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Thanks to a weak economy that won’t be booming anytime soon, plus a potent array of top- and bottom-line benefits, the lure of business intelligence softwarehosted in the cloud has triggered many refugees from the mature vendors – and prodigious amounts of venture capital.
So many companies have been launched over the past five years, and have gained traction in the market, that there has already been at least one casualty, LucidEra.
Dozens of start-ups have attracted not only VC money but also paying customers. Birst, PivotLink, GoodDataand others are providing either a full suite or a series of point solutions for customer analytics, performance management, workforce analysis or other tasks. Many of these feature were formerly only available from an on-premise implementation.
“A lot more new technology is coming that will delight people,” he explains. “It will be easier to deploy and manage. In addition, it will be more intuitive and more usable than what is already out there.”
As you read through the press releases, blogs and other hype, keep in mind the pedigree question: To properly gauge the value of a start-up, check out the backgrounds of the management team.
Dresner notes that a herd of “brilliant people who came out of the legacy companies” have launched an explosion of new Business Intelligence companies. He calls this new wave the BI 3.0 era. He should know when it’s a new era—he’s the author of several books on performance management.
Business Intelligence Legacy Players’ Rework in ProgressMeanwhile, the large established Business Intelligence players are rapidly moving to offer hosted versions of their apps in the short term. The challenge for some, though, is that they’re still digesting their last acquisition binge. SAP’s purchase of Objectsoft and Business Objects, IBM’s purchase of Cognos and SPSS and Oracle’s purchase of Siebel, PeopleSoft and Hyperion created a major interoperability challenge.
For certain customers, by the time these major players have forged a seamless interface among their broad Business Intelligence software suites, not everyone will care — it will be easier to just do it in the cloud.
Here’s a new scorecard of the legacy Business Intelligence software players I assembled from various articles and reports by IDC, Forrester, Gartner, Ventana Research and various independents. Use it to keep track of the leading vendors as they try to maintain their existing business model and adopt a radical new one at the same time:
Actuate: Hosted version of its performance management tools available since 2008.
IBM/Cognos: Already offering hosted BI apps and reworking existing on- premise apps to interoperate in the cloud.
Informatica: Cloud to on-premise integration tools introduced in 2009.
Microsoft BI: On demand version of SharePoint, which is the hub of its Business Intelligence stack, along with its Azure cloud development tools.
Microstrategy: Supporting SaaS by partnering with hosts.
Oracle BI: Offering hosted version of its Business Intelligence apps.
SAP BI: Offering products to integrate cloud with existing on-premise apps
SAS Business Intelligence: Building a data center for hosting apps.
Meet the leading Business Intelligence Software 3.0 players in the cloudWhen evaluating a start-up in the SaaS BI space, the pedigree of the alumni is a key criteria. I gathered nominations from reports and articles by analysts who I trust, and in a few cases added companies launched by people I know personally:
Business Intelligence Company: Alumni pedigree
Birst: Siebel.
PivotLink: Hyperion, SPSS, SAP.
Host analytics: Oracle, Hyperion.
Cloud 9 analytics: Cognos, Oracle, Informatica.
Vertica: Legendary DBMS developer Mike Stonebreaker plus alums from Oracle and elsewhere.
Proferi: In stealth mode, but management team from SAP, Siebel and Hyperion.
Indicee: Crystal Reports, Microsoft, Symmetrics and Business Objects.
The above is just a sampling of what is out there. The key take away is that a hosted Business Intelligence software solution is going to be in your future, whether you like it or not.
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10 Top Business Intelligence Software Solutions
The Business Intelligence software market is shaping up as a David vs. Goliath struggle. Behemoths like Microsoft, Oracle and IBM offer feature-rich BI suites along with their many other enterprise software products. Meanwhile, pure-play business intelligence software vendors — such as MicroStrategy and Tableau — have avid followers and are known for innovating around new features and quickly adjusting to the shifting marketplace.
The list below includes ten industry-leading BI solutions, from vendors large and not-so-large. If you’re looking for a bird’s eye view of this rapidly evolving market, the following condensed portraits should help.
10 Business Intelligence Software SolutionsNote: This list is NOT ordered “best to worst.” The question of what business intelligence software solution is best for a given company depends on an entire matrix of factors. This list is simply an overview of BI solutions, with the debate about quality left to individual clients.
SAP Crystal Reports
SAS Enterprise BI Server
Oracle Business Intelligence Enterprise Edition Plus
IBM Cognos 8 BI
IBM’s Cognos 8 BI offering is an inclusive suite featuring a range of BI capabilities including reporting, analysis, dashboarding and scorecards on a single, service-oriented architecture (SOA). The suite includes Report Studio, Query Studio, Analysis Studio, Metric Studio, Metric Designer, Event Studio, Framework Manager and PowerPlay Studio. IBM has declared business analytics as one of the most critical parts of its overall strategy. It has spent heavily on business intelligence and business analytics R&D, investing more than $12 billion in the last five years. That includes the $1.2 billion acquisition of SPSS in 2009, which added a predictive analytics element to its portfolio. (See a video interview of IBM’s Jeff Jonas on BI concepts.)
Microsoft PowerPivot
Two applications, Microsoft’s PowerPivot for Excel and PowerPivot for SharePoint, both leverage Office 2010, SharePoint 2010 and SQL Server 2008 R2 in an offering that uses the ubiquity of Microsoft’s applications to provide BI tools to the knowledge worker masses rather than BI experts. PowerPivot for Excel uses the Excel features users are already familiar with to provide interactive data analysis tools. PowerPivot for SharePoint provides the ability to share and collaborate on user-generated data analysis in Excel and in the browser. By leveraging technology already found in many companies and comfortable to most workers, Microsoft hopes to capture a much larger slice of the BI pie.
MicroStrategy Reporting Suite
MicroStrategy Reporting Suite is a free, commercial reporting tool composed of server software for core analytical processing and job management, an end-user Web interface, Web-based reporting software, desktop reporting software and a data architecting product. It outputs reports in HTML, PDF, Microsoft Excel and text. It can present data in tabular grid reports, graphs and charts, and combination grid-and-graph displays. It is available for Windows, Unix, Linux, Solaris, HP-UX, AIX, and any data source (including SAP BW and Microsoft Analysis Services). MicroStrategy Software is often layered over massive data warehouses, and it boasts the ability to support large-scale, demanding BI environments.
Salesforce CRM
TIBCO Spotfire Analytics
TIBCO Spotfire Analytics combines business process management (BPM), complex event processing (CEP), predictive analytics (PA) and visual data mining (DM) software. It handles everything from real-time data capture and streaming to data analysis, forecasting and interactive reporting on a single platform.
Information Builders WebFOCUS
Information Builders’ flagship WebFOCUS BI platform uses a purely Web-based architecture with no plug-ins. The company describes its approach as focused on BI applications and embedded BI rather than tools, noting that BI applications“are much simpler to use than tools.” WebFOCUS has been implemented at more than 12,000 customer sites and is used to build Web-based BI applications.
Tableau Business Intelligence Software
A pure-play BI software vendor, Tableau refers to its offering as “rapid fire BI.” It boasts drag-and-drop features that allow users without IT expertise to visualize information from any structured format. It claims to be the “only provider of data visualization and business intelligence software that can be installed and used by anyone while also adhering to IT standards.” Its offering is comprised of Tableau Desktop and Tableau Server. Tableau Desktop is a tool for graphically analyzing virtually any structured data to produce charts, graphs, dashboards and reports. Tableau Server adds enterprise-class security and performance to support large deployments.
Artificial Intelligence In Film Industry Is Sophisticating Production
Artificial intelligence in filmmaking might sound futuristic, but we have reached this place. Technology is already making a significant impact on film production. Today, most of the outperforming movies that come under the visual effects category are using machine learning and AI for filmmaking. Significant pictures like ‘The Irishman’ and ‘Avengers: Endgame’ are no different. It won’t be a wonder if the next movie you watch is written by AI, performed by robots, and animated and rendered by a deep learning algorithm. But why do we need artificial intelligence in filmmaking? In the fast-moving world, everything has relied on technology. Integrating artificial intelligence and subsequent technologies in film production will help create movies faster and obtain more income. Besides, employing technology will also ease almost every task in the film industry.
Applications of AI in film productionWriting scripts ‘Artificial intelligence writes a story is what happens here. Humans can imagine and script amazing stories, but they can’t assure that it will perform well in the theatres. Fortunately, AI can. Machine learning algorithms are fed with large amounts of movie data, which analyses them and comes up with unique scripts that the audience love. Simplifying pre-production Pre-production is an important but stressful task. However, AI can help streamline the process involved in pre-production. AI can plan schedules according to actors and other’s timing, and find apt locations that will go well with the storyline. Character making Graphics and visual effects never fail to steal people’s hearts. Digital domain applied machine learning technologies are used to design amazing fictional characters like Thanos of Avengers: Infinity War. Subtitle creation Global media publishing companies have to make their content suitable for viewers from different regions to consume. In order to deliver video content with multiple language subtitles, production houses can use AI-based technologies like Natural language generation and natural language processing. Movie Promotion To confirm that the movie is a box-office success, AI can be leveraged in the promotion process. AI algorithm can be used to evaluate the viewer base, the excitement surrounding the movie, and the popularity of the actors around the world. Movie editing
Artificial intelligence in filmmaking might sound futuristic, but we have reached this place. Technology is already making a significant impact on film production. Today, most of the outperforming movies that come under the visual effects category are using machine learning and AI for filmmaking. Significant pictures like ‘The Irishman’ and ‘Avengers: Endgame’ are no different. It won’t be a wonder if the next movie you watch is written by AI, performed by robots, and animated and rendered by a deep learning algorithm. But why do we need artificial intelligence in filmmaking? In the fast-moving world, everything has relied on technology. Integrating artificial intelligence and subsequent technologies in film production will help create movies faster and obtain more income. Besides, employing technology will also ease almost every task in the film industry.‘Artificial intelligence writes a story is what happens here. Humans can imagine and script amazing stories, but they can’t assure that it will perform well in the theatres. Fortunately, AI can. Machine learning algorithms are fed with large amounts of movie data, which analyses them and comes up with unique scripts that the audience love.Pre-production is an important but stressful task. However, AI can help streamline the process involved in pre-production. AI can plan schedules according to actors and other’s timing, and find apt locations that will go well with the storyline.Graphics and visual effects never fail to steal people’s hearts. Digital domain applied machine learning technologies are used to design amazing fictional characters like Thanos of Avengers: Infinity War.Global media publishing companies have to make their content suitable for viewers from different regions to consume. In order to deliver video content with multiple language subtitles, production houses can use AI-based technologies like Natural language generation and natural language chúng tôi confirm that the movie is a box-office success, AI can be leveraged in the promotion process. AI algorithm can be used to evaluate the viewer base, the excitement surrounding the movie, and the popularity of the actors around the chúng tôi editing feature-length movies, AI supports the film editors. With facial recognition technology, an AI algorithms can recognize the key characters and sort certain scenes for human editors. By getting the first draft done quickly, editors can focus on scenes featuring the main plot of the script.
Business Intelligence Vs Data Warehouse
Difference Between Business Intelligence vs Data Warehouse
Hadoop, Data Science, Statistics & others
Data Warehouse (DW) is simply a consolidation of data from various sources that set a foundation for Business Intelligence, which helps in making better strategic and tactical decisions. So I can say Data Warehouses have business meaning baked into them. Database stores data from different sources in a common format, and The Warehouse is like Godown (Big Building), where many things may be stored. Still, Data- Warehouse works with intelligent algorithms like Indexing, which helps locate and retrieve easily and on the same concept.
Data Warehouse is similar to a relational database aimed at querying and analyzing the data rather than for transaction processing. It usually contains historical data derived from transaction data but can include data from various sources. Data Warehouses hold data, in fact, Tables (Tables that cover numbers such as revenue and Costs) and Dimensions (Group Facts by different attributes like region, office, or week).
I will use certain abbreviations like BI for Business Intelligence and DW for Data Warehouse, as it’s easy to write. So far, I hope you have got enough understanding about both Business Intelligence and Data Warehouse concepts which are so commonly used in the Data Analytics Domain. These are so mistakenly used that even people in this domain are unsure what and when to use them.
Now let’s understand exactly what business intelligence is, which has created so much confusion in the Analytics industry as some people use both terms interchangeably. Lots of discussions are going on the internet.
A Business Intelligence system tells you what happened or is happening in your business; it describes the situation to you. Also, a good BI platform represents this to you in real time in a granular, accurate, and presentable form.
I will tell you why it is so intelligent; using Data is simple. Data is accumulated over a significant amount of time from several disparate sources.
But now, a fundamental question arises about where this data is. This data is stored in the Data Warehouse (DDS, Cubes). And BI systems use Data Warehouse data and let you apply chosen metrics to potentially massive, unstructured data sets and cover querying, data mining, online analytical processing (OLAP), reporting, and business performance monitoring predictive and prescriptive analytics.
So now let’s compare Business Intelligence and Data Warehouse to better understand by comparing.
Head-to-Head Comparison Between Business Intelligence vs Data Warehouse (Infographics)Below are the top 5 comparisons between Business Intelligence vs Data Warehouse:
Key Differences Between Business Intelligence vs Data WarehouseFollowing are the differences between Business Intelligence vs Data Warehouse:
BI means finding insights that portray a business’s current picture (How and What) by leveraging data from the Data Warehouse (DW).
BI is about accessing and exploring an organization’s data, while Data Warehouse is about gathering, transforming, and storing data.
DW outlines the actual Database creation and integration process along with Data Profiling and Business validation rules. At the same time, Business Intelligence uses tools and techniques that focus on counts, statistics, and visualization to improve business performance.
BI deals with OLAP, data visualization, data mining, and query/reporting tools. In contrast, DW deals with data acquisition, metadata management, data cleansing, data transformation, data distribution, and data recovery/backup planning.
DW teams use tools like Ab Initio Software, Amazon Redshift, Informatica, etc., while BI teams use tools like Cognos, MSBI, Oracle BI, Pentaho, QlikView, etc.
Software engineers, mainly Data Engineers, deal with DW, while top executives, Managers deal with BI.
Business Intelligence vs Data Warehouse Comparison TableBelow is the comparison table between Business Intelligence vs Data Warehouse.
Basis for Comparison Business Intelligence Data Warehouse
What it is System for deriving insights related to business. Data Storage: historical along with the current.
Source Data from Data warehouse. Data from several Data sources and applications.
Output Business reports, charts, graphs. Data, in fact, and dimension tables for upstream applications or BI tools.
Audience Top executives, Manager Data Engineers, Data Analysts, and Business Analysts.
Tools MSBI, QlikView, Cognos, etc. Ab Initio Software, Amazon Redshift, Informatica.
ConclusionSo I finally want to conclude this article as BI tools like QlikView, MSBI, and Oracle BI all access data from Data Warehouses. And let business users create more granular and presentable reports, graphs, and charts, which help top executives to make more effective business decisions in different functional areas like finance, supply chain, human resources, sales & marketing, and customer service.
Recommended ArticlesThis is a guide to Business Intelligence vs Data Warehouse. Here we have discussed head-to-head comparison, key differences, and a comparison table. You may also look at the following articles to learn more –
How Artificial Intelligence Impacts Business
AI isn’t new. People use it every day in their personal and professional lives. What is new is are new business offerings thanks to two major factors: 1) a massive increase in computer processing speeds at reasonable costs, and 2) massive amounts of rich data for mining and analysis.
This report from Harvard Business Review reflects the nascent use of AI in business, with the many respondents in the exploration phase.
Artificial Intelligence in Business: The AwakeningInfoSys in its survey report Amplifying Human Potential: Towards Purposeful Artificial Intelligence reported that the most popular AI technologies for business were big data automation, predictive analysis, and machine learning. Additional important drivers include business intelligence systems and neural networks for deep learning.
Artificial intelligence in business brings AI benefits – and challenges – into business areas including marketing, customer service, business intelligence, process improvement, management, and more.
Major Use Cases for Artificial Intelligence in Business
The biggest use cases driving AI in business include automating job functions, improving business processes and operations, performance and behavior predictions, increasing revenue, pattern recognition, and business insight.
3. Predict performance and behavior. AI applications can predict time to performance milestones based on progress data, and can enable customized product offers to web search and social media users. Predictive AI is not limited to traditional business: Disney Labs, Caltech, STATS, and Queensland University partnered to develop a deep learning system called Chalkboard. The neural network analyzes players’ decision-making processes based on their past actions, and suggests optimal decisions in future plays.
4. Increase revenue. Companies can increase revenue by using AI in sales and marketing. For example, Getty Images uses predictive marketing software Mintigo. The software crawls millions of websites and identifies sites that are using images from competitive services. Mintigo manages the huge sales intelligence database, and generates actionable recommendations to Getty sales teams. Northface uses IBM Watson to analyze voice input AI technology and recommend products. If a customer is looking for a jacket, the retailer asks customers what, when, and where they need the jacket. The customer speaks their response, and Watson scans a product database to locate two things: 1) a jacket that best fits the customer’s stated needs, and 2) cross-references the recommendation by weather patterns and forecasts in the customer’s stated area.
6. Business insight. AI can interpret big data for better insight across the board: assets, employees, customers, branding, and more. Increasingly AI applications work with unstructured data as well as structured, and can enable businesses to make better and faster business decisions. For example, sales and marketing AI applications suggest optimal communication channels for content marketing and networking to best prospects.
Based on the HBR report, predictive analytics is a leading business use of AI, followed closely by text classification and fraud detection.
AI Business ConcernsFor all its benefits, AI projects are often costly and complex and come laden with security and privacy concerns. Don’t let these issues blindside you: carefully research the business challenges around AI, and compare the costs of adopting an AI system against losing its benefits.
· AI is expensive. Advanced AI does not come cheap. Purchase and installation/integration prices can be high, and ongoing management, licensing, support, and maintenance will drive costs higher. Build your business case carefully; not just to sell senior management, but to understand if the high cost is worth the benefits – especially if a big business driver is cost reduction.
· AI takes time. Give installation plenty of time in your project plan, and build your infrastructure before the system arrives. High-performance AI needs equally high-performance infrastructure and massive storage resources. Businesses also need to train or hire people with the knowledge skills to manage AI applications, and complex AI systems will require training time and resources. Many businesses will decide to outsource some or all their AI management; often a good business decision but an added cost.
· AI needs to be integrated. There may also be integration challenges. If your AI project will impact existing systems like ERP, manufacturing processes, or logistics systems, make sure your engineers know how to identify and mitigate interoperability or usability issues. Businesses also need to adopt big data analytics infrastructure for predictive and business intelligence AI applications.
· AI has security and privacy concerns. Cybersecurity is as important for AI applications as it is for any business computing – perhaps more so, given the massive amounts of data that many AI systems use. Privacy issues are also a concern. Some of AI’s most popular use cases — ranging from targeted social media marketing to law enforcement — revolve around capturing user information. Businesses cannot afford to expose themselves to security or privacy investigations or lawsuits.
· AI may disrupt employees. Some positions will benefit from AI, such as knowledge workers who give up repetitive manual tasks in favor of higher level strategic thinking. But other employee positions will be reduced or eliminated. Although businesses must turn a profit, employee disruption is awkward, unpopular with the public, and expensive. According to Infosys, companies with mature AI systems make it a point to retrain and redeploy employees whose positions were impacted by AI automation.
Deploying AI systems is a big project, but is ultimately a business technology like any other system. Carry out due diligence. Research and build your expertise and infrastructure. Then deploy, use, refine, and profit.
Excel Powerpivot Disrupts Business Intelligence
Could the age of self-service BI (business intelligence) finally be near? And, if so, are organizations ready?
“It will spread like wildfire. As organizations upgrade to Office 2010, Excel users will adopt PowerPivot, whether the [IT staff] likes it or not,” said Gartner analyst Rita Sallam.
And thus far BI professionals seem ambivalent about PowerPivot. At a packed PowerPivot birds-of-a-feather session at the Microsoft TechEd conference last June, many admitted that the feature is powerful, even as they worried about the repercussions of its use within their own offices.
“Some of our concerns [are around] letting users loose, the size of the files that they want to share and the kind of data they want to share,” one attendee said.
As the name implies, PowerPivot is a PivotTable on steroids. With PowerPivot, you can pull into Excel large amounts of data from multiple database tables, databases or other sources of data, and sort and filter them almost instantly. Data can be reorganized around one column or compared against columns from another data source. You can divide the data by time, geographic origin or some other parameter. Since it runs Microsoft’s business intelligence software on the back end, it can do much of what a full-fledged BI application can do.
And PowerPivot can work blazingly fast too. Architecturally speaking, it replicates the technology found in many in-memory databases, allowing users to sort millions of rows of data within a few seconds.
But a potential danger lurks in this ease of use, said Andrew Brust, the chief technology officer for Microsoft integrator Tallan. (Brust also moderated the PowerPivot TechEd session.) Promiscuous use of PowerPivot may only aggravate a problem that has already become an issue for many data-centric organizations over the past decade, one that came about in large part due to the managerial popularity of Excel.
“There are a lot of people who are doing BI, but they just are not calling it that. They are doing a lot of their work in Excel and are not using mainstream BI technologies,” Brust said. “And they are doing it totally off-road, so IT doesn’t know about it.”
Worse, because they are in Excel, the reports being passed around often contain closely held company business practices, in the form of cell calculations, Collie noted.
Now, PowerPivot, with its ability to easily make reports of even greater depth, will only further muddy the waters of organizational insight, many fear. “Business users can combine data in a way that may not be compliant with corporate data sources or metrics,” Gartner’s Sallam said.
Nonetheless, if the organization puts a few rules and technologies in place, PowerPivot could actually diminish the proliferation of such spreadmarts.
This approach also allows the IT staff to keep track of what reports are the most popular, Oberoi said. The staff can then polish these reports and turn them into official, companywide summaries.
To some extent, PowerPivot may have BI professionals worried because it may put them out of a job. This probably won’t happen though.
“You need to make sure you have 100 percent agreement on the keys in the different datasets, and you need to be very careful how the join is done, otherwise the resulting data is meaningless. This is particularly hard with datasets that have differing levels of granularity,” he said, adding that these problems are solved using ETL (Extract, Transform and Load) tools.
“These tools are not out of reach for a seasoned Excel user to understand, but factoring in the data quality has to be done — even the flashiest analysis of bad data is going to lead you to make wrong decisions,” Dixon said.
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