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LEARN TO SPEAK DATA #5​ ​ Do you easily get lost in the jungle of data terms? To help you out, we put together a data glossary so you can comfortably take a deep dive into the fascinating world of data. You can find the previous posts in the feed! ​ ​ 1️⃣ The difference between artificial intelligence and analytics. Artificial intelligence is an umbrella term for solutions that are regarded as intelligent. Search engines, smart speakers, and self-driving cars are all examples of using artificial intelligence. AI is often associated with system autonomy and independence from human decision-making. Analytics, on the other hand, refers to data-based reporting and visualization produced for human decision-making.​ ​ 2️⃣ DataOps (data operations) refers to an operating model where various personnel roles and technologies are used to manage data pipelines automatically and to support data-driven business development.​ ​ 3️⃣ APIs and API Management. API is an application programming interface that allows parties to exchange data or initiate transaction in a system or a service. APIs enable faster development of new business services, improve efficiency and decrease costs related to integration landscape.​ ​ For the full data glossary, visit the link in bio! #data #dataglossary #digitaladvantage #datainsiders​

tietoevry 10 Aug

PRO'S AND CON'S ON REMOTE OFFICE👨🏽‍💻👉🏻 Kaja Drews, Consultant 📢: "The greatest part is to be able to wear clothes I normally don’t use! You know, the ones that may not be accurate enough for the office, but that I love wearing outside of work. I do believe that our meetings have become more efficient and it takes less time to attend a meeting: all you have to do is to click the “join meeting” button compared to the office where you may find a spare meeting room and often go to different floors! I find it challenging not to have an ergonomic office chair or proper workspace at home – and that I am most of the time physically alone".🙏🏻💛 ✅What are your feelings about remote offices? Please share your thoughts with us!😌 ✅Check out our hacks for how to master the art of working from home if you want some inspiration: https://www.tietoevry.com/en/blog/2020/06/13-hacks-to-really-master-the-art-of-remote-work/

tietoevry 10 Aug

• We are going back to work • PRO'S AND CON'S ON REMOTE OFFICE 👨🏽‍💻👉🏻 Robin Dronsfield, Head of EUS Advisory: 📢 ”Pro’s being that I save a lot of time and can get up later in the morning (I am a late sleeper✌🏽). I also appreciate the fact of being home as soon as my workday is over. I work with a greater focus until my tasks are finalized compared to when I am at the office. I also perceive digital meetings as more inclusive, it is easier for everyone to be part of discussions when using video🎥. On the downside, remote work requires a greater amount of meetings – at least for me who also has a social need to thrive at work. And I sit, even more, lacking activity and it is sometimes harder to log off from work when your workspace is also your home"💻🎧🚶🏼. ✅Do you agree with Robin? What are your feelings about working from home?🤓 ✅Check out our hacks for how to master the art of working from home if you need some inspiration: https://www.tietoevry.com/en/blog/2020/06/13-hacks-to-really-master-the-art-of-remote-work/

tietoevry 04 Aug

LEARN TO SPEAK DATA #4 ​ Do you easily get lost in the jungle of data terms? To help you out, we put together a data glossary so you can comfortably take a deep dive into the fascinating world of data. You can find the previous posts in the feed! ​ 1️⃣ Data and analytics platforms. A modern, cloud-based data and analytics platform combines traditional reporting with modern analytics and data scientists' services. It provides a platform, for example, for data-based applications that use artificial intelligence.​ 2️⃣ Industry 4.0 is a vision of an advanced industry where ecosystems, the industrial Internet, modern technology, and new business models are leveraged. The vision is based on the digital transformation of traditional manufacturing and production methods.​ 3️⃣ Predictive analysis. Machine learning and statistical methods allow us to model future events based on the data of previous events. Such modeling is called predictive analytics. Typical applications for predictive analytics can be, for example, customer attrition expectations, financial data predictions, and predicting machinery maintenance needs.​ Hungry to learn more? Check out the whole data glossary via link in bio! #data #dataglossary #digitaladvantage #datainsiders​

tietoevry 31 Jul

LEARN TO SPEAK DATA #3 ​ ​ Do you easily get lost in the jungle of data terms? To help you out, we put together a data glossary so you can comfortably take a deep dive into the fascinating world of data. You can find the previous posts in the feed! ​ ​ 1️⃣ The difference between a data warehouse and a data lake. ​ A data warehouse supports the organization's traditional core functions and obtains answers to defined questions from known source data. A data lake supports a more predictive and experimental approach. A data warehouse is mainly for structural information processing. A data lake enables the processing of all kinds of data in the organization.​ ​ 2️⃣ Data lifecycle refers to the different stages of data elements and data resources from the creation of information to its destruction. The stages can include storing, warehousing, transferring, using, and archiving the information.​ ​ 3️⃣ A data pipeline is a controlled function for data processing and data product creation that brings business value. A data product can be, for example, a report or a prediction produced by a machine learning algorithm that’s used via an interface.​ ​ Are you hungry to learn more? Check out the whole data glossary via link in bio! #data #dataglossary #digitaladvantage #datainsiders

tietoevry 24 Jul

LEARN TO SPEAK DATA #2 ​ Do you easily get lost in the jungle of data terms? To help you out, we put together a data glossary so you can comfortably take a deep dive into the fascinating world of data. 1️⃣Data management in manufacturing. The business of manufacturing companies depends on building equipment that is either sold or rented to a customer. Such companies collect plenty of information about their business operations. If this data is managed properly, the life cycle of devices can be accurately modeled.​ ​2️⃣ Data governance is about data ownership. The owner of a company's business units, equipment, and properties manages the usage of the company's assets and strives to maximize its business benefits. This should also be the case with company-owned data sets. The owner of a data set is responsible for ensuring the data is of good quality and making sure the user rights comply with the set rules. ​ ​3️⃣ Data architecture is a part of the overall architecture and can refer to several perspectives. It often relates to the artefacts of data architecture on multiple abstraction levels, such as data models, definitions, and descriptions of information flows and metadata.​ ​Are you hungry to learn more? Check out the whole data glossary: link in bio #datainsiders

tietoevry 21 Jul
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