There needs to be a way to take all of your data sources, finding new insights to put a company on the right path forward within any size business. An analytics platform combines technologies to meet enterprise needs across end-to-end analytics to make for the proper unified solution. This encompasses data storage, data management, and data preparations through either an on-premises database or cloud services. Businesses can empower users to make data-driven decisions across teams and foster innovation by implementing an analytics platform. Let’s take a look at what makes these platforms come to life.
The proper data analytics platform encompasses three business intelligence capabilities: visual analytics, data science, and streaming analytics. Visual analytics provide powerful data visualization with a reimagined way to explore an agile, artificial intelligence-driven module in real-time. This allows for easier interaction whether you’re a business user or an expert analyst. In addition, visual analytics allow for queries based on AI-powered recommendations or traditional direct manipulation of the data sources at their disposal.
Visual analytics works with data science to allow for automatic insights for businesses, creating one-click queries for any use cases in their data preparation. Visual analytics ties in with streaming analytics to create operational intelligence through a properly formatted database that is standardized for easier transparency among data sets. These capabilities can turn big data into the greatest solution to promote proper workflow for a supply chain.
Data science is a large, all-encompassing umbrella that takes on a multi-disciplinary approach to finding, extracting, and surfacing patterns in data sources. This includes a fusion of analytical methods, domain expertise, and technology. This approach generally consists of a variety of capabilities, including machine learning, forecasting, and data mining. With large amounts of data only expanding, understanding this science can allow organizations to forecast success, empowering knowledge through machine learning projects and tasks. This will enable companies to extract trends and opportunities with the help of data visualization tools.
This encourages business applications in every sector, from health care and education to government and social media. These open-source tools work with streaming analytics to create real-time model scoring within data pipelines. This leads to quicker model deployment, such as adjustments made to a virtualized form of a physical product. In addition, data science works with visual analytics to help business analysts discover automatic insights within a larger ecosystem. By having access to more relevant data in a large warehouse, businesses are in a better place to make strides for the future within their industry.
Streaming analytics is the driver of real-time analysis and lightning-fast business insights. This takes operations to the next level with intelligent business applications that deploy quickly and take action based on new decisions and models while curbing overhead costs. Streaming analytics comb through real-time feeds with low code and visual authoring. This allows for a shorter development cycle that, along with properly formatted templates, can create faster time-to-value for business decisions driven by analysis and simplicity.
Streaming analytics tie in with visual analytics to build greater business intelligence capabilities based on the real-time data sources at their disposal. This creates better operational intelligence in both customer interaction and back-end capabilities. Streaming analytics work with the greater field of data science for better model deployment based on what data experts are learning within the blink of an eye. Long story short, an analytics platform can help reduce time spent on data preparation, allowing business users to find insights that can be a game-changer. The best part is being able to use it for prediction and execution in real-time.