The concept of recruiting and hiring has always come with a certain degree of risk attached to it. All too often, hiring managers are drawn in by an impressive resume, fancy dress clothes, an upbeat and confident demeanor, and a handful of positive interactions.
While the initial meetings and interviews may set high expectations, companies never really know what they are getting with a new hire until months later. A single bad hiring decision can do a lot of damage to a company’s budget and reputation. From a financial standpoint, companies lose an average of $14,900 on every bad hire, according to CareerBuilder.
Fortunately, the rapid advancement and sophistication of big data have made its presence known in the hiring process. The results of this can do a lot to cut down on turnover. However, understanding how to make these algorithms work for your specific organization will require a good deal of time and commitment. Here is how to do it.
Know the data sources
For companies that are new to the whole concept of big data, one of the toughest parts can simply be knowing where to look for the most pertinent information.
In terms of hiring, these algorithms normally work within a relatively narrow scope of information. Many HR departments utilize three major source categories of data. These include:
Publically available data can come from a wide range of sources. These can include social media, demographic information of the area, pay scale, employment rate, and much, much more.
The background information is what hiring managers see on a resume, or any other credentials submitted by the applicant. These typically relate to skills, qualifications, and experience.
Interaction data refers to the small insights gleaned from how an applicant communicates with a company. These insights come from things like keystrokes, word choice, and answers to questions. The metrics can have a strong correlation with future job performance.
Once you have identified the data sources necessary for the hiring process, your data mining tool will be able to run analyses to find the context you need to make more informed choices.
Understand key variables for each position
Upon finding the ideal sources, one of the biggest data-related challenges companies face is knowing exactly which metrics pertain to their goals, and how to apply them. According to IBM, about 2.5 quintillion bytes of data are created every single day. That being said, locating the right information can seem like finding a needle in a haystack.
Depending on the position you are recruiting for, there will likely be a wide range of data variables that play into the equation. This is one of the areas where there tends to be a high margin of error. Keep in mind, algorithms can only work for you if you have all the information necessary.
Therefore, you need to have a crystal clear objective in mind for the exact variables that pertain to the job, as well as how you can leverage them to eliminate the guesswork. These may include college GPA, certain buzzwords from previous jobs, soft skill proficiency, certain personality traits, etc.
Fortunately, there are plenty of tools to help you with this part of the process. AI-driven “smart” recruiting tools like Harver are designed to automatically screen applications and background information to identify the ones with the strongest correlation to the open position. From here, it runs a number of specialized assessments to gauge the applicant’s interaction data related to problem-solving, communication skills, situation judgment, and more.
Once the candidate has completed the assessments, the system uses smart algorithms to determine the strength of each candidate and how well they fit the mold for not just the open position, but the company as a whole.
Even though big data can work wonders in making smarter hiring decisions, it’s important to remember that there will always be a good amount of human intuition and iteration involved as well. Big data algorithms are simply there to guide you.
Use each interaction as a predictive data point
Big data, in general, can best be described as a constant work in progress. Datasets are continuously building off of each other to become smarter and more precise.
As you begin to develop a bank of data relating to your hiring process, there will almost certainly be a number of patterns that will emerge. These patterns should serve as a reference to how people mesh with your company. For example, in terms of communication, the datasets might show that the best workers in your company were the ones who responded to messages from the hiring managers within one hour. Or, perhaps the ones who sent shorter and more concise emails had a better success rate in the company.
BI tools like Dundas make the concept of predictive analysis simple. The browser-based solution allows you to input any data source and view the trends in customizable, interactive reports.
From here, you can draw on previous datasets to justify decisions for the future. The goal of hiring managers is to stay one step of head of common issues like poor productivity, employee turnover, bad cultural fits, and more. If you use every single interaction as a predictive data point and keep a close eye out for trends, you are in a much better position to avoid mistakes and misjudgments down the road.
Over to you
Turning your company into a data center has many benefits. In regards to the hiring process, managers need to do everything they can to make smarter decisions and avoid the dreaded high turnover rate. In the age of constant-connectedness, a high turnover rate isn’t just bad for your budget; it’s a huge red flag for new talented candidates.
While there are very few guarantees in the business world, one of the safest bets is that big data is here to stay. The sooner you can get the algorithms working for you, the better you will be in the long run. Always remember, a business is only as good as the people it brings on board.
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Like many of the technological shifts of the past two years, the world of big data has marked a paradigm shift in how information is collected and stored across the world. Not surprisingly, legislation has fallen behind technology in this regard, but it’s aiming to catch up with the latest round of EU regulations, which are set to change the way client data is being handled not just across Europe, but in every significant market on the planet.
As currently drafted, the new legislation will force companies to require consent and be transparent with regards to their intentions when it comes to collecting data from consumers. While this law is necessary for many respects, it will certainly result in extra costs and will require each company to invest substantially in their data collection and consumer abuse departments. What’s more, the many gray areas that still remain in today’s legislation will make it hard to tell if a company is playing by the rules or bending them to their advantage.
Given the situation, it’s clear to see why the need for a better and transparent system has emerged. Luckily, that buzziest of today’s technologies – the blockchain, may provide significant aid in overcoming some of the most glaring issues plaguing big data management in the present. To that end, here are just three of the main ways through which blockchain technology can make a positive impact:
1. Decentralization
At its core, blockchain technology revolves around the idea that a decentralized, trustless system is not only inherently incorruptible, but also faster and easier to maintain than a traditionally centralized one. By putting big data on the blockchain, you’re ensuring its ultimate transparency for all parties involved. Shady behind-the-curtains dealing is completely eliminated, as is the need for the kind of costly maintenance that a centralized system typically requires.
2. Immutability
Another defining characteristic of blockchain technology is its inherent immutability. This means that once a transaction or an operation has been made, it cannot be rescinded or returned. While this principle may have its drawbacks in some areas, in big data it leads to more confident levels of testing data and creating models that work.
3. Fairness
Finally, and perhaps most importantly, the democratic nature of a blockchain will help shift the power of personal data back to consumers. Nowadays, people are unaware of just how much their data is worth, since it’s mostly being controlled by large corporations with little to no incentive in sharing the wealth. However, on the blockchain, a person can choose whether to share their data and with whom, and that may very well help users earn an income through the sharing of personal data alone.
As you can see, blockchain technology holds much promise with respect to big data, especially in the face of stronger restrictions, the kind that will likely become the new norm within the next several years. Still, taking full advantage of this fairly new and as-of-yet not all that developed technology will require enterprises to take the time to adequately gather the resources they need in order to make the transition as smooth and as graceful as possible.
To that end, hiring a quality software development company with a proven track record in the blockchain niche is a good start. However, finding one is not an easy task – Google only lists one blockchain developer on the first page, rest are informational and news resources. Good developers who are fluent in blockchain-adjacent technologies are few and far between, and may come at a high price. Likewise, finding insightful people to enlist as advisors may also prove to be a challenge, since the biggest players in the industry are highly sought-after for their consultation skills. Lastly, building a strong enough community to generate interest in any given blockchain-related project and help educate the masses is also essential.
No matter the struggles and hurdles that are sure to materialize, it appears that blockchain technology is here to stay. Whether we’re talking healthcare records or property deeds, the correct handling of data will be paramount in the coming years if one wishes to prevent any unpleasantly dystopian scenarios from coming true. Blockchain technology is not perfect, and still has ways to go before it is completely applicable, but it has so far shown immense promise for a variety of big data concerns, and is definitely deserving of further study on a global scale.
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Suresh Shankar, founder of Crayon Data, talks about how entrepreneurship is all about persistence and perseverance, through one of his favourite anecdotes on the Chinese bamboo tree. One that will get all you budding entrepreneurs fired up and ready to make a change! Catch him in conversation with the University of Oxford and Said Business School.
Suresh Shankar also discusses ‘obvious’ opportunities in the digital banking space and the financial disruption sphere. With burgeoning amounts of unstructured data, Suresh says the way forward is to utilize this and build products that will herald change.
Suresh Shankar is a big data and analytics evangelist, entrepreneur and innovator; he established his second start‐up, Crayon Data in Singapore in 2012. Recognized today as one of the world’s top big data companies, Crayon is on a mission to simplify the world’s choices with its flagship product, MAYATM.
Suresh spent the first 15 years of his 30‐year career in sales, marketing, advertising, media and banking. He has witnessed the transformation of marketing from a right to a left-brained pursuit. His expertise in customer analytics was the foundation for RedPill Solutions, set up in 2000 in Singapore. Business leader IBM acquired RedPill Solutions in 2009.
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The rapid pace of technological innovation and the sudden emergence of Big Data has left a lot of marketers feeling left behind. In less than a generation, the marketing industry has shifted in a way nearly unprecedented in its entire history. It has gone from a largely intuitive or psychological art to being heavily defined by data, analysis, and science.
This has created numerous challenges for marketing agencies, both new and old. Getting a handle on their data, using it properly, and finding new ways to reach out to consumers are challenges facing every marketer at work today.
What are some of the biggest problems faced by modern brands and marketing departments? And what could potentially address those problems? Here are some answers.
Problem 1: Getting a Handle on Big Data
One of the biggest key challenges simply involves the collection, storage, and access to data. Some organizations still find themselves struggling to get the information they need flowing in. Others have opened too many pipelines, and find themselves drowning in an ocean of data without clear ways of organizing it.
In either case, what’s called for is a data-collection plan. Don’t collect data for its own sake. Have clear goals in mind for what the data will be used for, then act accordingly. If you know what the data is for, it’ll be much easier to collect and sort it. Be forward-thinking, and focus on laying the good groundwork now that will pay off in the future.
Problem 2: Market Disruptors
If there is an industry that existed prior to the 21st century, it’s probably now seeing digital disruptors arise and create large changes to that industry. Retail is a perfect example: Amazon is putting retailers out of business across the country, and even causing problems for some of the biggest names like Wal-Mart, yet Amazon has (almost) no physical stores. Similar examples are arising constantly, such as the sudden boom of Uber and Lyft and their threat to traditional taxis, or the way Netflix is cutting into cable company profits.
The best solution here – if possible – is to become the disruptor. Go on the offensive. Read your data, look for trends, and ask “is there a digital solution to this problem?” Look at processes related to your industry where a long-established solution exists, then think of a better one. Re-invent the mousetrap.
If you don’t, someone else will. Don’t be on the defensive.
Problem 3: Consumer Distrust
If there’s one problem with marketing that a lot of companies really don’t want to address, it’s this: Most buyers and consumers don’t like us. Some outright hate us. Particularly when talking about younger buyers – those under 40 – there has never been an era when marketers have been more distrusted. And that’s a BIG problem.
Much of this has to do with how much information the public now has about businesses and their day-to-day interactions with customers. It’s easier than ever for buyers to learn of questionable behavior and organize themselves against it. Many have become so cynical that they simply distrust anything and everything that comes from marketing departments.
The solutions here are, broadly, twofold: First, spend more time cultivating brand ambassadors online. Find friends on social media, and YouTube, and other online outlets who genuinely support your product/services. Word-of-mouth is more powerful than ever in this age where advertisements are seen as propaganda. Use research and analytics to discover who your buyers trust, and get those people on your side.
The other solution is honesty. When you must openly market, be as transparent as possible. Cite sources. Don’t overstate facts or capabilities. Do not ever get caught in a lie. It is possible to build consumer trust on a brand-by-brand basis, but that trust must be earned through trustworthy behavior.
Problem 4: The Sales and Marketing Split
For too many decades, sales and marketing were treated as wholly separate entities within a business. In worst case scenarios, they even had something of an adversarial relationship, with each tending to blame the other for failures.
This simply does not fly today. Sales and marketing must be working hand-in-hand. They need to know what each other is doing, and they need to be sharing data – particularly since each will likely have access to key insights the other lacks. This is where a strong CRM-style solution can be extremely useful. By centralizing data where both sales and marketing can access it, they can form closer links and develop initiatives jointly.
Better yet, get product development in on the data-sharing too. In particular, this can eliminate the perennial problem of sales or marketing over-promising, and then getting stuck with a product which disappoints its buyers. Use smart data sharing to keep sales, marketing, and/or R&D on the same page.
Problem 5: Security
If you’re keeping data, you have to keep it safe. Unfortunately, as we’ve seen from many many headlines over the past few years, this is easier said than done. It seems like hardly a month goes by without another high-profile name turning into a high-profile embarrassment due to data breaches, ransomware attacks, or other cyber-criminal activity.
Unfortunately, there’s no magic bullet solution here. You simply have to be willing to spend the time and the money remaining abreast of the latest data security ideas and keeping your security systems up-to-date. If management balks at the cost of updating security, remind them that -according to IBM- the average cost of a data breach is between $3 and $4 million dollars. And it can be much higher.
The “it can’t happen to us” mentality has to be overcome because it can happen to anyone regardless of size. Be prepared.
Always Keep Informed
If there’s one unifying factor in this, it’s simply that knowledge is power. Both in terms of data and your own insights, keeping your eyes and ears open is the best way to ensure you’re aware of potential data challenges before they become major data problems.
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Although cognitive computing, which is many a times referred to as AI or Artificial Intelligence, is not a new concept, the hype surrounding it and the level of interest pertaining to it is definitely new. The combination of hype surrounding robot overlords, vendor marketing and concerns regarding job losses has fueled the hype into where we stand now.
But, behind the cloud of hype that is surrounding the technology currently, there lies a potential for increased productivity, the ability to solve problems deemed too complex for the average human brains and better knowledge based transactions and interactions with consumers. I recently got a chance to catch up with Dmitri Tcherevik, who is the CTO of Progress, about this disruption and we had a healthy discussion which led to the following insights.
Cognitive computing is considered a marketing jargon by many, but in layman terms it is used to define the ability of computers to replicate or stimulate human thought processes. The processes behind cognitive computing may make use of the same principles as AI, including neural networks, machine learning, contextual awareness, sentimental analysis, and natural language processing. However, there is a minute difference between both of them.
Difference between Cognitive Computing and AI
Both AI and Cognitive Computing may look extremely alike, but like we mentioned above there is a small difference between both methods.
Firstly, artificial intelligence does not work at mimicking human thought processes. The concept behind AI is to not mimic human thought and processes, but to solve a problem through the use of the best possible algorithm. This can be illustrated through an example of a car, which stays on course and avoids a collision. The processes in AI are not looking to process data in the same way as it would be processed by humans, but they’re looking to process it through the best known algorithm present. Processing data the way humans do it is a far more fault-prone and complex algorithm. And, we all know that a self-driven car isn’t giving suggestions to the driver, it’s responsible for all the decisions in driving.
Secondly, cognitive computing is not responsible for making decisions for humans, instead it is responsible for complementing or supplementing our own cognitive abilities of decision making. AI in medicine would be all about making the right decisions pertaining to a patient or the preferred mode of treatment, and minimizing the role of the doctor. Cognitive computing, on the contrary, would be more focused on achieving evidence that could supplement the human expert into making more flawless medical diagnoses.
Emerging Use of Cognitive Computing in Industries
We can gauge the success of cognitive computing and the development through the opportunities it has across industries. Cognitive computing is currently in a research phase, where research is going into properly implementing the technology in the fields deemed appropriate for its use. One can assess the opportunities for cognitive computing by looking at industries and industry specific scenarios where cognitive computing could make a big difference.
Customer services
Companies offering customer services deal with a lot of data which they have to accommodate with large processing requirements and are required to be efficient and flawless in advising customers to the right outcome. With so much happening, one can think about the opportunities for cognitive computing in this specific industry. At a consumer level, we can take the aid of robo-advisors that assist staff in advising new customers about what they can do and how they can go about creating a new account. There is also the concept of automated document processing that will limit human involvement and the flaws that come with it to a large extent. According to Dmitri: ‘Customer services are up for disruption, and the use of chatbots while booking airplane tickets or checking your insurance claim will go a long way in the future.’
Healthcare
Whenever we talk about Big Data, Machine Learning, AI or Cognitive Computing, the services that will be rendered through these technologies in healthcare always spring to mind. Human healthcare is certainly not at 100 per cent efficiency nowadays, which is because of the fact that there are certain flaws in the process. These flaws can be eradicated by giving machines the cognitive abilities required for going through a report and forming a basic judgment regarding the condition of any patient. The results can then be communicated to humans through a virtual display.
Industrial IoT
Most of the Industrial IoT giants that we have in industries such as car manufacturing, transportation, etc., have implemented exemplary data collection methods. These data collection methods do their job well, and hand over the necessary input to their patron organizations. Now, when the data is collected and stored off, the real challenge of anomaly analytics arises. Despite having stringent data collection and storage facilities, these firms don’t know what to do with their data and how to find actionable results.
The biggest problem facing businesses in today’s myopia is that only 20 percent of all problems or anomalies that occur are predicted and understood beforehand. This means that around 80 percent of the problems that businesses face are unpredicted, and the business is not prepared to handle them because of below par anomaly detection.
The Cognitive Anomaly detection is different from the traditional method, as it is a machine and data-first solution. The future for cognitive anomaly detection is seemingly bright, and it is now the time to move from a research phase to deployment.
How to Move to Deployment
The deployment of cognitive computing requires adhering to a certain set of levels for achieving the desired aims. The levels that should be used for proper deployment of the technique include:
With cognitive computing gaining center stage, it is expected that the concept will develop over time and will be implemented over numerous industries. Industrial IoT is expected to benefit a lot from cognitive computing as it can be used for deriving meaning out of the data they work with. In short, cognitive computing is currently leading the wave of the future as it holds the key to not only making healthcare, AI and Industrial IoT better, but also providing human thought processing and behavior that was needed here.
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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
MITS, an established leader in reporting and business intelligence solutions for the Wholesale Distribution market, is growing and we need a BI Solution Developer to join our team. Are you passionate about helping businesses make proactive.
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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