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AptivaCorp Data Offerings Services include data collection and processing from structured, semi-structured & unstructured IoT, social, cloud, mobile RDBMS, NoSQL DB, data visualizations and analytics from real-time information management systems, data operations like master data management, data security, and data tech transformations like migrations, license optimizations so on.
Structured & Unstructured IoT, Social Cloud, Mobile RDBMS
Information Management Real-time Data, Self-Serve Dashboard, Advanced Reporting
Data Security, Master Data Management, Data Governance
Technology Migrations, Data Migration to Hadoop, License Optimization
Neo4j is designed to be very visual in nature. Using nodes and relationships, users can easily model their data into something developers, analysts, and leaders alike can understand. The Cypher query language is also structured visually with ASCII-art to make query-building and maintenance easy to read and adapt.
Use JavaScript to build and deploy high performance graph visualization tools quickly.
The world is densely connected. If there’s an interesting relationship in your data, you’ll find value in graph visualization.
Using the KeyLines JavaScript graph visualization library, it’s quick and easy to build powerful graph visualization applications, roll them into analyst tools and workflows, and deploy them anywhere in the world.
Read moreBusiness intelligence (BI) is the combination of applications, processes, and infrastructure that, as Gartner explains 1, “enables access to and analysis of information to improve and optimize decisions and performance”. From expense management to supply chain visibility, sales pipeline management and beyond, modern BI tools are focused on extending the value of data across departments, roles, and increasingly, the ecosystem of partnerships that enable organizations to effectively operate and compete in today’s fast-paced business climate.
The emergence of business intelligence can be traced to the decision support systems of the 1960s, evolving in three major waves of innovation over the decades. The challenge is always the same: How can businesses analyze data to make discoveries that lead to competitive edge? Each generation has come a little closer to that promise but it’s the third generation of BI we are now entering that holds the greatest potential to spread the value of BI to every business user and unlock all the value in data:
Explore the top reasons organizations choose Power BI to meet their self-service and enterprise business intelligence (BI) needs.
Hume is a Graph-Powered Insights Engine. It creates a digital twin of your business in the form of a Collaborative Knowledge Graph which surfaces critical but previously buried and undetected relevance in your organization.
This newly accessible relevance can be surfaced and used in a variety of ways including unusually accurate and even predictive search, dynamic conversation and chat, proactive alerting on potentially high-impact unknowns, accurate connectedness recommendations, as well as advanced analytics and context aware visual exploration.
Master data management (MDM) arose out of the necessity for businesses to improve the consistency and quality of their key data assets, such as product data, asset data, customer data, location data, etc.
Many businesses today, especially global enterprises have hundreds of separate applications and systems (ie ERP, CRM) where data that crosses organizational departments or divisions can easily become fragmented, duplicated and most commonly out of date. When this occurs, answering even the most basic, but critical questions about any type of performance metric or KPI for a business accurately becomes a pain.
Getting answers to basic questions such as “who are our most profitable customers?”, “what product(s) have the best margins?” or in some cases, “how many employees do we have”? become tough to answer – or at least with any degree of accuracy.
Basically, the need for accurate, timely information is acute and as sources of data increase, managing it consistently and keeping data definitions up to date so all parts of a business use the same information is a never ending challenge.
To meet this challenges, businesses turn to master data management (MDM).