Category Archives: Data Mining
Accelerate Your Data Strategies and Investments to Stay Competitive in the Banking Sector

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2020 changed banks’ priorities, focusing more on data-driven insights that enhance resiliency and ensure exceptional customer experiences. Emerging technology (like predictive analytics, data management, machine learning, and artificial intelligence) can transform financial institutions, creating capabilities business-wide—from customer interactions to redesigned business processes to new risk and pricing models.
A recent IDC InfoBrief, sponsored by TIBCO, Connected Intelligence in Banking, states that “one out of every three customers said that their banking needs have changed due to disruption, including increased or decreased spending; needing more credit; or saving and paying down debt. This disruption puts pressure on banks to respond to individual needs.”
How can financial services rise to face changing customer needs? With a data platform as the foundation for the future, like TIBCO’s Connected Intelligence Platform.
Read on to learn about the digital capabilities that are revolutionizing the banking industry and how banks are using TIBCO Connected Intelligence to gain a competitive edge.
Personalize Your Offerings
What if you could connect to your bank on your favorite smart device whenever and wherever you needed? What if it was easier to upload checks, get access to your money, and update your account? What if your bank could analyze your portfolios and offer unique advice? With an increase in digitalization, customers now expect these streamlined, personalized services from their financial services.
By utilizing new data capabilities, banks can engage with customers in an omnichannel way, whether on mobile, web, social media, in-person, or over the phone. They can integrate AI-financial planning to offer timely and relevant financial advice and better connect their partner ecosystem with APIs to provide the most comprehensive solutions.
BNP Paribas goes beyond simple channel integration and ensures seamless omnichannel engagement so that a customer can start and complete interactions without losing any data, no matter which device they are on. Additionally, BNL, part of BNP Group, launched the first 100 percent digital mobile bank in Europe to satisfy a new generation of customers.
Drive Insights in Real-Time
Streamlining business operations with automation is the top priority for banks worldwide going into 2021. Instead of missing customer opportunities or getting insights too late, use real-time capabilities to harness the power of data.
Real-time models allow banks to monitor business-wide operations more efficiently. Whether it’s fraud detection, next action models, or dynamic pricing, banks can execute decisions based on immediate, accurate information. They can increase customer satisfaction by sending relevant offers in real-time or by speeding up responses to customer service issues.
Bank of Montreal saw three times the acceptance of customer offers after implementing advanced analytics solutions. By presenting more relevant and timely offers, the bank now gets real-time responses from customers and can target them based on interactions that happened moments before.
Predict Future Outcomes
Where will the market shift next? What will customers want next week, next year? Predictive analytics can answer these questions, and more, allowing banks to understand consumer trends, anticipate future events, and reduce instances of fraud. The main benefits of predictive analytics are below:
- Anticipate the needs of your customers
- Improve your portfolio risk with better models
- Reduce the cost of fraud management
Consorsbank uses predictive analytics to identify prospective customers, analyze account opening and closing processes, and discover potential risks when onboarding new customers. The bank reports a 20 percent increase in revenue after launching TIBCO Spotfire to analyze customer dialogues.
Simply put, 2020’s disruption has accelerated the banking industry’s digital transformation. Click To Tweet
Simply put, 2020’s disruption has accelerated the banking industry’s digital transformation. Banks should look to use data as a foundation for their future success. To holistically manage intelligent initiatives, consider using TIBCO’s Connected Intelligence Platform to solve all your data needs.
Read this IDC Infobrief, sponsored by TIBCO, Connected Intelligence in Banking, to learn how financial services can become more personalized, more predictive, and more real-time than ever.
3 Ways Data Virtualization is Evolving to Meet Market Demands

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Why is data virtualization so popular today?
More industry leaders are implementing data virtualization as part of their data integration strategy than ever before. Data virtualization technology has steadily evolved over the past fifteen years, so why has interest suddenly spiked?
Because modern data virtualization solutions are proving incredibly valuable to data consumers, IT, and technical teams.
- New capabilities are allowing companies to enable business-friendly data, faster provisioning of new data, and self-service data access.
- With the rising interest in self-service data discovery, these capabilities are attracting business users and enabling them to find and build their own views across disparate data sources.
- Technical teams are also benefiting from data virtualization as it reduces IT costs, enforces access controls for better governance and security, and supports data demands across the business.
To realize these benefits, more companies are looking to data virtualization. Below is a more in-depth look at the three major areas where data virtualization capabilities are evolving to meet growing market demands.
Data virtualization and self-service capabilities
Organizations are now seeing a rise in a new class of citizen data scientists and citizen data engineers who use self-service analytics tools. But their demands for data to access and consume can be overwhelming, requiring a data virtualization solution that gives them faster access to data while maintaining security and governance controls.
TIBCO® Data Virtualization includes several new self-service capabilities, including:
- A self-service web user interface that gives users the ability to rapidly create and publish their own views that are fine-tuned to best fit their needs. Users can create personalized views that will then be available for consumption by third party downstream apps.
- Data catalog functionality allows the users to search and find available data. Users can therefore gain a full understanding of what data is available for their consumption, on cloud and on-premises.
- With new virtual views, business analysts, data engineers, and developers can create datasets, perform complex SQL queries to manipulate data, and then publish the result set. Using a drag and drop feature, users can do so with limited or no knowledge of SQL.
- An intuitive and updated layout ensures users can easily understand what they’re looking at and overall understand how the view was originally defined before making any updates.
Accelerating cloud migration with data virtualization
Most organizations are looking to move to the cloud, but this journey can be complex and opens up new challenges. While some hold the common belief that moving to the cloud will eliminate your data silos, most organizations face even more data silos in the cloud. Moving to the cloud actually increases a company’s data integration requirements.
And while there are several data integration approaches, data virtualization is the best option. Data virtualization can help organizations integrate cloud data silos and ensure a smooth, rapid migration to the cloud.
So, before you get started on your journey, make sure you can answer the following questions:
- What are the top data challenges to migrating to the cloud?
- What are the requirements to simplify data access?
- How can data virtualization accelerate this migration?
For answers to these key questions, read this whitepaper or watch this webinar on best practices for accelerating workload migration to the cloud using data virtualization.
Data virtualization: a security layer for analytics
While enabling business users with self-service features may be a priority, businesses must strike the right balance between strong security and business agility. As data volume, variety, and complexity grows, requirements around compliance, protecting data assets, and mitigating risks becomes an increasingly important part of every data management strategy.
With TIBCO Data Virtualization, organizations can gain a unified view of all the data and a single consistent software layer for enterprise-wide data security management. This enables business and IT agility, inspires confidence, and increases productivity. Business users gain confidence in data, and IT benefits from better control and easier management of this critical enterprise asset.
To learn more about how to leverage data virtualization as a security layer for your analytics, check out this whitepaper.
The future of data virtualization
Every organization yearns for consistent, well-governed data that is easy to access and use. To achieve this, more businesses are turning to data virtualization and driving greater business value from their data.
Organizations are seeing a rise in a new class of citizen data scientists who use self-service analytics tools, requiring a data virtualization solution that gives them faster access to data while maintaining security and governance controls. Click To Tweet
Ready to do the same? To get started on your data virtualization journey, head to https://www.tibco.com/products/data-virtualization and begin laying the foundation for faster, smarter decision making.
New Data-centric Innovations for Your High-Tech Manufacturing Company

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For high-tech manufacturers, new data solutions are industry necessities. Digital transformation remains the number one priority for manufacturers to stay viable and competitive during unprecedented circumstances like disrupted supply chains and socially-distanced factories.
By 2021, Microsoft reports that 20 percent of G2000 manufacturers are predicted to depend on technologies like IoT, blockchain, and machine learning to automate large-scale processes. Here are some things your competition is already doing:
- Monitoring factory equipment remotely
- Detecting anomalies in complex processes
- Preventing equipment breakdowns
- Reacting to supply chain disruptions in real-time
Don’t get left behind by new data innovations. Read on to learn about how the high tech manufacturing industry is responding to change and what you can do to stay ahead.
Launch Your Digital Factory
Some of the most transformative innovations in high tech manufacturing come from businesses that apply integration, data, and analytics technology to all facets of their operations. Manufacturers can transform business outcomes by using data-driven insights and strategies to reshape their value chain.
Break free from legacy attitudes and embrace new business models to transform your enterprise. By introducing a Digital Twin and feeding it with data from various data sources, you can have a holistic view of the entire factory and simulate new business scenarios before actually starting the production of new products. Use digital twins to:
- Accelerate the adoption of new channels and ecosystems
- Generate new revenue streams
- Reinvent existing business models
Focus on Customer Experiences
High Tech Manufacturers who place customers at the center of their strategy and deliver their business model in sync with customer expectations build trust, deliver greater value, and develop sustainable loyalty.
Create a data-centric approach to customer satisfaction by connecting manufacturers with customers for more shared information. Use advanced analytics to:
- Measure and track customer engagement (such as product recalls, on-time deliveries, and defect rate)
- Optimize end-to-end customer journeys and improve supply chain resiliency
- Place customers at the center of business strategies, processes, and systems
Unleash the Power of Your Data
Data-driven insights and decision-making allow businesses to optimize every opportunity. Better insights lead to streamlined decision-making, improved return on capital investment, and to more optimized expenses.
Improve overall performance to become more efficient and profitable. Use predictive maintenance and operational 360 views to:
- Increase your overall equipment effectiveness (OEE)
- Proactively respond to events through real-time insights
Don’t Delay Operations Any Longer
Hemlock Semiconductor has already reduced its manufacturing costs and improved output quality and yield with its digital transformation. What are you waiting for?
Hemlock Semiconductor has already reduced its manufacturing costs and improved output quality and yield with its digital transformation. What are you waiting for? Click To Tweet
Read this e-book to start your journey to become a data-centric enterprise; it details success drivers, potential challenges, and competitive solutions for high tech manufacturers so you can optimize your business and increase your market growth.
New to Formula One Racing? The Top 5 Facts You Need to Know

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There are some big changes happening in Formula One, which means it’s a great time to start watching if you are new to the circuit. With the recent Mercedes-AMG Petronas car launch, new 2021 regulations, and shifting race schedules, there’s a lot to catch up on for long-time fans and newbies alike!
Want to impress your friends with your F1 knowledge? Here are the top 5 facts new F1 fans need to know.
1. The Language of Formula One: Explained
Formula One has a lot of industry-specific terms, which have come to the forefront with the recent car launches. New regulations talk a lot about downforce, dirty air, and floor edges. But what are they and what do they mean?
Here are some key terms, explained:
- Downforce, also called negative lift, pushes the car onto the track and helps with grip
- Dirty Air, related to slipstream, is the wind turbulence caused by the car in front–allowing the car behind it to reduce drag
- Floor Edges refers to the parts of the bottom of the racecar that help with aerodynamics (new regulations have changed what this can look like in 2021)
2. Racing All Over the Map: Why It Matters
If you’ve ever watched James Bond, you’re familiar with the prestige of Monaco. The flashing lights, shiny cars, bedazzled stars–all that is true of a race weekend–but don’t let the glamour fool you. Racing is hard work.
The most important thing you need to know about Formula One circuits is that each racetrack comes with its own set of challenges, whether it’s altitude, angles, speed, or climate–each team has to meticulously prepare for every race.
Here are some quick facts about some of the hardest tracks:
- Monaco Grand Prix: Cars race through the streets of Monaco, taking tight turns onto narrow alleys and through a tunnel–it’s the only race that doesn’t adhere to all F1 safety standards.
- Bahrain Grand Prix: This race has tricky turns and major environmental factors due to the sandy desert conditions. Sand blowing on the track can reduce visibility and traction.
- Singapore Grand Prix: Imagine sauna-like humidity and heat along with grueling driving conditions and blinding lights for two hours straight–this is the Singapore Grand Prix. Just one mistake can wreck a car–no runoffs included.
3. What’s With All the Flags? A Quick Explanation
Flags identify when cars must slow down or exit the track based on external conditions. Believe it or not, racing flags can actually change the outcome of a Grand Prix. A black flag can end a race for the best of drivers. Here’s what they mean:
4. Formula One in the Media: What to Watch
Formula One is no stranger to the movies and every die-hard fan has seen them all. The easiest way to understand the sport (and your racing friends) is to see F1 in action both on and off the big screen.
Here are some of the most famous movies and tv shows you can binge:
5. Mercedes Reigns Supreme: the Ultimate Racing Champions
So who should you keep an eye out for during race weekends?
Mercedes-AMG Petronas is a 7-time world champion in F1. Their premiere driver, Lewis Hamilton, broke over 4 major F1 records just last year. And over our five years of partnership, the Team has amassed more than 50 race wins by using data insights to inform car design, race strategy, and driver performance.
For the latest information on Formula One, keep up to date here, where you can learn how the Mercedes-AMG Petronas Formula One Team uses data and analytics as a competitive advantage to fuel victory on and off the track.
And follow along on all the excitement with TIBCO on our LinkedIn, Facebook, Twitter, and Instagram accounts with the hashtag #TIBCOfast. See you in Bahrain on the 26th!
Railway Analysts Talk Data-Driven Freight Transport Transformation

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North American freight transport provider, Norfolk Southern, is now leading its industry in data analytics. The Class 1 freight rail service provider, which covers nearly 20,000 route miles from Florida to Canada, uses its data to improve speed and consistency, keep customers informed, and even make necessary pivots/adjustments to accommodate COVID-19-related challenges.
A certain degree of transparency is expected in this digital age, particularly in the shipping and transportation industry. Customers today want to track the progress of their shipment from point of origination through delivery, in real time—a truly modern concept to apply to an industry like railroads that far predates computers.
Modern Solutions for an Established Industry
To modernize itself so it could meet twenty-first century digital business challenges, Norfolk Southern took a fresh look at its data analytics, teaming with TIBCO to make it possible. This process helped Norfolk Southern identify its strengths and weaknesses as well as how to better take advantage of its data assets.
Perhaps the largest benefit to come from digitizing Norfolk Southern was the ability to centralize its operations to one main dispatching center. In 2018, the company built a ‘home base’ for all of its operational functions, called the Network Operations Center. Every operating train is monitored in real time from this location, providing a 360-degree view of transport progress and delivery times. With all of its data and decision-making originating from the same source, improved communications and more efficient transportation services resulted.
In Conversation with the Railway Analysts
To capture the full story of the rail service provider’s digitization, TIBCO Chief Analytics Officer, Michael O’Connell, sat down for a Q&A with Jonathan Holliday, Director of Business Process and Systems, and Josef Kaufer, Superintendent of Locomotive Analysis, at Norfolk Southern. Check out their conversation to learn how the company invested in a twenty-first-century digital transformation to speed deliveries and improve customer transparency with the help of TIBCO.
And in case you weren’t in attendance, you can catch Norfolk Southern’s customer session from TIBCO NOW 2020 here.
Plant Seeds for Future Growth: What to Expect at TIBCO Analytics Forum 2021

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With spring comes the promise of new beginnings and the blossoming of new life. This year, embrace the spirit of spring at the TIBCO Analytics Forum (TAF) 2021 by learning about new analytics and data management technologies and approaches and how to foster growth in the coming years.
You can take the insights and knowledge harvested from TAF 2021 back to your organization and plant them as seeds for future growth.
Plan ahead with the agenda at a glance
At this two-day virtual event, you’ll hear all about the latest innovations and developments currently shaping the future of analytics and how industry leaders are applying these solutions to overcome today’s most pressing data challenges. Attend TAF 2021 for engaging presentations on successful applications across industries, including energy, manufacturing, healthcare and life sciences, consumer goods, retail, transportation and logistics, and more.
Explore the Agenda at a Glance for a breakdown of the two days awaiting you, including:
- Keynotes with TIBCO and inspiring guest speakers
- Breakout sessions with customers and partners on how to succeed across industries
- Product trainings for beginners and advanced users on data management, visual analytics, data science, and streaming analytics
- Virtual networking with peers and industry experts
- Spotfire deep dives and hackathon to test your knowledge and win exciting prizes
For more on what to expect, check out the highlights from TAF 2019 on the TAF community homepage. And get a head start on upping your analytics knowledge by exploring the TIBCO Community Blog and Spotfire demo gallery.
Customize your experience with curated tracks
To make sure you get the most out of your TAF 2021 experience, you can even customize your own event calendar along one of the following content tracks:
- Real-world Success: Learn how customers, partners, and product experts use analytics and data management solutions to turn data into insights that transform organizations and achieve results. Case studies and sessions will span industries such as oil and gas, renewable energy, manufacturing, pharmaceuticals, healthcare, and more. Use cases discussed will include digital transformation, business reinvention, customer intimacy, and operational excellence.
- Tech Deep Dives: Learn “how the magic happens” through a technical lens. Product capabilities, customer and partner success, and architectural approaches will be in the spotlight. Products discussed will include TIBCO Spotfire®, TIBCO® Data Science, TIBCO® Streaming, TIBCO® Data Virtualization, TIBCO EBX™, TIBCO WebFOCUS®, and more.
- The Future of Analytics: Learn about the opportunities provided by the changing face of analytics. Sessions will cover how to connect best practices in governance with ethical artificial intelligence (AI) and ways to immerse yourself in the open subsurface data universe (OSDU) conversation and be part of an open future. Also, learn more about the “better together” vision for TIBCO and ibi products, and sharpen your focus on the product roadmaps that matter most to you.
- Product Training and Demos: Build up your skills with TIBCO and ibi products. Learn how to use TIBCO EBX, TIBCO Data Virtualization, TIBCO Spotfire, TIBCO Streaming, TIBCO Data Science, and TIBCO WebFOCUS through lecture and product demonstrations. Get deeper knowledge into the advanced features and functions of Spotfire including IronPython scripting and Spotfire Mods.
Spring forward: register now!
Take a leap forward this spring. So much compelling content awaits you at TAF 2021. Join us and invest in growing the value of your analytics and data management programs. Register Now!
Plus, we are offering an academic discount of 50 percent off the full registration price! Just select “Government/Education” from the drop-down menu when you register to ensure you receive the discount.*
Take a leap forward this spring. So much compelling content awaits you at TAF 2021. Join us and invest in growing the value of your analytics and data management programs. Register Now! Click To Tweet
*To receive the discount, make sure to enter your educational institution email address. If you don’t have one, please reach out to events@tibcomeetings.com and provide proof of your involvement with an educational institution or send us a copy of your student ID.
The Road to S/4HANA: Is Your Business Ready to Seize SAP Transformation Opportunities?

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It’s more than fair to say that we have truly entered the era of digital transformation. Organizations have recognized what a valuable asset their data is and have begun to move to the next phase of evolution: transforming to create a more functional, connected ecosystem around this data to utilize it to its full potential.
And now, many of these businesses that have held off on their transformations are getting a push in the form of SAP migration. As SAP prepares to end support for SAP ECC ERP and enterprises prep for the move to SAP S/4HANA, there’s a significant amount of anxiety surrounding the migration process and the challenges involved.
SAP Migration: More Than Just a Move
Migration should be a priority for your business right now, and careful thought and planning are critical. However, it’s just as crucial not to get so caught up in the scramble to migrate that you forget why you’re doing it in the first place: to accelerate your agility and connectivity, gain more comprehensive data management, and set the foundation for post-migration innovation.
It’s true that the migration path is likely to contain challenges, but it’s also filled with opportunities to reach your destination with the tools to become a truly digital organization.
Transformation Challenges: It All Comes Down to Data & Business Process
Aside from macro issues like the cost of migrating a deployment to a newer system, most of the core issues and challenges you can expect to encounter when planning for your SAP migration will often involve data and the business processes that utilize it. Is the data across multiple ECC instances? How much of it is there? How accessible—or rather, inaccessible—is it? Add to this the issue that there’s rarely ever one master data model, syncing data models across systems, process compliance, data integration and connectivity…the list goes on.
Transformation Opportunities: Focused on the Future
But it’s not all doom and gloom. You can use your organization’s SAP transformation as a chance to solve these issues and, instead of migrating blindly, do it purposefully with the goal of future-proofing your organization. Define your business processes, and optimize and integrate your SAP data to drive operational insights and profit.
According to Constellation’s Q4 2020 Top CIO Survey, 77 percent of organizations have made digital transformation their top priority in 2021 and are determined to get left behind not just in the migration to SAP S/4HANA but in the race to getting the most value from SAP and their data as a digital business.
You can use your organization’s SAP transformation as a chance to solve these issues and, instead of migrating blindly, do it purposefully with the goal of future-proofing your organization. Click To Tweet
For many businesses, there are still many questions surrounding S/4HANA migration. But here at TIBCO, we have the answers. Check out our webinar in collaboration with Constellation Research for a deep dive into reducing migration risk, powering innovation with SAP, and why the path to digital transformation is only the beginning.
Transforming Big Data into Actionable Intelligence
Attempting to learn more about the role of big data (here taken to datasets of high volume, velocity, and variety) within business intelligence today, can sometimes create more confusion than it alleviates, as vital terms are used interchangeably instead of distinctly. However, when investigating big data from the perspective of computer science research, we happily discover much clearer use of this cluster of confusing concepts.
Before we dive into the topics of big data as a service and analytics applied to same, let’s quickly clarify data analytics using an oft-used application of analytics: Visualization!
Looking at the diagram, we see that Business Intelligence (BI) is a collection of analytical methods applied to big data to surface actionable intelligence by identifying patterns in voluminous data. As we move from right to left in the diagram, from big data to BI, we notice that unstructured data transforms into structured data. The implication is that methods of data analytics are applied to big data, the methods of data preparation and data mining for example, to bring us closer and closer to the goal of distilling useful patterns, knowledge, and intelligence that can drive actions in the right hands.
Hopefully this clarifies these complex concepts and their place in the larger analytics process, even though it’s common to see pundits and outlets tout BI or big data as if they were ends in themselves.
AI-driven analytics is a complex field: The bottom line is that datasets of all kinds are rapidly growing, causing these organizations to investigate big data reporting tools or even approach companies whose whole business model can be summed up as “big data as a service” in order to make sense of them. If you’ve got big data, the right analytics platform or third-party big data reporting tools will be vital to helping you derive actionable intelligence from it. And one of the best ways to implement those tools is to embed third party plugins.

Big data challenges and solutions
When you have big data, what you really want is to extract the real value of the intelligence contained within those possibly-zettabytes of would-be information. To best understand how to do this, let’s dig into the challenges of big data and look at a wave of emerging issues.
For starters, the rise of the Internet of Things (IoT) has created immense volumes of new data to be analyzed. IoT sensors on factory floors are constantly streaming data into cloud warehouses and other storage locations.
These rapidly growing datasets present a huge opportunity for companies to glean insights like:
- Machine diagnostics, failure forecasting, optimal maintenance, and automatic repair parts ordering. Intelligence derived from these systems can even be fed to HR teams to improve service staffing, which further feeds to enterprise HR management and performance solutions (AI-based analytics reporting to ERP solutions)
- Assembled products shipped also feed directly to ERP on updating supply chain solutions, improving customer awareness and experience
To put it bluntly, the challenge we face is that no cloud architecture yet exists which can accommodate and process this big data tsunami. How can we make sense of the data wont fit in the enterprise service bus (ESB)? (ESB is a middleware component of cloud systems which will be overwhelmed if a million factories were to all try to extract intelligence from their sensors all at once.)
One solution with immense potential is ”edge computing.” Referring to the conceptual “edge” of the network, the basic idea is to perform machine learning (ML) analytics at the data source rather than sending the sensor data to a cloud app for processing. Edge computing analytics (like the kind platforms like Sisense can perform) generate actionable insights at the point of data creation (the IoT device/sensor) rather than collecting the data, sending it elsewhere for analysis, then transmitting surfaced intelligence into embedded analytics solutions (eg. displaying BI insights for human users).
The pressure to adopt the edge computing paradigm increases with the number of sensors pouring out data. Edge computing solutions in conjunction with a robust business intelligence big data program (bolstered by an AI-empowered analytics platform) are a huge step forward for companies dealing with these immense amounts of fast-moving and remote data.
Big data analytics case study: SkullCandy
SkullCandy, a constant innovator in the headset and earbud space, leverages its big data stores of customer data regarding reviews and warranties to improve its products over time. In a twist on typical analytics, SkullCandy uses Sisense and other data utilities to dig through mountains of customer feedback, which is all text data. This is an improvement over previous processes, wherein SkullCandy focused on more straightforward performance forecasting with transactional analysis.
Now that SkullCandy has established itself as a data driven company, they are experimenting with additional text analytics that can extract insights from reviews of their products on Amazon, BestBuy, and their own site. Teams also use text analytics to benchmark their performance against their competitors.
SkullCandy’s big data journey began by building a data warehouse to aggregate their transaction data, reviews. A breakthrough insight/intelligence in product development occurred thanks to the text analysis of warranties through which SkullCandy was able to distinguish between product issues and customer education. The fact that AI-based analytics can delineate between product and education in a text message is groundbreaking. A common pattern was that clients were returning a product as broken when in fact they simply didn’t know how to use bluetooth connectivity.
Data-driven product development also benefitted: Big data analytics allowed SkullCandy to analyze warranty/return data that showed that one of their headsets, which was used more during workouts than previously thought, was being returned at a higher than normal rate. It turned out that sweat was causing corrosion in terminals, leading to the returns. The outcome was to waterproof the product.
Among the many successes SkullCandy achieved, we also see a pattern of value derived from big data.
Big Data as a Service: Empowering users, saving resources
Strictly speaking, “big data analytics” distinguishes itself as the large-scale analysis of fast-moving, complex data. Implicit in this distinction is that big data analytics ingests expansive datasets far beyond the volume of conventional databases, in essence combining advanced analytics with the contents of immense data warehouses or lakes.
In order to get a handle on these huge amounts of possible-information, the AI components of a big data analytics program must necessarily include procedures for inspecting, cleaning, preparing, and transforming data in order to create an optimal data model that will facilitate the discovery of actionable intelligence, identify patterns, suggesting next steps, and supporting decision making at key junctures.
Intelligence drawn from big data has real potential to transform the world, from text analysis that reveals customer service issues and product development potential to training financial models to detect fraud or medical systems to detect cancer cells. Savvy businesses will empower users, analysts, and data engineers to prepare and analyze terabyte-scale data from multiple sources — without any additional software, technology, or specialized staff.
Fortunately, it is now possible to leverage all of these potentials and to avoid the cost and time of in-house development, by embedding expert third party analytics. Recognizing the tremendous task of big data analytics in conjunction with the value of outcomes, the natural propensity exists to use it as a service, and thereby reap the benefits of big data as a service as quickly as possible.

Chris Meier is a Manager of Analytics Engineering for Sisense and boasts 8 years in the data and analytics field, having worked at Ernst & Young and Soldsie. He’s passionate about building modern data stacks that unlock transformational insights for businesses.