Tag Archives: Spotfire
Crunching Queries at Scale: How Spotfire Customers Get “Redshift Ready” Cloud Analytics with AWS
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For today’s most mature enterprise cloud architectures, ultra-quick querying capabilities on petabytes of data is what the modern pace of business demands. And for enterprises running AWS, Amazon Redshift is most certainly a part of the data warehousing picture given its size, flexibility, and scale. Fast, fully-managed warehousing services make it simple and cost-efficient to analyze all your data right within your business intelligence (BI) and analytics platforms.
That’s why we’re so excited to announce that TIBCO Spotfire® is now “Redshift Ready” as part of the AWS Service Ready program!
The Amazon Redshift connector for Spotfire has been enhanced in all facets from authentication to point-and-click Spectrum and geospatial support. Plus, as part of the AWS Service Ready Program, TIBCO Spotfire has been recognized as a member of the AWS Partner Network (APN), validating that it is a product that integrates with Amazon Redshift, and is fully supported for AWS customers. In addition, Amazon has verified that Spotfire meets a standard of demonstrating several best practices in its integration with Amazon Redshift and the AWS Cloud.
But what exactly does this partnership mean for TIBCO customers?
How Spotfire & AWS Combine to Create New Value for Current Customers
A primary advantage of having a fast database is the ability to perform in-database analytics, performing SQL queries both live and ad hoc. This may include data extracts that can be imported on demand, for example to feed data functions and custom expressions back into Spotfire analyses.
It’s an attractive solution for those aiming to optimize costs at scale, with datasets ranging from several hundred gigabytes to petabyte or more and costs roughly 10 percent of the amount of traditional data warehousing (less than $ 1,000 per terabyte annually).
By combining Spectrum with Spotfire® GeoAnalytics, the in-memory data engine combines the ability to drill down and across multi-layer map charts with ease all while connecting to geospatial files in S3 with just a few clicks. It’s as if you were connecting to regular Redshift data tables.
Why Redshift Matters for Spotfire Users
By pairing with Spotfire, Redshift complements “the need for speed” for joint users running ad-hoc analytics queries on vast amounts of data.
Simply connect Spotfire to your Amazon Redshift warehouse to explore your data with ease alongside the many other data sources—both at-rest and in-motion—all in one analysis environment for the most comprehensive view of your business.
Simply connect Spotfire to your Amazon Redshift warehouse to explore your data with ease alongside the many other data sources—both at-rest and in-motion—all in one analysis environment for the most comprehensive view of your business. Click To Tweet
A comprehensive understanding of your data is at the core of data-informed decision making models. Therefore, data access remains a hot topic among Spotfire users, working with an expansive list of data sources across vast ecosystems, demanding the very latest support for native self-service and custom data connectors. Of course, Spotfire connects to any data source: relational, cubes, files, NoSQL, REST, unstructured, cloud, or on-premises.
To learn the latest of all that’s new with Amazon Redshift, visit AWS resources for more.
The Journey of Generation A(X): Top 5 Highlights from Spotfire 10
Reading Time: 4 minutes
First introduced in the fall of 2018, TIBCO Spotfire®’s A(X) experience, an artificial intelligence (AI) driven analytics experience, delivers agile, augmented analytics for better, faster decision making. Building upon this momentum and listening to user feedback, TIBCO has prioritized several usability improvements and feature enhancements since the last Long-Term Supported (LTS) release (version 10.3) in mid-2019.
Several of these new capabilities—from “Interactive AI” to streaming analytics in the web client to native Python—provide organizations with more accessible data exploration and quicker insights for everyone.
Below are five highlights of the top technology advancements available by upgrading to the latest LTS release of Spotfire® version 10.10.
1. Interactive AI: Insights on Marked Data
With a simple lasso of a data point, AI-assisted exploration answers the questions that you didn’t know how to put into words! Spotfire®’s Recommendations engine automatically identifies interesting patterns in your data, guiding even less-experienced analysts to the most significant relationships to explore. “Interactive AI” aids users by isolating root causes of potential patterns (e.g. an outlier, shifting negative trend).
Takeaway: Interactive AI allows even non-technical users to borrow the brain of a data scientist and create analytics applications in seconds flat—without any expertise—thus shortening the road to insight by simply asking, “What’s different about these?”
2. Streaming Visual Analytics on the Web
Any business dealing with events data in terms of velocity, direction, and momentum can derive value from streaming visual analytics. Spotfire® provides the industry’s first true streaming business intelligence solution, offering one single visual analytics environment with built-in capabilities for diagnostic, predictive, and streaming analytics.
Streaming visual analytics transforms what was once static, historical rear-view monitoring into an immersive environment for predictive and prescriptive analytics. Streaming visual analytics enables richer insights, served up through dashboards and analyses, that support taking better actions. This is essential because the speed of business today demands:
- Adapting and responding to frequency of rapidly developing events and changing conditions
- Making urgent data-informed (at times automated) decisions based on deeper advanced analytics
Streaming visual analytics transforms what was once static, historical rear-view monitoring into an immersive environment for predictive and prescriptive analytics. Click To Tweet
Takeaway: Streaming analytics is not just delivering real-time data but also real-time analysis, blending with historical data to inform timely decision-making. Whether for logistics companies, manufacturers, or others seeking operational excellence and cost optimization, streaming visual analytics is imperative. Decision makers are better enabled to act on the most current state of the business when consuming real-time insights via “always-ON” web dashboards and apps.
3. Native Python Data Functions
Our feature in-focus, it’s a case of: you asked and we answered. By popular demand!
With support for writing and managing native Python data functions, Spotfire® provides an interface between visual analytics and advanced analytics with very tight integration. Python has the highest rate of growth among coding languages and is the language of choice for many data scientists, so it was natural for TIBCO to introduce support in Spotfire® for native Python data functions across the Analyst client, web, and Automation Services. Many Spotfire® customers’ IT leaders channeled the voices of their data scientist teams with this request, as this frees them up from manual low-value tasks of duplicating data across different environments. This all allows for the same ease and convenience that users of TERR and “R” language bundled packages have enjoyed for many years in Spotfire®—now for Python.
[*NOTE for users on earlier versions: It was already possible to use Python data functions prior to 10.7 through the data function extension available on the TIBCO Community. However, there’s no longer a need to use that extension as Python data functions are now supported natively right within the environment.]
Takeaway: One TIBCO partner endorsed this as, “…VERY complex analytics on the fly, without leaving [the] environment!” Writing and managing native Python data functions in Spotfire® consolidates several advanced analytics workflows—typically involving several tools—to cut the high cost of task switching.
4. Powerful Location Analytics Enhancements
Spotfire® location analytics extends far beyond merely plotting markers on a map. It’s truly immersive and automates the process of creating maps with instant geocoding, enriching the context for spatial patterns and relationships within multiple data layers, map services, and GIS data. It unlocks the use of advanced spatial processing techniques leveraging geographic services and data functions for seamless location-based exploration.
Spotfire®’s latest version now includes all of the following capabilities in map charts:
- Leverage geo-reference info from GeoTIFFs to position images on maps automatically at the right location and with the right projection
- Position images via drag and drop
- Calculate geo-spatial distance for streaming data
- Pan & zoom when Auto-Zoom is enabled
- Ease of use and productivity improvements
Takeaway: In a highly interactive visual application built upon embedded data science, instant recalculations of the models inform new sectors on-the-fly, offering the center of newly drawn areas accelerates the speed to insight.
5. More and More Data Connectivity Added
Native self-service connectors support 59 data sources (and counting!) right in the Spotfire® platform. This list is continually growing, with native connectors to the most popular hybrid cloud databases like Teradata, to applications like SharePoint Online and Salesforce, as well as those really hot cloud analytics data warehouses like Google BigQuery and Snowflake that combine data warehousing with your data lake, just to name a few.
Spotfire® users also connecting to streaming data are able to subscribe to 100+ streaming sources available through Spotfire® Data Streams to blend real-time analysis alongside your historical data.
Takeaway: Rich, disparate data is the core of any thorough analysis. TIBCO is always committed to adding new connectors—both native and custom—to Spotfire®, so customers gain an improved, holistic understanding of their business landscapes and markets.
With these advanced capabilities, innovative organizations can redefine what’s possible with analytics and business intelligence. For a closer look at all of “What’s new in Spotfire®,” explore the recent Dr. Spotfire sessions.
How to Get the Most out of Python in Spotfire®
Reading Time: 3 min 30 secs.
For recent grads and new data scientists coming into the fold, Python has clearly become the most popular programming language of choice over the past 24 months. Considering its rate of growth and widespread usage, evidenced by the sheer count of mentions in Data Scientist job descriptions, we can say with a fair degree of confidence: there’s a strong likelihood that Python is here to stay. And, our customers are clamoring for it. Well, you asked and we listened. You can now write Python natively within TIBCO Spotfire®, without the need for other tools. Read on for more exciting developments of how you can get the most out of Python in Spotfire®.
Write and manage Python natively
With the same ease and convenience of writing R language functions and managing those packages, Spotfire® users will find the Python experience released in 10.7 similar to how stats and ML libraries may be leveraged to build data functions as the basis for exploratory analysis, dashboards, or advanced analytics applications. The Spotfire® Analyst client now comes with a bundled Python engine for adding or importing Python packages for use with Spotfire® data functions. Writing Python functions natively within Spotfire® blends a streamlined offering for visual and advanced analytics by:
- Eliminating barriers for sharing value with more stakeholders throughout the enterprise when relying upon several disparate tools, some open source
- Simplifying the high-cost of task switching across various tools; translating single data science projects across platforms requires heavy manual work involving duplicative efforts (similar to the classic inefficiencies of legacy BI – ETL workflows)
A streamlined data science workflow benefits the enterprise
By embedding models into visual analytics, data science is driving better decision frameworks for improved outcomes. This makes each side of the “barbell” in large organizations more efficient: enabling citizen data scientists to self-serve quickly and use apps with advanced analytics underpinning them—essentially running Python functions as an engine within dashboards—all while liberating data scientists from low value, time-consuming tasks.
Just as with the classic data preparation inefficiency of basic formatting tasks over actual analysis, these are wasteful activities for highly valuable, and expensive, data science resources. Furthermore, this duality also frees up Data Scientists to spend time modeling deeper analyses by running workflows created by TIBCO® Data Science. In this way, Spotfire® satisfies coders and non-coders alike, expanding the reach of data science across organizations by creating more advanced analytics applications.
As embedded analyses are widely shared and consumed through dashboards, this also prompts the savvy analyst or citizen data scientist to explore them or even customize completely new low-code custom apps through self-service analytics. It’s through the automation of these workflows that Spotfire® serves end-to-end analytics use cases across the enterprise.
[NOTE: Spotfire® 10.7 provides native Python support in Spotfire®. For those users on earlier versions of Spotfire®, there are still other options for using Python. For more info, visit our TIBCO Community resource]
All with the Appropriate Governance
All of this is made available while showing appropriate care and concern for governing which packages are used across an organization. Designs of a seamless, invisible workflow in Spotfire ensures that all Python users utilize the same packages and versions of packages. Administrators or superusers can control which packages and versions of packages are automatically available, providing the potential for embedding Python packages in Spotfire for individual or group-level access privileges.
This is your opportunity to deliver more business value to your stakeholders and expand the reach of data science across your organization with advanced analytics applications using Python. For more on how to get the most out of Python in Spotfire® 10.7, watch our On-demand Dr. Spotfire® session with TIBCO Data Scientist, Neil Kanungo. Also, check out additional resources on TIBCO Community for more information about how to use the new native Python Data functions in Spotfire®.
A Look at My First 30 days at TIBCO Through the Lens of Spotfire
If you’re a data analyst who’s evaluating TIBCO Spotfire, or a Spotfire customer, I’m delighted to virtually meet you—my name is Lori Witzel, I’m the new Director of Product Marketing for Spotfire, and a large part of my role for Spotfire will involve listening to and learning from analysts and customers like you. In this blog, I’ll share a non-traditional look at my first 30 days with TIBCO, using a metaphorical framework that echoes some of what I’ve heard about Spotfire.
Augmented intelligence makes for better outcomes
Listening to and learning from many different sources is a core function for Product Marketing Managers (PMM) and research shows that more sources make for better outcomes. These diverse sources augment a Product Marketer’s own experience and intelligence, making it more likely that the products a PMM supports will delight their customers. (This constant learning is also a key reason why I love being a PMM.)
In my first 30 days with TIBCO, it’s clear that augmented intelligence is not just what our analytics products provide–it’s infused throughout the organization, in the quality and volume of information shared among teams and via business systems to enable better real-time decision-making. In a less metaphorical way, what I’ve heard from analysts like you: Spotfire’s ability to seamlessly consume disparate data, whether at-rest, streaming, on-premises, or in the cloud, augments business intelligence and enables better decision-making.
Adaptive intelligence transforms—and is transformed by—the process of learning
There are few things more exciting than starting something new–whether it’s a new job or a new high-impact project—learning through discovery changes the way we fit into and improve the world around us. I’m learning and discovering new things every day at TIBCO, and being stretched and challenged in all the best ways. As I adapt and transform myself, I’ve heard that my new-to-TIBCO perspective is transforming how our teams engage with data analysts like you.
My first 30 days at TIBCO have been the start of a great adventure in learning, and you can help me as I learn and evolve. I’d love to connect, and would love to learn if you’ve explored AI-infused discovery in Spotfire X. Message me on Twitter at https://twitter.com/loriaustex.
Working and learning at the edge
Because of the importance of listening and learning for a PMM, metaphorically speaking, the role embodies aspects of streaming intelligence. We’re “at the edge,” gathering information from people like you, from disparate sources through real-time engagement and interactions. This information becomes something we can act on to generate insights for our colleagues and our peers. Those insights, in turn, are shared back out to the source – analysts like you – in the form of product enhancements, which in turn leads back to further information gathering. In these early days at TIBCO, the new information I’m exposed to is daunting—but it’s part of working at the edge, and I wouldn’t have it any other way.
Let’s augment our intelligence together
For data analysts who are ready to evolve their careers and their organizations, Spotfire is the de facto standard for analytics augmented intelligence. My request? Let’s take that next step in augmented intelligence together. If you’re a Spotfire user, an analytics professional, a data scientist, I want to learn from you and hear your analytics stories–what’s important to you, what your use cases have been, how a recent discovery via analytics has made a difference. Look for me at TIBCO NOW London, at other upcoming Spotfire events, or on Twitter at https://twitter.com/loriaustex. I hope you’ll connect, and help make my next 30 days as exciting as my first 30 days.
C
Learn all about Spotfire, join the Spotfire community and watch the Dr. Spotfire channel on YouTube.
Hang 10: Spotfire Cited as a Leader in Forrester Enterprise BI Wave
In The Forrester Wave™: Enterprise BI Platforms (Vendor-Managed), Q3 2019, TIBCO Spotfire® is positioned as a Leader following an extensive evaluation of vendors across several key measures. Given its placement, Spotfire® will continue to ride a strong tide of market momentum. According to the report, “customers especially praised Cloud Spotfire for its ease of use and search UI/data discovery and exploration.”
TIBCO believes that Spotfire®’s scores in the ‘Current Offering’ category are due in large part to the generational set of capabilities introduced in Spotfire® X (version 10). Spotfire® X capabilities extend well beyond basic business intelligence (BI) dashboarding to serve a variety of analytics needs across the enterprise. Spotfire® X’s rich support for artificial intelligence (AI)-powered recommendations along with Natural Language Querying (NLQ) capabilities enables business users to have a conversation with data and explore freely. Additionally, automated workflows for simplified data wrangling further enhance the Spotfire® X experience.
Below is a breakdown of some of the criteria that Spotfire® received the highest possible score in, as well as TIBCO’s take on how it addresses those criteria:
Augmented Business Intelligence. TIBCO Takeaway: Spotfire®’s machine learning, predictive analytics, and advanced analytics capabilities empower business analysts and other “non-data scientists” to become citizen data scientists with automated, guided workflows.
Big Data. TIBCO Takeaway: With Spotfire®, users have the ability to connect to and analyze various complex and disparate data sources with ease. Users can ingest and analyze billions of rows of raw, unmodeled data and push query processing to a distributed in-cluster platform.
Data Preparation. TIBCO Takeaway: Today, analytics workers spend approximately 80 percent of their collective analytics time preparing data for analysis. So, while data is widely viewed as the “new oil,” it (much like oil) remains a raw material in need of much refining for the desired byproduct of business insights. Spotfire® is that refinery. And its ranked highly in part for how well it simplifies data wrangling workflows with automated tasks.
Deployment Options. TIBCO Takeaway: Spotfire® is designed to scale to a large number of users, data, and physical locations as a versatile best-in-class solution for meeting customers’ unique business requirements whether deployed on-premises or in the cloud.Read the full report to learn why TIBCO Spotfire® is rated as a leader among Vendor-managed Enterprise BI Platforms.
Optimizing MMA Fighting with TIBCO Spotfire® Software
What does TIBCO and MMA fighting have in common? TIBCO Spotfire® software.
Attendees at TIBCO NOW had the pleasure of watching professional MMA fighter, Allen Crowder spar while watching the readout of his body stats in real time. The fighter has been working with his trainer, Jesse Rey, a veteran employee who uses Spotfire software at Goya Foods, and Rishi Shah, at TIBCO, to experiment with Spotfire software as it relates to MMA fighting. Since Rey works at Goya Foods and interacts with Spotfire software on a regular basis, he thought it would be an interesting experiment to run.
With Spotfire, Shah and Rey found a way to tap into the human body as a source of data. They were able to receive signals from muscles to better understand contractions, intensity, velocity, and ultimately the point of fatigue. This data can then be displayed on a live dashboard to help Rey and Crowder optimize training and performance.
From a coaching perspective, Rey wants to understand the data, whether it’s a heart rate, punch count, oxygen level, or more. He sees using science and technology as an opportunity for improvement to take Crowder to another level.
For the demo, Shah put sensors on certain muscle groups of Crowder’s—two on the deltoids and two on the legs. These wires collect the data and show live muscle activation, fatigue, and intensity. As Crowder punches against the trainer, they can see body behavior changing in real time. What’s more, is that Crowder can now better identify his body’s limits based on what the sensors tell him. This helps Rey and other coaches understand how Crowder’s body responds to the training to make sure he isn’t overworked.
Data provides an advantage for better training
Rey is able to quantify the data, through the dashboards and visualizations, and provide feedback to Allen as he’s fighting to recognize how his body feels at the point of fatigue. At that point, Allen can change his fighting approach to both optimize his “available resources” and allow his depleted muscles to recover. The ability to connect a quantified event brings the value forward, with the data becoming increasingly crucial for better, more efficient training.
“As a coach, sometimes we think that we haven’t pushed the fighters enough,” said Rey. “If we can get data points where we can see that, that’s a good advantage for training and preparation.”
In addition to the visual results of the fight, data is also collected prior to Crowder’s fight. Rey and other coaches can compare both sets of data to adjust their training moving forward. However, it’s not an exact science; there is a multitude of factors that come into play such as heart rate and stamina level.
Crowder’s heart rate is an example of this data-based training adjustment put into action. With this data, Crowder finds that if he maintains his heart rate at 155, his performance is optimized, compared to a heart rate of 160, when his body starts to breakdown. Crowder finds this a tremendous help to ensure that he stays optimal for as long as possible.
“You don’t want to send a fighter into a fight at 50 percent; you want them to be 100 percent,” said Crowder. “With this data and the information that we can gather and collect, I can make sure that every time… I’m going to be 100 percent.”
Follow Crowder on Twitter at @AllenCrowder6, on Instagram at @allen_prettyboy or pb_crowder, or on Facebook.
Verisk, aided by Spotfire, Protects Insurers from Weather-Related Claims Fraud
When insurance companies are faced with substantial claims that must be verified, they turn to Verisk. When Verisk needed a data visualization tool to assist those insurance companies, it turned to TIBCO.
Insurance companies have a difficult task of ensuring the claims they accept are valid, in order to prevent loss due to fraud. With the world’s largest, most comprehensive claims database, Verisk is there to help. One of the main uses of the database is to verify weather patterns and natural disaster-related damage claims—insurers can easily detect fraud if a claim doesn’t match up with the weather at the time.
While Verisk’s database used to only turn up text reports for adjusters to check claims against, now it provides them with visual, multilayered analyses of weather patterns supported by TIBCO Spotfire®. TIBCO Spotfire allows users to view a wide range of dashboards with visual representations of the claims currently in Verisk’s database, making it easier to detect fraud based on visual anomalies in the data. On the other hand, if there is a series of claims that have already been verified at that time and place in the database, adjusters can be more certain that they have a valid case.
One of the best examples of how useful data visualization is to insurance adjusters is hurricane season. Verisk is able to track the weather around hurricanes and create predictive models based on where the related damage occurred. “By laying claims data on a map where storm damage occurred, we empowered our customers to confirm losses—as well as detect outliers that could be indicators of opportunistic fraud,” said Anthony Fiorino, vice president of engineering and chief data officer at Verisk.
To learn about the various other ways TIBCO Spotfire is helping Verisk help customers, check out the full case study now.
Scaling Enterprise Analytics: TIBCO Spotfire Rated #1 for Self-Service
With more citizen data scientists and non-technical knowledge workers needing to make sense of data, the demand for guided insight discovery has never been greater. This has been a trend across the analytics landscape for quite some time and is often categorized as “Decentralized Analytics” or self-service analytics.
Gartner defines Decentralized Analytics 1 as supporting “a workflow from data to self-service analytics, and includes analytics for individual business units and users.” In the most recent Critical Capabilities report for Analytics and Business Intelligence Platforms, TIBCO Spotfire® was recognized by Gartner, again, as the number one platform for the Decentralized Analytics use case.
We believe, the key factors driving TIBCO’s placement in the Decentralized Analytics use case were performance across each of the following highly-weighted capabilities: ease of use, visual appeal, workflow, data storage and loading, interactive visual exploration, analytics dashboards, and data preparation.
Analytics for Everyone
Line-of-business knowledge workers (often the internal “customer” of the data scientist) create a constant flow of ad-hoc reporting requests from all corners of an organization. Especially when all corners of the organization are requesting highly formatted exports or customized dashboards, this can be a waste of an organization’s valuable data science resources. As an asset, data science talent is capable of driving far greater value back to the business through deeper analysis.
Spotfire® has proven to maximize efficiency on both sides of this challenge in how it:
- Enables line-of-business workers and citizen data scientists to self-serve quickly and build simple dashboards.
- Empowers data scientists by not only liberating them from basic reporting requests but also providing a powerful underlying engine for the deeper dives into modeling advanced analytics applications.
Data Connectivity and Preparation
With a range of connectivity across disparate sources including relational databases, Hadoop, AWS, and other cloud apps like Salesforce, Spotfire®’s data connectivity (with the highest score possible, 5 out of 5) enables knowledge workers to connect and self-serve on analysis of a variety of sources.
As noted, this also frees up data scientists from the arduous manual tasks of data preparation. It’s hard to fathom that in 2019 we’re still spending about 80 percent of our collective analytics time preparing data for analysis. So, while data is widely viewed as the “new oil”, it remains a raw material in need of much refining. That refinery is Spotfire®, recognized for simplifying data preparation by opening up new avenues for deeper exploration across varied datasets. A full data lineage may be leveraged entirely by anyone across an organization with automatically captured dataflows, recording all workflow steps in-sequence on a fully editable canvas.
Suitable for Any Use Case in the Enterprise
In addition to its top ranking score for Decentralized Analytics, TIBCO also placed in the top two for each of the following use cases: Agile Centralized BI Provisioning, OEM or Embedded BI, Governed Data Discovery, and Extranet Deployment.
An adaptable platform for both simple and advanced use cases, Spotfire® is recognized for scaling across the continuum to serve a wide breadth of enterprise software users from frontline operations to analysts to citizen data scientists to data scientists.
For more on Spotfire®’s placement in the 2019 Gartner Magic Quadrant for Analytics and Business Intelligence platforms, read the full report.
Gartner, 2019 Magic Quadrant for Analytics and Business Intelligence Platforms, Cindi Howson, James Richardson, Rita Sallam, Austin Kronz, 11 February 2019
Gartner, Critical Capabilities for Analytics and Business Intelligence Platforms, James Richardson, Rita Sallam, Austin Kronz, 14 May 2019
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
1“Decentralized Analytics” as defined in 2019 Gartner Magic Quadrant for Analytics and Business Intelligence Platforms
Gilead Uses TIBCO Spotfire to Aid in Drug Safety
Gilead is a research-based pharmaceutical company that discovers, develops, and commercializes medicines for unmet medical needs such as HIV/AIDS, liver diseases, hematology/oncology, and inflammatory diseases. With over 25 products, Gilead is committed to ensuring global access to healthcare.
At Gilead, digital transformation is a continuous process as pharmacological research, development, and manufacturing are constantly changing. And as an FDA regulated industry, drug safety is paramount. To provide the insights required to thoroughly test drug safety, Gilead needed an analytics platform that would allow for deeper data discovery.
Gilead turned to TIBCO Spotfire for data discovery use cases spanning day-to-day operations monitoring as well as longer-term trend analysis. The tool is used across many teams in the organization; the clinical trials group uses it for statistical analysis, the operations team for live monitoring of data, the submissions team for trend analysis and live monitoring of submissions, and the Maximum Segment Size group for supporting signal detections. The implementation of Spotfire has helped everyone at Gilead better understand their data.
Across this llitany of use cases, Gilead has seen the following benefits from the implementation of Spotfire in its organization:
- Enabling early detection of unexpected drug safety problems by using Spotfire to identify outliers and patterns in complex, disparate data sources, which saves the business time and increases confidence
- Cross-team collaboration to find similarities in common data, allowing exploratory data analysis and working with data to be fast and easy for everyone
- Ability to extract quantitative attributes so teams can see day-to-day operational data and compare it to historical trends while identifying the root cause of outliers
Looking to the future, Gilead’s goals include automation, artificial intelligence, and accelerated use of Spotfire Towards these, the company is also looking to add TIBCO Data Virtualization as a way to expand data sources as well as machine learning to gain even more insights.
For more about how Gilead uses Spotfire to improve drug safety and aid in continuous innovation, read the full case study.
Spotfire Lands TIBCO in the 99th Percentile for March Tournament Brackets
The country’s most exciting college basketball tournament of the year has come to an end, and TIBCO pulled out a win once again. For the second year in a row, the TIBCO Data Science team landed in the 99th percentile with its forecasted bracket, including predicting the tournament champion, Virginia.
But how did they do it? With the help of predictive analytics tools TIBCO Data Science® and TIBCO Spotfire®. The two solutions are integrated to create a seamless data analysis experience. TIBCO Data Science finds and extracts data patterns, while the Spotfire platform delivers self-service analytics for everyone and works with all kinds of data, including historical sports data. By relying on the power of data science, TIBCO was in the top one percent of more than 70 million tournament brackets. Here’s how:
First, we had to provide Spotfire with data upon which it could build a model. We put historical public data sets for past teams into TIBCO Data Science, with statistics like offensive efficiency, rebounds, turnovers, points per game, and three-point shots. The platform took those data points and decided which were the most relevant for each team, based on game wins. The topmost relevant data points were then used to create an algorithm.
Next, we had to test our model. We ran our algorithm on the historical data, to see if we could ‘predict’ the outcomes that in actuality had already happened. When the predictions lined up with the true outcomes, we knew we had an accurate machine learning model.
Finally, when the full tournament lineup was announced on March 17, historical data for each of the 68 participating teams were put into Spotfire, and the model was run for each initial match-up followed by the predicted winners of each round.
Those predictions panned out, as TIBCO employees, as well as those who attended our Metrics of Data Madness happy hour and webinar, can attest: they have TIBCO to thank for the cash in their pockets from winning their own personal bracket pool.
Data science can give you a leg up in your bracket predictions—just think what it can do for your business. To learn more about how you can leverage TIBCO Spotfire on the court and in the office, download a free trial today. Make sure to check back in next March to get in on the action before the tournament begins, so that you can be the winner of your office pool.