, more accurate data. Maybe the data is difficult to collect and requires continuous effort to extract and amalgamate information from a variety of sources, such as emails, phone calls and Excel documents. Maybe the data requires a significant amount of verification, depending on active investigation and corrections to achieve the accuracy desired. Or possibly the data captured in the source systems are no longer correct and making adjustments in Excel is just easier.

In the eyes of the person maintaining or creating this data, this provides an unparalleled ability to increase the veracity of the data; however, for the data consumers, who may be several times removed from the creation process, there is little consistency or stability in the data they depend upon.

When these types of exercises to produce reliable data succeed, the organization often becomes a victim of its own achievement.  As more people in the organization consume the trusted data, maintenance becomes a full-time endeavor. At this point, the curator of this data begins to push back and say no to change requests and stops serving the “customers” that have begun to rely on this data, simply because of the insurmountable task of keeping everyone happy.

This is where a technology solution can shine the brightest. At this point, the curator of the data should be ready for help in automating or alleviating the burden of sustaining the data source and serving it to the various interested parties. Or they may have turned, out of self-preservation, to rejecting any requests outright, creating the perception of selfish motives. Regardless, in order to achieve progress, change must occur.

The paradox of using technology to help us maintain the integrity of our data is that, although the pace of change is drastically slowed down (even sometimes to the point of frustration), it forces a measured and thorough approach to change — a phenomenon that can be tremendously beneficial in some circumstances.

With the introduction of technology to help with capture, cleansing and storage, an evaluation of the significance of the data and the process for creating the data is forced. The business and IT must come together and cooperatively determine where process improvements must be made in the lifecycle of the data. Do new requirements need to be placed on the initial capture of the data? For example, does data that was traditionally communicated through emails or phone calls need to be entered properly into an application? For data that requires a robust validation process, do data integrity management solutions need to be considered, or do the capabilities to perform updates to the source data need to be built in?

The drawback is that these changes take time and a great deal of effort to get right, and it is all too easy to look back with unjustified favorability upon old methods. This is a where a consistent and sound process for determining when technology needs to be brought in becomes invaluable. Some of the questions that must be asked are:

  • How important is it to be accurate?
  • How many groups, teams or individuals rely on the data?
  • What is the effort to automate data capture and validation?

Depending on the answers to these questions, technology can replace the process fully, creating maximum rigidity and stability in the process; or a blend of technology and good process can be used to support the limitations of the other.


Culture can be thought of as the moral, social and behavioral norms for an organization that dictate how information is “thought of.”

Culture is one of the most important factors in effectively utilizing data to move your business forward. For example, simply investing in new networked infrastructure won’t guarantee that a company’s data is suddenly shared across the organization, not if the company’s culture continues to accord hero status to those so-called “data mavericks.” If the culture is geared toward rewarding individuals as opposed to recognizing “team efforts” when it comes to sharing data, a cultural shift will be needed.  A new networked infrastructure should be complemented with a reward/recognition program that encourages sharing data between departments, to illustrate the benefits of sharing information with the entire company, and ultimately creating an enterprise level information sharing strategy.

When it comes to the individuals within your organization who are primary cultivators and sustainers of culture, it is important to be aware of the behaviors you are incentivizing. As people gravitate toward the path of least resistance, you will need to encourage collaboration over isolationism when it comes to data.

Becoming a Data-Centric Organization

To take an organization from one data-centric level to the next, enterprise-wide perspective will be required when addressing each of the discussed factors: people, process, infrastructure, culture.

Ultimately, building trust in the data depends on consistency. A strong data culture knows this and looks to embrace technology where it can help reduce manual work, but also supports the development and improvement of these solutions, without the expectation that technology is or even needs to be a perfect antidote to all data problems.  

If your business is able to harness the power and benefits that data offers — defining and following through on sound processes and building the technology to support these — then you will be well on your way to fostering a data-centric culture within your organization. Microsoft Dynamics 365 is one of the latest and most innovative solutions that is helping to accelerate this data centric revolution and help forward-thinking companies implement and maintain a data-driven culture. By combining the power of CRM, ERP and BI, along with a multitude of apps, all specific to your unique industry, Dynamics 365 enables you to maintain and leverage data in one central repository — offering a 360-degree view into the business and allowing you to capitalize on the vast amount of information hidden within your currently siloed data sources.     

Finally, regardless of the technologies implemented, if an organization lacks well-thought-out processes and employees who are committed to following them, even top organizations will struggle to foster a data centric mentality.

If you are interested in learning more about how your business can implement a data centric culture, contact us. With deep industry experience and a close partnership with Microsoft, the team at Hitachi Solutions understands how technology can help businesses just like yours move forward by harnessing the power of data.

This post is published by Hannah Story on behalf of three members of the Hitachi Solutions team who collaborated on this post: Brad Lagore, Patrick Putnam, and Rowan Sciban

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