Category Archives: Sisense
I’m a Tech Recruiter — These Stories from Women in Analytics Inspire Me
In honor of Women’s History Month, I sat down (virtually) with a diverse panel of women from across Sisense in an open interview format. We dug into their unique challenges and successes to learn what inspires them as they blaze their trails in the tech world. From juggling work and life responsibilities and raising children to lifting up the voices of other marginalized individuals, these intrepid Sisensers do it all and are helping build a tech powerhouse at the same time!
It will take a lot of work to continue building a more equitable world and to forge the way for the next generation of women in tech; hopefully after hearing from these Sisensers, you’ll understand a bit about how far we’ve come and reaffirm your commitment to helping build a better future.
Women inspiring women
CNBC reported that in 2021, women’s workforce participation (the portion of the population actively searching for work) hit a 33-year low — just 57%. After decades of positive trends of women in the workforce, the global pandemic has forced women out of the workplace like never before. Women — far more often than men — find themselves having to stay home with their families due to school closures and lack of childcare options.
But at Sisense and many other high-tech companies, women are still a powerful part of the team.
“I’m in constant awe of working mothers who balance their home responsibilities with their day jobs,” said Melanie Tantingco, VP, Talent Acquisition.
As the number of women in the workforce is being forcibly reduced by the pandemic, it’s more important than ever to support the women on our teams. Being able to balance life and work is critical to keeping women in the workforce. But it does require some give and take from our teammates and the companies themselves. It can be done, but even under the best conditions, it’s not easy.
“Something that I love about working in tech is that I have always had the ability to work from home or even work in a hybrid model,” said Elise Woodard, Social Media Manager. “Since COVID hit in 2020, I’ve been home and working remotely with my four kids. Even though it’s incredibly difficult some days, I feel good setting the example for my kids that women can have powerful careers. This is especially important for me to show my daughter.”
Helping young girls find pathways into tech is so important. While some girls have mothers or other family members as examples, others are inspired by women who run organizations that encourage girls to explore STEM activities.
“I am inspired by Reshma Saujani, Founder of Girls who Code,” said Susanna Tharakan, Diversity & Inclusion Program Manager. “She’s a woman of color, like me, who paved her own path to success while bringing up other women alongside her. Her TEDx talk called ‘Teach Girls Bravery not Perfection’ was both inspiring and validating.”
Mentors matter
No one gets anywhere in life alone; we all need help. Since women understand the unique challenges that we go through in the workforce, it’s especially important that we support each other, whenever and however we can! In this spirit, one question we asked our panel was, “Who are some women who have helped you in your career?”
“My honest answer is my mom,” said Cody Young, Talent Acquisition Partner. “Although she doesn’t work in tech, she has had a successful career and has always been there to provide me with authentic guidance and has embodied what it means to balance career, home, and passion in the workplace. Growing up with a strong example of what it means to be a woman of color in the workplace without compromising her own values has helped me to lead with confidence without having to compromise my own values.”
The tech world can be challenging, but startups and even bigger companies like Sisense can offer women unique opportunities to support each other and help women grow and succeed.
“While working for my first startup, [a leader named] Osha Kondori advised me to negotiate a promotion that I clearly wanted but didn’t feel qualified to ask for,” said Mirijam Stewart, Customer Success Manager. “She knew my worth before I did. Then, she referred me to Periscope Data [which merged with Sisense]. In the past year I’ve been able to join her team during COVID-19 and tackle difficult conversations.”
Paving the way for the next generation of women in tech
When women succeed, we all succeed. Our society and our planet have a long way to go to create equality for women and young girls. Women who have already made inroads in the tech world can help those just starting their careers or who are still learning the skills they’ll need to succeed. Many people ask themselves, “How can I help the next generation of young women succeed in the tech world and beyond?” Our panel offers this guidance:
“Get involved with nonprofits or resource groups in underrepresented communities,” said Shannon Woodward, Account Development Representative. “Take an intersectional approach to involving yourself.”
“Do well yourself and talk about it,” said Shruthi Panicker, Senior Technical Product Marketing Manager. “Become friends with others and be part of the community.”
All children need support and encouragement, but especially girls who are interested in male-dominated worlds like tech, the sciences, and math.
“Celebrate when girls explore their interests in STEM programs,” Melanie offered. “Gift them with non-gendered or science-based toys. Encourage them to take tech internships or seek out women mentors early in their career.”
“It’s important to talk to girls about technology at a young age,” Elise said. “I think we’ve made strides in the last generation, and I already see so many opportunities for my daughter in this field. We need to continue to foster an environment that encourages girls to thrive in tech.”
Celebrating Women’s History Month
Although I think we can all agree that women’s history should be celebrated beyond just one month of the year, it’s amazing to have a month dedicated to increasing awareness of women’s issues in society and celebrating the trailblazers who paved the way for women to get where we are today.
“It’s a time to be thankful for the women who have broken barriers that have allowed us to be in the rooms we are now,” said Cody.
“For me, Women’s History Month means celebrating how far we’ve come while recognizing the women in our lives who have helped us get to where we are today,” said Susanna. “Women supporting women. Women recognizing women. Shining a spotlight on those who have been overshadowed, talked over, and just not given the same attention as our male counterparts.”
Envisioning a better future for women in tech
There’s no telling what tomorrow will bring or what the future of tech will look like; however, we can all play a role in creating more opportunities for diverse communities and people who have historically been barred from progress or leadership.
“Elevate more women who do not look like you,” said Mirijam. “Seek them out. Ask them about their perspective. Don’t ask for similar experience; ask for their story and look at what they’ve achieved.”
The past is not one story; nor is the future. It will be woven by the countless individuals who go out, every day, and try to make the world a better place, helping support each other — people with different backgrounds, outlooks, dreams, and goals.
“I believe organizations need to do more in order to recruit and retain women in the tech industry,” said Mimi Mbaye, People Partner. “We need to support diversity, equity, and inclusion so more women can enter the space.”
Julia Casey is a Talent Acquisition Partner at Sisense. She has almost 3 years of experience in the tech industry recruiting top talent, creating a quality candidate experience, refining Sisense’s employer branding, and co-leading the in-house women’s Employee Resource Group, Zenith.
Transforming Big Data into Actionable Intelligence
Attempting to learn more about the role of big data (here taken to datasets of high volume, velocity, and variety) within business intelligence today, can sometimes create more confusion than it alleviates, as vital terms are used interchangeably instead of distinctly. However, when investigating big data from the perspective of computer science research, we happily discover much clearer use of this cluster of confusing concepts.
Before we dive into the topics of big data as a service and analytics applied to same, let’s quickly clarify data analytics using an oft-used application of analytics: Visualization!
Looking at the diagram, we see that Business Intelligence (BI) is a collection of analytical methods applied to big data to surface actionable intelligence by identifying patterns in voluminous data. As we move from right to left in the diagram, from big data to BI, we notice that unstructured data transforms into structured data. The implication is that methods of data analytics are applied to big data, the methods of data preparation and data mining for example, to bring us closer and closer to the goal of distilling useful patterns, knowledge, and intelligence that can drive actions in the right hands.
Hopefully this clarifies these complex concepts and their place in the larger analytics process, even though it’s common to see pundits and outlets tout BI or big data as if they were ends in themselves.
AI-driven analytics is a complex field: The bottom line is that datasets of all kinds are rapidly growing, causing these organizations to investigate big data reporting tools or even approach companies whose whole business model can be summed up as “big data as a service” in order to make sense of them. If you’ve got big data, the right analytics platform or third-party big data reporting tools will be vital to helping you derive actionable intelligence from it. And one of the best ways to implement those tools is to embed third party plugins.
Big data challenges and solutions
When you have big data, what you really want is to extract the real value of the intelligence contained within those possibly-zettabytes of would-be information. To best understand how to do this, let’s dig into the challenges of big data and look at a wave of emerging issues.
For starters, the rise of the Internet of Things (IoT) has created immense volumes of new data to be analyzed. IoT sensors on factory floors are constantly streaming data into cloud warehouses and other storage locations.
These rapidly growing datasets present a huge opportunity for companies to glean insights like:
- Machine diagnostics, failure forecasting, optimal maintenance, and automatic repair parts ordering. Intelligence derived from these systems can even be fed to HR teams to improve service staffing, which further feeds to enterprise HR management and performance solutions (AI-based analytics reporting to ERP solutions)
- Assembled products shipped also feed directly to ERP on updating supply chain solutions, improving customer awareness and experience
To put it bluntly, the challenge we face is that no cloud architecture yet exists which can accommodate and process this big data tsunami. How can we make sense of the data wont fit in the enterprise service bus (ESB)? (ESB is a middleware component of cloud systems which will be overwhelmed if a million factories were to all try to extract intelligence from their sensors all at once.)
One solution with immense potential is ”edge computing.” Referring to the conceptual “edge” of the network, the basic idea is to perform machine learning (ML) analytics at the data source rather than sending the sensor data to a cloud app for processing. Edge computing analytics (like the kind platforms like Sisense can perform) generate actionable insights at the point of data creation (the IoT device/sensor) rather than collecting the data, sending it elsewhere for analysis, then transmitting surfaced intelligence into embedded analytics solutions (eg. displaying BI insights for human users).
The pressure to adopt the edge computing paradigm increases with the number of sensors pouring out data. Edge computing solutions in conjunction with a robust business intelligence big data program (bolstered by an AI-empowered analytics platform) are a huge step forward for companies dealing with these immense amounts of fast-moving and remote data.
Big data analytics case study: SkullCandy
SkullCandy, a constant innovator in the headset and earbud space, leverages its big data stores of customer data regarding reviews and warranties to improve its products over time. In a twist on typical analytics, SkullCandy uses Sisense and other data utilities to dig through mountains of customer feedback, which is all text data. This is an improvement over previous processes, wherein SkullCandy focused on more straightforward performance forecasting with transactional analysis.
Now that SkullCandy has established itself as a data driven company, they are experimenting with additional text analytics that can extract insights from reviews of their products on Amazon, BestBuy, and their own site. Teams also use text analytics to benchmark their performance against their competitors.
SkullCandy’s big data journey began by building a data warehouse to aggregate their transaction data, reviews. A breakthrough insight/intelligence in product development occurred thanks to the text analysis of warranties through which SkullCandy was able to distinguish between product issues and customer education. The fact that AI-based analytics can delineate between product and education in a text message is groundbreaking. A common pattern was that clients were returning a product as broken when in fact they simply didn’t know how to use bluetooth connectivity.
Data-driven product development also benefitted: Big data analytics allowed SkullCandy to analyze warranty/return data that showed that one of their headsets, which was used more during workouts than previously thought, was being returned at a higher than normal rate. It turned out that sweat was causing corrosion in terminals, leading to the returns. The outcome was to waterproof the product.
Among the many successes SkullCandy achieved, we also see a pattern of value derived from big data.
Big Data as a Service: Empowering users, saving resources
Strictly speaking, “big data analytics” distinguishes itself as the large-scale analysis of fast-moving, complex data. Implicit in this distinction is that big data analytics ingests expansive datasets far beyond the volume of conventional databases, in essence combining advanced analytics with the contents of immense data warehouses or lakes.
In order to get a handle on these huge amounts of possible-information, the AI components of a big data analytics program must necessarily include procedures for inspecting, cleaning, preparing, and transforming data in order to create an optimal data model that will facilitate the discovery of actionable intelligence, identify patterns, suggesting next steps, and supporting decision making at key junctures.
Intelligence drawn from big data has real potential to transform the world, from text analysis that reveals customer service issues and product development potential to training financial models to detect fraud or medical systems to detect cancer cells. Savvy businesses will empower users, analysts, and data engineers to prepare and analyze terabyte-scale data from multiple sources — without any additional software, technology, or specialized staff.
Fortunately, it is now possible to leverage all of these potentials and to avoid the cost and time of in-house development, by embedding expert third party analytics. Recognizing the tremendous task of big data analytics in conjunction with the value of outcomes, the natural propensity exists to use it as a service, and thereby reap the benefits of big data as a service as quickly as possible.
Chris Meier is a Manager of Analytics Engineering for Sisense and boasts 8 years in the data and analytics field, having worked at Ernst & Young and Soldsie. He’s passionate about building modern data stacks that unlock transformational insights for businesses.
Analyzing Data from Multiple Sources: The Key to More Powerful Insights
Simple datasets just won’t cut it anymore. To get truly powerful insights, you need to pull in data from multiple sources. The more complex and diverse your datasets, the more surprising and potent the insights they’ll produce. Additional data sources increase your chances to inform actions, fueling top-line and bottom-line growth.
How does it work in real life? Read on to find out how Measuremen optimizes workspace utilization, Skullcandy minimizes product returns, and Air Canada improves airline safety.
Measuremen: Optimizing facilities use with data from numerous sources
When Measuremen CEO Vincent le Noble began the company in 2005, he wanted to help his clients make the best use of their workspaces. He leaned into his facilities-management experience, taking careful note of how organizations used desks, chairs, meeting rooms, and amenities. At the start, Measuremen observed and recorded utilization data and began to log the kinds of activities each space enabled (such as collaboration, individual work, and high-concentration work).
Since those early days, Measuremen has broadened its data sources. A mobile app is used to collect granular information about how each component or meeting space is used. The app not only documents utilization data, but allows users to add subjective inputs such as personal preferences. According to Vincent, it allows Measuremen to ask customers questions such as: what made users come to the office, how they foresee the future for their departments, whether they will grow, whether they will shrink, and what kind of activities they will be doing.
In the last two years, Measuremen has added location-based sensors, which record data on a more permanent and real-time basis. The result of the various inputs — self-reporting, app-based logs, static sensors, and other data sources — allows Measuremen to assess much more than unused desk spaces or underutilized meeting rooms. It can give businesses a better understanding of how employees experience the workplace and let companies tailor their resources to better suit employee needs. As a result, companies can proactively address more challenging problems like employee productivity, retention, and work-life quality. And Measuremen is hardly finished.
“We’ve been deepening our analysis for the last four years now with Sisense,” said Vincent. “All the data streams combined … give us the insights and decision-making power to help users to improve workplaces and work life for employees. And that journey is still going on.”
Skullcandy: Listening to the market
The datasets you collect, the way you combine them, and the insights that can come from them depend on your industry, the realities of your business, and your imagination. Personal audio brand Skullcandy had a huge dataset and a huge challenge: using analytics to explore returns and reviews data to inform future product decisions.
Machine learning and predictive modeling allowed the company to use complex historical warranty claim and cost information, previous and new product attributes, and forecasting data to create a predictive data model for future warranty costs. The information not only helps Skullcandy with resource allocation for future warranty fulfillment, but can also drive design improvements.
Skullcandy’s methodology included delving into sentiment analysis gleaned from online reviews and other customer feedback, which gave rise to exciting revelations. For example, when customer comments focus on a particular defect, Skullcandy can pinpoint the problem, examine the future warranty claims effects, and engage its engineers to make design modifications to help head off these returns. Skullcandy is also exploring ways to use disparate data streams to inform decision-making that improves customer relationships, customer education, and e-commerce ecosystems.
Air Canada: Taking data to new heights
The value of large, varied data sources is becoming obvious in air travel, too. Air Canada uses Sisense to collect and translate a wide variety of safety, quality, environmental, and security data. Safety Analytics & Innovation Manager Shaul Shalev said, “We collect hundreds of gigs of data … but unless you have a clear method of slicing and dicing that data and presenting it to users, it’s not really useful. With a tool like Sisense, it changes the game altogether.”
The ability to collect data and make it useful allows Air Canada to identify important insights and extract actionable intel so frontline employees can respond to it in real time. In a more forward-looking posture, AI can employ the data to predict component failure, so Air Canada can replace parts before they cease functioning.
Seek out varied datasets to transform your business
The power of vast, varied data sources isn’t constrained to any segment of the economy. Here are a few more stories of companies finding success with large, complex datasets:
As you can see, leveraging diverse datasets to generate game-changing insights spans industries. The takeaway is that you should cast the widest net you can and leverage whatever data you can get your hands on. The benefits you’ll reap from bringing that data together can help drive revolutionary change at your business and further evolve you in a tumultuous business world.
Adam Luba is an Analytics Engineer at Sisense who boasts almost five years in the data and analytics space. He’s passionate about empowering data-driven business decisions and loves working with data across its full life cycle.
Experimenting to Win with Data
When deploying analytics for your company or your customers, you can feel stuck between the twin poles of “What data is available?” and “What do we want it for?” The key to coming up with the best insights lies in delving deeply into both questions; the answers you discover can help you get even more out of your data. The experiences of Measuremen, an international consultancy that helps organizations optimize facilities use, illustrate this point.
Determining data goals, making a plan
Since 2005, Measuremen has helped its clients study and optimize facilities use. Founder and CEO Vincent le Noble started out studying and logging when, how much, and why employees used chairs, desks, conference rooms, and corporate amenities. Armed with data, he could advise companies on which facilities costs were good investments and which weren’t.
Even now, Vincent says, his engagement with his customers starts with a conversation about their goals:
“Each and every one of them begins with a change that they want to make to improve their workplace, so we try to get that as clear as possible. What is the change that they want to facilitate, and what data elements can help them in deciding how to improve or facilitate that change?”
Those client-generated objectives are the jumping-off point for Measuremen.
“So that’s what we start with,” continues Vincent, “and then we [add] all these data collection methodologies that will gather that information needed to make those insights.”
Multiple methods to collect maximum data
After writing out a properly scoped, well-defined set of goals and a way to collect the needed data, Measuremen begins its research period. That could entail surveys, studies, and app-based workplace sampling or involve a limited period when Measuremen observers personally monitor workplaces and record data.
When clients want a more permanent, passive method of collecting data, Measuremen places sensors in meeting rooms or at workstations. Many organizations want to understand the human element, too: How do their employees feel about the environment? What improvements would they recommend?
After that, says Vincent, Measuremen invites the client to a key insight meeting, “where we show the data … in an interactive session.” Measuremen can visualize the data in its portal, evaluate current trends, and recommend changes. Later, Vincent adds, “we’ll do another study to validate that the changes that they’ve made have resulted in the expected results they were aiming for.”
Applying data to goals
After engaging end users about their goals, it’s time to shape data models based on their responses. And as the process continues, further refinements take place.
“Our customers … had a really good idea of what utilization and occupancy numbers would mean for them,” Vincent says. But soon their examinations of the data would result in questions like, “I can see there’s a low utilization of this meeting room compared to the other meeting rooms. Why is that?” and similar inquiries they couldn’t answer with the data they had on-hand. This would steer the research team in the direction they needed to go and tell them what data they needed to collect.
According to Vincent, there’s important work to do with the data after it’s collected. It’s crucial “to be able to slice and dice and go into that detail as you go along because not only do you want to provide information on a holistic view or a high level, but you want to be able to dive deeper.”
Not all data journeys start that way, though. Some organizations have only a loose idea of what the data could do for them or of what data they need to track to see the desired outcomes.
But don’t let a lack of data — or a lack of a clear direction — stall your search for usable intelligence. Sometimes it’s best to simply start with the data you have and see what you can get out of it.
Charles Holive, Sisense managing director for data monetization and strategy consulting business, says this:
“Sometimes I meet customers or prospects, and they would say, ‘I don’t have enough data.’ And I always reply, ‘Start somewhere.’ And then build from there, because there’s always enough to get started … asking the right questions, finding those high-value questions.”
Failure to launch: Avoiding analysis paralysis
Just as often, companies have the opposite problem: too much data. So much data that users can’t make sense of it. It’s the challenge that cloud software provider Quali had andovercame through disciplined data sifting and visualization with an intuitive dashboard.
Whether there’s too much available data or not enough, whether you’ve got a clear direction or only a hazy idea of where you want data to carry you, data-driven insights infused into workflows are a must-have for every organization. The time to start asking questions is now. Think about the data you have (and need) and start experimenting with it; you’ll be surprised what you uncover.
Mindi Grissom is Director of Product Marketing at Sisense. She has over 5 years of experience in the technology industry, helping thousands of organizations transform their business with data and analytics.
DNV Illuminates the Utilities Industry with Insights and Data
At DNV, our mission is to safeguard life, property, and the environment. We do this throughout a variety of industries, and as the head of the electric grid ecosystem and product center at DNV, my focus is on helping optimize risk assessment, remediation, and overall reliability of critical assets at our electric utility customers. In order to do this, my team uses data to identify problem areas and potential issues for our customers (ideally before they happen).
Like many organizations, our utility company clients are sitting on copious amounts of data from a variety of sources, including mobile applications and streaming data captured by grid-edge technologies. However, without an advanced analytics solution that could surface relevant trends and information, this data was an underutilized asset.
That led us to build Cascade Insight, an enterprise solution that infuses data and insights into a comprehensive platform that utility companies use to optimize their operations, reduce risk, and improve the reliability of their electrical equipment. This process necessitated us getting a handle on the data we were already collecting, making sense of the constant stream of new information our clients were pulling in, and presenting it all in an easy-to-understand interface.
Unique challenges and opportunities in the utilities industry
As opposed to typical for-profit enterprises, utility companies were created to serve the public good. One of their main focus areas is driving operational efficiencies to maintain affordable rates and deliver better service for customers.
Utilities employ skilled professionals as knowledge workers, but creating a simple, visual way to analyze their data is a hard skillset to find in abundance. DNV’s effort to create an analytics consortium was driven by the opportunity to bring knowledge workers from different utilities together to collaborate on challenges with their peers. Much in the same way that researchers are able to collaborate on projects across time and space, we wanted a consortium of utility providers to be able to pool efforts on major problems affecting the industry beyond their individual companies.
Capturing and transforming data to transform the utilities industry
The key to making this collaboration possible would be having an analytic application that could pull in data from across a number of sources. Each utility company generates a tremendous amount of data from a variety of touch points, making the first challenge one of volume and ingestion: The destination system has to be able to handle data from disparate sources and in a range of formats, especially from mobile and field sensors/equipment. Transforming this data into something usable becomes more complicated when you realize that utilities often have different data naming conventions in addition to capturing different pieces of data.
This presented the first challenge for our product team in building Cascade Insight: What is the data that is most important to capture? Being IT professionals and software vendors, my team initially focused a lot on the technology. However, defining the data requirements was important for understanding what data you need to measure to provide analytical insights. And my team at DNV struggled with that in spite of having all this data and domain knowledge. Fortunately, the team at Sisense was able to come out and help us with this process.
The right data, the right platform, the right partner
The experts at Sisense live and breathe data analytics — and the Sisense platform is designed from the ground up to connect with disparate datasets and infuse insight from those sources anywhere users need them. Working with the Sisense team, we were able to nail down the data requirements that were needed to create the high-level insights, such as the overall health of a transformer, that would improve the lives of our utility company end users. Armed with this understanding, we were able to use Sisense to develop the Cascade Insight platform in just six months, a rapid timeline for building an enterprise product from scratch!
We also had to ensure that the information captured across our utilities was consistent. For example, when considering data captured on/by a transformer, a couple of the obvious data points are make and model. However, a utility may have another 40-plus items about that transformer that it’s capturing data on that may either be documented with a different naming convention or not even captured by other utilities. So, when building out Cascade Insight, we chose 16 key dashboards that our clients would benefit from and ensured that the data captured from each of the clients was clean and consistent and could be ingested into our analytics application.
Driving immediate impact with analytics
Cascade Insight had an immediate positive impact for our utility company clients. Professionals are now able to quickly identify equipment and processes that are at risk or are performing suboptimally, drilling down to specific components on a map that are having problems. From there, they can proactively provide maintenance to those components and help keep the lights on for millions of customers. But this is just the starting point for Cascade Insight.
By creating this consortium of utility providers that collaborates across utilities, we hope that this will encourage ongoing communication among the utilities. By collaborating on best practices, and as new challenges are discovered, this coalition can begin defining future dashboards and coming up with standards for the associated data that must be gathered to drive meaningful insights for them and users like them. This collaborative work style, uniting a number of utilities, is a practice that can be replicated across other industries, in particular health care. Bringing together really talented professionals and providing them data in an accessible application instead of jumbled numbers in a spreadsheet helps drive positive, high-value results.
Whatever your industry and your company, you’ve got some data you can use to drive revenue. The key is to understand that data, learn to capture and prepare it properly, then present it to your users in a way that is meaningful to them. The right BI platform can go a long way toward making that process possible, and the right partner (like Sisense) can give you the support and expertise you need to make your dream a reality. To hear more about DNV’s analytics journey, check out our conversation here!
Ron Howard is head of electric grid ecosystem & product center for DNV. He has over 35 years of experience in the utilities industry with a focus on IT and software products. Having worked for both software vendors and utilities, he has devised and delivered many successful software products and has spoken at numerous industry events and conferences.
Mastering Product Life Cycle Management with Analytics
Although many publications compare product data management and product life cycle management — commonly framing the debate as “PDM versus PLM” — that can create confusion. The functionality referred to as a product data management framework is more accurately a subset of a product life cycle management framework. This distinction can sometimes be blurred by the various marketing efforts of software apps.
The matter may even be further complicated depending on whether the purchasing agent is an engineer or a manager, as there are varying degrees of overlap in PDM and PLM app functionality. An engineer may focus on PDM functionality, while business managers and executives in BI are more likely focused on an enterprise-level solution. An enterprise software selection team should represent all aspects of business to ensure the company makes a decision that will fit the needs of all stakeholders. In particular, buying a standalone PDM framework may leave the enterprise without the broader functionality of a PLM. In this article, we’ll explore the nuances of offerings along the “PDM versus PLM” spectrum.
As we clarify the functional distinction between the two types of frameworks and summarize the rationale for choosing one or the other, an exciting new event in the evolution of product life cycle management emerges: The application of machine-learning-based (ML) analytics is sharpening PLM frameworks. We’ll begin by defining PDM as an integral part of PLM. We will then see how business intelligence within PLM is leveraging AI analytics toward new levels of insight and productivity.
What is product data management, exactly?
Briefly, PDM refers to domain knowledge expert engineering theory and its associated software tools. PDM manages engineering data about a product, particularly computer-aided design (CAD) data, and handles product revisions. Although PDM can be used to handle the design release process, most design release processes are external to PDM framework capabilities. And this defines the most important difference between PDM and PLM: PDM provides one of many inputs to the more comprehensive enterprise PLM process.
An engineering team commonly uses PDM software to collaborate in planning and organizing product data. Engineers can use a PDM framework to handle revisions and rollbacks, manage orders to change design specifications, and create and send a bill of materials. Engineering teams can save substantial time and remove trial and error by collaborating in a central CAD-based PDM. The CAD component includes product prototypes and models based on actual parts’ manufacturing instructions. Refinement and debugging are a natural component of PDM. A modern PDM system interfaces and shares product data with a variety of other software applications, especially enterprise PLM. Here are some of the many important methods and functions integral to PDM frameworks:
- Augmented reality and virtual reality for real-time syncing of design data
- Product CAD file data management
- Product revision control as well as revision and rollback history
- Search functions within CAD files — recycling product specs
- Scalability tools for spec replication
- Output data to integrate with PLM, enterprise resource planning (ERP), and materials requirement planning (MRP)
- Security management to authenticate permission levels for engineers
- Automated workflows such as engineering change orders
A favorite function of PDM frameworks among engineers is the ability to collaborate on designs and share comments and feedback. An equipment manufacturer can improve product development and productivity by sharing product data with suppliers and marketing teams. An engineering team typically leverages a PDM through the following aspects of product development:
- Initial product design
- Product prototyping
- Release to manufacturing
- Engineering change notice (ECN) process
ECNs are also called ECOs (engineering change orders) and are a practical procedure greatly enhanced and optimized by PDM frameworks. The release to manufacturing, mentioned above, streams naturally to many of the functions of the PLM that we will explore shortly. PDM often releases product CAD files to manufacturing. Bill of materials (BOM) data is streamed to the PLM and ERP. This final step unifies the product development in the global enterprise BI model.
Important benefits of PDM
Direct CAD integration is now standard in competitive product development. Computer vision is an important area of AI now enhancing many aspects of CAD in product development. Fast product data searches, product spec replication and recycling, as well as secure data access, can make the difference between a successful product release and a problematic one. Augmented reality 2D or 3D views have now elevated product design to a new level. Automated engineering change orders are likewise an industry standard that PDM frameworks have brought to automated fruition. Many previously disparate functions are now central to PDM, including:
- Revision control
- BOM management
- Spec recycling and reuse
Now that we have defined the engineering assistance provided by PDM frameworks, it will be easy to transition toward understanding the enterprise solution of PLM frameworks.
Full-spectrum product life cycle management
Now that PDM is defined as a subset functionality of PLM, what is a product life cycle management framework exactly? Briefly, PLM manages all aspects of a product from the initial design phase to product termination. Equivalent enterprise-level apps that tend to be unified within PLMs include ERP, customer relationship management, and manufacturing execution systems frameworks.
In the product development scope, PLM ideally integrates the other solutions centrally as a product data sharing hub. PLM is therefore envisioned as a business intelligence strategy with the ultimate objective of maximizing profitability. The PLM framework thus seeks to promote innovation in the context of product introduction, product maintenance, and all the way through to a product’s end-of-life planning.
An ideal PLM framework will track a product through its history, charting designated aspects of production and sales (by way of PDM inputs, service requirements, and spec revisions), all the way to product retirement. Generating vast data stores along the way, the PLM framework is now a primary beneficiary of ML-based data analytics. As such, the PLM framework unifies all business processes and enterprise applications, with the ultimate outcome of uniting people in the achievement of their best possible outcomes. When the PLM is integrated fully, the entire manufacturing supply chain benefits. Run correctly with integrated analytics, PLM optimizes product development, informs manufacturing and production rates for order attainment, and drives accurate marketing campaigns.
Some ways this manifests include:
- Tracking for all project development progress in designed phases with milestones, assessments, and projected outcome assurance
- Assessment of product-related business process performance at a glance
- Interfacing for all enterprise solutions with streaming product data in real time on development issues, hardware issues, and software QA events
- Strategic materials sourcing through supplier relationship management
- Context-driven and role-specific authentication and collaboration to improve data sharing, automated workflows, and BI tools and services toward actionable insights
- Actionable insights leading to concise decisions through ML-based data analytics on product development projects
- Flexible reporting based on portfolio management analytics
- Occupational health awareness and product safety
- Product and process quality assurance
- Improved operational control through monitoring of product revisions, resource constraints, and cost factors
- Interface PDM and engineering processes in product data with other BI processes and the business model on the whole
Comprehensive benefits of PLM
Having defined PLM as an enterprise solution for product development, let’s talk about the broad benefits of such systems: First, PLM creates a foundation for streamlined initial product designs and ECN processes. It also helps reduce development time and costs, speeding up time to market.
Ideally, PLM frameworks tend toward:
- Product designs that are universally visible throughout the organization, promoting collaboration and BI outcomes
- Reduced redundancy; increased design reuse and recycling
- Efficient sourcing and inventory investment, leading to further improvements in manufacturing productivity outcomes
- Documentation updates leading to higher standards of QA
- Inception of BI models that embrace innovative ML models and analytics
The analytics edge in PLM functionality
All processes that generate data can be analyzed to produce valuable insights about how to improve the efficiency of those processes. Both human and machine productivity can be dramatically improved with insights gained from models trained with live data from CAD and production facilities. Translating DevOps processes into the product design context is the natural evolutionary next step in PLM. API integrations for third-party analytics platforms like Sisense now make it easy to harness the forecasting power of AI.
Whatever you’re working on, the right analytics for your PDM or PLM data can help you build better products, services, and experiences that will delight your users and stand the test of time. Backed with insights and the right framework, you’re ready to build boldly and change the world.
Vandita Manyam is product manager for Cloud Data Team at Sisense. She has 5+ years of experience in product management and business analytics, improving the product experience for business intelligence companies like Interana and Tellius. Vandita has spoken for events in Gartner Data Conference and company webinars.
From Data to Decisions with Actionable Insights
There’s no industry that couldn’t be improved by actionable insights, and over time, every industry will be. For example, one fintech problem requiring actionable insights today is credit risk assessment. A well-structured fintech algorithm could easily use machine learning (ML) to advise a human agent to approve or reject a business loan application.
That advice is a quintessential actionable insight: The algorithm models the applicant’s probability of success by training on a mountain of historical data and market factors. The resulting insight is the algorithm’s classification of the applicant as an acceptable or unacceptable risk. The action, in this case, is the human agent’s decision to grant or deny the loan. Businesses of all kinds today are dependent on such actionable insights, ideally infused into workflows, driving better business outcomes without users having to leave their primary tasks to look for answers in the data.
Actionable insights: The core of business intelligence
In every industry, actionable insights arising from analytics and AI are no longer a luxury, but a necessity for achieving competitiveness. E-commerce platforms require high-level fraud detection systems to advise their human reps on courses of action. Actionable insights have a special connotation in the field of data analytics: An algorithm evaluates a vast data store and effectively advises a human agent on the best action to take. Implicit among assumptions about machine learning is that algorithms will identify a pattern or outcome in time-series data, for example, which human beings could not do in practical time. There are countless opportunities for turning data into actionable insights in industries like:
- Finance: Optimal portfolio trading
- Healthcare: Diagnosis, intervention, administration
- Advertising: Market analytics and placement
- Pharma: Protein folding and drug synthesis
- Retail: Demographics and style trending
- Education: Assessment and teaching methods
The race to put actionable insights into the hands of users is on. When one player in a field turns data into actionable insights, all competitors must follow suit to keep pace. Innovative use of AI and ML methods in all fields will only accelerate this arms race, putting more powerful actionable insights in the hands of workers of all kinds.
Actionable insights example: Broadridge
Undoubtedly money is the great motivator, and AI solutions must improve profitability. Therefore, let’s see how ML models in asset management and optimal portfolio trading produce actionable insights. Broadridge Broadridge Asset Management Solutions is a $ 4.5 billion revenue assets management company with clients generating unique data stores in diverse containers. An analytics challenge here is the integration of multiple data streams to feed an analytics platform and subsequently generate actionable insights that are valuable across a portfolio spectrum.
On the fundamental level, preparing data for analysis is often called wrangling. To business intelligence execs, the top-level view is the integration of everything from containers to ML technologies and visualizations. Analytics must cope with both structured and unstructured data to achieve optimal results. As we’ll see, very few ML and AI analytics platforms today embody the data science and engineering sophistication to accomplish such a feat with an outcome of actionable insights whose profit justify their cost.
A particularly profitable outcome achieved by Broadridge is the use by its portfolio managers of ML analytics to forecast the best portfolio positions. Broadridge accountants are also leveraging analytics to generate actionable insights that include forecasting accounts receivable and payable outcomes leading to sharper awareness of overall assets.
Infusing actionable insights into workflows — the future of business
Turning data into actionable insights is where BI and data analytics platforms like Sisense shine. Modeling complex and multidimensional data parameters in innovative ways and presenting those insights within workflows will change the future of every industry. For example, these systems will be able to evaluate millions of model configurations and dynamically make corrections to them based on live event streaming data in order to continuously update business-facing users with the best possible insights. The result will be more businesses where all or most decisions are made on underlying data, not gut or guessing, leading to increased conversions, lower churn, and high revenues for those who embrace actionable insights. Those who don’t won’t have much of a future to speak of.
Eitan Sofer is a seasoned Sisenser, having spent the last 13 years building and shaping our core analytics product, focusing on user experience and platform engineering. Today, he runs the Embedded Analytics product line which powers thousands of customers and businesses, making them insights-driven. Eitan is also an avid music fan and surfer.
“Without Data, Nothing” — Building Apps That Last With Data
Every company is becoming a data company. Data-Powered Apps delves into how product teams are infusing insights into applications and services to build products that will delight users and stand the test of time.
In philosophy, a “sine qua non” is something without which the phenomenon under consideration cannot exist. For modern apps, that “something” is data and analytics. No matter what a company does, a brilliant app concept alone is insufficient. You have to deftly integrate data and analytics into your product to succeed.
Whatever your audience, your users are getting more and more used to seeing data and analytics infused throughout apps, products, and services of all kinds. We’ll dig into ways companies can use data and analytics to succeed in the modern app marketplace and look at some now-extinct players that might have thrived with the right data in their platforms.
Sentiment analysis in customer messages
Yik Yak was an anonymous chat app that looked promising initially but failed because of problems that could have been resolved with data and analytics. What made Yik Yak popular was the exotic feature that enabled members to chat anonymously with others in the same geographic vicinity. Unfortunately, that feature was also the cause of the app’s demise: Yik Yak capitalized as a startup with about $ 75 million and grew to a value of $ 400 million before uncontrolled cyberbullying ruined its reputation. After Yik Yak’s name was spoiled as a result of abusive chat, the company could not sell ads on its platform, meaning it could no longer monetize its innovative concept.
How could Yik Yak have used data and analytics to avert disaster? Luma Health showed how message data can be analyzed for mood and meaning by using AI/ML methods on a data lake of chat messages. Yik Yak could have tagged message content with the originating IP address and then quickly blocked messages from that IP after abusive language was detected. This hindsight can now become foresight for other enterprising companies.
The benefits of leveraging collective data
Color Labs was another successful startup whose failure could have been avoided with the right analytics. Although the company’s investment in AI and convolutional neural networks (CNNs) may have been significant, in retrospect, an innovative use of these technologies on the right data could have given it a better shot at survival. The basic service model behind Color Labs’ app was that users would share images and then see images from other users who were posting pictures in the same vicinity (a media-based counterpart to Yik Yak’s concept). The app failed in part for reasons that new dating apps often fail: Needing to go live with a million users on day one! Color Labs’ users joined up only to find little or nothing posted in their vicinity, giving them little incentive to post and share. and leaving them feeling alone in an empty room. The company ultimately folded.
How could data insights have solved this problem for Color Labs? Leveraging the right collective datasets with CNNs could have identified images tagged to a geographical place already freely shared on the internet. Those images could be used to populate the app and get the user engagement ball rolling. Using CNNs in that way is expensive but justifiable if it means keeping the company afloat long enough to reach profitability. New dating app startups actually use a similar trick — purchasing a database of names and pictures and then filling in the blanks to create an artificial set of matches to temporarily satisfy new subscribers’ cravings for instant gratification (one such database is marketed as “50,000 profiles.”) The gamble is that new subscribers will remain hopeful long enough for a number of subscribers to join up and validate their existence. Color Labs could have benefited from existing media with a much lower cost in terms of ethical compromise as well.
Forecasting and modeling business costs
Shyp was an ingenious service app that failed for a number of reasons, but one of those reasons could have been fixed easily with data insights. The basic innovation of Shyp was to package an item for you and then ship it using a standard service like FedEx. The company’s shortcut, which turned out to be a business model error, was to charge a fixed rate of $ 5 for packaging. Whether the item to ship was a mountain bike or a keychain, the flat rate of $ 5 for packaging was a hole in Shyp’s hull, one that sank the company in short order.
Shyp’s mistake could have been resolved cleverly by using the wealth of existing data about object volume, weight, fragility, temperature sensitivity, and other factors to create an intelligent packaging price calculator. Such a database could even have included local variations in the price of packing materials such as foam peanuts, tape, boxes, and bubble wrap, and have presented the calculation at time of payment. Flat fees are attractive and can be used as loss leaders when trying to gather new customers or differentiate oneself in a crowded market, but if you aren’t Amazon, then you need to square the circle somehow. A data-driven algorithm for shipping prices (or whatever your service is) doesn’t just make good business sense — it can even be a selling point!
Social vs. personal networks: Sentiment analysis in data
“Path” fashioned itself an anti-Facebook: According to its founder, former Facebook developer Dave Morin, Path was a “personal network,” not a social network, where people could share “the story of their lives with their closest friends and family.” And for a moment it almost looked like Path might allow people to do just that. The startup boasted a whopping $ 500 million value with steadfast investor confidence that lasted all the way until it faded into obscurity, ultimately being purchased by a Korean tech firm and then removed from app stores. Path intended to enforce its mission to provide personal networks of true friends by limiting each user’s friend count to 50. The friend limit was perceived as detrimental to Path’s success at a time when Facebook users often had thousands of friends, but this alone did not account for the apparent irrelevance of the novel app. What was the missing piece? Data analysis.
Path could have sustained itself as a stalwart alternative to Facebook users disenchanted with the endless mill of likes and heart emojis. The key would have lain in sentiment analysis of user message content: By using natural language processing methods to distinguish close friends from distant acquaintances, Path could have offered its users an innovative platform for knowing who their “real friends” were.
Data analytics and the competitive future
We have seen that startup apps based on ingenious concepts and with funding levels over $ 100 million failed for a variety of reasons that could have been ameliorated or averted with savvy, transformative uses of data, analytics, and insights. One of the original e-hailing taxi companies failed for no other reason than the founding designers’ lack of awareness that Yellow cab drivers in New York at that time did not carry mobile phones!
Data is not only useful for calculating and forecasting the future, it’s a must-have for your app. Every company with a novel concept to unleash into the market must face the reality, as these companies did, that a good idea alone won’t guarantee an app’s success. Innovative use of data in concert with that idea is something that no modern app can survive without.
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.
How Profectus Delivers Value from Data
Every company is becoming a data company. In Data-Powered Businesses, we dive into the ways that companies of all kinds are digitally transforming to make smarter data-driven decisions, monetize their data, and create companies that will thrive in our current era of Big Data.
Streamlining data management across high-volume transactions
Profectus is an international technology and services company that provides leading technologies for rebate and deal management, contract compliance and accounts payable audits. Founded 20 years ago and with offices in Australia, NZ, USA and The UK, their solutions are leveraged by 100 ASX listed companies, including Westpac and HSBC, Coca Cola Amatil, Vodafone, Coles, Kmart, JP Morgan, and Rio Tinto, just to name a few.
For Profectus, data is absolutely everything. From accounts payable data coming in as direct feeds from ERP finance systems, to hundreds of thousands of invoices Profectus’ solutions ingest on behalf of its customers, along with any agreement data that their customers have with their suppliers.
“We crunch enormous Accounts Payable data files, and thousands of rebate agreements, and invoices,” Profectus’ Chief Technology Officer, Mark Webster told attendees. “In the retail sector, for one of our biggest customers, we have 4TB of data that we crunch through every few months. That’s billions and billions of rows of data that we go through to find the different variances in order to find the best value for our customers.”
Part of Profectus’ suite of services is ensuring that every transaction is aligned with a particular deal. But Mark revealed that despite the data-rich services the company provides, a lot of teams still use Excel spreadsheets.
“These have their limitations due to their size and data sets,” he explained. “And when you become a large organization, spreadsheets just aren’t going to cut it for you anymore.”
According to Mark, Profectus found that on average, somewhere between 3.5-4 transactions per 10k transactions contained an error. This number may seem relatively insignificant, but when repeated across millions or even billions of transactions, these errors add up.
“With our solutions, we’re able to save millions of dollars for our clients, simply because we are able to find the details of these transactions buried deep in the data,” Mark explained. “And the reason why we’re able to do that is because we really pride ourselves on focusing on the detail and accuracy of our data analysis. We don’t use aggregate data, we don’t use rollups. We use full detail — and that’s where we find the full value.”
Leveraging smarter data tools to unlock deeper insights
Profectus does a lot of processing, with around 90 people in their office busy “crunching” through row by row of data. But with the company growing fast, the challenge is finding a better way to boost the productivity, efficiency, and accuracy of processing these vast volumes of data at scale.
“Our COO was wondering, how could we possibly bring on more customers and then try to grow the team?” Mark said. “If a customer signs up, well sales are doing their jobs properly. But as they bring all these extra customers on, who can service them? Our business is growing, but our cost base is growing with it, because we just have to hire more and more people to trawl through more and more spreadsheets — that can’t be the sustainable way to do it.”
Profectus began looking for technology to take over and find a solution to automate the process of extracting extremely large volumes of data.
“We wanted to have algorithms, ‘visualization stations’, that actually tease out the differences in the data in a lot more automated way, so that we’re not just throwing more and more human capital at it, but actually leveraging smarter technology,” he added. “Spreadsheets just die at a certain size, and communicating the results becomes extremely difficult.”
“Think about the resources taken for teams to carefully handcraft and curate large spreadsheets, then attach them into an email. Then the customer comes back with various edits and more attachments. Trying to merge all the edits and figure out which version is the right one just gets out of control. And this whole process just breaks down at scale.”
Discovering the “single source of truth” with Sisense
For Profectus, having a streamlined, automated online system, where there’s a single source of truth was their “holy grail” solution.
“We did a very thorough and rigorous examination of the BI space and we put all of the different platforms through the ringer, but Sisense came out as the leading BI solution on the market,” Mark said. “With Sisense, not only is the data stored safely and securely, but we can extract the full value from our data and we can get the consistent repeatable and scalable answers our business needs.
“We also are using embedded analytics, with a portal that our customers can log in to and see easily for a more unified customer experience — and Sisense allows us to do this far more easily.”
Importantly, it was the sheer scalable power of Sisense’s solution that Profectus found was unmatched in the market.
Unlocking data in Snowflake to deliver insights through Sisense
With a high-powered data warehouse in place, Profectus needed a tool to unlock data that answered critical business questions. Through a combined pairing of Sisense and Snowflake, the Profectus team is now able to unlock the data in Snowflake with datasets they provide, including CSVs, spreadsheets, and third-party API integrations. Snowflake’s speed supports the live connections, ensuring Profectus sees the freshest data in its warehouse whenever up-to-date metrics are needed.
“My team now relies on Sisense and Snowflake to simplify a variety of recurring data aggregation workflows, from reports to spend analysis. Anything that used to require manually aggregating and merging spreadsheets can be pulled out of Sisense.”
“As an example, we ran a representative data set that we had in our Snowflake data warehouse through a competing solution, but we killed the process at 20 minutes because that was already unacceptable both from a customer experience and cost perspective,” Mark explained. “With Sisense, we ran the same data set, and it processed the query within 20 seconds! That was our aha moment.”
“This sort of data efficiency gain is a big deal for us, because it helps us to achieve the scale we need to serve our customers and grow as an organization.”
The data-driven vision for the future
Moving forward, Profectus is excited to reap the benefits of its new “project Delta,” which involves leveraging Sisense’s solution as part of a revolutionary shift towards smarter data-driven decision making.
“Project Delta for us is all about leveraging the right technology solutions to instigate new and exciting change,” Mark explained. “We want to enable behavior change in our customers, and for our customers to be able to optimize their business decisions, transform the way they do business with their suppliers, and help them enjoy much greater value. We’re confidently shifting towards automating a lot of our processing, taking the problem away from all the 90 people who have to manually check line after line of data, and actually getting the computer to do the job.”
“Importantly, we’re putting the right visualizations online to solve our communications problems, so our customers, their suppliers, and our own analysts can all log into the same solution and look at the same source of data treatment. They can all actually see the same story at the same time consistently, with full version control and no errors.”
Ultimately, Profectus wasn’t just looking for a “software vendor,” but a technology and business partner to work together, to help bring these great solutions to market.
“This is where Sisense really shines for us, because they have very much the same vision that we have around how to unlock insights from data and then take powerful actions based on those insights,” Mark added. “Sisense has a very compelling vision, which fits perfectly with what we’re trying to achieve.”
David Huynh is a Customer Success Manager with Sisense. He holds a degree in Business Information Systems and has spent the last 9 years in a variety of fields including sales and project management. David is passionate about helping businesses leverage data and technology to succeed. When not in the office, he enjoys cooking, travelling, and working on cars.
Sisense and Signals Analytics Bring the Power of External Data to the Enterprise
We’re stronger when we work together. In our Partner Showcase, we highlight the amazing integrations, joint projects, and new functionalities created and sustained by working with our technology partners at companies like AWS, Google, and others.
Business teams constantly want to know how their companies are performing — against their internal goals and those of the market they compete in. They benchmark their performance against their previous results, what their customers are asking for, what their customers are buying, and ideally what their customers will buy. To get their answers, businesses typically rely on data sources that are all internal, showing decision-makers only part of the picture.
That’s now in the past. Today, through a strategic partnership, Signals Analytics and Sisense are making it easy to incorporate external data analytics into a company’s main BI environment. The result is a broader, more holistic view of the market coupled with more actionable and granular insights. Infusing these analytics everywhere, democratizes data usage and access to critical insights across the enterprise.
Organizations who are truly data-driven know how to leverage a wide range of internal and external data sources in their decision-making. The integration of Signals Analytics in the Sisense business intelligence environment gets them there faster and seamlessly, without the need for specialized resources to build complex systems.
Kobi Gershoni, Signals Analytics co-founder and chief research officer
Why external data analytics?
The integration of Signals Analytics with the Sisense platform delivers on the promise of advanced analytics — infusing intelligence at the right place and the right time, upleveling standard decisions to strategic decisions, and speeding the time to deployment. Combining internal and external data unlocks powerful insights that can drive innovation, product development, marketing, partnerships, acquisitions, and more.
Primary use cases for external data analytics
External data is uniquely well-suited to inform decision points across the product life cycle, from identifying unmet needs to predicting sales for specific attributes, positioning against the competition, measuring outcomes, and more. By incorporating a wide range of external data sources that are connected and contextualized, users benefit from a more holistic picture of the market.
For example, when combining product reviews, product listings, social media, blogs, forums, news sites, and more with sales data, the accuracy rate for predictive analytics jumps from 36% to over 70%. Similar results are seen when going from social listening alone to using a fully connected and contextualized external data set to generate predictions.
The Sisense and Signals Analytics partnership: What you need to know
- Signals Analytics provides the connected and contextualized datasets for specific fast-moving consumer goods (FMCG) categories
- Sisense users can tap into one of the broadest external datasets available and unleash the power of this connected data in their Sisense dashboards
- The ROI of the analytics investment dramatically increases when combining historical data, sales, inventory, and customer data with Signals Analytics data
Integrate external data analytics in your Sisense environment in three easy steps
Step 1: Connect
From your Sisense UI, use the Snowflake data connector to connect to the Signals Analytics Data Mart. The data can be queried live in the Sisense ElastiCube.
Step 2: Select
Once the data connection has been established, select the data types needed by filtering the relevant “Catalog.”
Step 3: Visualize
Select the dimensions, measures, and filters to apply, then visualize.
More data sources, better decisions
Your company is sitting on a large supply of data, but unless and until you find the right datasets to complement it, the questions you can answer and the insights you can harness from it will be limited. Whatever your company does and whatever questions you are trying to answer, mashing up data from a variety of sources, inside the right platform, is vital to surfacing game-changing insights.
To get started on the next leg of your analytics journey start a free trial or become a partner.
