The team at Maana is thrilled and honored to be recognized with this coveted designation. Such acknowledgment by the industry thought leaders represents a huge vote of confidence in the paradigm shift being led by Maana from data mining to knowledge mining and, along with it, away from the data lake and to the Enterprise Knowledge Graph.
When it comes right down to it, businesses must be able to make data-driven decisions about their operations in order to bring about some desired outcome. Getting to this point, as anyone who has done it will attest, is no mean feat.
Today’s “big data” ecosystem grew out of what Google was doing in their datacenters in the early 2000s, approaches they have long since abandoned. Besides, it was designed for performing a certain style of computation (MapReduce) over a large dataset. It turns out that this is not the primary use case for asset and business performance improvement processes. (Yes, Spark helps immensely, but this is far from being sufficient.)
There has also been a pendulum swinging across the storage trend spectrum – NoSQL, SQL, NewSQL, … but these debates also miss the point: how do we represent the business concepts, the structural relationships between them, the dynamic processes and behaviors that govern them, the statistical and probabilistic distributions, correlations, anomalies, clusters / classes, similarities, … This is the stuff of knowledge. It isn’t about how much data you have or what format you store it in. It’s about what do you know about your business and what options do you have to improve it?
Maana has focused on what really matters: getting from raw data to operational solutions in the shortest time possible with the fewest resources possible, while leaving behind something of lasting value and reuse.
We have designed Maana from the ground-up to serve this purpose. It is not a Frankenstein’s monster of off-the-shelf tools stitched together in creative ways. As such, Maana’s user-guided, machine-assisted technology is always there to remove as much drudgery as possible, by automatically parsing files, suggesting joins, classifying fields, renormalizing flat data, etc.
These capabilities are provided through a rich plug-in system, allowing 3rd party and custom algorithms to provide domain-specific capabilities in, pharmaceuticals, healthcare, manufacturing, finance, fraud, …
Lastly, once a solution model has been developed, Maana’s APIs and libraries enable the solution to be immediately put into production by integrating with existing LOB tools and put directly into the hands of thousands of employees.
Having worked together now with many businesses to solve their biggest challenges, we appreciate the vital role that analysts and domain or subject matter experts play. These folks have the instinct for where the issues are. Maana differs in that we have built a rich user experience around maximizing the productivity of these key roles. A clinical researcher, a drilling superintendent, a supply chain manager – these folks know where the losses and opportunities are in their respective operations. If we can empower these folks and save our IT and data science resources (for which there is a severe shortage) for the projects that really need them, then we can help overcome one major bottleneck to business improvement.
A data lake is just that – a giant pool of data. It isn’t structured or organized. There aren’t nice tools for being able to find and understand what’s there and how it all fits together. The purpose of the Maana Knowledge Graph is to change this. It is to give structure and coordination to all this data as they map into business concepts. The raw data is only interesting once it has been interpreted. The Maana Knowledge Graph captures the understanding of the data, not just the data. This understanding will evolve with you, especially as you take action that affects business outcomes.
Not only does Maana make it easy to find things, we make it easy to get answers, explore the conceptual space, achieve statistical understanding, see the impact of hypothetical situations, and much more. Any insight gained can be integrated right back into the Maana Knowledge Graph, making it available for others to see and use. Such value accrues across business units and divisions, benefiting all future projects.
We have been very fortunate at Maana to have had real-world challenges from our early supporters and partners. This has focused us from the start on delivering business value, not technology for its own sake. All of our innovation has been driven out of a desire to make it easier and faster for real users to tackle challenging asset and business operations improvements and improve their asset profitability. We appreciate being included in this report and look forward to the increased awareness about the importance of knowledge it brings.
Gartner Cool Vendors in Internet of Things Analytics, 2016, Rita L. Sallam, W. Roy Schulte, Gerald Van Hoy, Jim Hare, 11 May 2016.
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