This is the third part of our “Diving deep into Gartner’s Top 10 Data and Analytics Technology Trends for 2019” blog series. In this series, we’ll explore the top 10 Data & Analytics 2019 trends identified by Gartner. If you haven’t yet seen the second post on Augmented Data Management check it out here.
In this post, we’ll cover another up-and-coming data & analytics technology that could help businesses save massive amounts of time when it comes to making their data ready-for-use, Continuous Intelligence (CI).
Definition
A design pattern in which real-time analytics are integrated within a business operation, processing current and historical data to prescribe actions in response to events.
Gartner / Striim
Continuous intelligence leans more towards a business model rather than a specific technology. It is essentially the combination of the six following concepts.
- Fast: Everything must be in real-time
- Smart: Feeding all data into analytics platforms and machine learning
- Automated: Limiting or eliminating human intervention to ensure insights are untainted
- Always-on: 24x7x365 (or 366) days a year, no excuses
- Embedded: Integrated into the business, as well as the applications it supports
- Outcome focused: Responding in a timely manner and delivering measurable value
These six items in unison can create a business environment super-charged to make the best use of real-time, automated, and actionable data streams in order to drive massive business change across your whole organization.
Why It Matters
Database technology has significantly matured over the last few decades, the next 10 years have the potential to mark some massive changes in what we can expect out of a database. The problems around storing persistent, redundant, and consistent data across multiple zones are getting easier and easier. This beckons the question…what next?
The next step in this is not a wholesale technology offering (yet), but rather an attitude or perspective towards what it means to be a truly real-time data platform organization. Continuous Intelligence is that next step, it matters because as data processing becomes faster, easier to scale, and is backed by self-guiding artificial intelligence technology such as machine learning and natural language processing, then we will need a framework to bring all these efforts together to make a legitimate business impact.
When the application market place for tools delivering advanced data & analytics technologies such as Augmented Analytics and Augmented Data Management, Continuous Intelligence will become a feasible option for many modern enterprises. There will be multiple areas of the business world that will be significantly changed by a Continuous Intelligence approach, from internal tooling such as Development Operations procedures for product development to ultra-intelligent fraud detection and prevention in the financial world.
Applications
There are no standout holistic Continuous Intelligence applications out in the market as of right now. There are some companies, such as Sumo, that claim they have CI for a nice market, in this case, DevOps.
Conclusion
Continuous Intelligence software sounds like a stretch, and it is without a doubt. However, much like the off-the-shelf software, we have available today which 20 years ago only a massive enterprise could afford, continuous intelligence will become a much more feasible approach for modern companies to take as they reach full “Enterprise Consciousness”.
Anant
Interested in learning more about Data & Analytics? Our practice is currently focusing on technologies such as Spark, Cassandra (check out this post on Cassandra vs Databases), Graph, Kafka and DataStax Enterprise and the following are our go-to places for our Data & Analytics brief.
Disclaimer: We recently made our Cassandra.link website open to the public. We previously used this site internally, but now want to share with others everything Apache Cassandra that we find informative or beneficial.
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Photo by Josh Riemer on Unsplash
