Category Archives: Sisense
Winning with Data: A COVID-19 Relief Package for Customers
Together, we face a truly unprecedented situation. The global Coronavirus (COVID-19) crisis affects all of our families, our businesses, our communities, and our very way of life. In times of great disruption, our collective ability to continue to march forward and identify solutions will ensure we persevere.
As a data and analytics company, we are doubling down on doing everything possible to help enable both those on the front lines who fight this crisis directly and those impacted by it across so many important industries, from small businesses to Fortune 100 companies. During this time, Sisense is doing everything in our power to support customers and the broader industries and markets that we serve.
Tackling new challenges in the age of COVID-19
Personally, I’ve spent most of the last couple of weeks talking (over video, of course) to our customers, as well as sourcing ideas from our employees about how we can help them. Dozens of customers shared their stories about new challenges that their businesses are facing. Our reality has changed — from WFH and social isolation to tele-everything and healthcare capacity challenges, to mass reductions of flights and ever-shifting supply chains. We live in a new normal that is unlike anything we’ve experienced.
I’ve spoken to a credit union using analytics to identify new unemployed members to provide direct support. I’ve spoken to a railroad that needs to redirect all of their routes to meet the new demand curve. I’ve met a healthcare company that is learning to optimize capacity and advance resource planning. I’ve met an eCommerce company using data to understand and adjust for a sudden burst of activity.
We’ve worked with a municipality whose elected leaders are using dashboards to communicate critical information for decision making, as well as a state that strives to balance the challenges of the crisis between ensuring both public health and economy, a balancing act that reviews different scenarios to make critical decisions. We’ve helped a company tracking the impact of the virus in the UK to guide citizens accordingly, and a NY entity that joined the NY COVID-19 task force.
Everyone is impacted. Data will be central to how many organizations rethink how to manage inventory, reallocate staff, optimize supply chains, or reassess marketing activities. Our customers need to respond quickly using data and analytics, as well as AI and machine learning to make better, smarter decisions. They cannot afford to wait months building new data warehouses or IT projects. They need nimble decision-making tools and empowered data teams.
Launching a COVID-19 Relief Package to Our Customers
We are incredibly grateful to the 2,000+ customers around the world who entrust us with their analytics needs. Sisense is a global company that is now fully remote and distributed across three continents. In addition to the many individual discussions and help we regularly provide, we’re formally announcing a comprehensive relief package for all customers to ensure their success and effective reaction to this crisis:
- Free unlimited analytics: Each and every one of our customers will now receive free licenses for uncapped, unlimited use of our analytics for 90 days to expand the use of Sisense to others within their organizations to provide insights that improve decision making.
- Free help/professional services: Data is at the heart of many of the most important business decisions, so we’re offering a few hours of help and technical consulting at no cost to address near-term business problems or conduct strategic planning projects. This can help take advantage of all the free licenses or be used for other initiatives.
- Strategic consulting with our leaders: Our experienced executive leadership team has built a library of best practices and tools they use to run their departments during this special time and shared it with all our customers. They are also available for business counsel, to guide our customers where analytics will provide the greatest impact.
Acting today for a stronger tomorrow— helping to empower those directly fighting COVID-19
Analytics plays an essential purpose on the world stage and provide great value during this crisis. Darwin once said, “It is not the strongest that survive, but the most responsive to change.” The world’s response to this crisis is extraordinary, as much as for what it has required from us as people to what it has revealed around how we work, and the impact we can each make.
We also want to contribute on a global scale. To those organizations who are on the front lines (committing their own resources, time, and energy to battle this crisis), we’re offering our software and professional experts at no cost to any organization engaged directly in the fight against COVID-19, to rid the world of this very real pandemic. We will make it free for the next 90 days as a start, and more as needed.
Our talented team at Sisense, as well as so many other good and resilient people, will rise to meet — and defeat — the challenges posed by this virus. If there are any critical issues or if you are aware of any opportunities for us to help others with our technology or analytics expertise, please let us know at [email protected].
I will continue to communicate with you around what else we are doing and know that we always value your questions, ideas, and feedback. We will overcome this challenge together.
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 by orders of magnitude.
Building Better: Ethics and Product Development
As software’s role in business proliferates, the ethical implications of decisions made by developers only become more profound. In Big Questions, we outline the biggest concerns facing development teams and look at how companies are grappling with them.
In our increasingly digital world, software has a fundamental role to play in almost everything we do. It’s the backbone of how our global workforce collaborates, it underpins much of our industrial operations, and even ensures the smooth-running of our cities.
A failure of these critical software systems, whether intentional or accidental, can result in damaging, even fatal, consequences. Unfortunately, these failures are all too common. It seems that every week we hear of another privacy infringement, security breach, or technology misuse in the news. With all this in mind, it’s never been more important for software developers to be aware of their ethical responsibilities.
Building a more secure future
The information means that a company’s data and infrastructure are its greatest assets, and weaknesses. According to Mandiant in their 2020 “M-Trends Report,” 22% of attacks on companies were for IP theft or corporate espionage, while a whopping 29% of attacks were for direct financial gain. Interestingly, only 4% of attacks covered by the report were to weaken the target’s systems for another hit later. This is worth knowing because 31% of customers covered in the report who suffered one attack saw another within the following 12 months!
The Internet of Things only makes the rise of attacks on companies more likely and more challenging to deal with as it continues to grow; more than 20 billion new devices are forecast to connect to the internet this year alone. Malware creators are ready and waiting to infiltrate the software underpinning these devices. This mounting threat landscape is something that developers cannot afford to ignore.
“The greatest ethical challenge facing software developers today is how to secure information in to reduce the risk of intellectual property loss to external threats (like hackers) in shared environments without affecting ease of use for the end user,” said Thomas Holt, a professor in the school of criminal justice at Michigan State University, whose research focuses on computer hacking and malware.
“If encryption or security becomes too cumbersome, employees will be less likely to use them or create workarounds, so there must be thought given as to how to protect vulnerable information in a distributed and networked environment.”
A Code of Ethics developed by the Association for Computing Machinery (ACM) states that computing professionals should “design and implement systems that are robustly and usably secure.” They should do this by integrating mitigation techniques and policies, such as monitoring, patching, and vulnerability reporting. It’s also important that developers take steps to ensure parties affected by data breaches are notified in a timely and clear manner.
The inspiration to consider the repercussions of a product team’s actions can come from any source:
“As a practicing security professional in tech, I’m grateful to see that many modern engineers were influenced by Isaac Asimov, inventor of the Three Laws of Robotics,” says Ty Sbano, Sisense Chief Security and Trust Officer. “Rule number one is ‘A robot may not injure a human being or, through inaction, allow a human being to come to harm.’ Even though the term ‘injure’ may be severe, if you take a step back, the mandate for product teams remains the same when it comes to acting ethically: In the case of privacy and ethics, or any other impact your creation could have on users and the world, you really need to think these things through.”
“Where machines learn like humans and from humans, unconscious bias is as much a threat as with humans.”
Lothar Determann
User acceptance testing and other best practices can help developers avoid implementing security precautions that are too confusing, are situationally inappropriate, or otherwise inhibit legitimate use. ACM is clear that there should be no compromise here: in cases where misuse or harm are predictable or unavoidable, it says the best option may be to not implement the system.
Give your customers (and everyone else) privacy
Software developers are often asked to create solutions that enable the collection, monitoring, and exchange of personal information. Our increasingly global workforce collaborates via a host of digital technologies spanning multiple countries and territorial borders. Employers can use many of these technologies to monitor employees, but at what point is it considered an invasion of privacy? How far do software developers integrate this ability to monitor into their solutions?
“Respect privacy” is featured high up in ACM’s Code of Ethics, which says that software developers should only use personal information “for legitimate ends, and without violating the rights of individuals and groups.” This means taking precautions to prevent the re-identification of anonymized data or unauthorized data collection, ensuring the accuracy of data, understanding the provenance of the data, and protecting it from unauthorized access and accidental disclosure. Personal information gathered for a specific purpose should not be used for other purposes without the person’s consent.
Fight algorithmic bias to deliver better products
Algorithmic bias describes systematic and repeatable errors in a computer system that create unfair outcomes. This danger looms greater than ever today thanks to machine learning techniques that are fundamentally changing the way software is made.
“Instead of coding instructions for machines from A-Z, engineers provide massive data sets to programs with high-level instructions to figure out solutions on their own,” said Lothar Determann, partner at multinational law firm Baker McKenzie, and professor of law at the Free University of Berlin. “Where machines learn like humans and from humans, unconscious bias is as much a threat as with humans.”
ACM’s Code of Ethics states to “be fair and take action not to discriminate.” It says that technologies and practices should be as inclusive and accessible as possible and software developers should take action to avoid creating systems or technologies that disenfranchise or oppress people. Failure to design for inclusiveness and accessibility may constitute unfair discrimination. Determann believes that success requires developers to code prohibitions into algorithms.
“We must not rely on machines learning on their own what is and isn’t prohibited,” he said. “We need to do our best to avoid replicating unconscious human biases by training machines with insufficient data (e.g. outdated history books), supervising teams (e.g. lacking diversity), and procedures (e.g. failing to flag for machines where data sets are known to be incomplete). We have to develop countermeasures to reduce the risk of replicating human unconscious bias in AI. Diverse teams and constant validation and questioning should be part of the solution.”
Think before you build
Software developers are the first, and last, lines of defense against the misuse of technology. Our current era of software culture may have been dominated by Facebook founder Mark Zuckerberg’s now-famous motto “move fast and break things,” but that’s not what users want from their software anymore. However, inside software companies, there can still be pressure on developers to get software to market quickly, making it tempting to skip rigorous testing.
“Software developers are often infatuated with the technology itself — what it can do and how we can apply it,” said Michael S. Kirkpatrick, associate professor in the department of computer science at James Madison University in Virginia, and education coordinator for ACM’s Committee on Professional Ethics.
Kirkpatrick argues that it’s the responsibility of developers to ask more questions about how their technology might be used. “All too often, developers are given a task to develop a piece of code, without information on the context in which it might be used. This needs to change. Developers need to be more proactive in finding out how the code is going to be used and anticipating how it might be misused. They should also question what policies and procedures can be put in place to prevent misuse.”
Kirkpatrick believes that multidisciplinary development teams are crucial to success here. “Software developers tend to have a very narrow focus on technology, so it’s really important that they work with ethicists, anthropologists, sociologists, and other people who bring a different perspective on the context of how a piece of technology might be used, and how misuse can be prevented.”
Developing a better world
It’s clear that a lot of responsibility rests in the hands of software developers. The ACM’s Code of Ethics is a useful framework for helping software engineers to live up to their ethical obligations, but it doesn’t provide a solution for solving ethical problems; rather it serves as the foundation of ethical decision-making.
Ultimately, it is up to software developers to act responsibly, taking the time to consider the wider impacts of their work, which should consistently support the public good.
“Technology amplifies power,” concludes Kirkpatrick. “Governments, large corporations, and other actors can use it to extend their influence in all aspects of our lives. It’s no exaggeration to say that the unquestioning adoption of technology poses risks to fundamental human freedoms and civic rights. So it’s really important for software developers to listen to different voices, to ask more questions and to think more proactively about how their software can be used and, more importantly, how it might be misused.”
Lindsay James is a journalist and writer with over 20 years’ experience creating compelling copy for some of the world’s biggest brands including Microsoft, Dassault Systemes, Exasol, and BAA. Her work has appeared in The Record (a magazine for the Microsoft partner community), Compass, and IT Pro.
The Data Behind: Helping Healthcare Get Better
Today, every industry is data-driven. In The Data Behind, we dig into the data creating change in rapidly evolving industries.
Healthcare is experiencing a digital transformation, shifting how the medical ecosystem operates and the way that care is delivered. And all of this change comes down to one little word: data. In 2013, the healthcare industry produced 153 exabytes of data; in 2020, that volume is estimated to increase over 15-fold to 2,314 exabytes. It’s projected that healthcare data is expanding faster than in manufacturing, financial services, and media.
That’s right — we produce more data at the doctor’s office annually than we do swiping our credit cards or surfing Netflix. It follows that unlocking the power of all that data is the key to transforming the future of healthcare with quality and precision in mind, across clinical, financial, and operational processes.
As Big Data continues to expand, what are some of the major trends that data leaders in the healthcare industry are addressing in 2020 and beyond? In this piece, we explore the data that impacts decision-making within the healthcare industry, and how this data helps practices tackle the challenges facing the communities that they serve.
Conquering mountains of health data
Electronic health records, bolstered by BI, change the game for clinicians. Traditionally, the immense amounts of data produced by patients and doctors was confined to paper records in a doctor’s filing cabinet. Electronic health records (EHRs) were a revolution because of the mobility they granted health data and medical records to move between multiple doctors and clinics. Now these records have the potential to vastly improve clinical outcomes for patients. But many of these systems fall short when it’s time to bring together financial, clinical, and operational data to help practitioners make informed decisions about patient care.
Hamza Jap-Tjong is the CEO of Gerimedica Inzicht, the analytics subsidiary of a SaaS company focused on delivering the best software and service for healthcare professionals in the eldercare sector. For Hamza, embedding a BI solution within Gerimedica’s EHR was key to maximizing the efficiency of each caregiver’s time on a particular hospital ward.
“Caregivers now have access to alert dashboards that highlight patients who are struggling or not making anticipated progress. Caregivers are able to connect the dots between clinical and operational data and focus their discussions on a handful of patients.” – Hamza Jap-Tjong, CEO of Gerimedica Inzicht
As is the case in many data-heavy industries, it’s often the employees on the job who produce the largest amount of data and who need those insights the most. Yet they have the least amount of time to sift through heavy data exports to put the pieces together. Healthcare providers are skilled at providing care to their patients, and making the lift lighter on the way to gaining critical insights allows them to do what we all hope our caregivers are prioritizing: the highest quality care.
Digital transformation within Healthcare improving visibility for communities in need
Population health management (PHM) is the practice of aggregating numerous sets of patient health data across a specific geography to produce a single record from which health care providers can make important clinical and financial decisions. Presently, these solutions combine public health and technological disciplines to achieve high-level health outcomes for broader populations.
A compelling example of this practice was also employed within a network of state clinics. With 16 clinics already in practice, the organization’s data team aggregated patient records from all of these clinics in order to select the neighborhood that would be best served by a specialty outpatient clinic for Chronic Obstructive Pulmonary Disease (COPD) and Congestive Heart Failure (CHF). With the ability to blend patients’ diagnostic history (clinical data) with their addresses (operational data), the clinic could be built where the highest concentration of potential patients were already living, maximizing clinical outcomes for local patients and financial outcomes for the new clinic.
A great example of this is D-tree International, that is deploying these solutions to enable international volunteers to pass actionable insights to government health organizations in Africa.
“Recently, a local government leader noticed that pregnant women were facing unusually high costs following visits to healthcare facilities in a specific region. That’s not how healthcare should work in Zanzibar — costs should be limited when going to public healthcare facilities. Upon digging further into the visualizations, they discovered that a very important product was out of stock at the facility and patients were required to buy and pay for it themselves.” – Sam Lilienfeld, D-Tree International Technology and Data Manager
With a clear view of the causal link between clinical data (women with a pregnancy diagnosis), operational data (clinic locations), and financial data (heightened aftercare costs), there were immediate calls for policy change in the region that lead to budget reallocations to cover these vital pregnancy products.
Building the future of healthcare with data
According to Andy Slavitt, former Acting Administrator of the Centers for Medicare and Medicaid, “Doctors are overloaded on data entry and yet rampantly under-informed.” Slavitt’s assessment highlights the immense amount of data going into healthcare systems, as well as the unreasonable amount of pressure on caregivers to make sense of that siloed data fast enough to make long-term clinical and operational changes that will benefit future patients.
Through the successful adoption of advanced data strategies, clinics and hospitals, healthcare organizations, and government health initiatives can aspire to achieve healthier outcomes for patients with precision, accuracy, and superior efficiency.
Spending her early career as a travel writer, Emily Arent found a home in Copenhagen and Denver before settling in Tel Aviv. She’s spent the last 8 years contributing to content programs at agencies and start-ups across Israel.
Three Steps to Actionable Analytics: From Data to Insights to Outcomes
Developing analytic apps is a bold new direction for product teams. The Toolbox is where we talk development best practices, tips, tricks, and success stories to help you build the future of analytics and empower your users with the insights and actions they need.
What is the ultimate goal of using data internally at your organization? You probably answered something like “Make smarter, data-driven decisions.” That’s the goal for a lot of companies! But how will they accomplish this goal? Companies of all kinds are sitting on more data today than ever before, but business intelligence adoption remains low. To understand this problem, let’s take a quick look at the history of BI and then talk about the future: analytic apps.
Centralized reports were in-depth, but slow
Let’s go on a little journey through history that many of us have been on, starting with IT-focused “reports.” You or someone in your company needs some insights from your data stores, so you go to the IT team and ask for it. They put you on a backlog list, and at some point — maybe a few weeks or a few months later, you get a report. Now say you have another question, you go back again and wait some more. We all are familiar with this story and it’s not a good one!
Self-service BI began to empowering end-users
In order to solve this endless waiting game, the industry invented self-service BI which completely revolutionized how we interact with data. Self-service BI made it easier to combine multiple sources with a UI that included drag and drop, drill through, filter functions, and more. These tools are easy to use and users of all skill levels can find insights on their own and share them as well, without learning SQL or other languages. Self-service BI made huge inroads in making BI mainstream and more accessible. It continues to do so and is extremely relevant today.
But, even though for the last two decades — maybe even longer — “BI and Analytics” has been at the top of the technology agenda for most organizations with massive and increasing investments, pervasive BI adoption has continued to remain a challenge with organizations only seeing about 30% adoption (and in some studies up to 50% but still low).
Now, a hint at the underlying problem is that less than a third of organizations claim that they have fully connected this data effort to the actions that they take as a business. Just doubling down on traditional solutions that do not fix this gap between data and insights and actual actions is clearly not sustainable.
Combining actions and insights — the rise of analytic apps
This latest wave of actionable BI is the key to bridging the gap between insights and action by enabling users to act at the point of insight. This is one of the driving sentiments around our three new Sisense packages, for Product Teams, Cloud Data Teams, and Analytics and BI teams. All three groups have a mix of users with different needs, but all contribute to and/or profit from building analytic apps that connect insights and actions.
Organizations at the top of their industries are engaging in digital transformation strategies that go far beyond a drive toward efficiency and traditional BI practices. These organizations are completely changing the way they operate. They are focused on driving action within their organizations based on insights and on creating better partnerships with vendors and customers by delivering differentiated analytic apps, both inside and outside their organizations.
Actionable analytics, analytic apps, and embedded analytics will drive this third wave of BI. These are highly-interactive, action-oriented data experiences that co-exist seamlessly with existing applications and workflows.
Actionable analytics will lead to superior outcomes
What do we mean by analytic apps? Let’s take an example of an app we are all familiar with: Google Maps.
Google Maps is a quintessential example of a great app experience. Not only do we view data and metrics (directions, travel time, weather, etc.) but over the years, they have also added actions (making reservations, calling businesses, calling Ubers and so on) allowing for a seamless experience.
Do you remember how annoying it was to go OpenTable or Yelp or another website to take all those actions? Google Maps makes our workflow seamless from deciding where to go to making the reservations to finding directions and even hailing a cab — all without leaving the context of the application!
Why should analytics be any different? It is time to go beyond the dashboard and integrate analytics within workflows through actions to build a fully data-driven organization. Actionable BI is the key to increasing adoption and building a truly data-driven organization.
Analytic apps allow automation and the ability to act upon insights
Write-back for closed loops: Imagine that you are running an important space mission with critical metrics to track. It is a complex project and mission-critical project where you are tracking not just revenue and costs but also have to provide daily status updates and careful oversight.
An analytic app will enable you to provide these updates seamlessly within your workflow without requiring you to leave the context of your work. For example, you can edit data in a project management tool or you can or submit forms for the daily status update that goes to another application right within the analytic app. This removes friction and reduces the number of steps leading to better outcomes.
Integrate for seamless workflows: Analytic apps will allow you to integrate your analytics into third-party applications whether they are a communications app like Slack or Gmail or you’re sending data to an application like Salesforce or Gainsight, etc. to kick off a project or process. Or, in the case of Luzern, clients can track their market spend in a Sisense BloX actionable analytics widget, and dig into an individual ad’s performance. As they’re digging into performance metrics, they can mark it successful, put it on a watchlist, or pause it, all without leaving the analytics interface.
Feature-rich, interactive, and visually appealing: Analytic apps will allow you to purpose-build what your user needs or wants. It allows you bimodal integration — you’re not just embedding analytics into other applications or sending data there, but also bringing those applications into the analytics workflow. Last but not the least, analytic apps are customized to be aesthetically and visually pleasing, making your users more likely to actually enjoy using them (and thus, actually keep using them).
With analytic apps, users shouldn’t be able to tell where analytics end and operations start.
Shruthi Panicker is a Sr. Technical Product Marketing Manager with Sisense. She focuses on how Sisense can be leveraged to build successful embedded analytics solutions covering Sisense’s embedding and customization capabilities, developer experience initiative and cloud-native architecture. She holds a BS in Computer Science as well as an MBA and has over a decade of experience in the technology world.
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.