Four cornerstones of Data Driven company

We have developed a unique The DDC-CMM framework© that can help you to get step-by-step to your „Data Driven Company“ target.

We believe these are the key cornerstones on which you can build Data Driven company.

• forge company-wide DATA CULTURE & STRATEGY
• empower business users with proper DATA LITERACY & SKILLS
• leverage state-of-the-art technologies into robust DATA PLATFORM & SERVICES
• ensure DATA CREDIBILITY & CLARITY through data asset management and governance

Data Culture

Data driven company must acknowledge data as significant asset across the organization — and manage it accordingly. To manage, you need to measure (quality, relevancy, impact on business etc.) and quantify economic benefits — like a balance sheet asset. What is information cost or market value, contribution to a revenue stream can help with decisions on investments. Though this is as well about organization strategy supporting data sharing/collaboration, data value perception. Change to forward looking attitude — from ex post analysis to predictions. And most important, outweighing facts over intuition. Data illiteracy should not be hindering data driven decisions. Commitment starts with leadership and culture transformation happens best top-down.

Data Value Perception

Data are organizations key asset such as a good chef for a good restaurant. Data can bring differentiation on the market. Data management & development is part of organizations strategy.

Decision Making

Decisions are made mainly based on facts = data over intuition. Data are used not only for understanding past, describing present, but as well to predict future.

Data Practice

Organization is on high level of data literacy = is consciously using relevant data to improve its daily business.

Data Collaboration

Data assets are available across the company, minimizing “local silos”, “different language” and redundant development.

People & Skills

Data literacy cannot be only „data scientist“ responsibility in Data driven company. Organization needs to build proper attitude and motivation in teams. Provide people with useful tools and trainings. Organization as well needs to extend the level of data literacy and governance across the teams and maximize self-service approach. Data need to be presented in a simple, storytelling way. There were already a lot of investments into technology — it’s a good time now, to invest into people and skills.

Data Attitude & Motivation

I do understand organization strategy and what is expected from me personally. I know, that creative use of relevant data can help me to do my work better. I understand benefits of utilizing data in daily business.

Data Literacy & Creativity

I can analytically work with data to support the decision process. I use data in daily business, because it can bring the difference. I do my decisions based on data.

Data Tools Proficiency

I know what tools and techniques are available for data analysis. I am proficient user of tools which are relevant for my daily business.

Data Governance Leverage

I do understand, that there are data assets available in my organization. I know where to find data I need or where to ask for new ones. I understand importance of having data in a good quality and described in a common language.

Data Services

To second Culture and People, Data driven company must provide data „as a service“, and not as a “product”. Users must have scalable access to data in a form they understand. Data infrastructure must create transparent access to data independently from data type, form, origin or stability. Users need to have proper tool set, which supports their daily business and data creativity. Data ecosystem of organization should be flexibly extendable — not only by internal data, but by data from business partners or even outer world as well.

Data Infrastructure

Data infrastructure must provide flexible access to all data assets (regardless of type, form, origin…) and possibility to quickly extend them with new ones. Real time processing, virtualization, sharing, transparency and scalability are key factors hand in hand with access management. Data need to become part of organization processes.

Enablement Technologies and Accelerators

Together with scalable data infrastructure, data for users must be available where it is needed and how it is needed = “real-time”. To leverage usage of data sandboxes, data boutiques, gateways, automated decisions etc. are to be introduced and utilized.

Personal Data Tools

Personal Data tools need to overcome current complexity of using the data. There need to be a relevant set of tools (including trainings) for different groups of users. Starting with low2no code and ending with DS, ML and AI.

Data Ecosystem

To really utilize data, the borders do not lay within your organization. Data ecosystem consist of multiple layers. Transparently to the user, you can use data from 1) your organization, 2) your ecosystem (Customers, Suppliers, Partners…) 3) outside of your business (registers, data marketplaces, social media listeners etc.).

Data Credibility

Data credibility is on one hand built based on FAIR principles supported by strong ownership. Data need to be findable, accessible (in time), interoperable and reusable. Rendered in business terms, accompanied with story-telling. On the other hand, it is crucial to keep Data Governance principles as well. Without delivering data in time and in proper quality, well documented, data will lose its credibility very fast and shadow solutions will strive. Too much flexibility can inhibit organization in the end.

Data Democratization

To allow users to become data driven, organization need to provide them with relevant data and description. In time, where they need them and how they need them. Besides infrastructure and tools, sharing and collaboration on existing data assets needs to be utilized.

Data Management

Mastering the data is the key factor to build a common understanding (and stories) and trust of users. Having “one language” is a must if organization wants to utilize sharing and reusing of existing data assets to prevent siloed solution and problems with different interpretation. Too much flexibility proved to be contra productive.

Data Quality

Data quality is a second key factor for building a trust in outcomes/stories and leveraging benefits. Data must not only be correct, complete, up to date, consistent etc. It also need to be delivered in time, in correct frequency and so on.

Data Ownership

Strong role in utilization of existing data assets takes ownership. Owner of a data asset is on one hand responsible for metrics definition, proper description, business meaning and quality of the data. On the other hand (s)he supports reusing of existing assets by other users by becoming a partner for them, helping with use-cases/stories.