Let’s talk about:
- Creating the metadata magic – how analysis can get a great data quickly
- How not to spend 80% of your time on data preparation for the data scientist
- How to use data management to serve data to the business quickly
- Automated self-service metadata management as the foundation of usable analytics and trustworthy data management
For business intelligence professionals it is no surprise that the data management landscape is highly complex and can be quite difficult to navigate. As Big Data and NoSQL technologies are creating a variety of languages and skills, which many organizations do not have internally. This creates a system that is very difficult to navigate, organize, and certainly does not ensure consistency.
New technologies such as Pig, Hive, Hadoop, Spark, Scala, MongoDB, HBase as well as new emerging environments were created, and yet still make the data orchestration increasingly complex.
As we move toward a Big Data, algorithmic, machine learning and an artificial intelligence based world, most companies do not have the means in order to extract meaningful insights in their traditional data warehouse due to siloed proprietary technologies. This can of course cause a company to feel in the dark and overwhelmed, often unsure where to turn in order to receive and easily understand consistent reports.
The emergence of an easy-to-use centralized metadata governance solution is imperative and essential to move efficiently throughout the data journey and data value chain. A system that is easily accessible by organization, reduces cost and ultimately saves the organizations even months of valuable time and energy.
With a better metadata management system, organizations will be able to make, with more confidence, important decisions with clearly organized and searchable metadata reports. Metadata management is the underlying skill to enable organizations to focus on discovering new patterns, predicting future events and simulating different scenarios that is easy, efficient and transparent but must be treated as foundational and strategic importance of the modern data enterprise.
In order to harness this change, glean new insights and make decisions faster, companies must look at their data operational processes and look for efficiencies. Metadata management has strategic, efficiency, financial and regulatory implications that must be addressed to break data value bottlenecks.
Evidence-based, data-driven organizations and digital transformation is the imperative for the next-generation organizations. A metadata revolution is needed to bring a systemized and easy to use platform that will level the playing field. It should be obvious that all companies should be able to access important information without the long and tedious process BI Managers do every day to chase the data through its journey.
The benefits of enabling visibility and control of metadata that is scattered across a BI landscape are innumerable. Better metadata understanding with smart algorithms, and modeling and indexing of all metadata types will enable organizations to quickly located and understand cross connections.
In the spirit of, “it takes one to know one” Octopai was founded by former BI leaders in organizations where they constantly struggled with silo based tools. They decided to apply their frustration to innovation and created Octopai, an automated platform that enables BI groups to quickly and precisely discover and govern shared metadata.
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