In April 2016, the European Union sought to replace their 1995 Data Protection Directive with a new set of regulations. Which aimed to protect the internet rights of EU residents. Last week, the new and much awaited General Data Protection Regulations (GDPR) finally came into effect. It reinforces data privacy on a much larger scale than any of the previous national data privacy laws, both territorially and digitally. For the first time, one set of regulations protect the internet rights of residents of the EU and the EEA. And while the GDPR may not be a hundred percent perfect, it unifies data protection regulation across Europe and places the power quo into the hands of individual internet users rather than organizations.
A revised set of Internet rights
The GDPR provides individuals from the EU a set of fundamental data subject rights as well as contextual rights which they can exercise under particular conditions and with exceptions. These include the right to be forgotten, the right to object, the right to access and the right to be notified among several others.
Essentially, this implies that an EU resident’s data cannot be collected, used or stored until the organization has received explicit permission from the user for processing their data. These organizations are also required to clearly explain why their data is being gathered and how it is being used.
Of course, these data subject rights are not absolute, and as mentioned, are subject to conditions and exceptions. They can be influenced as well by already existing rights. For example, the right to information and the freedom of expression which could affect the right to erase one’s data. Of course, there would be cases in which organizations have their own legal stipulations and obligations which could out-weigh the data rights of users. However, in such cases, there are guidelines set up by the European Data Protection Board for organizations to follow.
The new regulations called for significant changes in company policy and infrastructure. The EU had given a grace period of two years so that companies could make the necessary changes in order to be GDPR compliant. To both European companies and those outside the EU.
Is Asia ready?
Asia, of course, is one of the regions which had to sit up and take notice of the GDPR. But do enterprises based in Asian countries need to be bothered by a data protection law for European citizens?
The matter of fact is that they do. Given that the GDPR focuses on protecting the individual rights of internet users, these regulations apply to any organization which collects and holds data of EU residents. The GDPR particularly applies to companies who (a) process personal data outside of the EU but have establishments, such as branches or subsidiaries, in the EU; (b) provide goods and services to users in the EU, or (c) monitor the behavior of individuals in the EU.
Given that many Asian countries have adopted stricter data privacy laws in recent years – like Japan’s Personal Information Protection Act (PIPA) and Singapore’s Personal Data Protection Act (PDPA), the GDPR completely raises the bar when comes to data privacy. So, is it surprising that there are very few Asian companies who are compliant despite the two-year grace period? In fact, studies have shown that even a month before the GDPR was scheduled to take effect, less than one-third of Asian companies were ready for it. While organizations in Europe have been preparing for the GDPR for past two years, many Asian companies are only just beginning to comprehend and assess the impact of the GDPR on their businesses.
What happens if Asian companies are non-compliant?
The consequences of being non-compliant are pretty dire. If the event of Facebook’s data breach had happened after the 25th of May, the social media giant would have been answerable to the EU. Then perhaps, the company wouldn’t have escaped unscathed.
An organization could face multiple lawsuits and a €20 million fine. Or would have to give up to 4% of their annual global turnover, whichever is the greater amount. However, these are the legal actions that European companies will face.
The one beacon of light in the confusion surrounding the GDPR is that the EU regulators would most likely focus on European companies before they look towards Asia and the rest of the world. It still appears to be unclear how the regulators plan to enforce GDPR overseas. The suggested route of action is that Asian companies caught would be required to appoint a representative as a point of contact in Europe. The point of contact could be a subsidiary, branch or representative office. In cases where they run into roadblocks while taking action against a non-compliant Asian company, it would most likely be that regulators would take enforcement action against this European contact.
However, the fact remains that overseas companies who fall into the GDPR compliant bracket have to ensure that they will be ready. Even if they seem too late for the game, Asian companies, including small businesses and start-ups, need to consider whether they are required to comply with the GDPR or not. And then take the necessary actions to accommodate these changes.
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ConnecTechAsia Summit speaker, Sharala Axryd, CEO, ASEAN Data Analytics Exchange, shares about the transformative potential of Artificial Intelligence, issues of bias and ensuring the objectivity of AI.
The vision of an all-knowing, omni-present intelligent being that forms the backbone of our everyday lives has been portrayed in movies that captivate the imagination of many. Today, that vision is not too far from reality, and we are seeing this at work through artificial intelligence (AI) – from AI-powered voice assistants like Alexa, to helping solve traffic issues, enabling the sequencing of DNA, tackling business problems and transforming industries such as tech, healthcare to logistics and fintech.
Current AI technologies are estimated to have the potential to automate about 50 percent of work activities in ASEAN’s four biggest economies – Indonesia, Malaysia, the Philippines and Thailand, according to McKinsey.
Even as AI increasingly finds its way into our everyday lives, the transformative power of AI is underpinned by a deeper issue that threatens the very fabric of our society – the presence of bias within it.
Uncovering the roots of bias
Much of AI’s capabilities as an intelligent, cognitive system rely on it being programmed and trained. At its core, AI operates on algorithms and data sets, the driving force of the digital economy in the 21st century. However, AI also unfortunately inherits and reflects the existing bias of its creators through the data it is given.
For example, when used in recruitment, a biased AI could be trained to shortlist potential candidates based on selected profiles of high-performing employees, which may not be representative of the company’s workforce nor consider diversity and inclusion as a factor for hiring, and potentially skew the hiring demographic.
The capabilities of AI are only as objective as the quality of the data inputs, as well as the assumptions around this data. When this data is not carefully selected, AI may not only validate the biases we hold, but further perpetuate them.
Leaving this unaddressed could pose issues for society, given that AI has already found its way into sectors such as telecommunications, medical, legal and finance. For example, a biased AI system might deny a bank loan simply because the borrower is located in a poorer neighborhood.
The possible scenarios are endless, though the conclusion resoundingly clear – bias in AI needs to be swiftly addressed while AI is still at its teething stages, before it progresses too far to root out issues that lie at the conception.
Part of the reason behind the existence of bias in AI points to the lack of diversity, particularly of gender, of the tech industry. Even more so for a highly specialized field like AI, and it has been found that just 18 percent of C-level executives in AI or machine learning companies are women.
Can AI be truly objective?
A key agent of change is to ensure that the data used to train AI is representative across various socioeconomic factors including race, religion, sexuality, education, career background and financial status.
Additionally, it is essential to expand and diversify the talent pool of people working on the next generation of AI. These should include women, creatives, sociologists and various industries that can together identify the lacking aspects of AI and provide the needed perspectives to weed out bias. Bringing in the alternative perspectives of women can boost creativity within the industry and cultivate gender diversity, and also prevent AI from becoming a skewed, gendered technology.
AI presents an exciting new frontier for the human race, and could possibly be the defining technology that will change our world like never before. The healthcare industry has been a key driver for AI, with recent breakthroughs including the world’s first AI-powered stethoscope, a Malaysian invention that enables precise surveillance and detection of heart and lung diseases.
However, as with any new technology, a cautious approach is needed in its design and implementation. While AI is designed to make our lives easier, our responsibilities and ethical obligations cannot be outsourced to machines. A global framework and increased governance of AI is essential to ensure that the very technology we are designing to help us does not do the contrary.
It has been said that bias is an inherent human trait and impossible to eliminate, however the goal is not to eliminate bias, but reduce it to a negligible level. When we bring together the greatest minds in the industry and involve partners across industries, communities and people from all walks of life, efforts to use AI to make the world a better place, transcending race, religion, color and gender, will deliver unparalleled outcomes for mankind.
For more insights, join Sharala Axryd, CEO, ASEAN Data Analytics Exchange (ADAX) at the ConnecTechAsia Summit on Conference Day 3, 28 June 2018 speaking on the panel ‘Man vs Machine – Who is the Biased One?’
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Social media giant Facebook has been in the news quite a bit recently and in fact, it’s gotten to the point where it turns out the old saying of “there’s no such thing as bad press” may not be as true as we’d assumed.
For the sake of discussion (and because we really don’t have that kind of time), let’s remove politics from the equation entirely and take a look at the cold, hard facts. In March of 2018, it was revealed to the public that personal information of (what was then thought to be) 50 million Facebook users was sold directly to a political data analysis firm that was working on the 2016 presidential campaign.
But you yourself are not running for president. You’re running a business. So what does all this mean in that specific context?
Let’s find out together.
Facebook and Cambridge Analytica: What is Actually Happening?
While the idea of Facebook taking your personal data and using it for something else is nothing new, the specific way that this happened was notable. It all happened via an app that was created by a company called Global Science Research. While it’s absolutely true that 270,000 people volunteered to use the app and thus give up their information, Facebook’s own API also allowed data to be collected from friends of people using the app – meaning people who probably had no idea that it was going on in the first place.
To make matters worse, both Facebook and Cambridge Analytica threatened to sue The Guardian and their reporter Carole Cadwalladr who was in the process of breaking the story should it actually run. It ran anyway, they didn’t sue and now a bad problem has been made that much worse in the court of public opinion.
Note that a few things have happened since then. First, Facebook has publicly apologized and Mark Zuckerberg even testified in front of Congress about the whole thing. Then, it was revealed that the total amount of affected users was probably closer to about 87 million people.
A high-profile boycott called #DeleteFacebook was also launched at roughly the same time, with both everyday people and notable names like Elon Musk urging people to delete their Facebook accounts once and for all. Indeed, research has suggested that both the number of people talking about quitting Facebook and the number of those who are actually taking that step are at a record level.
But at the same time, for small and medium-sized businesses in particular, Facebook is still one of the dominant ways to take full advantage of the social media revolution and connect with your audience. But can you still do that if Facebook is losing users in droves? What, exactly, does this current Facebook situation mean for your business?
Facebook Moving Forward
According to a recent piece that ran in Forbes, the answer is probably some variation of “not much.”
Even as soon as March 21, there was a clear peak in the amount of chatter about people packing up and heading off of Facebook in favor of something else. Even when you compare the number of people who left Snapchat after that service rolled out a new interface that was universally panned, the damage was A) not nearly as bad, and B) subsided far sooner.
So as of today, Facebook remains one of the best ways that you have to connect with your audience in a deep, meaningful way. It’s still a great place to use all those social media graphics you create with a tool like Visme (which I founded). It’s still an opportunity to publish all of that other visual collateral that you’ve been working on.
But what this situation does remind us is that nothing lasts forever, especially in terms of social media. If anything, it’s a perfect example of why you should never put “all of your eggs in one basket,” so to speak.
Facebook is still enormously popular all over the world right now. But even if the Cambridge Analytica scandal had never happened, that wasn’t a guarantee that it would continue indefinitely. Social networks come and go and your business model – as well as your marketing efforts – should not be built on the foundation of Facebook. But the thing is, they should never have been in the first place.
They should have been built around your customers, plain and simple.
If you’re working hard, pulling out all the stops and creating meaningful content that resonates with your audience first and thinking about the distribution side of it second, things like this matter less and less when they do actually occur.
If you’ve built your business on the idea that Facebook is always going to be around, rest assured that there will come a day where you wish you hadn’t. But if you built your business on the idea that you should prioritize relationships with your customers, the channels you use to create those relationships can pivot far easier than they otherwise would. It’s why you should also focus on creating other types of content like flyers, or even scatter plots too.
In some ways, it’s not too dissimilar to businesses from a few years ago that built entire revenue streams on their ability to A) stuff content with keywords and little value so that it would rank as highly as Google as possible and then B) sell ads on that content. When Google changed its algorithm to penalize exactly this type of behavior, a lot of those businesses essentially disappeared overnight.
But the ones that made it a priority to solve problems on behalf of their users, or answer questions, or make their lives better in some way, weren’t affected. Because that’s what we should have all been doing in the first place.
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When working on a data-related project, it’s essential to perform tests and make sure the specifics of what you’re doing function as they should. It’s often impossible to make that verification without using test data sets.
The internet offers numerous places to get those, thereby keeping your project on schedule and boosting its chances of success. Here are six of them.
FiveThirtyEight is a current affairs website that provides the public with the data used for its articles and infographics. It got its start as a polling aggregator solely focused on political topics but has since branched out to cover sports, societal matters and more.
You can also visit the FiveThirtyEight Github. The data there ranges from information about which states have the worst drivers to the economic worth of different college majors. The broad range of information makes it an excellent resource for continuously curious people.
2. Kaggle
This website has a wealth of information beyond data sets, but it’s easy to narrow down your search. After arriving at the Kaggle homepage, look for the search box at the top of the page. Then, use the “in: datasets” tag.
For example, to get data about shopping, enter “shopping in: datasets” into the search box. Alternatively, click the Datasets menu at the top of the homepage to browse instead of getting specific. There is also a search box at the top right of the primary data section.
Digging into a particular data set is simple. Click on the link associated with one of them. Then, choose the Data tab at the top of the page to get the necessary files.
3. Data.gov
Representing an initiative from the U.S. government to make the data it collects more accessible to the public, this website is one of the places offering free data sets for people who need or want them.
The site is refreshingly user-friendly and breaks down the data by topic in addition to enabling keyword searches. Also, Data.gov offers more than 100,000 data sets with more added every night.
Many people say machine learning is taking over our lives for the better. Whether your data science project is for something related to machine learning or not, Data.gov highlights how so much of the information collected today is associated with human existence and has the potential to improve it.
4. Software With Sample Data Sets Included
Some tools come with built-in data sets for you to use.
“By displaying location or address-based business data against an accurate map, the map viewer can visualize their typical business data in a new way,” says Geoffrey Ives, President of Map Business Online. “By including both location-based map layers and demographic data in Map Business Online we have increased the value of these map visualizations.”
If you need data related to geography or population, Map Business Online sources material from the U.S. Census Bureau and Geolytics, Inc for users. Plus, it includes data from Canada and the United Kingdom. You can get statistics related to ethnicity, occupation, marital status and much more.
Similarly, people who purchase the Statistics and Machine Learning Toolbox from MathWorks get various sample data sets to work with as well. They include simulated data about hospitals, mileage information for particular kinds of cars and even statistics about popcorn.
If you’d rather not search for data to import into the tools you use, consider options like those discussed directly above. The built-in information they offer could streamline your data science processes.
There are data sets for numerous purposes, and you may need a particular type for a current project. If you’re making a tool that gives recommendations to people, the GroupLens site offers its MovieLens data sets that could help you.
As the name suggests, it has information about films — specifically, the ratings attributed to those movies by the people who watched them. One of the data sets offers 20 million ratings.
Most of the data sets mention the number of movies and ratings contained within. If you’re experimenting with big data, pay attention to those figures in your research.
6. Climate Data Online
The information on Climate Data Online is in expandable sections related to seasonal temperatures, wind direction, hourly precipitation and other topics related to the Earth and its detectable characteristics.
Click on one of the topic headers to expand the information below it. Use the Documentation and Data Samples drop-down menu to get a spreadsheet’s worth of content for your project.
Reliable Data Without Hassles
These sites highlight how valuable data is only an internet search away, and much of it is available for free — either through a trial period or entirely open access.
Instead of relying on too much guesswork when working on data-centric projects, use these sets to test for the desired function.
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Do you remember that feeling when you plan everything very precisely, but something happens unexpectedly and ruins your plans? It’s always an awkward situation, but it can also be a costly mistake in case it’s related to your business.
Making a poor estimation is not uncommon in big data. According to the research, more than 80% of companies are trying to be data-driven, but only a third say they do it successfully. It seems like huge volumes of information that keep piling up can be a genuine riddle for many business analysts.
In this article, I will briefly explain 8 common pitfalls that can ruin your predictions.
1. Lack a Business Case
Big data can draw meaningful conclusions out of seemingly unrelated information, but you still need a concrete business case to make use of these results. This is the only way to make big data truly applicable. For instance, you cannot simply analyze brand awareness on social media.
Instead, you need to use big data to improve brand image by setting clear parameters such as direct and indirect influence, geolocation, engagement, etc. Once you detect followers’ behavioral patterns, you can adjust social media strategy so as to increase brand awareness.
2. Poor data quality
The outcome of big data analysis depends on the quality of information. This is particularly the case with unstructured and semi-structured data because they need a pre-processing adaptation. Business intelligence managers at Rushmyessay UK explained that you should filter textual information through language correction libraries to polish the content. Image and video data quality are acquired from the source, but you always need quality data to generate accurate results.
3. Data Lifecycle
Timing plays a key role in comparative analytics, but many predictions go terribly wrong because they don’t take data lifecycle into the calculation. Let’s say you started importing a product in April 2017, so there are no sell-in parameters for the first quarter of the year. If your import prediction for Q1 2018 equals zero, you’ve made a big mistake.
It only suggests you should add more indicators to the research and come up with a more accurate estimation. For example, you could compare this product’s sellout with similar items you already had in your portfolio. Such data lifecycle awareness will lead you to the completely different outcome.
4. False Aggregations
Creating complex forecasts, you will often need to take into account individual events of a larger phenomenon. Some analysts don’t realize it and make false aggregations, which is the wrong way to analyze multilevel processes. If the first phase of an event is likely to occur in February, while the last should take place in October, the process itself will not end in June. There is no in-between result, so don’t make this kind of false aggregations.
5. Overfitting
While some companies create forecasts based on low-quality data, others make the mistake of overfitting. They add various highly specific indicators to the formula, but still expect to obtain a useful general prediction.
To put it simply, a good prediction would be to say that Cleveland Cavaliers win if LeBron James scores more than 40 points. On the other hand, overfitting happens if you claim that Cavs always win when:
- James scores 41-43 points
- The number of spectators is over 16 thousand
- The opponent ranks 3rd in Western Conference
- The number of fouls does not go under 31
6. Forecast What You Can Measure
Big data operates with huge resources of information, but it doesn’t mean you can use it to extrapolate everything using the same formula. On the contrary, you can only create forecasts based on measurable indicators. If you have to design daily transportation and delivery plans, the setup is completely different than weekly predictions. But in case you rely on weekly projections, your day-to-day planning will probably end up chaotic. Read the odds of foretelling rains and why monsoon prediction is hard.
7. Don’t Realize Data Complexity
Data pile up in different formats, leaving most people confused and unprepared. For instance, you might want to analyze social impact of the brand, Twitter and LinkedIn in particular. The two platforms are completely opposite in nature – while tweets take not more than 140 characters, LinkedIn posts are usually much longer and descriptive. Each data set here demands a different combination of processing cycles, so you must adapt it to gather the same type of results for both networks.
8. Not Measuring Big Data Efficiency
Big data is not perfect and you need to measure its efficiency. First of all, it will help you to understand the accuracy of the prediction model. Secondly, the business is changing and you will have to adapt your big data technique at some point. And thirdly, if your forecasts turn out to be too bad or too precise, there is probably something wrong with it, so you should find and fix the error.
CONCLUSION
Big data has the potential to give a fresh boost to your business, but it can also ruin it in case you make false predictions. A lot of data analysts make mistakes while designing plans and projections, so you need to be aware of the most frequent cases.
In this post, we showed you 8 common pitfalls that can ruin your predictions. Did you face any of the problems already? Do you know other examples of false big data estimations? Share your experiences in comments and we’ll be glad to discuss it!
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This article aims to list all top BI (Business Intelligence) products available on the market. It should help interested users to compare and select the best solution for their needs. According to the list of best business intelligence tools prepared by experts from FinancesOnline the leading solutions in this category comprise of systems designed to capture, categorize, and analyze corporate data and extract best practices for improved decision making. The more advanced the system is, the more data sources it will combine, including internal metrics coming from different company departments, and external data extracted from third-party systems, social media channels, emails, or even macroeconomic data. Ultimately, business intelligence software helps companies gain insight on their overall growth, sales trends, and customer behavior.
1. Sisense
Sisense is one of the leaders in the BI market and a winner of the Best Business Intelligence Software Award for 2016 from FinancesOnline, one of the most popular business software review platforms. This solution capable to effectively simplify complex data analyses, and make big data insights accessible even for startups and small companies. The competitive edge of Sisense is primarily its capacity to collate data from multiple sources without pricey preparations (sources can be Salesforce, Google Analytics, AdWords, and many more). Users will also enjoy the tool’s very efficient use of in-chip technology in a database that processes data 10 times faster than traditional systems. Sisense also works with the innovative ElastiCube technology, which means it can import large sets of data and work with any CPU layout without compromising the quality of your results. If you are interested to learn more about its features you can actually try out the software yourself with a great free trial plan they offer. You can easily sign up for Sisense free trial here.
2. Actuate Business Intelligence and Reporting Tools (BIRT)
BIRT project is a flexible, open source, and 100% pure Java reporting tool for building and publishing reports against data sources ranging from typical business relational databases, to XML data sources, to in-memory Java objects. BIRT is developed as a top-level project within the Eclipse Foundation and leverages the rich capabilities of the Eclipse platform and a very active open source community of users. Using BIRT, developers of all levels can incorporate powerful reporting into their Java, J2EE and Eclipse-based applications.
3. icCube
icCube is a SaaS end to end BI platform, specialized to be embedded in your application. Deploy it on premises, in the cloud or make use of one of their managed services and enjoy a short time to market for custom feature requests. It integrates seamlessly with any application because of the on-the-fly-authentication and authorization (up to cell level), the ability to connect and combine any custom data source, direct access to Java and R, a web based dashboard builder and the ability to graphically design widgets from scratch. Basically, icCube is the dream for any software developer who needs to provide predefined dashboards or a solid web based self service BI solution, to their end-users.
4. Domo
Domo’s Business Optimization Software brings together the people, the data, and the insights business users need to deliver a detailed view of what’s happening in your organization. Connect all of your crucial business data, collaborate with fellow employees, and get powerful visual data—all within one customizable platform.
5. Board Management Intelligence Toolkit
BOARD toolkit combines various BI and CPM functionalities within a single graphical software environment. BOARD’s BI capabilities include multi-dimensional analysis, ad hoc querying, dashboarding and reporting, while its CPM capabilities include budgeting, planning and forecasting as well as “other finance-related activities”.Like Business Intelligence software in general, BOARD is used in an effort to improve productivity and decision making while lowering costs. It does not require any programming skills to build BI and CPM applications.
Clear Analytics is incredibly intuitive Excel-based solution with minimal training required. Employees with a basic knowledge of Excel can learn the system rapidly, so businesses can implement a fully-operational, self-service Business Intelligence system with little downtime and almost no learning curve. Clear Analytics offers a variety of BI-specific features to help generate, automate, analyze, and visualize a company’s key data and information. Clear Analytics also enables consolidation of data from multiple data sources and all within excel.
7. Ducen
Companies need to keep an eye on every revenue generating event and cost saving opportunity while improving customer satisfaction and retention. By combining historical data with real-time operational data for analysis, business users can make more informed, proactive decisions. However, to achieve these efficiencies, data must be available real-time.
8. Gooddata
GoodData powers the All Data Enterprise by offering an Open Analytics Platform that supports both IT’s need for Data Governance, security and oversight and business users desires for self-service Data Discovery.The platform consolidates data of any size, typically found both inside organizations and in the cloud, creating an analytic experience that is both fast and agile for users, yet protected, managed and secured for IT.
Information silos, multiple platforms and excessive reliance on spreadsheets can hinder the process of analyzing your business data to understand performance and recommend improvements With business analysis software from IBM, you can explore information from different angles and perspectives and compare it with data in motion and trends for a more extensive view of your business. The facts you need for better results are right at your fingertips.
10. Insightsquared
Successful sales strategy is dependent on understanding the customer. But for small and medium businesses building up the kind of intelligence database needed can be time consuming and take staff away from the task of actually selling. It can be many months before the implementation of a traditional sales intelligence platform bears fruit.
11. JasperSoft
The Jaspersoft Business Intelligence Suite offers a number of ways for end users to perform interactive analysis. For the most casual user, this might involve simply changing a filter setting on a report to view a different slice of data. For a data analyst this could mean writing powerful, multi-dimensional expressions.
12. Looker
Looker is a data-discovery platform that helps companies make better business decisions through real-time access to data. Data, no matter the size, can be analysed within Looker’s 100% in-database and 100% browser-based platform. Looker analytics integrate with any SQL database or data warehouse, such as Amazon Redshift and Greenplum.
Microsoft Business Intelligence platform include Analysis Services, Integration Services, Master Data Services, Reporting Services, and several client applications used for creating or working with analytical data. This section of the SQL Server Setup documentation explains how to install these features. Analysis Services and Reporting Services can be installed as standalone servers, in scale-out configurations, or as shared service applications in a SharePoint farm. Installing the services in a farm enables BI features that are only available in SharePoint, including PowerPivot for SharePoint and Power View, the Reporting Services ad hoc interactive report designer that runs on PowerPivot or Analysis Services tabular model databases.
14. MicroStrategy
From local spreadsheet data to enterprise data systems to cloud-based data, MicroStrategy provides effortless access to all business data from one place. Use data connectors that are optimized for each source, and allow queries to reach their greatest performance potential. Connect to one source or many, separately or in combination. Gain the pure play advantage of superior R&D focus on strong technology partnerships and high speed analytics.
15. MITS
Over 1,000 companies are using MITS Distributor and Manufacturer Analytics to empower everyone—from the CEO to purchasers to sales reps—to make better decisions with tools designed specifically for their role. By combining flexible, user-friendly business intelligence tools with premade and customizable reports, dashboards, and scorecards ready to run for your business systems, MITS helps users quickly gain value from their analytics and business system investments through improvements in cash flow, profitability, and business growth.
16. OpenI
OpenI provides a web-driven interface to build and publish interactive reports from OLAP data sources. Going beyond that, OpenI aims to provide consolidated analysis from all the key data components of an intelligent application. Our key goal is to take away the complexity of creating and publishing reports for business users. OpenI does this by providing a clean, intuitive interface to connect to different types of data sources, and to publish web-based interactive reports. If you want to build web-based intelligent applications that interact with your OLAP data sources.
17. Oracle BI
Oracle BI is a comprehensive collection of enterprise business intelligence functionality that provides the full range of business intelligence capabilities, including dashboards, full ad hoc, proactive intelligence and alerts, and so on. Typically, organizations track and store large amounts of data about products, customers, prices, contacts, activities, assets, opportunities, employees, and other elements. This data is often spread across multiple databases in different locations with different versions of database software.
18. Oracle Enterprise BI Server
Oracle Business Intelligence Enterprise Edition 11g is a comprehensive business intelligence platform that delivers a full range of capabilities including interactive dashboards, ad hoc queries, notifications and alerts, enterprise and financial reporting, scorecard and strategy management, business process invocation, search and collaboration, mobile, integrated systems management and more. OBIEE 11g is based on a proven web service-oriented unified architecture that integrates with an organization’s existing information technology infrastructure for the lowest total cost of ownership and highest return on investment.
Oracle acquired Hyperion, a leading provider of performance management software. The transaction extends Oracle’s business intelligence capabilities to offer the most comprehensive system for enterprise performance management. The acquisition of Hyperion extends our business intelligence product strategy. Customers are increasingly using performance management and business intelligence together. Hyperion adds complementary products to Oracle’s business intelligence offerings including a leading enterprise planning solution, world-class financial close and reporting products, and a powerful multi-source OLAP server. Coupled with Oracle’s BI tools and pre-packaged analytic applications, the combination redefines business intelligence and performance management.
20. Palo OLAP Server
Palo is a memory resident multidimensional (online analytical processing (OLAP) or multidimensional online analytical processing (MOLAP)) database server and typically used as a business intelligence tool for controlling and budgeting purposes with spreadsheet software acting as the user interface. Beyond the multidimensional data concept, Palo enables multiple users to share one centralised data storage.
21. Pentaho
Pentaho addresses the barriers that block your organization’s ability to get value from all your data. Our platform simplifies preparing and blending any data and includes a spectrum of tools to easily analyze, visualize, explore, report and predict. Open, embeddable and extensible, Pentaho is architected to ensure that each member of your team — from developers to business users can easily translate data into value.
22. Profit base
Profitbase SIM is a full scale financial planning and simulation tool for budgeting and forecasting where Profit & Loss, Balance Sheet and Cash Flow statements are fully integrated. SIM enables management to simulate business scenarios and immediately see the financial impact. SIM delivers a wide selection of standard reports, graphical charts and features seamless integration with Profitbase Studio and WebPlan.
23. QlikView
The QlikView Business Discovery platform delivers true self-service BI that empowers business users by driving innovative decision-making,Develop, enhance, re-engineer, maintain and support QlikView applications to create robust services around business requirements to inform business decision-making and Understand all the data that the business holds and create sustainable reporting solutions ensuring the accuracy of the data.
24. Rapid insight
Rapid Insight is a leading provider of business intelligence and automated predictive analytics software. With a focus on ease of use and efficiency, Rapid Insight products enable users to turn their raw data into actionable information. The company’s analytic software simplifies the extraction and analysis of data, enabling clients ranging from small businesses to Fortune 500 companies to fully utilize their information for data-driven decision making.
Predictive analytics give your decision makers the insight they need to predict new developments, capitalize on future trends, and respond to challenges before they happen. SAP’s market-leading combination of real-time business intelligence (BI) and predictive analytics make it easy for you to extract forward-looking insights from Big Data, harness the power of R, and create stunning data visualizations with ease.
SAP BusinessObjects Analysis, edition for Microsoft Office is an Office add-in that allows multidimensional ad-hoc analysis of OLAP sources in Excel. It also allows, Excel workbook-based application design and creation of BI presentations in PowerPoint. It perfectly connects to SAP NetWeaver BW and SAP HANA.
27. SAP NetWeaver BW
Quickly Capture, store, and consolidate your vital information with our real-time data warehouse platform. Tightly integrate your warehousing capabilities for a single version of the truth, decision-ready business intelligence, and accelerated operations.Supercharge your data warehouse environment with SAP Business Warehouse powered by SAP HANA.
28. SAS BI
According to Forrester, SAS has not only been a market leader in advanced predictive analytics, but also a provider of a formidable BI platform. Customers select SAS for its well-integrated, one-stop platform, a significant part of which is its BI capabilities. SAS provides scalability, excellent data integration, multiple query languages, internationalization, customization through a rich set of APIs, advanced analytics tools, MDM, performance management, and reporting and querying. SAS ranks eighth on number of Forrester BI inquiries. Recent market survey data indicates that 14% of corporate customers depend on SAS for their BI needs.
29. Silvon
Business Intelligence solution provider Silvon Software, Inc.to bring a powerful, web-based business analysis software interface to retailers. Under the terms of the agreement, RPE will market Silvon’s Viewer interface for Performance Analysis by IDEAS, a client-server BI application for JDA Software Group’s Merchandise Management System. This new optional interface for Performance Analysis by IDEAS will provide many added features for today’s mobile professionals.
30. Solver
The solver in excel is part of an analysis tool known as “what ifs analysis”. You can use solver to ascertain an optimal value in one cell known as the “target cell”. Basically, solver is used for a group of cells that are directly or indirectly related. Constraints can also be applied to minimize the value that can be used by Solver. This article will provide step-by-step guide on how to use solver to find solution to a business problem.
31. SpagoBI
SpagoBI supports the real-time monitoring, analysis and presentation of business data and processes. You can keep business processes under control by constantly monitoring their state.SpagoBI allows you to go further than this: you can detect inefficiencies and bottlenecks in your business processes, promptly react to events requiring quick decision making, as well as discover new business opportunities hidden in your own data.
32. SQL Server Analysis Services
Server Analysis Services platform, build high performance analytical models (multidimensional and tabular) that can be used for interactive data analysis, reporting, and visualization. SQL Server provides a comprehensive analytical and modeling experience to support rapid solution prototyping and support for the largest enterprise-grade solutions.
Style Intelligence is business intelligence software for dashboards, reporting, visual analysis, and data mashups. It blends enterprise strength with a small, 100% Java footprint. Unlike traditional BI platforms, Style Intelligence does not require specialized BI skills or consultants to implement or use. It delivers maximum self-service that is both end-user and IT-friendlier than other BI solutions.
Syntel’s Technology Outsourcing services deliver value and provide solutions that transcend platforms. Leverage Syntel’s expertise in managing business processes, systems and platforms in order to reap the benefits of an innovative and collaborative outsourcing partnership. Syntel understands your pain points and offers a set of distinctive services that enhance your operations across the applications and IT environments. Syntel designs a client-specific strategy to achieve your desired objectives, and our services help you create a strategy based on the value to your business.
35. Targit
TARGIT fights all unnecessary clicks that only make your life difficult. TARGIT BI Suite has a very unique and intuitive user interface you have to see it to believe it! You will experience an integrated and ready-to-use set of tools which enables you to create intelligent dashboards, revealing analyses and insightful reports in fewer clicks than with any other Business Intelligence solution on the market. TARGIT will accelerate decision making, increase operational awareness, and improve performance across the organization. TARGIT BI Suite is so easy to use that all employees can follow trends, create all types of analyses, and make decisions.
36. Vismatica
Vismatica by IronRock Software is powerful data visualization solution geared toward small to medium businesses. Dashboard development tools make up the core of this system, but Vismatica also empowers you to create powerful data collection forms and conduct data analysis. It can be deployed on premise or over the web as a hosted solution. Vismatica comes with additional features for sharing documents and designing web applications.
37. WebFOCUS
The WebFOCUS Business Intelligence and Analytics platform empowers everyone in your organization to make smarter, more confident decisions. WebFOCUS extends to your customers and partners, too, giving them easy access to analytic apps and tools from any browser or mobile device.
38. Yellowfin BI
Data to dashboards Yellowfin delivers a brilliant analytical experience. Our interface is more than beautiful it provides all the data discovery features that you will ever need. All this whilst providing a fine balance between the ease of use business users require and the governance needs of enterprise IT.
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