Cookie Settings

By clicking "Agree," you consent to the storage of cookies on your device to enhance site navigation, analyze site usage, and support our marketing efforts. For more information, please refer to our Privacy Policy.

Reject
Agree
Glossary
Data Culture & Processes

Data literacy

Data literacy is a key competence for companies that want to be successful in the digital age.

Meaning in detailContact us now

What is data literacy?

In a world dominated by data, data literacy, the ability to read, understand, analyze, and communicate data, is more important than ever. It is the basis for well-founded decisions, optimized processes and data-driven innovations.

Data literacy is a key competence for companies that want to be successful in the digital age. By promoting data literacy, companies can strengthen their competitiveness, improve decision-making, and drive innovation.

Why is data literacy important for companies?

Data literacy is not an option, but a necessity for companies that want to remain competitive in the digital age. It offers companies:

  • Improved decision making: With the ability to analyze and interpret data, companies can make more informed decisions based on facts and insights.
  • Increased efficiency: Data analyses can optimize processes, save resources and increase productivity.
  • Lower risk: Data literacy can help reduce risks associated with data loss, data breaches, and bad decisions.
  • Fostering innovation: Data analytics can provide new insights and innovations that create competitive advantages.
  • Strengthened collaboration: Sharing and understanding data promotes collaboration and teamwork.

How can you promote data literacy in companies?

Promoting data literacy in companies requires a comprehensive approach that covers various levels and areas:

  • Management Commitment: Top management must recognize and actively promote the importance of data literacy.
  • Training and continuing education: Employees should be trained in data literacy skills that are tailored to their respective roles and tasks.
  • Data-friendly culture: A corporate culture must be created that supports data usage and analysis.
  • Technological infrastructure: Companies must provide appropriate tools and technologies to facilitate the use of data.
  • Continuous improvement: Data literacy is an ongoing process that must be continuously adapted and developed.

Our services related to Data culture and Data organization. They support the holistic approach of a long-term data learning culture in the company.

Note: Our team benefited from the support of AI technologies while creating and maintaining this glossary.

Abstrakte Form eines Pfades

Contact now
Abstrakte Form eines Pfades

Do you have questions around
Data literacy
?

Let´s TalkContactFollow us on LinkedIn

Passende Case Studies

Zu diesem Thema gibt es passende Case Studies

Alle Case Studies entdecken
Babymarkt is data-driven
eCommerce
5
Minuten Lesedauer

From data audit to individual architecture sketch

eCommerce thrives on data — babymarkt wanted to make even better use of it.
Couple reading a newspaper
Medien
5
Minuten Lesedauer

MediaPrint reinvents itself in terms of data technology

The publisher wanted a clear structure and the development of a data culture.
Hub & Spoke Model and Data Roll Out
Medien
4
Minuten Lesedauer

Hub & Spoke Model and Data Roll Out

Program developed for organization, architecture, culture; mission & vision for data organization

Which services fit to
Data literacy
?

<svg xmlns="http://www.w3.org/2000/svg" width=" 100%" height=" 100%" viewBox="0 0 32 32"><path fill="currentColor" d="m16 23l-2.139-1.013A5.022 5.022 0 0 1 11 17.467V11h10v6.468a5.021 5.021 0 0 1-2.861 4.52zm-3-10v4.468a3.01 3.01 0 0 0 1.717 2.71l1.283.608l1.283-.607A3.012 3.012 0 0 0 19 17.468V13zm-8.88 4.656A2 2 0 1 0 1 16c0 .789.46 1.464 1.124 1.79a13.946 13.946 0 0 0 9.569 11.535l.614-1.904a11.954 11.954 0 0 1-8.186-9.765m25.205 2.652l-1.904-.616a11.954 11.954 0 0 1-9.765 8.187A2 2 0 1 0 16 31c.789 0 1.464-.46 1.79-1.124a13.947 13.947 0 0 0 11.535-9.568m.551-6.098a13.946 13.946 0 0 0-9.569-11.535l-.614 1.904a11.954 11.954 0 0 1 8.186 9.765A2 2 0 1 0 31 16a1.992 1.992 0 0 0-1.123-1.79M16 1c-.789 0-1.464.46-1.79 1.124a13.947 13.947 0 0 0-11.535 9.568l1.904.616a11.954 11.954 0 0 1 9.765-8.187A2 2 0 1 0 16 1"/></svg>

Data Governance

Not boring or stuffy at all: Aligning ideas and rules

<svg width=" 100%" height=" 100%" viewBox="0 0 62 62" fill="none" xmlns="http://www.w3.org/2000/svg"> <g clip-path="url(#clip0_5879_2705)"> <path d="M7.75 50.375H15.5V58.125H7.75V50.375ZM27.125 50.375H34.875V58.125H27.125V50.375ZM46.5 50.375H54.25V58.125H46.5V50.375ZM48.4375 31H32.9375V27.125H29.0625V31H13.5625C12.5351 31.001 11.5501 31.4096 10.8236 32.1361C10.0971 32.8626 9.68853 33.8476 9.6875 34.875V46.5H13.5625V34.875H29.0625V46.5H32.9375V34.875H48.4375V46.5H52.3125V34.875C52.3115 33.8476 51.9029 32.8626 51.1764 32.1361C50.4499 31.4096 49.4649 31.001 48.4375 31ZM17.4375 3.875V23.25H44.5625V3.875H17.4375ZM21.3125 7.75H25.1875V19.375H21.3125V7.75ZM40.6875 19.375H29.0625V7.75H40.6875V19.375Z" fill="currentColor"/> </g> <defs> <clipPath id="clip0_5879_2705"> <rect width="62" height="62" fill="currentColor"/> </clipPath> </defs> </svg>

Data Organization

So that the handling and responsibility of data is structured and everyone knows its responsibilities.

<svg width=" 100%" height=" 100%" viewBox="0 0 62 62" fill="none" xmlns="http://www.w3.org/2000/svg"> <g clip-path="url(#clip0_5879_2976)"> <path d="M60.0625 58.125H56.1875V52.3125C56.1875 50.7709 55.5751 49.2925 54.4851 48.2024C53.395 47.1124 51.9166 46.5 50.375 46.5H42.625C41.0834 46.5 39.605 47.1124 38.5149 48.2024C37.4249 49.2925 36.8125 50.7709 36.8125 52.3125V58.125H32.9375V52.3125C32.9375 49.7432 33.9581 47.2792 35.7749 45.4624C37.5917 43.6456 40.0557 42.625 42.625 42.625H50.375C52.9443 42.625 55.4083 43.6456 57.2251 45.4624C59.0419 47.2792 60.0625 49.7432 60.0625 52.3125V58.125ZM46.5 23.25C47.6496 23.25 48.7734 23.5909 49.7293 24.2296C50.6851 24.8683 51.4301 25.7761 51.87 26.8382C52.31 27.9002 52.4251 29.0689 52.2008 30.1965C51.9765 31.324 51.423 32.3597 50.6101 33.1726C49.7972 33.9855 48.7615 34.539 47.634 34.7633C46.5065 34.9876 45.3377 34.8725 44.2757 34.4326C43.2136 33.9926 42.3058 33.2476 41.6671 32.2917C41.0284 31.3359 40.6875 30.2121 40.6875 29.0625C40.6875 27.5209 41.2999 26.0425 42.3899 24.9524C43.48 23.8624 44.9584 23.25 46.5 23.25ZM46.5 19.375C44.584 19.375 42.711 19.9432 41.1179 21.0076C39.5248 22.0721 38.2831 23.5851 37.5499 25.3553C36.8167 27.1254 36.6248 29.0732 36.9986 30.9524C37.3724 32.8316 38.2951 34.5578 39.6499 35.9126C41.0047 37.2674 42.7309 38.1901 44.6101 38.5639C46.4893 38.9377 48.4371 38.7458 50.2072 38.0126C51.9774 37.2794 53.4904 36.0377 54.5549 34.4446C55.6193 32.8515 56.1875 30.9785 56.1875 29.0625C56.1875 26.4932 55.1669 24.0292 53.3501 22.2124C51.5333 20.3956 49.0693 19.375 46.5 19.375ZM29.0625 42.625H25.1875V36.8125C25.1875 35.2709 24.5751 33.7925 23.4851 32.7024C22.395 31.6124 20.9166 31 19.375 31H11.625C10.0834 31 8.605 31.6124 7.51494 32.7024C6.42489 33.7925 5.8125 35.2709 5.8125 36.8125V42.625H1.9375V36.8125C1.9375 34.2432 2.95814 31.7792 4.7749 29.9624C6.59166 28.1456 9.05572 27.125 11.625 27.125H19.375C21.9443 27.125 24.4083 28.1456 26.2251 29.9624C28.0419 31.7792 29.0625 34.2432 29.0625 36.8125V42.625ZM15.5 7.75C16.6496 7.75 17.7734 8.0909 18.7293 8.72958C19.6851 9.36827 20.4301 10.2761 20.8701 11.3382C21.31 12.4002 21.4251 13.5689 21.2008 14.6965C20.9765 15.824 20.423 16.8597 19.6101 17.6726C18.7972 18.4855 17.7615 19.039 16.634 19.2633C15.5064 19.4876 14.3377 19.3725 13.2757 18.9326C12.2136 18.4926 11.3058 17.7476 10.6671 16.7918C10.0284 15.8359 9.6875 14.7121 9.6875 13.5625C9.6875 12.0209 10.2999 10.5425 11.3899 9.45244C12.48 8.36239 13.9584 7.75 15.5 7.75ZM15.5 3.875C13.584 3.875 11.711 4.44316 10.1179 5.50764C8.52481 6.57211 7.28314 8.08509 6.54992 9.85525C5.81669 11.6254 5.62485 13.5732 5.99864 15.4524C6.37244 17.3316 7.29508 19.0578 8.6499 20.4126C10.0047 21.7674 11.7309 22.6901 13.6101 23.0639C15.4893 23.4377 17.4371 23.2458 19.2072 22.5126C20.9774 21.7794 22.4904 20.5377 23.5549 18.9446C24.6193 17.3515 25.1875 15.4785 25.1875 13.5625C25.1875 10.9932 24.1669 8.52916 22.3501 6.7124C20.5333 4.89564 18.0693 3.875 15.5 3.875Z" fill="currentColor"/> </g> <defs> <clipPath id="clip0_5879_2976"> <rect width="62" height="62" fill="currentColor"/> </clipPath> </defs> </svg>

Data Culture

A culture and the processes that make everything possible together.

Follow us on LinkedIn

Stay up to date on the exciting world of data and our team on LinkedIn.

Follow on LinkedIn
Abstrakter Pfad des Data Institutes