Sisense, the business analytics software company that offers business intelligence products has revealed in a new study, an increase in “confidence” in the UK tech sector through 2014.

Analyses of a decade worth of startup data on 12,000 UK companies against data on approximately 300,000 companies in the CrunchBase database sheds light on investment patterns, leading technologies and geographical distribution providing “a bird’s-eye view of industry trends and developments,” explains Sisense.

“Our analysis indicates the rise in, and assurance of both the UK market and the overall European tech scene,” says Co-Founder and Chief Evangelist of Sisense, Adi Azaria. “Until now, there was much talk and buzz about a resurgence. In just a few minutes, our dashboard provides the evidence to support this, along with the details on where and how much.”

Key findings of the study indicate that:

• A 140% growth in total investments in startups in UK was charted in 2014 compared to 2013 and 270% (vs 2009) over the past five years

• On average, UK startups raised more Series C and D funding than startups from any other country globally in 2014 for those same rounds, including the US

• The greatest number of investments in 2014 were in software, with mobile attracting the most funding and the return of bio-tech as a key UK sector

• London, Cambridge, Edinburgh and Dublin startups attracted the most investment

• UK startups attract Series A funding faster than those in the US, averaging around 6 months. In comparison, startups based Stateside usually take 18 months to raise Series A. However, while Series A and Series B rounds tend to be weaker in the UK, series C and D rounds are stronger than anywhere else around the world.
• In 2014, the UK tech sector grew, with a total investment of $6.7 billion (£4.4 billion) as compared to 2013’s $2.7 billion (£1.76 billion), with 80% of it fashioned for early stage startups. Older tech outfits garnered funding larger than those raised by companies globally.
• Software, mobile and biotech have been the leading tech sectors. Mobile witnessed a 465% year-on-year growth to £650 million ($900 million), while the biotech sector saw renewed interest in 2014, with the highest median raise (87%) of investment per company at £3 million ($4.7 million).
• Territorially, London managed nearly two thirds of total tech investment, securing £2.9 billion ($4.5 billion) from more than 460 companies, while Cambridge startups, raising a funding, totaling £135 million ($209 million) across 34 companies in biotech, software, manufacturing and mobile, stood a distant second. Edinburgh’s 21 startups secured just over £50 million ($79.9 million) and the 72 startups based Dublin attracted £200 million ($310 million), reports Sisense.

A dedicated, interactive Dashboard that is updated in real-time based on the CrunchBase database and is part of the Crunch Analytics project, provides investors and entrepreneurs with a worldwide view of the same data.

Image credit: Sisense

BI and analytics startup Sisense revealed last week that for the fourth year in a row its subscription revenues have tripled. They attribute their success to the ascent in demand globally for its full-stack business intelligence and analytics software.

“The business world is moving beyond ‘Excel 2.0’ data visualization products, as companies large and small realize they require a strong back-end to handle huge data sets coming from a multitude of sources,” explains Sisense CEO Amit Bendov in the news release.

“We saw incredible growth in 2014 with customers embracing our full-stack solution, which offers interactive dashboards paired with the power, speed and ease of use of our proprietary In-Chip technology, which far outruns traditional in-memory products,” he further added.

In June last year, the outfit garnered $30 million in Series C round, and has since then grown to a record number of customers in a single quarter, tripled its North American headquarters on Wall Street and doubled the size of its R&D center in Tel Aviv. The workforce has also spread across 50 countries, and fortified its executive staff with hires including a new vice president of sales, and its first channel chief.

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(Image credit: Sisense)

When it comes to this years big data funding stories, the first thing that probably comes to your mind is Intel’s $740 million investment in Cloudera, or MapR’s $110 million financing round led by Google Capital. You’ll also probably recall funding rounds of companies like Hotonworks, MongoDB, and DataStax too. But aside from these popular Hadoop and database vendors, there has been a lot of interesting funding activity in the big data space.

Take Alteryx, for example, who raised a whopping $60 million in funding last month; or ContextRelevant who went and raised $28 million in just 28 months. Undoubtedly, 2013 was an extremely busy year when it came VC activity in the big data space. This year, however, has been equally exciting!

Below are 7 significant funding stories you may have missed so far this year:

1) InsideSales.com Raises $100M to Predict Sales

InsideSales.com, a company specialising in could-based sales acceleration technology, announced in April this year that it has raised $100 million in series C funding. Polaris Partners and Kleiner Perkins Caufield & Byers led the funding round, with participation from Salesforce, Acadia Woods, EPIC Ventures, Hummer Winbald, U.S. Venture Partners and Zetta Venture Partners.

InsideSales.com provides software-as-a-service (SaaS) to more than 1,000 customers, including large enterprises like Microsoft, Fidelity, Groupon and Marketo. The company sits in between marketing automation services like Eloqua and Marketo and CRM vendors like Salesforce and Microsoft. The aim of InsideSales.com is to use resources from both of these fields to help sales teams become more effective in their operations.

2) Kreditech Raises $40 Million at $190 Million Valuation

The big data finance company Kreditech secured $40 million Series B investment from new and existing global investors in June. It was the largest funding round ever for a German financial services technology company and one of the largest rounds in Germany in 2014. The lead investor is Värde Partners, a global investment manager with fund assets in excess of 8.5 billion USD. Co-lead investor is existing shareholder Blumberg Capital. Other shareholders, including Point Nine Capital, also participated in the round.

Based on automated big data and machine learning credit scoring, Kreditech extends loans to individual customers across the globe in real-time using a fully automated credit scoring system and banking backend infrastructure. The company operates independently from credit bureaus and traditional banking infrastructure. In just 20 months, Kreditech has scored more than 1.5 million individual loans – using a current average of 15,000 data points per application.

3) Big Data Ecommerce Outfit Qubit Scoops Up $26m in Series B

E-commerce analytics startup Qubit secured $26 million in Series B funding in late September, with Accel Partners leading the round. Original investors Salesforce Ventures and Balderton Capital, also participated.

Qubit specialises in tools which assist online retailers to monitor and optimize sales through A/B testing and user centric personalization of content. TechCrunch reports that, with approximately 150 customers in the U.K. and the U.S., including Hilton Hotel, Jimmy Choo, Staples, Farfetch, Topshop and Uniqlo, the company revealed a 260% year on year growth in sales in the six months to June 2014.

4) Sumo Logic Plan to Globalise, Following $30 Million Funding Round

Sumo Logic, whose software manages and analyses IT log files, raised $30 million in its latest round of funding (May this year), bringing their total funding to $80.5 million. The funding round was led by Sequoia Capital, with exisiting investors Greylock Partners, Sutter Hill Ventures, and Accel Partners also participating.

Sumo Logic is a cloud-based software solution which mangages system log files to give companies a greater insight into the functioning of their IT systems. The software offers an insight across a company’s IT infrastructure, including the company’s apps, servers and network. Sumo Logic also uses machine learning in its LogReduce feature to sort log file data into patterns, meaning anomalies and errors and can be identified before the become major issues.

5) GoodData’s BI Platform Rakes in $25.7M in Series E Funding

GoodData, a Cloud based Business Intelligence startup, landed $25.7 million in Series E funding just last month, led by Intel Capital and existing investors Andreessen Horowitz, General Catalyst, Tenaya Capital, TOTVS, Next World Capital, Windcrest, and Pharus Capital. The total funding received so far has now gone up to $101.2 million.

According to Ben Kepes, contributor at Forbes, their analytics platform “allows companies to manage, analyze and visualize all from one environment. He adds that “this approach really democratizes data, allowing managers and business people to generate their own insights rather than having to wait for IT departments and data scientists to run queries for them”

6) SiSense Gains Momentum with $30m Series C Funding

SiSense, a business intelligence startup based in Tel Aviv, announced in June that it had raised $30 million in Series C funding. The round was led by DFJ Growth and included participation from existing backers Battery Ventures, Genesis Partners and Opus Capital.

The company’s software is designed to make big data analytics easier for ordinary, non-technical business users. Workers can combine data from multiple sources for analysis, create visualisations and web-based dashboards, and gain insights through both mobile and desktop devices.

Among the 500 customers of SiSense are eBay, Target, Samsung Electronics and ESPN. The company said that the money from the funding round would be used to grow its workforce, expand into new markets and build its customer base.

7) Marketing Intelligence Startup Radius Picks Up $54.7M in a Bid to Change Conventional Marketing

It was announced in September that Radius, a marketing intelligence platform, secured $54.7 million in series C funding. The funding came from companies and individuals such as Founders Fund, Glynn Capital Management, John Mack formerly of Morgan Stanley, Charles Songhurst formerly of Microsoft, and actor and entrepreneur Jared Leto.

Radius provides sales and marketing teams with access to accurate, updated business information on 30+ million small-and-medium-sized businesses, with a team of more than 50, working out of San Francisco. The new funding will be used to expand its data science initiatives with intentions to remodel market and customer engagement strategies used by companies.

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Our three part Business Intelligence series has looked at the key developments in BI from the 1960’s all the way to the late 1990’s.

In the first edition, we focused on the way data storage changed from hierarchical database management systems (DBMS), like IBM’s IMS in the 60’s, to network DMBS’s and then to relational database management systems (RDMBS) in the late 70’s.

The second part of the series investigated the technological advancements through the 80’s and 90’s, predominantly mapping the evolution from mainframes to personal computers, DBMS’s to RDBM’s, and the emergence of new methods and tools like Data Warehousing, Extract Transform Load (ETL), and Online Analytical Processing (OLAP).

In this edition, we will take a brief look at how BI transitioned from a tool based, IT-centric activity, to one that is now accessible to technical and non-technical users alike.

The transformation of BI 1.0.

BI 1.0 refers to an era of BI that existed through the late 1990’s and early 2000’s. With the advent and development of data warehousing, SQL, ETL and OLAP, data was consolidated into a unified system and queries could be written to extract data from many tables at once, ultimately helping companies access and store their data more effectively.

At its core, BI 1.0. could be distilled into two components: data and reports, or aggregation and presentation. As Neil Raden, Principal Analyst at HiredBrains Research commented, “most of the effort in BI…[was] focused on data integration, data quality, data cleansing, data warehouse, data mart, data modelling, data governance, data stewardship.”

However, within this period, the major problem with BI projects was that they were still owned by IT departments, data was siloed, and reports often took extended periods of time to be delivered to management. BI solutions were predominantly designed for an analytics-trained minority and those who were already capable of understanding data models.

BI 2.0 and the Influence of Web 2.0: Mid-2000’s

The mid-late 2000’s marked a significant step forward for BI as it entered its acclaimed 2.0 phase; it went far beyond simple data and reporting by integrating near real-time processing, collaboration, self-service, discoverability, as well as offline and online access.

Whereas BI 1.0 centered mostly around the refinement of different tools – the aforementioned data warehouse, OLAP, and ETL technologies – BI 2.0 focused mostly on using the connectivity of the Web to create a BI environment that would encourage access, flexibility and getting the right data to the right people.

Many of these changes were influenced by the direction that the Web began to take in the early 2000’s (often dubbed “Web 2.0”) with social networking and web applications. One significant example was the arrival of platforms like Facebook, Twitter, and even Google, where the consumer became an important source of critique – anyone could exchange opinions on widely accessible sites, as well as gain instant information on competitors.

Businesses in the mid-2000’s therefore required access to immense amounts of real-time information in order to, among other things, track customers’ reactions to their products, what their competitors were offering, and, with the advancement of mobile and tablet technologies, the best interfaces on which to approach their consumers. In other words, the “new” Web environment demanded a simultaneous reconstitution of BI technologies that emphasized agility, dynamism, and immediacy.

BI 2.5 and the Democratization of Data: 2000’s – Current

After this explosion of data throughout the 2000’s, businesses in our current environment now require visualization tools – interactive dashboards, bar graphs, animations – to effectively analyse the information coming from inside and outside of the organization. BI is becoming jointly governed by IT and business users themselves, and is aimed at empowering the ‘Data Explorer’ through content delivery and creation.  The emergence of visualisation tools and other techniques means that BI uptake across the organisation is rising, essentially empowering business users  with the ability to independently explore their data.

Everyone from huge IT companies like Oracle, IBM, SAP, SAS, Microsoft, as well as other

companies like Tableau, Birst, Qlikview, Tibco Jaspersoft, SiSense (the list continues), are all competing to make data easier to store, more accessible across devices, and processable at a speed like never before.

As such, the battle between BI companies today is to provide speed, affordability, and high capacity storage. With mobile technology and PC’s generating such incredible amounts of data – estimated at 2.5 quintillion bytes a day — companies are no longer looking for access alone. Rather, the data has to be accessed in breakneck speeds across all devices, instantly analysable and stored in a cost effective manner.

It is no surprise, therefore, that the BI market is expected to reach $20.8 billion by 2018, at an estimated CAGR of 8.3%, of which $4 billion is expected to come from cloud-based BI. The same goes for data visualisation, with forecasts suggesting the market will grow at a CAGR of 9.21% to reach $6.40 billion by 2019.

Whether BI is set to enter yet another phase – BI 3.0? – and what it will look like is as yet undetermined. But as Brian Gentile suggests, the fierce competition among BI vendors may already have reached its tipping point:

“We joke about it inside TIBCO Jaspersoft ‘here’s the new competitor of the week’. Everyone apparently thinks that they can do analytics because that’s what it looks like. While this will clearly generate some good ideas, not all these companies are going to make it. Many of them are going to fail, or be acquired, and so on along the path.”

With this in mind, the History of Business Intelligence series has come to a conclusion. However, we will now look at dispelling the myths around BI, compare vendors against one another and offer guidance as to which technologies are right for your specific business needs.

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Furhaad Shah – Editor

Furhaad worked as a researcher/writer for The Times of London and is a regular contributor for the Huffington Post. He studied philosophy on a dual programme with the University of York (U.K.) and Columbia University (U.S.) He is a native of London, United Kingdom.

Email: furhaad@dataconomy.ru

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PREVIOUS ENTRIES:

The History of BI: The 1960′s and 70′s

This is the first edition to a three part series and gives a brief overview of the history of business intelligence. Starting in the 1960’s and 70’s, the article looks at the advancements made in data storage, database management systems, and companies that were pioneering BI from the early stages.

The History of BI: The 1960′s and 70′s

The second edition of our business intelligence series takes a deeper look at the transition from DBMS’s to RDBM’s, and the emergence of Data Warehousing, ETL, and OLAP. The 1980’s and 90’s were revolutionary in many aspects for BI, and ultimately transformed the way businesses extracted value from their data.

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So far in the “Understanding Big Data” series, we’ve looked at what “big data” actually means; what the big data landscape looks like; and some of the infrastructural approaches you might look into when you’re taking the first tentative steps away from the data warehouse. In this edition, we’ll be examining different analytics platforms- how they work, what opportunities they offer enterprises, and how they differentiate themselves from their competitors.

Around the turn of the millennium, most enterprises had turned to modern business applications to offer them insights. Companies had been implementing CRM and ERP systems to capture data, and the rise of  parallel processing techniques made data storage and processing cheaper and quicker than ever before. But garnering this data was not, in itself, helpful- this data had to be analysed to be of any value. That’s where analytics platforms come in.

Analytics Platforms

Guavus; source

At their core, analytics platforms harness the data and transform it into something comprehensible for the end user. They transform the data into reports and analytics dashboards which companies can use to fuel decisions and optimise their services. The most powerful tools in this field pull in multi-structured data from multiple sources and delivers actionable insights in real time. Some big names in this field include:

• Guavus- Guavus provide an end-to-end view across operations in real time. Guavus is capable of processing over 2.5 petabytes of data a day- that’s equivalent to 250 billion records a day, or 2.5 million records a second.
• Datameer- Datameer markets itself as the only end-to-end big data analytics application purpose-built for Hadoop. Datameer takes your multivarious data spread across the cloud, legacy databases and spreadsheets and helps you to integrate it all into Hadoop. It also allows you the option to choose whether you want a one-time input or continuous integration as new data comes in. You can then analyse and visualise your data from a single source.

Business Intelligence Platforms

Pentaho; source

In 1958, IBM researcher Hans Peter Luhn employed the Webster’s dictionary definition of intelligence to discuss Business Intelligence: “the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal”. The same holds true for Business Intelligence tools today; presenting the interrelationships between data in such a way that it illuminates decision-making and pushes enterprises forward. Companies developing tools within this field include:

• Jaspersoft- Computer Weekly described Jaspersoft as “business intelligence on the cheap”- whilst this might not sound like the most favourable review, Jaspersoft’s open source offerings have proved extremely popular. As well as open-source, Jaspersoft provide commercial products, support and service. Some of their unique features include mobile compability, integration with Google, MongoDB, Cassandra and Hadoop, and cloud BI for less than a dollar an hour.
• Pentaho- The Pentaho model also offers a community and enterprise offerings. Jaspersoft’s and Pentaho’s models are fairly similiar, but Jaspersoft is said to be stronger in terms of reports and community support, and Pentaho is more adept at OLAP analysis and dashboards.
• Birst- In our interview with Southard Jones, Birst’s VP of Product Strategy, he defined his product like this: “The key to Birst is the ability of our product to automate the hardest part of BI – which is all of the raw, ugly, un cleansed data that is all over your organisation and is multiplying at insane rates )[…] In essence, we create a business library that appeals to the way a business person thinks about their business.” One of their most recent innovations was introducing support for SAP HANA.
• SiSense- SiSense markets itself as the database which allows “non-techies” to “skip the data preparation nightmare” by combining data sources into a single repository. It allows users to drag-and-drop data from sources such as Excel, Google AdWords and Analytics and CRM information, and move quickly and painlessly to analytics and visualisation.

Visualisations

Tableau: source

Visualisation involves taking the raw data and presenting it in complex, multi-dimensional visual formats to illuminate the information. Although many analytics programmes offer visualisation as part of their product, some companies primarily focused on visualisation include:

• Tableau- As easy-to-use tool which allows you to drag and drop elements to create visualisations on the fly, and is accessible for users of all skill levels. It also promises speeds up to 10 times faster than those of traditional Business Intelligence tools. They also have a Cloud version, and can scale from small companies to enterprises with tens of thousands of employees, who can all use and modify the visualisations.
• Visual.ly- Visual.ly is a community platform for data visualisation and graphics. As well as a gallery of user-designed infographics, their in-house design team build a range of ad-hoc infographics, including analytics.

There are a dazzling range of analytics solutions out there, from free offerings capable of giving basic analytics in real time to programmes that can produce complex visualisations of immense datasets. In the next installment of “Understanding Big Data”, we’ll be looking at Machine Learning- moving from understanding patterns in the data you have to predicting trends you may encounter in the future.

(Image credit: Birst)

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Eileen McNulty-Holmes – Editor

Eileen has five years’ experience in journalism and editing for a range of online publications. She has a degree in English Literature from the University of Exeter, and is particularly interested in big data’s application in humanities. She is a native of Shropshire, United Kingdom.

Email: eileen@dataconomy.ru

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Accessibility has been the flavour of the week, with several big names making announcements which will shake up the analytics market and put greater focus on making tools easier to use.

The main announcement was Facebook’s decision to open source Haxl. Written in Haskell, Haxl is an internally-developed library which makes it easier to pull data from multiple sources. Haxl acts as an abstraction between front-end applications and back-end databases and web services. It allows users to execute a single query across multiple sources, and caches the queries for future use.

Jaspersoft have also been making waves in accessibility. The recent release of Jaspersoft 5.6 placed alot of focus on blending sources (particularly relational/non-relational), and using enhanced connectors to do away with manual integration. As we reported last week, they’ve also announced a new visualisation product, visualise.js, which allows for better embedding of visualisations and support for new chart types.

SiSense also announced $30 million in funding. The money will go towards expansion of the product line and pushing for the adoption of their namesake product, which aims to make large-scale analytics easier to use for traditional enterprises. This is achieved by their columnar database, which utilises CPU cache, RAM or disk depending on what is most appropriate for the task, improving performance and reducing hardware requirements.

The move towards greater accessibility is growing trend; hopefully, this will lead to more enterprises being capable of using big data analytics, and gaining greater insights as a result.

Read more here.
(Image credit: Jaspersoft)

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SiSense, a business intelligence startup based in Tel Aviv, announced yesterday that it has raised $30 million in Series C funding. The round was led by DFJ Growth and included participation from existing backers Battery Ventures, Genesis Partners and Opus Capital.

The company’s software is designed to make big data analytics easier for ordinary, non-technical business users. Workers can combine data from multiple sources for analysis, create visualisations and web-based dashboards, and gain insights through both mobile and desktop devices. In February, the company released SiSense 5 with new functions and capabilities for analysing terabytes of data on mobile devices.

Although the analytics company has considerable competition in this field – including Tableau, Qlik, Birst, DataRPM, BIME Analytics, to name just a few – SiSense’s CEO, Amit Bendob, said,

“Traditional BI software is robust but complicated and not too user friendly. Then the newer generation, like Qlik or Tableau, are much easier to use but they can’t handle the range of requirements that the older tools have,” he said.

Indeed, SiSense’s recent funding round is another sign of how next-generation business analytics software companies – like the ones mentioned above – are challenging established products like SAP’s Business Objects, IBM’s Cognos, and Oracle.

“We’re not going head-to-head against SAP HANA or Oracle,” said Bruno Aziza, SiSense’s former vice president of marketing, in an interview with SilinconANGLE. “Most of the people who can afford solutions like that have a lot of time, a lot of money and big teams.  And it turns out that’s probably 500 companies.”

As such, SiSense claims that its software makes business analytics financially viable for a “much broader range of organizations through the use of a homegrown columnar database that utilizes CPU cache, RAM or disk depending on which resource type is most appropriate for the specific task at hand.”

Among the 500 customers of SiSense are eBay, Target, Samsung Electronics and ESPN. The company said that the money from the funding round would be used to grow its workforce, expand into new markets and build its customer base.

Read more here

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