Thursday, January 18, 2018

Knowi Product Update Q4 2017

To see these exciting new capabilities in action, please join Lorraine Williams, Head of Success at Knowi, for a web demo on Wednesday, January 31th at 11:00 AM PT.


Lovin' Query Management

Our query capabilities are at the heart of what makes Knowi different.  We constantly add capabilities but in the past few weeks we've focused our efforts and added a number of enhancements:  

Join Builder
In addition to performance improvements to the Join functionality, we now provide query join assistance from within the Query page. The supported joins types are listed along with auto-detected key field candidates for each side of the join. You can, of course, still enter the join criteria manually.

Join Query Help
Save Query as Draft
You now have the ability to save a query in progress without creating an associated dataset and widget.  Go for that coffee break!

View Query Change History
Wondering who messed up your query?  Wonder no more.. you now have the ability to view an audit history of query changes. This is only applicable if the user has edit permissions for the query in question. From the Query Listing page, a history icon is now available.  When clicked, the user will username and timestamp of when each change was made.



Query Filters Suggestions
In Query, filters auto-suggestions and hit list filter capabilities can now be seen from within the Query Builder itself.

Join Post-Processing
You can now apply Cloud9QL functions to a dataset post join.

Preview Data at Each Join Step
You can now preview dataset results at each join step.  This can be especially useful when you have multiple join steps.




We're Getting Slacky

Slack integration allows you to trigger actions in your slack channel(s) for a given condition triggered by an alert. When the condition is triggered, we'll send a message to a predefined channel(s) including the attachment of full data or conditional data depending on the options selected.




Stranger Danger

Enterprise data security is top of mind for everyone.  Whenever we can, we leverage new security capabilities from our database partners as quickly as possible.

SSL Support
We now support SSL enabled MarkLogic and Datastax/Cassandra

Role-based access control (RBAC) Support
We now support RBAC in Couchbase 5.0

Access Control List
The system now supports the ability to create white and black lists of datasource assets (tables/collections/indexes). This will allow the datasource creator to specify those assets available to subsequent queries. The datasources that support the ACL functionality are currently: Elasticsearch, Oracle, Knowi Elasticstore



Other Cool Stuff

Email Reporting Improvements
Parametrized Report Templates The Email Report function has been enhanced to pass in user-lever query filters, ensuring only the data the recipient is allowed to see is contained within the report. Any dataset attachments also adhere to the passed in parameters.

Analyze Grid Formatting
A number of usability enhancements we made including:
  • Ability to view statistical data for numerical columns
  • Added formatting options for numeric and data columns: currency, date, percent and decimal place
  • Ability to resize columns
  • Added Count option for column aggregation
  • Added 'does not equal' as an operand in the conditional grid formatting options 
Embed API Formatting
An option has been added into the JS Embed API that allows for auto-sizing of content based upon the full height of the dashboard. 

 New Datasources
Added support for
  • Apache Hive
  • Couchbase 5.0
Cloud9QL Enhancements
Cloud9QL Function AutoComplete
When adding a function in Analyze or preview modes, the system now gives a dropdown list of C9QL functions available along with autocompleting capability

A new CLoud9QL function has been added that allows you to control the display of numerical values. The format is NUMBER_FORMAT(<number>,<format>), and an example is:   select number_format(clicks,##,###.00) as Number of clicks

If your data is a JSON string, the PARSE function can be used to convert it into an object which can then be further manipulated and processed.

Provide an alternate value to be used in case the specified field doesn't exist or the value is NULL.

Wednesday, January 10, 2018

5 Most Influential Data Analytics Trends for 2018

2018 Data Analytics Trends

Data Analytics Trends 2018
Happy New Year!  As we enter the year, the question always comes up, “what are the data analytics trends that we should pay attention to in 2018?”  The technology trends we see are emerging to support a couple of large-scale business analytics trends.

First, there is a slow but steady movement away from process-driven towards data-driven decision-making.  Future thinking organizations understand that 1+1 does not always equal 2 if other factors are considered so following a linear decision-making process may not always result in the desired outcome.  Data provides a clear picture of internal and external influences that may impact outcomes, positively or negatively.  Getting up-to-date information in the hands of the right people up and down the organization, in a context they can use, is becoming critical to ensure future competitiveness. 

Second, there is widespread realization that data is an asset and a valuable one at that.  While analysts have been talking about data monetization for a while, most organizations are still trying to get their data to work for them internally.  In 2018 many will continue to focus their analytics thinking inwards, but those who have figured it out are starting to look outward.  They are changing how they think about analytics to include how their customers and partners might also benefit. It may be a while before we see a proliferation of data marketplaces, but organizations are starting to think about how they can use their data to create new data-driven products and services and open up new revenue streams.

With these high-level business drivers in mind, here is our take on the 5 Most Influential Data Analytics Trends for 2018. 

An analytics architecture rethink is underway.

Data Architecture Team
For most companies, single stacks data architectures just don’t fit anymore.  Modern data stacks optimize for different types of data and use case, so most enterprises have a mix of RDBMS, SQL, NoSQL and Cloud APIs.  Trying to funnel all the right data into a data lake, data warehouse or other reporting data store is increasingly more difficult and time-consuming, as business and data move faster than ever before.  To keep up with business demands for agility and rapid experimentation data analytics architectures will need to adapt and modernize to cope with an evolving future.

In 2018, this means analytics platforms that venture into this modern data environment will stop being simply data visualization tools.  The next wave of BI solutions will take on more data management (data plumbing) capabilities to eliminate the need for ETL and data prep tools and simplify the data analytics stack.  They will natively interact with structured, unstructured and semi-structured data sources, be highly extensible, embeddable, and include published APIs to insert into operational workflows.  These capabilities will allow for the merging of analytics and applications into data-driven applications and transform BI tools from monolithic single destination applications to analytics distribution frameworks that fuel business transformation.

Data science, data discovery and data engineering converge to support rapid experimentation.

Data science, data discovery and data engineering convergence
Is 2018 the year Big Data just becomes data?  I don’t know, but it seems that as NoSQL technologies become mainstream (note: MongoDB IPO), it makes less and less sense to distinguish Big Data from any other kind of enterprise data.  Business teams want to explore large data sets, add context from different sources and build advanced analytics.  For the most part, Big Data and AI are segmented off and use their own set of tools and resources which limits the ability of an enterprise to do interesting things beyond a few use case level experiments.

2018 will be the year that CTOs and CDOs realize that to transform into a data-driven enterprise they need a unified view of all their data as well as a data architecture and culture that encourages experimentation.  New technologies like AI need to be integrated so the work done by data scientists and data engineers can be shared with a broader audience and leveraged enterprise-wide.  This is more than just a shift away from old data architectures but requires rethinking how business teams, data engineers, and data scientist work together in an iterative, agile, development process that is better suited for rapid experimentation.

Data-driven applications and services create new revenue streams.

Data-driven application drive revenue growth
Forward-thinking companies understand that analytics can change experiences for the better.  By providing analytics as part of product or service offering, customers stay longer and do more, making these new data-driven applications more valuable to the consumer.  This value converts to a willingness to pay more, higher retention rates, and long-term strategic relationships which are the stuff of every product managers dreams.

In 2018, product managers will ride the wave of experimentation focused analytics architectures on driving top-line growth.  Product managers will need the ability to experiment with all available data, easily and securely embed analytics into applications and iterate quickly in a self-service environment. As an example, one of our customers, Boku, is monetizing their analytics by providing financial reporting to their merchant and carrier partners.  You can read more here.

Public cloud, private cloud, on-premise deployment?  Yes, please.

analytics architectures must support hybrid environments
Cloud-first strategies continue to be the favorite option for analytics, including Big Data analytics, because of the reduced onboarding friction and greater flexibility.  However, enterprise data stacks are not necessarily on the same path.  With regulatory, security, variable costs, and performance concerns, many enterprises have opted out of the cloud for some applications.  Additionally, NoSQL technologies are optimized to store certain types of data and serve specific use cases.  For example, enterprises use MySQL to store customer information, MongoDB to store customer interactions from their website, and Elasticsearch is used to enable customers to search large data sets very quickly.  Reporting platforms need to pull data from potentially all these sources to add context and answer even basic questions around how engaged a customer is with a product or service.  There is little indication that enterprises will standardize on a single stack or environment, in fact, the opposite is true.

In 2018, there will be a push to modernize analytics architectures to handle increasingly fragmented data and applications architectures without requiring data to be moved and transformed as this limits the ability to be agile and experiment.  Analytics platforms that play nicely in the cloud API’s and can navigate any variety of hybrid environments will have the advantage.

Reporting moves beyond single destination dashboards.

future analytics move beyond dashboardsMass adoption of analytics is often a barrier to transitioning into a data-driven enterprise.  The reasons are two-fold.  First, analytics platforms typically don’t serve different users with varying skills sets well.  Second, getting up-to-the-minute information in the hands of the right people is pretty hard.  These gaps leave groups of people either reliant on other team members for reporting while others just give up. 

In 2018, the push toward data-driven enterprises continues, and analytics platforms will start to move the boundaries of where reporting happens to challenge long-standing barriers to enterprise-wide adoption.  Logging into an analytics platform and looking at a set of dashboards isn’t going away anytime soon.  However, what you will see in 2018 is a broader use of highly contextualized analytics pushed to where the user lives.  This includes targeted analytics built into data applications and starting to leverage AI to make analytics more interactive.  While talking to your analytics platform or type a question into Slack and having a dashboard appear sounds a bit gimmicky today, did you think five years ago it would be normal to ask a device sitting in your kitchen to turn on the lights?  We are still in the early stages of this transition away from desktop dashboards to analytics everywhere, but 2018 will see a significant step forward on the journey.