Category Archives: Sisense

Infographic: Top 5 BI Trends You Need to Know Right Now

Trends move fast these days. It seems like just as soon as we take the last sip of our unicorn frappuccinos the next big thing will be here. And then the next. And then the next. How are we meant to keep up? It’s all too much! Business Intelligence is moving at a similar pace, and although we can’t help you translate the latest lingo the kids are using, when it comes to trends in BI you need to know, we’ve got your back.

For example, did you know that two-thirds of BI users check the status of their KPIs daily or hourly and that 55% of them prefer visual alerts but 57% would like to interact with bots or voice activated data assistants in the future? Or how about that only a small 5% of BI users are IT-savvy analysts?

From self-service BI for all types of users in your organization to BI that literally speaks for itself via AI and machine learning, below are the top five trends in BI we think you need to know right this second.

Trends2017 4 01 Infographic: Top 5 BI Trends You Need to Know Right Now

Want to hear more about the biggest trends in BI for 2017? Watch to our webinar with Howard Dresner, President, Founder, and Chief Research Officer at Dresner Advisory Services; Noel Poler, CTO at EREA Consulting; and Ani Manian, Head of Product Strategy at Sisense.

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Sisense Leaps From Cool Vendor To Visionary, And 5 Key Takeaways From the 2017 Magic Quadrant

After several weeks of tense anticipation, the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms has officially been released. This is probably the most influential report in the BI space – describing the current state of the industry, and influencing its future by impacting buyer behavior, vendor strategy and market awareness.

After last year’s Magic Quadrant redefined business intelligence, this year’s report gives us a fascinating snapshot of an industry changing into something completely different, driven by technological breakthroughs and a crowded marketplace.

I strongly recommend everyone with even a fleeting interest in BI to read the MQ. These are my own 5 key takeaways (and my own opinion, unless stated otherwise):

1. Sisense Leads the Way in Innovation, Customer Success

I can’t help but start with mentioning how immensely proud and grateful I am of our amazing global team. Together, we have managed to achieve the largest organic shift in this year’s Magic Quadrant: moving from Cool Vendor to Niche Player to Visionary, all in the span of just three years.

gartner magic quadrant business intelligence 2017 Sisense Leaps From Cool Vendor To Visionary, And 5 Key Takeaways From the 2017 Magic Quadrant

This is, above all, a testament to our core values: we innovate, care and deliver solutions with a single-minded focus on customer success. We are leading the way in innovation – reinventing core BI technologies, providing the most flexible embedded analytics platform in the market, and transforming the way business users receive insights. But this is never innovation for its own sake: everything is done with a strong, constant focus on business value and empowering our customers.

From my experience at the helm of several software companies, this spirit is absolutely vital for long-term success – and Sisense has an abundance of it. Based on information and feedback from our clients, Gartner rated Sisense in the top quartile for user enablement and achievement of business benefits.

2. Simplifying Complexity

The BI industry is maturing from departmental projects into enterprise endeavors, creating demand for more robust functionality, more data sources and more analyses. As Gartner notes:

“…buyers want to expand modern BI usage, including for self-service to everyone in the enterprise and beyond. They want users to analyze a more diverse range and more complex combinations of data sources (beyond the data warehouse or data lake) than ever before — without distinct data preparation tools.”

Indeed: we’ve talked before about the rise of complex data. Modern enterprises are dealing with troves of data generated from more sources than they can wrap their head around, and business departments are demanding more than visualization. Today’s data-driven professional needs the ability to navigate a wide variety of disparate data sources in a self-service environment, and derive insights before making a decision. Enterprise data tools should empower business units to be data-driven in this sense, rather than retroactively justifying decisions with canned reports.

It all boils down to simplifying, removing barriers, and giving more power to everyday users while enabling “data heroes” to truly unleash their analytical prowess.

3. A Move to Consolidate

More than ever we see the market gravitating towards full-stack and single-stack solutions, replacing the infamous “assembly line” of database, ETL, querying and visualization tools. This is reflected in Pentaho and Alteryx – two good companies with a very loyal customer base – moving backwards in this Magic Quadrant due to focusing mainly on back-end, data preparation features.

I believe this trend will continue and expand. It is simply unsustainable for organizations to maintain three, four or five different and expensive systems for analytics. Instead, modern business intelligence should be a seamless value chain that is purchased, owned, and operated mainly by the individual business units. If Marketing or Sales have dashboards, but any new data source or query has to go through centralized IT systems, the bottleneck has merely moved elsewhere. BI vendors that can deliver on this premise will lead the industry in the near future.

4. The Future is Hands Free and Machine Guided

bot transparent 370x334 Sisense Leaps From Cool Vendor To Visionary, And 5 Key Takeaways From the 2017 Magic Quadrant

Another clear trend is the emergence of next-generation technologies – machine learning, natural language processing, and artificial intelligence – as core components of modern BI solutions. Gartner predicts that:

“By 2020, natural-language generation and artificial intelligence will be a standard feature of 90% of modern BI platforms.

By 2020, 50% of analytic queries will be generated using search, natural-language processing or voice, or will be autogenerated.”

It would seem that BI is set to converge with AI. Machine learning is already being used to serve analytical insights to end users with close to zero human intervention. Couple this with the amazing advancement of voice and natural language processing, and it’s safe to assume that the way businesses interact with data is going to very different, very soon.

I am once again extremely thrilled to see our own product vision aligning with the marketplace – with Sisense leading the way in the use of machine learning for automated anomaly detection, alerting and performance optimization, as well as integrating natural language into analytical workflows through chatbots and smart devices.

5. An Overcrowded Market

Finally, if there is one conclusion that is undisputedly evident just from a cursory glance at this year’s MQ, it’s this: there are a LOT of vendors out there. Gartner’s report is merely the “cream of the crop” with dozens of other tools available, offering everything from verticalized dashboards to advanced statistical analysis.

So how can you make an informed buying decision in such an overcrowded market?

I would start by asking three questions:

  1. Will the vendor give me the tools I need to succeed as a customer?
  2. Do I believe in the vendor’s vision and roadmap?
  3. Will the vendor bring me to where I want to be with my data today? Tomorrow? Three years from now?

In other words, focus on the future as well as the present. Business intelligence is evolving, perhaps more so than any other enterprise technology. Data is changing and growing in complexity; technology is moving towards consolidation, automation, and smarter workflows; and business itself is changing and becoming more and more dependent on data for its operations. In this time of sea change, future-proofing is the way to go.

These are definitely exciting times, the best and most dynamic I have experienced in my own career (and possibly yours). I for one can’t wait to see how the next iterations of data and analytics will transform the enterprise.

You can get your copy of the full Magic Quadrant for Business Intelligence and Analytics Platforms right here.

gartner banner for blog Sisense Leaps From Cool Vendor To Visionary, And 5 Key Takeaways From the 2017 Magic Quadrant

Copyright notice:
Gartner, Magic Quadrant for Business Intelligence and Analytics Platforms, by Rita L. Sallam, Cindi Howson, Carlie J. Idoine, Thomas W. Oestreich, James Laurence Richardson, Joao and Tapadinhas, published 16 February 2017
This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Sisense.
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and is used herein with permission. All rights reserved. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

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The Ultimate Guide to Compare Embedded Analytics Solutions

The Inevitable Challenge: What to Do with All This Data?

Businesses across all applications and in every industry are faced with mountains of data. Finding a meaningful way to manage this data has become a necessity, especially when it’s data that can help your customers or partners succeed. The only question is: will data be your company’s weak point or competitive differentiator?

In today’s ambitious business environment, customers want access to an application’s data with the ability to interact with the data in a way that allows them to derive business value. Afterall, customers rely on your application to help them understand the data that it holds, especially in our increasingly data-savvy world.

Sisense OEM vs Alternatives small The Ultimate Guide to Compare Embedded Analytics Solutions

Embedded Analytics Is the Most Popular Answer

Embedded analytics is defined as when analytical capabilities such as data management, reporting and visualization are built into other business applications and solutions. Service providers, including on-premise, cloud-based or hybrid solutions, can then offer customer-facing dashboards, reports, and services such as software infrastructure, platforms, and processes.

Many companies today are embedding analytics into their application so users can access insights from their data in easy to understand reports and dashboards. Gartner reported that today 25% of analytics capabilities are embedded in business applications, while other industry research firms stated that as high as 40% percent of organizations are embedding analytics. Both numbers show the incredible growth of embedding analytics and point to a high-value return on investment.

The Hidden and Not-So-Hidden Benefits

The value is actually two-fold: service providers who are using embedded analytics to help their customers be more successful are simultaneously creating a powerful competitive differentiator. The outcome? Happier, more loyal customers, and a strong competitive advantage for the company.

Aberdeen Group discovered that 53% of service providers are embedding analytics to drive competitive advantage, and the top service providers who did saw a 31% year over year increase in customer base. Other top benefits service providers are experiencing include improved user experience, new revenue streams, and increased average customer value:

pic 2 oem 770x369 The Ultimate Guide to Compare Embedded Analytics Solutions

Becoming an OEM Partner for BI & Analytics

Today, it is most common to embed an business intelligence solution by working with a BI partner and embedding their product through an original equipment manufacturer (OEM) agreement. Becoming an OEM is proven to be the easiest and most-effective way to offer business intelligence because it allows you a fast time to market using an established BI solution and technology.

Many of the resource allocation and budget issues dissipate by embedding a BI solution, especially one with technology built on cost effective infrastructure and that can easily scale to your current and future data needs. In fact, top BI analyst and researcher, Wayne Eckerson described the movement towards embedding analytics as:

The best way to simplify and operationalise BI is to embed it directly into operational applications and processes that drive the business. This is the definition of embedded analytics, and it’s the next wave in BI.

Eckerson went on to say that future of BI and its continued success in terms of user adoption and extensive deployments is dependent on embedded analytics.

How to Make Analytics Your Competitive Differentiator

To capitalize on the value of their information, many companies today are taking an embedded approach to analytics and delivering insights into the everyday workflow of their users through embedded analytics and business intelligence (BI). However, in order to successfully expose analytics to customers and partners, companies are faced with three main challenges:

  • Manage complex data (big and disparate datasets) quickly
  • Securely share data and insights
  • Ensure the solution is built on scalable, cost effective infrastructure

Learn how to overcome those challenges by seeing a series of matrixes comparing popular embedded analytics technologies and vendors in our new eBook The Ultimate Guide to Comparing Embedded Analytics. You’ll get a general overview of embedded analytics as well as indepth analysis of the different approaches to embedding BI and analytics, and the benefits and challenges of the most common BI solution technologies that offer OEM partnerships. By the end, you’ll have a strong sense of the embedded analytics marketplace and understand the most strategic way your company can benefit from embedded analytics.

The Ultimate Guide to Comparing Embedded Analytics Solutions

Is it better to build or buy? What are the different cost factors? What differentiates the many OEM vendors? Download our new eBook The Ultimate Guide to Comparing Embedded Analytics, and learn:

  • Pros and cons to building vs. buying embedded analytics
  • Pros and cons to building vs. buying embedded analytics
  • See how Sisense stacks up against other vendors
  • Get real-life use cases of embedded analytics

Download Now

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Beyond Dashboards: Introducing BI Virtually Everywhere

BIVE LinkedIn Beyond Dashboards: Introducing BI Virtually Everywhere

Today we announce an exciting new initiative and another step forward in our quest to simplify the way business users consume, interact and engage with business data. Sisense BI Virtually Everywhere takes data out of the 2D screens in which it “lives” today – and gives it a new, physical presence, to inspire immediate data-driven action in response to changes, as they happen.

The private beta launched with two Sisense-enabled devices – a smart IoT lightbulb that integrates with Sisense to show the way your department or business is performing against a certain KPI (for example, changing to green once the sales reps hit their daily targets); and an Amazon Echo device which enables you to ask questions about your data and receive questions, all in natural language. Here’s what some of the first users are saying:

Needless to say, we will still be providing business intelligence software – we’re not moving away from data models and dashboards quite yet! But we are very excited about BI Virtually Everywhere, because this initiative fits like a glove with three of Sisense’s core mission statements: simplifying complex data, building new and innovative technology, and delivering unparalleled user experience to our customers.

We’ve got a lot more planned in the future – the possibilities that can come from combining data analytics with the Internet of Things and new innovations in VR and AR tech are mind boggling. And as usual at Sisense, we don’t develop anything for the sake of novelty –but in order to deliver better, smoother and more effective products to end users. At this point BI Virtually Everywhere is a private beta, and we’re already getting almost more requests to join than we can handle. However, for now registration is still open – so go ahead and apply!

Learn more

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Sisense Bots: Introducing Your Very Own, Personal Data Assistant!

sisensebot Sisense Bots: Introducing Your Very Own, Personal Data Assistant!

Learn More About Sisense Bots or Join the Beta!

Since July 2016, Sisense has been proving that you don’t have to be glued to a traditional dashboard to receive insights from your data (that is, unless you want to). In July, Sisense gave you the first glimpse of its Sisense Everywhere initiative, the revolutionary way to interact with data using Amazon Echo integration and IoT light bulbs.

A total sensory experience, this initiative was among the first of its kind in BI, allowing users the ability to interact with their data through light and sound – either by asking Amazon Echo a question and hearing the answers in real time, or by quickly glancing at the color of their Sisense bulbs to understand key performance indicators. Now, Sisense is excited to present yet another seriously cool way to engage with your data.

The next phase in our “Sisense Everywhere” initiative is the Sisense Bot– your very own personal data assistant, which allows you to seamlessly interact with your data through messenger apps that you already use day-to-day, like Slack, Skype, Facebook Messenger or Telegram. All users need to do is type a natural-language question within the chat box or choose from an existing list of pre-set questions. For example, if you wanted to understand your sales figures from a certain dashboard, you would just type: “Summarize sales dashboard” and the Sisense bot would automatically retrieve the data, understand the main insights and send you the answer through chat. Sisense Bots are fully functional in both one-on-one chat and group-chat settings, to keep entire teams up-to-date and on the same page.

“This allows people to use natural language in order to ask for information from this bot and receive a combination of natural language responses, much like you would when you communicate with a friend, and widgets, images, and charts, that allow you to both consume the data visually but also drill and analyze the data.

“You will be able to connect with this bot and reach out to such a bot and ask a question and get a response the minute you need the data. I don’t need to go into a deck board to drill or look for the right widget or right visualization in order to get the information I need,” Guy Levy-Yurista, Ph.D., Sisense Head of Product, said in an interview with VentureBeat.

The Bots framework is another step in our continuing journey to simplify complex data and make business insights available to business users – at any place, any time, and in a straightforward manner. It’s great to see some of the first Bot users already gaining real value from these features.

We’ve got a lot of great features planned for these last two months of 2016 – so stay tuned for Sisense 6.5, which should be out in the next few weeks and introduce even more ways to stay connected to your business data. And if you want to get on board and join the Sisense Bots beta program – you’re more than welcome to do so.

Learn More About Sisense Bots or Join the Beta!

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Putting an End to “I’ll Get Back to You” Analytics

Putting an End to “I’ll Get Back to You” Analytics

Imagine this scenario: You’re at an important meeting discussing next quarter’s upcoming sales strategy. One of the team members is presenting your company’s performance in the previous quarter, and tells you that telemarketing was on average 20% more effective than web sales in Germany, France and Italy. Intrigued, you want to learn more and ask for a breakdown by country. “Right”, says the coworker, “I’ll get back to you on that one.”

You schedule another meeting for tomorrow, and the same team member informs you that telemarketing was 40% more effective in France, 10% more effective in Italy, and 30% less effective in Germany. You still feel there’s not much insight to be gained from this – and ask to hear absolute values. “Sure”, says your data-savvy associate, “Let me get back to you.”

The next day you hear the numbers, and want to start looking at possible plans of action. So you ask to see demographic information about your customers in each one of those countries. The response? “No problem. I’ll get back to you tomorrow.”

If you’re thinking – sounds like this employee isn’t doing his job properly, you’re absolutely right. But oddly enough, most businesses are willing to accept the same kind of “I’ll get back to you” attitude when it comes to their Business Intelligence tools.

The Problem of Data Granularity

The amounts of data modern businesses tend to generate is staggering. Coupled with existing external data sources – either publicly available or purchasable – many companies, which previously could have relied on a couple of Excel spreadsheets, suddenly find themselves looking at dozens of gigabytes or terabyte-scale data, with more and more of it being generated every day. And obviously, the more historical data that a business wants to analyze, the larger the dataset becomes.

Since ‘big data is the new gold’, no one wants to exclude raw data from their analysis – and rightly so. A professional data analyst, or even a statistically-inclined business user can often find surprising connections and insights by analyzing data – connections and insights that might not have necessarily been obvious in advance, before crunching the data using their BI tool.

But has Business Intelligence solutions kept up with the times, in terms of the amounts of data it can handle? Not entirely. Most analytics software today tends to run into one or both of these bottlenecks:

  • Hardware limitations: The increased size of the datasets requires increasingly powerful hardware, with increasingly larger amounts of RAM, to effectively process data.
  • Data preparation: The time-consuming need to combine data coming from disperse and often disorganized sources, particularly if this has to be done by the company’s IT department which often has other tasks on its hands.

So when the data starts to accumulate, performance inevitably drops. The company’s BI system – which was quite robust and flexible when it was dealing with 1 gigabyte of data and could provide answers to new queries almost immediately, starts groaning and moaning when it’s fed 50 gigabytes of data.

caa blog image2 1 Putting an End to “I’ll Get Back to You” Analytics

The common solution to this is to reduce the granularity of the data. For a simple example – instead of working with sales information from the past 24 months, the end-user works with the last 12 months; instead of seeing sales-by-city, they see sales-by-country. The rest of the data is still in the database – but retrieving it takes time.

Whether this time is spent waiting for IT to rebuild the database to accumulate new sources, or waiting for the software itself to rearrange its storage to analyze the required information (as it can no longer store the entirety of the database in-memory and is forced to use slower disk storage for unused parts of it) is irrelevant; the fact of the matter is, the company can’t make immediate decisions and instead postpones decisions while waiting for data.

In other words, the end-user finds out that whatever new question he or she is asking their Business Intelligence software, the answer is once again: “I’ll get back to you.”

Sound bleak? Luckily more modern BI technologies do exist, ones which have the ability to display the full scope of an organization’s data.

Immediate Answers for Actionable Insights

Business Intelligence that is truly agile is not limited to a set of predetermined queries, or predefined datasets. To avoid the dreaded wait for answers, modern BI tools look to solve the two above mentioned bottlenecks:

  • Joining multiple sources can be done by the software itself when queries are made and in automated manner. Self-service business analytics software should be designed to be able to mash-up data coming from diverse sources and create a single source of truth, without the need for extensive human (and specifically IT) intervention.
  • Overcoming hardware limitations: As long as BI software continues to rely on processing every new query entirely in RAM, it will always start lagging when data size increases. However – using innovative caching, decompression and data storage algorithms, newer software solutions can now process terabytes of data without dropping a beat, and without requiring major investments in hardware.

When questions are answered immediately, business processes can be improved in real-time, and decision making becomes much more data oriented and accurate. In highly competitive and fast-paced business environments this is by no means a “nice to have” – but an absolute necessity.

What about you: Are you getting all the answers you want from your data, when you want to get them? Are you seeing all your data, or are you limited to the narrow scope through which your software tools allow you to see it? If not – it might be time to think about the factors that are holding you back.

Want to learn more about common Business Intelligence problems? Read our free guide: 5 Most Common BI Problems & How To Resolve Them

By |November 11th, 2014|blog|0 Comments

About the Author:

 Putting an End to “I’ll Get Back to You” Analytics
Marketing expert with years of tech experience and a new-found love for building brilliant dashboards that always show incredible and novel insights.

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SiSense | The Big Data Analytics Blog

Leveraging the CPU Cache and Columnar Storage for High Speed BI

Leveraging the CPU Cache and Columnar Storage for High Speed BI

Watch co-founder and CTO Eldad Farkash explain some of the ingredients in Sisense’s Secret Sauce – the groundbreaking technology that allows Sisense BI Software to achieve unparalleled performance on commodity hardware (e.g. crunching 10 terabytes of data in 10 seconds on a single server node).

Eldad’s lecture covers 3 of the main bottlenecks currently impeding the performance of Business Intelligence software, and outlines the technological innovations Sisense uses to actually resolve these problems, rather than the usual workarounds and makeshift solutions that currently prevail in the BI industry.

Eldad Farkash: Leveraging the CPU Cache and Columnar Storage for High Speed BI

Topics by Timestamp

0:00 Intro: 3 Bottlenecks of Data Analytics
1:18 Bottleneck 1 – Disk: Columnar Storage
5:08 Bottleneck 2 – Memory: Decompression in the CPU
9:58 Bottleneck 3 – Multiple Users and Queries: Crowd Accelerated BI
19:56 Summary

By |November 6th, 2014|blog|0 Comments

About the Author:

 Leveraging the CPU Cache and Columnar Storage for High Speed BI
Professional writer and tech enthusiast with a passion for discovering innovative technological tools that can transform the world in which we are living.

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