DataOps

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Data is changing the way we do business. The amount of information available to us as business owners and that we should be processing and using to our advantage is staggering. The amount of digital data, made and distributed, is 79 zettabytes. A zettabyte is one sextillion bytes. It’s a lot. By 2025, that number […]

Data is an essential topic in today’s business world. Every business owner wants to talk about innovative ideas and the value that can flow from data. The data regarding markets, customers, agencies, other companies, and publishers are considered to be valuable resources. Statistics and data are only useful if they are of high quality.

A look into how software developers are taking on more responsibility, and what this means for traditional tech rolesPhoto by Peter Gombos on UnsplashHistorically, IT departments have been structured across lines of technology, such as the app, UX, and database teams. More recently, the 2-pizza DevOps team has emerged and evangelized restructuring around lines of business. A single team is now responsible for a particular business capability end-to-end. This has ushered in the era of the full-stack developer — an engineer who can contribute to any facet of the system.

Master data management definitionMaster data management (MDM) is a set of disciplines, processes, and technologies used to manage an organization’s master data. Master data is data about business entities or objects (customers, suppliers, employees, products, cost centers, etc.) around which business is conducted. It is used to provide context to transactional data and is typically scattered around the business in various spreadsheets, applications, and even physical media.

DataOps (Data Operations) has assumed a critical role in the age of big data to drive definitive impact on business outcomes. This process-oriented and agile methodology synergizes the components of DevOps and the capabilities of data engineers and data scientists to support data-focused workloads in enterprises. Here is a detailed look at DataOps.

AIOps, a marriage of machine learning and IT operations, can transform businesses by automating key processes.

MLOps, AIOps, DataOps, ModelOps, and even DLOps. Are these buzzwords hitting your newsfeed? Yes or no, it is high time to get tuned for the latest updates in AI-powered business practices. Machine Learning Model Operationalization Management (MLOps) is a way to eliminate pain in the neck during the development process and delivering ML-powered software easier, not to mention the relieving of every team member's life.

Over the last years, many organizations have been investing substantially in data and analytics. The objective is to become more data-driven, become a tech style organization.

Data is more present and more powerful..

DataOps would allow various teams handling data to collaborate better with the team deploying data into applications.

Building a machine learning model is great, but to provide real business value, it must be made useful and maintained to remain useful over time. Machine Learning Operations (MLOps), overviewed here, is a rapidly growing space that encompasses everything required to deploy a machine learning model into production, and is a crucial aspect to delivering this sought after value.

Organizations large enough to have one or more data teams typically have a mix of data scientists, data engineers and data analysts on those teams. However, as companies become increasingly digital, they must be able to utilize massive amounts of data intelligently in a timely manner at scale. Achieving all that may require the addition of a DataOps engineer who can help the company operationalize its data.

DataOps is the use of agile development practices to create, deliver, and optimize data products, quickly and cost-effectively. DataOps is practiced by modern data teams, including data engineers,…

Modern data development tools and how data quality impacts ML resultsPhoto by NASA on UnsplashML is all around us! From healthcare to education, it is being applied in many domains that affect our daily activities and it’s able to deliver many benefits. Data quality carries a very important and significant role in the development of AI solutions — just like the old “Garbage in, garbage out” — we can easily understand the weight of data quality and its potential impact in solutions like cancer detection or autonomous driving systems.

Organizations across the globe are striving to provide a better service to internal and external stakeholders by enabling various divisions across the enterprise, like customer success, marketing, and finance, to make data-driven decisions. Data teams are the key enablers in this process, and usually consist of multiple roles, such as data engineers and analysts. However, […]

Several leading health systems got together recently to announce the formation of Truveta, an independent company that will pool patient medical records from the participating health systems and analyze them for insights to drive healthcare outcomes. The announcement highlighted the benefits of sharing de-identified data for driving research, new therapies, and improved health outcomes.

Thousands of customers are building their data lakes on Amazon Simple Storage Service (Amazon S3). You can use AWS Lake Formation to build your data lakes easily—in a matter of days as opposed to months. However, there are still some difficult challenges to address with your data lakes: Supporting streaming updates and deletes in your data […]

Microsoft today announced Azure Percept, its new hardware and software platform for bringing more of its Azure AI services to the edge. Percept combines Microsoft’s Azure cloud tools for managing devices and creating AI models with hardware from Microsoft’s device partners. The general idea here is to make it far easier for all kinds of […]

Chances are, your brand has data scientists and operations professionals on the team, and while they do their best to collaborate, they each have their own areas of expertise. This could lead to miscommunications and misunderstandings. The data scientists can interpret the data, but they likely don’t have the background to manage business operations. Likewise..