Category Archives: Sisense
Humanizing Data: How Data Can Actually Save Lives
Data can change the world, really. In Humanizing Data, we spotlight how organizations are using data to help build a better world. We explore the journey they took, the people they affected, and the change they drove.
Recent coverage of the Indiana Donor Network showcased the amazing story around how data and technology is helping in an unexpected way: it’s saving more lives. Located in Indianapolis, they advocate for patients in need of organ, tissue, and eye donation and transplantation. Nationwide, there are over 114,000 people waiting for donors and every 10 minutes someone is added to the US national transplant waiting list. Simply put, the demand exceeds the available supply, so the nationally-driven matching process and care approach are only getting more complex.
The Indiana Donor Network aims to use technology to modernize its business and leverages data in the form of demographics, locations, and all kinds of other elements to minimize the time it takes to market, match, and measure effectiveness. They work in a tough business that is both devastating and rewarding, supports a valiant cause, and works on not just matching but also the aftercare of patients — with a passionate staff that has its eyes wide open around its current challenges.
This past year, through using Sisense as it continued to digitally transform its business, it saved (or greatly affected) nearly 700 lives, an increase of about 30%. In the truest sense of the word, they are a model for other organizations that want to plan, execute, and measure their results more effectively. The following is a Q&A with some key members of their staff around their story and the journey they are on.
Sisense: Tell us a bit about your organization — what do you do and how do you measure success?
Steve Johnson (Chief Operating Officer): We are a non-profit organization. We are one of 58 Organ Procurement Organizations which exist to facilitate the donation process. But most importantly, we are here to save lives and measure our progress closely. Every number is a real person — so it’s truly meaningful work. Everything is focused on that singular goal. We cover the state of Indiana but work with other states to partner on cases that are across state lines.
Sisense: How do you differentiate and market yourself and who is affected by your work?
Steve Johnson: With limited resources, we lean on education for awareness in traditional media markets (in radio), to sports marketing (Indy 500, Colts), and more locally in schools. Like any other organization, we use data to drive decisions and these decisions are aimed at saving lives. All of us are passionate about it and this cause motivates us.
Sisense: What types of data do you measure?
Steve Johnson: We use data for our marketing and operations — so we’re aiming to understand the demographics of individuals to drive outreach and to help inform events. And often the way we communicate is still less electronic and more through phone calls for a more personal and memorable experience. But our events generally are more about education than specifically raising money. We use data to identify the start of a donation at a hospital (e.g. when a patient is taken off life support). That is the most crucial time to ensure it’s handled properly and notifications are sent out.
Sisense: How broad or expansive is that data?
Courtney Tillotta (Manager, Aftercare): Through data, we are able to better understand our population. Our aftercare relates to both the donor and recipient. As an example, we are caring for 3,500 next of kin because we can more easily track these families and provide better care (like grief support). The organization and relationship of data has made us more efficient.
Sisense: What other benefits do you provide to patients that might surprise people? What’s an example of a type of person you would typically help?
Courtney Tilotta: With data in hand, we can understand our populations much better. As an example, we’ve seen older females in their 60s affected after their husband passes, so with this information, we can tailor programs and better anticipate their needs. We also commonly have next of kin outside Indiana (48 states affected in all) so we work to connect those families with local organizations to support the grieving and support of any family member — no matter where they live.
Sisense: What is the problem you aimed to solve using data?
Bill Janczak (Report Writer): I joined 4 years ago and at that point, most of our data was managed in Excel. Like most organizations, we lacked a master reporting setup and were working manually in Excel, looking at reports 30 days after activity. The organization wanted to improve speed and accuracy.
Sisense: How has data changed the way you manage your business and how has that transformation occurred?
Bill Janczak: We aimed to better automate our process and use that to build internal trust with others so that we could be more efficient through technology. Today, every screen in the office uses dashboards which deliver insights to others across the organization.
Sisense: Why do you need a BI platform like Sisense?
Bill Jancazk: The amount and locations of data is only getting more complex, so Sisense helped at the collection and analysis phases. We have call centers, patient lists, and hospitals that we work with so just like any other business, we can use the power of technology to manage the way we work and where we spend our time. The analytic apps and dashboards that we build help inform what we do, they don’t drive it.
Sisense: What’s the end product look like? What’s the right balance between using data versus human decision making?
Bill Jancazk: We have a “magic board” that includes 20+ dashboards we created. This identifies trends and allows a human to drill down to affect an action, or the next step. Data has surely made these decisions quicker and easier. We rely on Sisense at this point. It helps us reach our cause of saving lives.
Brian Becker runs content marketing at Sisense. He’s previously held similar roles at Chase and Thomson Reuters and is a regular speaker at marketing conferences and quoted in media outlets such as Digiday, AdAge, Business Insider and the Content Strategist.
Sisense Hackathon 2020 Recap: Dreaming Big, Building Bigger with AWS
At Sisense, we are builders. Create This is your firsthand look at what that means to us. Read on for Hackathon stories, new product developments, moonshot ideas, and more.
We’re always working on something new at Sisense. Innovating and experimenting with new ideas are core parts of our DNA; our R&D and AI teams never stop building the future of data. With all that creativity in the air, it’s no wonder that our annual Hackathon takes the energy to a new level. This year, the entire effort was built from the ground up, allowing the organizers to better manage the event and encourage more participation.
“The Hackathon is a very human celebration,” said Lio Fleishman, one of the organizers and a Solutions Engineer in our Partnerships and Alliances team. “We rejoice in what makes us human and what keeps pushing us forward: problem solving, imagination, and curiosity.”
The response company-wide was amazing! A grand total of 39 teams, from 6 of our offices, across 4 countries formed, collaborating on a wide array of projects. A whopping 80% of teams took advantage of the industry-leading power of Amazon Web Services (AWS)! If you read our Hackathon announcement, you know that AWS’s partnership wasn’t just cosmetic. We even had visitors from AWS pop in at our Tel Aviv, New York, and San Francisco offices to offer support (and tasty treats) as the festivities got underway.
Amazonians on site!
Amazon and Sisense have very similar DNA, from our customer obsession and ceaseless self-improvement to our abilities to work hard and have fun at the same time. Nothing could exemplify our partnership better than a Hackathon. Here’s what our AWS visitors had to say, in their own words.
Bala and Gary from San Francisco
“[It was a] great experience supporting the Sisense Hackathon. Loved the customer sentiment analysis using the data from Support teams with AWS Comprehend to understand in real-time what your customers are saying,” said Bala Keelapudi, Startup Partner Solutions Architect. “It was great to see the developers explaining the ease of getting up and running with AWS Comprehend.”
“So impressive to see the level of customer-centric innovation possible in 24 hours or (much) less, using AWS! And, using the latest AWS managed services, such as Comprehend, Lambda, API Gateway, RedShift…”
Gary Stafford, AWS Emerging Partner Solutions Architect
Jon from New York City
“Within my first few minutes on-site we were already troubleshooting SSH connectivity from Sisense office to an ec2 instance in us-east-1. It was really educational to work directly with the team to walk them through the network configuration and to check and verify that everything was in place for connectivity,” reported Jon Myer, Partner Solution Architect. “I really enjoyed my visit and seeing how everyone was working hard but having fun during the hackathon.”
Oren and Eran from Tel Aviv
“During our time in Sisense offices we witnessed several interesting initiatives that leverage AWS services and abilities,” said Oren Reuveni, a Solutions Architect at AWS SaaS Factory. “Data ingestion and analytics mechanism using Kinesis-analytics and Athena, an end-to-end serverless architecture that enables users to consume insights about their social network profile data and more.”
“I was deeply impressed seeing the culture of innovation and collaboration at Sisense Hackathon. Looking at the team translating the user interface using Amazon Translate shows truly customer-centric thinking.”
Eran Sidi, AWS Account Manager
It’s always fun to have friends pop in for a visit, but let’s get down to the serious business of talking about the ground-breaking projects our enterprising teams put together. The overall winner was the Magnificent Unicorn team with live-streaming analytics. The Kyiv office winners called themselves Wasted Potential and used machine learning and an open camera to figure out when the cafeteria lines were shortest. And, maybe most vitally, Big BIC Energy used a Raspberry Pi computer to handle the critical task of tracking how much beer is in the NYC office keg.
Read on to explore these amazing ideas. (Other than the world winner, they’re in no particular order!)
World winners, Magnificent Unicorn: Make faster, smarter decisions
The Sisense platform handles data of every kind, from every conceivable source, even allowing you to mashup live and cached data. The Magnificent Unicorn team took this to the next level with their streaming analytics project. For users with rapidly-changing datasets who need to see changes to their data as they happen, this is life-altering. Real-time BI is possible! And of course, AWS is a key to that possibility: pulling data into a Kinesis Stream Producer, then into Kinesis itself, a Lambda function, and finally through Socket.io into Sisense. The power of AWS undergirding Sisense is a proven winner and could change the way users understand and interact with their fast-moving data streams. No wonder they took world prize!
Wasted Potential: No more lunch lines
No matter where you are, this group created an application that removes long lunch lines. Our Kyiv winners know this, so rather than keep suffering in silence whenever they go down to the cafeteria for food, they made tech work for them. Using the publicly-available camera in the lunchroom, some AWS horsepower, and a little Machine Learning (ML) they came up with an effortless solution that lets them know exactly when it’s the best time to grab a bite.
By teaching a computer to visually recognize where the line forms and when there are humans on it, their algorithm is able to understand approximately how long the wait might be and advise users whether they should wait, or hurry up and eat. Users access the functionality via Slack (who doesn’t love Slack?). Then the information from the camera is pulled into an AWS visual recognition neural network (who doesn’t love neural networks?) and an answer is produced. Every request (and the associated queue time) is stored in Sisense, so that users can look at historical data and patterns.
All this was made possible by Amazon EC2, Amazon Lambda, Amazon Rekognition, and the Amazon API Gateway (and Sisense, of course).
Big BIC Energy: Beer barometer
It’s no secret that after a hectic week, a lot of us here at Sisense in NYC love to unwind with a cold beer (or nonalcoholic drink), so the keg holds a treasured place in many of our hearts. Oh the wails of consternation when someone pulls that lever and receives only a gout of foam and sadness! Those days are gone now, thanks to the BBE team “Beer Mate” project (with some help from AWS and a Raspberry Pi).
By attaching a flowmeter to the beer keg, using some handy Python code, and piping the data into a MySQL database on AWS, they’ve enabled the Sisense platform to achieve its most significant use case yet: letting everyone know how much beer is left! The system also informs our intrepid office manager, Sabrina, through Sisense Pulse Alerts when it gets low so that she can order more. Sisensers can also use Beer Mate’s integration with
Untappd to rate the beer and leave their thoughts. This is a great demonstration of just how outside-the-box Hackathon thinking can be, and how fun and useful the results often are!
What will you build?
We hope this brief glimpse into the Sisense 2020 Hackathon has given you a few ideas on setting up and running your own hackathon. There’s a reason why tech companies of all shapes and sizes have been holding these events for decades (and show no sign of stopping). New ideas from Hackathons end up in production all the time, including Sisense BloX to name one notable example. Whatever form your company’s hackathon takes, remember, the most important thing is to have fun, connect with your teammates, and build what matters to you!
Jack Cieslak is a 10-year veteran of the tech world. He’s written for Amazon, CB Insights, and others, on topics ranging from ecommerce and VC investments to crazy product launches and top-secret startup projects.
Predictive Analytics in Manufacturing: A Winning Edge
The modern manufacturing world is a delicate dance, filled with interconnected pieces that all need to work perfectly in order to produce the goods that keep the world running. In Moving Parts, we explore the unique data and analytics challenges manufacturing companies face every day.
Building an accurate predictive analytics model isn’t easy. It requires a skilled data team, advanced tools, and enormous amounts of clean data from the right combination of inputs. It’s a difficult process, but an effective predictive analytics engine is an enormous asset for any organization.
Big challenges, big rewards
Manufacturing companies are in a unique position regarding data: they create and capture tons of it every day. The process of producing goods is an enormous opportunity for data optimization. Raw materials need to be ordered, received, constructed, packaged, and shipped out for sale in the most efficient manner possible. Because the steps are repeated so many times through the process, a small edge created via predictive analytics in manufacturing will be magnified at every repetition to produce significant benefit.
Because of the cyclical nature of the manufacturing process, data-driven companies are building superior processes to create bigger and bigger advantages. Here are a few examples of companies using manufacturing analytics to win the future:
Predicting return rate
Skullcandy’s dive into predictive analytics started with the challenge of understanding return rates on new products. The logic was that if the team could predict certain features or aspects of a product that would lead to a return, they could optimize those policies around returning products. They used BigSquid to blend and analyze historical data related to returns and added their learnings to features and products on their roadmap. From there, the team could ask new questions of that dataset to understand the way customers were interacting with their products and ultimately build a better warranty policy for products before they were even released. This data was also useful for product managers, giving them a clear picture of what was making customers adopt Skullcandy’s products (or not).
Once the return rate questions were answered, the team focused their efforts on uncovering insights around reviews and warranty claims to generate insights about positive and negative drivers. Data like this is ideal for making decisions for product roadmaps. All those customer insights can be used in a number of creative ways to better focus resources and improve products.
Improve forecasts and maximize revenue
Gentex made the most of their budget to optimize their incoming revenue. Just six months after implementing predictive analytics, their ecommerce sales increased by 50%!
Gentex deployed Sisense to comb through millions of records after switching over from an outdated ERP system. They needed a platform that could churn through all that data quickly and deliver quick intelligence about both current and future revenues. Initially, Gentex created dashboards for their sales and operations teams that collected information about sales, quotes, and orders across the company. Those dashboards answered immediate questions about the current state of the business.
To answer more forward-looking questions, Gentex creates a sales forecast for an entire year using just a few months of data. They use predictive models to forecast revenues based on spending. They even incorporate trend data to improve accuracy over time. Currently, Gentex builds visualizations of year-to-date revenue data to forecast up to 15 months into the future.
Operating off those accurate forecasts, Gentex made the most of their budget to optimize their incoming revenue. Just six months after implementing predictive analytics, their ecommerce sales increased by 50%!
Improve inventory management with demand forecasting
Making a product that consumers want to buy is only useful if a company can find a way to get that product in front of the consumers who demand it. Several of today’s most cutting-edge manufacturers are blending historical customer data and external factors to predict demand for goods so they can increase production when demand will be high and decrease production when demand will be low. These companies aren’t just building for the future, they’re building the future.
The need to accurately forecast demand is crucial to these manufacturers. Assessing demand in real-time is ineffective since companies need to make decisions about demand far enough in advance to complete an entire production cycle and get that product in front of customers. With a solid predictive analytics model in place, manufacturers can create exactly the right amount of products (and the right variety of those products) to satisfy future customers.
These forecasts optimize sales revenue, but it also avoids unnecessary costs associated with producing, shipping, and stocking items that won’t sell. Accurate predictions are a win-win for any manufacturer.
Build your manufacturing business with analytics
Predictive analytics in manufacturing have gone from being science fiction to being a make-or-break addition to any company’s technology stack. Using a platform like Sisense for manufacturing analytics, combining internal and external information into a series of accurate forecasts is incredibly invaluable to any manufacturer. Improving any step of the manufacturing process is an advantage over the competition, but improving every step is a data-driven way to become an industry leader faster.
Adam Bonefeste is a veteran content marketing manager. When he isn’t writing copy, he’s probably reading books, running through San Francisco or getting lost in YouTube holes about math/logic problems.
Periscope Data Is now Sisense for Cloud Data Teams
Sisense News is your home for corporate announcements, new Sisense features, product innovation, and everything we roll out to empower our users to get the most out of their data.
Uniting to empower data analytic teams
Last May, we made a decision to join forces with Periscope Data to build a true end-to-end analytics platform. Our vision was a single data platform that combined features custom-built for data, product, and BI teams for every step in the analysis process. Both companies shared the common vision that the most successful companies are transforming into data companies. The end product that we envisioned would allow these customers to extract deeper insights from their data and make those insights more actionable across every line of business.
Today, I’m proud to announce that we’ve hit another important milestone on our merger journey. We no longer have any reason to talk about Sisense and Periscope Data as separate entities. I couldn’t be more excited to say that effective today, Periscope Data will be renamed Sisense for Cloud Data Teams.
Building the future for advanced data teams
Periscope Data has officially been a part of Sisense for quite some time now. The effect can be seen on the combined company culture, product roadmap, and especially our current data offerings. The announcement of Sisense for Cloud Data Teams illustrates the progress we’ve made as part of the merger.
The platform will still provide the same powerful in-app experience that our thousands of customers have come to love, now as an official part of the Sisense product family. The name will be changing, but the offering will remain the same: world-class technology for advanced data teams to discover powerful insights in data. We’re 100% committed to executing on our roadmap and continuing to innovate Sisense for Cloud Data Teams going forward.
Supercharging advanced data teams
Part of the new name is an appropriate recognition of the community of builders that has partnered with us to evolve the combined Sisense-Periscope offering to the world-class solution it is today: cloud data teams. These data professionals are driving the data revolution that is changing the way companies win with data. They’re asking new questions and continually pushing the boundaries of what can be done with our products.
In addition to the broad range of capabilities already offered as part of the Periscope Data product, such as data pipelines and advanced ad-hoc analysis, the Sisense for Cloud Data Teams package will enjoy numerous benefits from being a part of the core Sisense platform. Over time users will be empowered with self-service dashboard creation and a more interactive exploratory environment.
These builders are the reason Sisense invests so heavily in delivering new technology to elevate the effectiveness and enhance the impact of data and insights. The announcement of Sisense for Cloud Data Teams is an acknowledgment of the value these experts provide and a promise to execute on an aggressive roadmap for them.
Building a common Sisense vision
From the beginning, the merger of Sisense and Periscope Data was always about finding a way to create a single data platform that would deliver the most value to every user, regardless of their data needs or technical ability. However, Sisense for Cloud Data Teams is a huge step for advanced data teams. This data analytics platform gives those teams a more powerful way to make their insights operational for other leaders within their organizations.
The co-founder of Periscope and now one of my partners on this journey, our CMO Harry Glaser, has brought his own perspective and energy to Sisense’s vision of a true end-to-end data platform and has expressed his thoughts here.
Sisense for Cloud Data Teams is the operational realization of the vision we shared last year at the outset of the merger. It’s the result of years of work to build a platform that enables advanced data teams to accelerate organizations all over the world. We couldn’t be more excited about the journey to deliver this functionality to modern data builders.
While delivering Sisense for Cloud Data Teams is an important milestone, it’s only the beginning of our goal to deliver the best data analytics platform. I’d like to thank the customers who have come this far with us and encourage builders everywhere (including product and BI teams) to continue innovating with Sisense. Power to the builders; insights for everyone!
Amir Orad is the CEO of Sisense, a successful entrepreneur and a Big Data, cybersecurity, and financial technology thought leader, with proven success in leading and scaling businesses.
Sisense for Cloud Data Teams: a Step Forward for Builders Everywhere
Sisense News is your home for corporate announcements, new Sisense features, product innovation, and everything we roll out to empower our users to get the most out of their data.
A vision for builders
Nearly eight years ago, I joined my friend and Periscope Data co-founder Tom O’Neill (now Sisense Chief Cloud Officer) on a journey to build an advanced analytics platform that empowered data teams to build amazing new things. Last May, that journey took a big leap forward with the decision to merge Periscope Data and Sisense. In the time since that announcement, our team has been working extremely hard to combine technologies, customer bases, and internal operations into a single organization that achieves the goal we set out to accomplish in 2012.
Recently, that merger matured to a point where we felt it necessary to change the way we talk about the products. Internally, we were no longer keeping Periscope Data and Sisense separate products in our long-term vision and we wanted to make the external conversations we were having consistent. Rather than building two separate data analytics platforms for two separate parts of the overall data analysis process, we are focusing on building a single data platform with tools optimized for every user.
To reinforce that cohesion, we renamed Periscope Data as Sisense for Cloud Data Teams. When we began setting important checkpoints for the progress of this merger, this change was always an important milestone. I’m proud to be able to say that we’ve officially achieved it.
Building a shared culture
To this point, the merger has been a success story. Part of that success is due to our incredible customer base, which has been eager to listen to the shared vision and work with us to make it a reality. The truth is that building an ambitious product like this requires equally ambitious customers who can be relied on to take our data analytics platform to new heights and provide feedback along the way. It is neither a nicety nor an oversimplification to say that we could not have done this without the brilliant data professionals who use Sisense every day.
At this time last year, Periscope Data and Sisense both had strong company cultures. Merging them together required shared focus and developing a new culture around that commonality. For us, that common goal was a dedication to customers. Sisense for Cloud Data Teams is a testament to how well the new culture has jelled around servicing our combined customer base.
While the two original products focused on different users and unique parts of the data analysis process, the end-to-end platform available today is the result of a shared passion for empowering customers to create value from data. There’s a lot of enthusiasm about the data platform we are building and how it will be used to help out customers move their organizations forward.
Rewarding our internal builders
Another big pillar of the culture at Sisense is a commitment to employee success. Independently, both Sisense and Periscope Data had made several “best places to work” lists before the merger, which is illustrative of the priority given to employee satisfaction. There’s no pressure to produce perfect results, we’ve built an atmosphere that encourages continual experimentation and rewards those who help others. We set high standards for our work, give employees the resources they need to hit their goals, and trust that they’ll succeed.
That attitude is a huge reason why Sisense for Cloud Data Teams is available today and why future innovations at Sisense will set new industry standards for data platform possibilities. Renaming a product is not a simple process. It’s one that takes cooperation from all parts of the organization.
While the announcement of Sisense for Cloud Data Teams is a big reason to celebrate, there’s an understanding around the company that we’re not just building a product, we’re building a culture. The success of one goes hand-in-hand with the success of the other.
Building for the future
The announcement of Sisense for Cloud Data Teams is a milestone commemorating our dedication to building tools for advanced data professionals. It’s an industry-leading tool that already plays a crucial role in the way the most advanced companies are analyzing data. The success of that product so far is the result of individual people — both customers and employees — who share a vision for the possibilities of data and work to achieve it.
Sisense for Cloud Data Teams is also the next step forward in the journey to empower data teams. It removes existing workflow barriers between data professionals and the rest of their teams, streamlining the path from actions to insights. This tool is a leap ahead for modern data teams. The unified team Sisense and Persicope Data tea is building something truly incredible.
Harry Glaser is the Chief Marketing Officer and General Manager of San Francisco at Sisense. He was the co-founder and CEO of Periscope Data, which merged with Sisense in May 2019. Prior to founding Periscope Data in 2012, Harry was a Product Lead at Google AdWords and graduated from the University of Rochester with a bachelor’s degree in computer science.
Deriving Value from Data Lakes with AI
Artificial Intelligence and machine learning are the future of every industry, especially data and analytics. In Growing Up with AI, we help you keep up with all the ways this pioneering technology is changing the world.
Use AI to tackle huge datasets
Let’s talk about AI and machine learning (ML). Does your company actually have the tools and processes to use these innovations? Or do you just say that you have them? “AI-washing” is a disturbing trend among tech companies today, where they (correctly) ascertain that AI will be the deciding factor in the next generation of technologies, but don’t actually have the AI horsepower to back up their product, service, or claims. They slap a term like “AI-powered” or “AI-enhanced” to their offering and hope it’s enough to keep customers engaged.
“AI-washing” is a disturbing trend among tech companies today, where they slap AI language onto their product or service, but don’t actually have the AI horsepower to back up their claims.
However, half-measures just won’t cut it when it comes to handling huge datasets. Data is growing at a phenomenal rate and that’s not going to stop anytime soon. Many companies are already sitting on datasets so massive that it’s not possible for humans to deal with them unaided. AI and ML are the only ways to derive value from massive data lakes, cloud-native data warehouses, and other huge stores of information. This is both a daunting challenge and an inspiring opportunity, since effective use of AI and ML can cut through about 80% of data preparation (the annoying, routine stuff), leaving humans to handle the remaining 20%, the actual modeling and optimization.
Overcoming the obstacles between you and revenue
Once your data is prepared for analysis, the next question is: how else can AI help you? There’s a belief held by many in the AI and analytics worlds that the Holy Grail of data strategy is the ability to turn every question the business could ask (strategic, tactical, and operational) into requirements that an AI system can understand. Then the AI would go into the data, find the respective answers, and serve them up in easy-to-digest language that even a nontechnical team member can handle. This simple goal of “business questions to ML” would massively alter the business world, allowing users of any technical skill level to effortlessly draw deep insights out of their data. However, this sweeping paradigm shift will require the work of countless AI experts to tackle challenges that the industry simply hasn’t figured out yet.
There just aren’t enough AI and data science practitioners to go around to tackle this lofty goal. A recent Gartner report estimates that “by 2020, 50% of organizations will lack sufficient AI and data literacy skills to achieve business value.” Apply that metric to any other business-critical function. Can you imagine if 50% of companies didn’t have coders or sales reps or any of the other vital roles that keep the lights on? It’d be considered a crisis and an emergency. That’s the state of AI. Implementation of AI elements is expected to almost triple in the US while the overall supply of AI experts will stay significantly lower than demand.
Speaking of the demand, there’s another hurdle to AI success in the short-term. Business teams are looking for very specific tactical insights from their AI utilities (profitability, sustainability, brand, customers, etc.) compared to the Data Science/AI teams, which are more interested in strategic improvements including Natural Language Processing, computer vision, optical character recognition, and neural nets. Further complicating matters, research and development groups focus on bread-and-butter issues like scalability, security, reliability, and performance. Communication between these disparate teams, each with their own goals and desires, means that often novel AI solutions and applications are created and abandoned because stakeholders elsewhere in the company don’t understand the value they produce.
Get money out of your data
So where does this leave companies that want to survive and thrive in the coming AI apocalypse?
Most existing business models are somewhere in the “diminishing returns” region of the economic value curve (investing more resources while revenue stays flat). More and more funds are poured into R&D with no additional associated revenue coming in. The right data and analytics platform can help you bridge the gap between your current AI and analytics paradigm and where you want your company to be in the future.
Your platform should be able to effortlessly connect to numerous large datasets: cloud-native data warehouses and data lakes with millions or billions of rows. It should also be able to handle live datasets that constantly update and allow you to mash them up with cached data. If the system also allows for in-database preparation and materialized views that can be optimized for faster queries and lay the foundation for advanced analytics with Python and R, even better. All these utilities put powerful tools into the hands of data engineers to perform more complex analysis and use ML on huge datasets.
Whatever you want to do with AI and ML in the long term, the right analytics platform will be key to building it. It’s also a better way to monetize your data in the short term. It’s a win-win that more and more companies will rush to embrace as they undergo digital transformations and cloud migrations. It’s the future.
Inna Tokarev-Sela, Sisense’s Head of AI, has over 15 years’ experience in the tech industry. She spent the last decade at SAP, driving innovations in cloud architecture, in-memory products, and Machine Learning video analytics. A frequent speaker at industry events like IBC, NAB, WonderlandAI, and Media Festival, Inna holds a BS in Physics and Computer Science, an MBA, and an MS in Information Systems, having written her thesis on Neural Networks.
Periscope Data Expands to Israel, Empowering Data Teams with Powerful Tools
With data growing at a staggering rate, managing and structuring it is vital to your survival. In our Event Spotlight series, we cover the biggest industry events helping builders learn about the latest tech, trends, and people innovating in the space. In this piece, we detail the Israeli debut of Periscope Data.
Driving startup growth with the power of data
Growth. It’s the aspiration of every startup. The challenge is to do it right, and a crucial way to achieve it is with decisions based on data and analysis that drive measurable business results.
This was the key learning from the Sisense event heralding the launch of Periscope Data in Tel Aviv, Israel — the beating heart of the startup nation. We hosted over 150 people from more than 100 companies, who gathered to learn why data can supercharge their companies and how harnessing the huge power of data can take business from startup to unicorn.
An exciting slate of presentations took them on a journey from why to how they should use data analytics to optimize their operations successfully and maximize their business opportunities.
There are plenty of opportunities for startups, but huge challenges too, with more than 50 percent of US startups failing to last longer than four years. That’s why it has become increasingly vital for companies to leverage fresh data and deploy accurate analytics, so they can make smarter decisions that will enable them to prosper. Data teams have become the key to many organizations’ success, and the transformation of data into insights is now a critical business process, as Sisense Israel Sales Country Manager, Davidi Boyarksi, explained in his introduction.
Periscope Data provides the ideal platform for data teams to establish an effective analytics environment in the type of emerging companies that are so prevalent in Israel. It’s why Sisense, having merged with Periscope Data in May 2019, chose to host this event in Tel Aviv.
What VCs want from startups
The day’s first guest speaker, Itzik Parnafes, General Manager of global technology-focused VC investor Battery Ventures, set the scene, with an enlightening overview of the KPIs that VCs focus on during startup growth. He outlined how critical measurable results are to help VCs make major investment decisions — metrics such as revenue, net vs gross earnings, sales, costs and projections, and more. The importance of giving this information to VCs reinforces how significant a role data plays in measuring and predicting growth and enabling companies and investors to gain insights that drive growth.
The rise of the data team: from startup to unicorn
With Itzik’s wisdom fresh in everyone’s minds, Scott Castle, Sisense General Manager, Data Business, shared his view on the role of modern data teams. Scott whisked us through the history of business intelligence from its first definition in 1958 to the current rise of Big Data.
He talked through how the mind-blowing escalation of data and the drastic reduction in the cost of its storage has led to more complex, sophisticated uses of data and a shift in the way it’s managed and consumed. And he explained that the leap to visual-based data discovery put analytics in the hands of data experts working in different lines of business, rather than in IT, where bottlenecks could previously occur, and reporting could take much longer.
Scott outlined how this change has driven a shift in the role of data teams, who now occupy strategic business positions. These teams now play a far more significant part in shaping company strategy than ever before, because the opportunity of Big Data necessitated skilled data professionals not just to crunch the numbers but to find value in the information. Scott said:
Periscope Data’s mission is to turn data teams into super heroes because we see our role as unlocking the potential that data teams already have.
Scott Castle, Sisense General Manager, Data Business
And he emphasized why they should be treated differently. They’re no longer simply a repository for information. Instead they operate as an engine for transforming their organizations, using predictive technologies to answer business questions that shape decisions, roadmaps, and future strategies. He concluded that data teams can influence the transformation of startups into unicorns. A platform like Periscope Data is what makes this happen, by elevating data’s prominence and the role of data teams beyond merely a source of visual-based data discovery.
Responding to Scott’s presentation, attendees from AppsFlyer, Ziv Ben Naim, Client Services Analytics & Strategy Lead, and Maayan Dukas Kfir, Data Analyst had this to say:
It has been good to get aligned with what’s out there from a data team’s point of view. We really liked Scott’s story of the development of BI and Big Data and the vision he presented. It was interesting to get such a data orientated perspective, today, and as data specialists, it really resonated with us.
Ziv Ben Naim, Client Services Analytics & Strategy Lead, AppsFlyer
Maayan Dukas Kfir, Data Analyst, AppsFlyer
The impact on customers
The impact of the changes in the data ecosystem has already taken effect in numerous startup customers in Silicon Valley, and Kyle Dempsey, Senior Sales Engineer at Sisense shared some great examples of how Periscope Data has helped them grow from infancy to unicorn. Quoting Keystone Research, he opened with the finding that:
Companies who use data effectively have 18% higher gross margins and 4% higher operating margins
Keystone Research
And he demonstrated how the Periscope Data platform overcomes the challenges of huge data volumes that can’t be easily modeled by traditional BI. Citing Tinder as a major example, Kyle explained how it constantly uses data to enhance users’ interactions and calibrate the user experience. No surprise then that Tinder beat Netflix to become the highest-earning non-game app on both Google Play Store and the Apple Store.
Similarly, Kyle outlined how Flexport, the world’s first international freight forwarder and customs brokerage built around an online dashboard, uses Periscope Data to analyze billions of records, and get answers in seconds. Periscope Data enables Flexport to investigate critical business issues, identify opportunities and recommend ways to streamline business and operational processes by providing the company with the fastest time to insight, and powerful capabilities to answer any business questions, whether they derive from BI reporting, advanced analytics, or data prep for machine learning. Kyle said:
We empower data analysts to create more business value than any other BI platform.
Kyle Dempsey, Senior Sales Engineer, Sisense
These customer examples demonstrated how impactful smart data management and analytics can be for every part of customer organizations, from data teams to marketing, sales, and beyond. Another of Kyle’s case studies reinforced this point: Oscar Health, a disruptor in the private health insurance sector. The company has integrated data analysis throughout its organization to power decision making. From a startup in 2012, it is now valued at $ 3.2 billion. A true unicorn.
Optimizing data pipelines: How Kongregate uses Periscope Data
All these presentations provided rich food for thought, but there was more to follow. We were delighted to welcome Max Murphy, Software Engineering Manager for one of our customers, Kongregate, a leading mobile and PC game developer, publisher, and web gaming portal.
Kongregate has been using Periscope Data since 2013. Max demonstrated how Periscope Data enabled Kongregate to replace its ETL (extract, transform, load) system and apply software development best practices from end to end. The key element was standardizing its data pipeline from event source to charts, enabling Kongregate to gather, manage, and analyze a larger volume of data from across all data sources.
Additionally, Max discussed how embedded analytics offers huge benefits both to the company and third-party developers with whom they partner. He explained:
The big piece is embedding analytics in the Kongregate platform so game developers can see the performance of their games
Max Murphy, Software Engineering Manager, Kongregate.
The easy set-up and access to embedded analytics enable them to measure KPIs, get game statistics, monetization and retention statistics that help them to optimize players’ experience, hone best practices and benchmarks, and maximize stickiness and profitability.
Diving deeper into the datasphere: Data lakes — best practices
The day’s journey from the “why” through the “how” of data analytics then continued. We dived deeper into the mechanics of building the most efficient data pipelines to access and deliver data for analysis, even with the challenges of Big Data.
Omid Vahdaty, CTO of Jutomate Ltd., shone the spotlight on best practices with data lakes. As a specialist in Big Data solution architecture and systems engineering, Omid shared lessons from his extensive experience designing systems in startup environments. He addressed the challenges presented by today’s huge volume of data that’s enabled by cloud storage services. This data is often complex and difficult to model, particularly when it can be stored in both transformed and raw, structured, and unstructured formats. Omid discussed how to build data pipelines using data lake technologies, including the architecture and the guidelines needed to build your own data lake, using leading cloud vendors.
And extending this deep dive, Kyle stepped up once more to walk everyone through the ways Periscope Data handles data. His comprehensive demo showed how the SQL, R, and Python languages can be utilized and can work together to enable data teams to optimize performance.
This particular capability caught the attention of Dror Katz, Lead Data Analyst at Toluna (market research company), who was at the launch. He said:
We wanted to get familiar with new technology. We were familiar with Sisense before this event. It has been great to learn what capabilities Periscope Data has in comparison to other BI platforms. What stood out was the integration with Python and the added value that Periscope Data can offer.
Dror Katz, Lead Data Analyst at Toluna
And his views were reiterated by the AppsFlyer team attending the event, who learned about Periscope Data for the first time, and as a result, felt that:
. . . now we know more about what tools we’re missing, specifically regarding the integration of Python, SQL and R, which we don’t have with our current tools.
Ziv Ben Naim, Client Services Analytics & Strategy Lead, AppsFlyer and Maayan Dukas Kfir, Data Analyst, AppsFlyer
How Namogoo built a data-driven culture with Periscope Data
Of course, seeing how Periscope Data works in practice adds more color to the theory. Our final guest, Ohad Greenshpan, Co-Founder and CTO of Namogoo, presented a fascinating case study. Namogoo is a SaaS company that helps online businesses prevent customer journey hijacking. Its technology detects and blocks unauthorized ads injected into consumer browsers that redirect website visitors to competitor products and promotions.
He explained how Namogoo has used Periscope Data to grow its business and develop a data-driven culture at the company. Using the platform has empowered its teams to identify opportunities and take ownership of developing strategies informed by insights. Ohad explained that it has helped them discover things that they never even thought about, which has beneficially influenced decisions. He articulated the biggest benefit of all in one of his closing remarks:
The power of Periscope is in its simplicity.
Ohad Greenshpan, Co-Founder and CTO of Namogoo
Making an impression
Our launch of Periscope Data in Israel clearly left a positive impression. Dan Postar, CEO, Business Wise Solutions (Big Data and analytics consultancy), who already knows Sisense, said:
I think Periscope Data is a great addition to the Sisense capabilities. It was great to get an introduction to Periscope’s capabilities and an overview of what we can get from the Sisense suite.
Dan Postar, CEO, Business Wise Solutions
The event demonstrated that advanced BI and analytics are invaluable for startups facing intensified challenges from an environment that’s increasingly competitive and rich with data. Managing data so that organizations can more confidently make smart decisions is fast becoming a prerequisite for growth.
Periscope Data is perfectly designed to help dynamic startups and fast-growing companies achieve this. It adds to the armory of innovations that gives Sisense the capability to support organizations at every stage of growth, from startups to enterprise to OEM.
We’re excited to have launched Periscope Data into this fertile market, and we look forward to the opportunity to help accelerate the growth of many fantastic businesses.
Adam Murray’s early career PR started in London, then moved on to New York and Tel Aviv. He’s spent the last ten years working with tech companies like Amdocs, Gilat Satellite Systems, and Allot Communications. He holds a Ph.D. in English Literature and when he’s not spending time with his wife and son, he’s preoccupied with his beloved football team, Tottenham Hotspur.
6 Tips for Data Teams to Improve Collaboration
Everyone wants to get more out of their data, but how exactly to do that can leave you scratching your head. Our BI Best Practices demystify the analytics world and empower you with actionable how-to guidance.
In the right hands, data is the ultimate means to answer important business questions. The problem is that when data is used incorrectly, it still provides answers (just bad ones). The best way to avoid decisions made based on that bad information is to improve the relationships between the employees analyzing data and the employees acting on that information.
Today, data questions often involve someone from a line-of-business team and someone from a data team. Both people bring their own individual expertise to the collaboration, but these projects can easily fall into the trap of unclear expectations and insufficient communication.
Before building a dashboard to answer questions, data experts need to sit down with their line-of-business counterparts and have a discussion about the purpose of that dashboard. Here are a few tips for data experts to get the most out of that meeting:
Go into every dashboard with an open mind
It’s crucial to start every new data inquiry with a fresh mind. Every assumption the data experts make is an opportunity for the overall insight to lose value. On the other side, provided assumptions and business context can accelerate the data work for a faster time to value. The goal of every dashboard creator in a data request meeting should be to fully understand the data consumer’s needs and workflow. That means listening to their individual context and letting the investigation go where the data leads. If you drive the conversation or ask leading questions, you’ll end up at the resolution that you want, not necessarily the one that’s most valuable.
Get to know the individual requesting the data
Like most collaborative projects, empathy is critical to success. I like to start data request meetings by asking the data customer to walk me through their typical day. In some cases, it even helps me to shadow them for a while. What I’m looking for is a complete picture of the way that individual uses data. There might be pain points or missed opportunities for data to be used that I can help integrate. Asking someone about their typical day is also an easy way to get them communicating openly. It disarms them and puts the focus on the personal connection rather than a business problem.
Understand the business value behind a data request
An easy way for a data inquiry to get off track is for the dashboard creator to receive a request and cut straight to building a dashboard. Sometimes a request for a specific metric might miss the bigger question that data can solve. For example, a CS person might say “give me churn” but what they mean is “we need to find a way to minimize churn.” If you were to build a chart that simply listed churn over time, you’d miss all the other data points that correlate with churn. It’s often the data team’s job to connect data from different teams and maximize business value. Narrowing in on one team’s KPIs is an easy way to take your eye off the real goal. If you understand what is at the heart of the data request, it’ll be easier to work backward and find the right questions for the data to answer.
Work in pairs if possible
It’s true that two heads are generally better than one, but this concept is less about brainpower than it is about having one set of hands dedicated to note-taking. While a second data expert can definitely help understand the bigger questions in a meeting about data, it is invaluable to the flow of the conversation to have someone designated as a note-taker. Since the conversation moves at the speed of the slowest participant, it is extremely disruptive to have one person focusing on both moving the conversation forward and also documenting the important information for later. If a paired approach isn’t possible, it’s always an option to record the conversation, but I also recommend sketching out potential charts during these sessions, and that doesn’t translate well through audio.
Start general, then get specific
It’s likely that the initial dashboard request will be for a very pointed metric. This isn’t a bad way to start, but it’s the data expert’s job to guide the bring the focus to a bigger general question that this data can solve and uncover complementary data that can be included in that dashboard. Consider the example of churn from earlier. Presumably, the company’s goal is to maximize revenue and the inquiry into churn is being done to that end. It’s probably valuable to first create a variety of charts that illustrate the effect of churn on revenue. From there, it might also be useful to consider churn for individual cohorts, find factors that correlate in some way with churn or locate levers that will improve churn. Organizing the questions you ask from general to specific will also help you organize the charts on your dashboard when it’s time to create that asset.
Build an unsorted list of questions to answer with data
At the end of the meeting, the best thing to come away with is a list of questions that can be translated into data queries. An easy way to build this list is to open a document and just list the big questions that come up as you have exploratory conversations and try to understand the bigger business issue. If you can work as a pair, your note taker can do this part too. When the meeting is over (or maybe afterward if you recorded the conversation and want some time to go back and review notes), you can submit the list of questions to the dashboard requester and make sure that you have everything covered. Once that list is approved, you can start mapping each of the questions into a chart that will be organized and placed on your final dashboard.
Thinking long instead of short
Building a dashboard isn’t a straight line: there’s a lot of back and forth, questioning, editing, and iterating between the initial request and the finished product. These tips will help you build a strong foundation for this process and leave you in the best position for creating a dashboard that can really help your users make smarter decisions. Try embracing a collaborative process; you’ll be glad you took the time, asked the hard questions.
Christine Quan is a seasoned data and analytics veteran, focused on data visualization theory and building tools to empower data teams. She’s an expert at constructing SQL queries and building visualizations in R, Python, or Javascript.
Better Data Requests = Better Data Results
Building a Data-Driven Future
It’s important for everyone at a company to have the data they need to make decisions. However, if they just work with their data team to retrieve specific metrics, they are missing out. Data teams can provide a lot more insights at a faster rate, but you will need to know how to work with them to make sure that everyone is set up for success.
Data teams can be thought of as experts at finding answers in data, but it’s important to understand how they do that. In order to get the most value out of your collaboration, you need to help them understand the questions that matter to you and your team and why those questions need to be answered. There are a lot of assumptions that get built into any analysis, so the more the data team knows about what you are looking for, the more knowledge they may find as they explore data to produce their analysis. Here are four tips to make more productive requests from members of your data team:
Approach data with an open mind
It’s important to treat the data request process as an open-ended investigation, not a way to find data that proves a point. A lot of unexpected insights can be found along the way. Make your goal to ask questions and let your data team search for the answers. This approach will allow you to get the best insights, the type of unknowns that could change your decision for the better. If you put limitations on what you’re asking the data, you’ll end up putting limitations on the insights you can get out of your inquiry.
To really dig into this, think about how questions are answered scientifically. Scientists treat any bias as an opportunity for the insight to be compromised. For example, let’s say you are looking to improve customer satisfaction with your product. Requesting a list of customers with the highest and lowest NPS scores will give you a list of people who are happiest or most frustrated, but it is not going to let you know how to improve customer satisfaction. This request puts too much attention on the outliers in your customer base rather than identifying the key pain points. That’s part of the picture, but not all of it. If you’re trying to create a program that targets your goal, let your data team know the goal, give them a few optional starting points, and see what they come back with. They might surprise you with some sophisticated analysis that provides more insight and helps you launch a fantastic program.
Start with a conversation, not a checklist
The single biggest mistake a line-of-business professional can make when requesting data is to present a data expert with a list of KPIs and tell the data team to just fill in the blanks. This approach misses so much of the value a data team can provide. Modern data teams have technology and abilities that allow them to go much further than just calculating numbers. They can guide analytical exploration through flexible, powerful tools to make sure you’re getting the most valuable insights out of your data.
Instead of a list of metrics, think about starting your data request as a meeting. You can provide the business context needed and a list of questions that you want answered. You can even present some initial hypotheses about what those numbers may look like and why they might move in one direction or another. This is a great way to kick off the conversation with your data counterpart. From there, you can benefit from their experience with data to start generating new and more informed questions from their initial inquiries. The data team’s job is to get you information that helps you be more informed, so give them as much context as possible and let them work as a problem solver to find data-driven recommendations.
Data should recommend actions, not just build KPIs reports
A lot of standard business KPIs measure the results of company efforts: revenue, lead conversion, user count, NPS, etc. These are important statistics to measure, but the people tracking them should be very clear that these numbers track how the company is moving, not why it is moving that way. To make these data points actionable, you need to take analysis further. Knowing that your NPS is going up or down is useless if it doesn’t inform a customer team about the next step to take.
A good data team will map important KPIs to other data and find connections. They’ll comb through information to find the levers that are impacting those important KPIs the most, then make recommendations about how to achieve your goals. When you get a list of levers, make sure to understand the assumptions behind the recommendations and then take the right actions. You can always go back to those KPI reports to test if the levers are having the intended effect.
Data requests are iterative, give the data person feedback
Communication about data should not end when the data has been delivered to you. It’s important to dig into the analysis and see what you can learn. Instead of reporting that data or taking action on it right away, you should check with your dashboard creator to make sure that he or she can verify that you’re reading all of the data properly and that the next steps are clear. There are a lot of ways to misinterpret data; a good way to prevent mistakes is to continue communicating.
Even if you’ve gotten the right takeaways from the data, it’s still good to consult with your dashboard creator and go over your interpretation of the information so they know how you read data. You may need a follow-up meeting to restart with the overall question you want to answer, then see what additional data needs to be collected or what modifications are needed to make the report or dashboard work best for your intended use-case.
Sisense at AWS re:Invent — Helping Enterprises Win in the Analytic Apps Economy
AWS re:Invent is the single most important gathering of builders every year. As an AWS Data & Analytics Competency partner, we’re honored to join and power this innovative community.
It’s an innovative community because it’s being fueled by a thriving economy — the analytic apps economy. Executives, business leaders, and Chief Data Officers are leading the way when it comes to generating new value and new revenue for their customers with analytics products and services. Companies that aren’t taking advantage of this trend will be left behind.
If you want to unlock new value for your customers and generate millions of incremental revenue leveraging Sisense and AWS, we’d love to help. Our executive leadership team will be at re:Invent to share their experiences. Register here to set up a time to meet!
A proven track record
If you’re not attending AWS re:Invent, but your company is on a journey to generate new value and new revenue for your customers, you’re in good company:
For instance, the leaders at GE Healthcare were early believers in the idea that they could drive more financial, operational, and clinical outcomes for their partners and customers by extracting actionable insights from the data they created and managed for them. Leveraging Sisense, they created millions in recurring revenue and added a strong market differentiator.
At Sisense, we work across industries on many of those opportunities. It’s now become clear that:
- All companies can participate in the multi-billion analytic and AI apps economy, creating new go-to-market and revenue streams
- Sisense truly is the best platform to support those innovations at scale, leveraging existing infrastructures, people, domain expertise, and markets
Maximizing return on existing investments
As an enterprise executive, it’s important to realize early in your career that a core path to success is not always the “rip and replace” or “different must be better” approach. It is far more beneficial to a company to find ways to accelerate what we call return on existing investment — the ability to create revenue from existing assets like data, market penetration, domain expertise, technology decisions, people, and operational excellence. Building a strategy to monetize incremental value from those assets is key to success in existing or new markets.
In fact, large enterprises are the only ones capable of innovation at scale. These enterprises are perfectly situated to successfully compete with the startup ecosystem, leveraging the following strengths:
That’s exactly what successful developers and product managers building apps on AWS and Sisense do: they select best-in-class technologies, design a flexible platform architecture, enable business lines to focus on delivering incremental value to their customers, differentiate through outcome selling, and experiment cost-effectively.
Sisense works with Fortune 500 companies across industries, unveiling opportunities that add up to millions of incremental, high-margin revenues, delivering five times that value to their customers. Every day our strategy consulting business and customer success teams help identify opportunities for millions in new revenue and advise businesses about how to organize, budget, and go to market. Some have built-in advantages: those with a solid AWS deployment with centralized data, fast time to build AI algorithms, and a developer-friendly interface to manage workflow applications have a head start in leveraging Sisense to embed actionable insights into their core products and monetize those assets.
Building vs. buying the platform
When technology was less mature, it might have made sense for enterprises to build and own both the platform and the apps. But that leads to more resources spent on technology and fewer resources invested in using your domain expertise to solve customer problems and deliver outcomes. That’s the opposite of what a smart business wants to do!
Companies can avoid that tradeoff by using a microservice-oriented architecture to create that new revenue. This year, the dream came true when in August we released the most modern native-cloud architecture available for builders. This technology is exactly what any company needs to significantly increase its AWS ROI (it was built for Amazon Linux 2). We worked closely with AWS on the product and continue to do so as we roll it out to customers.
If you’re not a platform company, you must understand the “build vs buy” debate: Competing on quantifiable outcomes is a much better use of existing internal resources (vs competing to develop the best platform) and customer time. Once you’ve set up the right AWS/Sisense infrastructure, all you have to do is to find what high-value questions need to be answered. Consider what types of new value you can create for your customers, your suppliers, and your end-users. Then deliver on that value and get a share of that outcome.
Meet you at re:Invent
Sisense is the prime way to supercharge your Amazon Redshift and overall AWS investments in today’s analytic apps economy. Let’s talk more at re:Invent! Register here to meet with the Sisense executive team or visit us directly at booth #837.