Here we go! Another peak conference season has started. This month we saw a wide variety of releases from JupyterCon and Strata NYC. Next month should be even more exciting with announcements from Gartner Symposium, Tableau Conference, TIBCO, Pyramid Analytics, Microstrategy, AtScale, Frontline Systems and more.
The Forrester Wave: Enterprise BI Platforms
One of the hot topics this past month in BI channels was the latest research from Forrester. The Forrester Wave™: Enterprise BI Platforms Q3 2017 has been released with an odd twist. The vendors were divided into on-premises or cloud BI offerings. Even if a vendor had solutions for both on-premises and cloud – they were only covered in one report.
Aside from the division chatter, the findings were insightful and seem a bit less biased than other research firm reports. I can’t post pictures or the Forrester police will contact me. This group is much more stringent on sharing report summary images than other research firms. A free copy of the full on prem BI report and the full cloud BI report can be downloaded from several vendors.
- On Prem BI: MicroStrategy, IBM, TIBCO, and Qlik Lead The Pack
Forrester’s research uncovered a market in which MicroStrategy, IBM, TIBCO Software, and Qlik lead the pack. Information Builders, Looker, Pyramid Analytics, Tableau Software, SAP, OpenText, Yellowfin, Sisense, and SAS offer highly competitive options.
- Cloud BI: Oracle, GoodData, 1010data, and Birst Lead The Pack
Forrester’s research found Oracle, GoodData, 1010data, and Birst are market leaders. Domo, Salesforce, Microsoft, InsightSquared, Altair, and AWS also offer cloud BI.
These reports use 22-criteria evaluations of business enterprise solutions that analyzes current offerings, strategy, and market presence through vendor surveys and product demos. The Forrester Wave™: Enterprise BI Platforms reports are a good read for business intelligence buyers, architects and planners.
Google Launches Public Beta of Cloud Data Prep
Google recently announced that Google Cloud Dataprep—the new managed data wrangling service developed in collaboration with Trifacta—is now available in public beta. This service enables analysts and data scientists to visually explore and prepare data for analysis in seconds within the Google Cloud Platform. By leveraging a serverless, auto-scaling data processing engine (Google Cloud Dataflow), Cloud Dataprep can handle any size of data, located anywhere in the world.
Amazon Web Services Analytics Highlights
As usual in the cloud world of constant updates, Amazon had a couple tid bits worth noting.
- New Kinesis Analytics stream processing functions adds 6 new stream processing functions in your Amazon Kinesis Analytics applications.
- New Quick Start: Build a Data Lake Foundation on the AWS Cloud This Quick Start deploys a data lake foundation that integrates Amazon Web Services (AWS) Cloud services such as Amazon Simple Storage Service (Amazon S3), Amazon Redshift, Amazon Kinesis, Amazon Athena, Amazon Elasticsearch Service, and Amazon QuickSight.
Tableau 10.4 Released
In late September, Tableau released 10.4. Here are a few highlights. I expect to hear much more about innovations in Tableau next week at the annual conference – devs on stage – keynote. It is one of my favorite events of the year!
In this incremental release, Tableau improved on data source certification to promote approved data sources with recommendations. Powered by machine learning, data source recommendations help you sort through the noise to find the right data source
A super useful new feature is smarter conversations between teams with discussions and visualization snapshots. The visualization snapshots store filters, selections, and actions taken by other users.
My favorite enhancement is the cool MATLAB model integration. For engineers and scientists, you can use MATLAB models in Tableau for predictive insights or pre-process data using MATLAB and save it as a Tableau data extract.
Geospatial also improved with linear geometry shapefile support for visualizing custom networks from hiking trails to pipelines. Updates to the Tableau Web Data Connector allow to import spatial data from the web, including points, lines, and polygons, by connecting directly to GeoJSON.
For more information on Tabeau 10.4, check out the official product team blog.
Tellius AI-Powered Trend Insights
Tellius, an innovative leader in next generation business analytics for the enterprise, continues to expand on their artificial intelligence-powered analytics capabilities. At Strata Data Conference in New York, a new feature called Trend Based Insights was showcased. Trend Insights allows anyone to instantly understand changes in key metrics over time with a single button click.
H2O.ai Driverless Feature Engineering
H2O.ai finally released a private beta of the previously announced H2O.ai Driverless Feature Engineering capabilities. I did receive my invite and I can’t wait to play with it one of these days. Supposedly puppies sleep 16-20 hours a day – ha! My puppy did not get that memo.
Upcoming SAS Platform Developments
SAS announced several new enhancements coming to the SAS Platform in 2017 including:
- Embedded AI capabilities in the SAS Platform, starting with SAS Visual Data Mining and Machine Learning and SAS Visual Text Analytics. Building on a long history of machine learning, these SAS products leverage deep learning and natural language understanding to enhance the depth of insights derived from data.
- Single interactive interface that spans a wide range of analytics tasks to help non-coders solve complex problems faster.
- SAS Data Preparation for self-service, visual data wrangling, transforming, blending and cleansing data, with application-generated code fit for IT scheduling environments.
- SAS Visual Text Analytics, a modern, flexible and end-to-end text analytics framework that combines text mining, contextual extraction and categorization, sentiment analysis, and search. It automates feature extraction and business-rule generation using modern machine learning approaches.
- Cloud deployment through the SAS Cloud, Amazon Web Services, Microsoft Azure and others, including SaasNow.
R-Brain Unveiled at JupyterCon
R-Brain is a next generation platform for data science built on top of Jupyterlab with Docker. It was recently unveiled at JupyterCon in late August. Don’t let the name fool you. R-Brain currently supports R, Python, Structured Query Language (SQL), and more. It has integrated intellisense, debugging, packaging, and publishing capabilities. This cool new solution also has analytics workspace collaboration and marketplace features for personal, professional and enterprise use cases.
Datameer Big Data Visualization
At Strata NYC,Datameer announced and demonstrated a new interactive data visual exploration for big data discovery. Built on patent-pending dynamic indexing technology, Visual Explorer extends Datameer’s core big data platform beyond integration, preparation and governed democratization of big data for analyses, to now add unconstrained interactive visual exploration of extremely large datasets. This feature does not require modeling or development of an OLAP cube on top of a big data source like many other solutions do today.
On a side note, I tested several visualization solutions earlier this year to understand where they fall down when it comes to big data discovery, who can really do it and who says they can but can’t. I have not tested Datameer Visual Explorer yet.
Yellowfin 7.4 Preview: Augmented Data Discovery
We are starting to see more automation and augmented analytics being baked into existing offerings. Last month YellowFin released a preview of these capabilities in their solution. By using some smart algorithms, Yellowfin will soon prompt you and guide you through your data. At the same time, the platform will learn what’s important to you. Once it knows what you are trying to discover in the data, it can then present that back. By taking a user-focused approach, data and insights become more relevant. For more information on what is coming from them, check out their official product team blog.
Next Version of Looker
New Looker 5 features were discussed at their annual customer event, Looker JOIN 2017. Amazingly, Looker has acquired over 1000 customers as a newcomer in an almost commodity space. From discussions with peers and a great article by Mark Rittman, it sounds like they gained momentum in Silicon Valley by being an OEM solution baked into other startup solutions.
- Action Hub – Allows users to take action on insights directly from within Looker. Examples include changing Zendesk support tickets, shipping reports and dashboards to Google Drive or Box, or pausing a Google Adwords campaign. The Action Hub will launch with direct built-in connections to these services as well as hundreds more through middleware tools like Zapier and Segment.
- New Looker Blocks – New Looker Block types include: Viz Blocks which instantly provide 10 beautiful new visualization types (with more coming soon) and the ability to create any other custom visualization users can dream up; Data Tools that give end users highly curated analytic experiences (e.g. a Google Analytics-like Web Analytics tool); and Data Blocks that make it simple for customers to join their data with pre-modeled external datasets like weather data or demographic data.
- Applications – Applications by Looker are new end-to-end solutions for users to dig into data that’s important to specific departments. They leverage all Looker’s capabilities to access, analyze and take action with data. The first three offerings will be Marketing Analytics, IT Operations Analytics and Event Analytics.
- Data Merge: Intuitively merge results from two or more disparate datasets, even if that data lives in different databases. This will help customers examine time-series data, geographic data, and other summary data from multiple sources in a single interface.
- Druid Support: Looker now supports Druid, the high-performance, column-oriented, distributed data store. Quickly ingest massive quantities of event data in Druid and use Looker to run low-latency queries on top of the data.
- Statistical Functions: 57 new functions that support statistical procedures have been added to Looker’s extensive library of functions and operators.
- SQL Runner Visualizations: Looker’s SQL Runner has gained significant new functionality to better enable Looker’s most technical users. Analysts can now search, sort, save, and visualize their data directly in Looker’s SQL Runner.
Looker 5 will roll out to customers in October 2017.
Informatica AI-Driven Data Catalog
Informatica®, the Enterprise Cloud Data Management leader accelerating data-driven digital transformation, announced a new set of solutions in the areas of intelligent data lake management and enterprise data cataloging and discovery that enable organizations to turn data lakes into business value using Artificial Intelligence (AI) and the cloud to drive intelligent disruption.
Powered by the amazing CLAIRE™ engine. With clairvoyance in mind and AI at the center, CLAIRE is the industry’s most advanced metadata-driven AI technology embedded in the Informatica Intelligent Data Platform. CLAIRE delivers intelligence to Informatica data management products and solutions by applying machine learning to technical, business, operational and usage metadata across the entire hybrid enterprise, on-premises and in the cloud.
Although Informatica claims to be the industry’s only intelligent data catalog that integrates with Hortonworks Atlas – I don’t know if that really is true or a marketing exaggeration. Waterline Data and several other catalog vendors mentioned Atlas integration in briefings.
Alteryx, Inc. announced new Alteryx Promote, a component of the Alteryx Analytics Platform that empowers both data scientists and citizen data scientists to deploy predictive models directly into business systems via an API and then manage model performance over time. Alteryx Promote is the result of the company’s acquisition of Yhat, an end-to-end data science platform for developing, deploying, and managing real-time decision APIs.
Until Next Month
That wraps up the filtered analytics industry updates. Here are several upcoming webinars that might also interest you.
In this webinar, I’ll show you how to extract data and prepare data from unstructured files such as Adobe PDFs, reports and log files. I’ll also discuss different business models for monetizing your data. Large organizations have appointed chief data officers (CDO) to take on the mission of maximizing data value and producing new revenue streams. Honestly, anyone with a good idea can monetize data. The barriers to market entry have fallen. The technology is readily available and easily scalable. There has never been a better time to start scraping and shaping your data into digital gold.
Machine Learning has become a competitive differentiator in a big data world. Vast amounts of data are already overwhelming existing BI tools and analytics processes. When faced with hundreds of variables, a human’s ability to efficiently identify new insights or detect changing patterns manually has also been exceeded. To address these challenges, BI and analytics professionals are adopting user-friendly, automated machine learning solutions. In this one, I’ll cover:
– An Introduction to Machine Learning
– Popular Machine Learning Use Cases
– CRISP-DM Methodology
– Common Algorithms: Regression, Clustering, Classifiers, and More
– How to Get Started with Automated Machine Learning