As businesses across the world continue to grow through the power of data science and machine learning, a major deficiency is emerging in the systematic integration of data.
Ever-expanding data sources, batch data movement, rigid transformation workflows, growing data volume, and distribution of data across multi and hybrid environments all make integration of data an increasingly strenuous task.
Though the process of data collection from various sources itself is straightforward, enterprises must then integrate, process, curate, and transform all of those data with other sources and then deliver an extensive view of the customer, partner, product, and employee.
Is your head spinning yet?
An end-to-end data management process is an amalgamation of real-time connected data, self-service, and a high degree of automation, speed, and intelligence. Thus, it needs efficient and unconventional management strategies to reduce costs and manual efforts.
Knowledge graphs make it much easier for decision-makers to derive insights from the massive amount of data at hand. Knowledge graph offers businesses all kinds of benefits, from self-service abilities and support to end-to-end data management. That includes ingestion, transformation, preparation, discovery, data catalog, integration, governance, and security. These graphs usually exist on top of the organizational data, linking them together, usually to enhance knowledge of relationships between objects, events, and abstract values. Graph databases help human users, programmers and machines alike interpret data. Remember the good old days of using endless numbers of JOINs to query strictly defined rows and columns? Mercifully, those days have gone the way of dial-up connection.
If you think of Knowledge Graph as the tool that defines relationships between key business resources, operations, and stakeholders, think of DataBlaze as the system that prepares data to be represented sensibly on the graph and helps you derive insights out of it. If Knowledge Graph is the story, DataBlaze is the storyteller.DataBlaze provides unique and structured solutions. Its end-to-end management capabilities provide a one-stop solution for your data management troubles.
#5 Insightful graph algorithms that pave the way for efficient decision making:
#4 Analytical Models accelerated through Knowledge graphs:
#3 Crucial ways in which DataBlaze uses knowledge graphs to save your business precious time and effort:
Weaves together your organization’s structured and unstructured data into an Enterprise Data Fabric.
Effectively connects, harmonizes, and governs data in your DataBlaze Software while also eliminating the need to stitch together multiple data tools and write custom code.
Uses data science algorithms like correlation, profiling, distributions, and entropy analysis to automatically connect and segment data for analysis.
#2 Places where businesses use Knowledge graph :
Google searches – When you search a movie title on Google and it shows similar movies, cast names, movies by the same director, etc., that’s Knowledge graph at play. Google uses Knowledge graphs to provide options to their users quickly and efficiently.
Amazon searches – When we ask amazon to show us the best leather shoes for $200 – it calls upon its knowledge graph to understand how it can best answer this demand.
#1 Place you definitely need a Knowledge graph
Your enterprise-level business intelligence.
Imagine if, in addition to having connected data sources, applied strategic templates, and multiple effectively-run pipelines, your data was also easy to understand and question. It’s not a fantasy! With DataBlaze, you can actually get all of that. Seeing is believing.
Contact us today for a demo!