Magic Quadrant BI 2018 – Gartners Business Intelligence
SAP Business Objects Business Intelligence 4.2 Πακέτο υπηρεσίας 05 αναμένεται να κυκλοφορήσει στο τέλος του 2o17.
SAP BusinessObjects BI (επίσης γνωστή ως BO ή BOBJ) είναι μια σουίτα του front-end εφαρμογές που επιτρέπουν στους χρήστες των επιχειρήσεων να δείτε, είδος και να αναλύσει τα δεδομένα επιχειρηματικής ευφυΐας. Η σουίτα περιλαμβάνει τις ακόλουθες βασικές εφαρμογές:
- crystal Reports — Επιτρέπει στους χρήστες να σχεδιάσουν και να δημιουργήσουν αναφορές
- Xcelsius / Dashboards — Επιτρέπει στους χρήστες να δημιουργήσουν διαδραστικές πίνακες που περιέχουν χάρτες και γραφήματα για την οπτικοποίηση δεδομένων
- Web Intelligence — Παρέχει ένα περιβάλλον self-service για τη δημιουργία ad hoc ερωτημάτων και ανάλυση των δεδομένων τόσο online όσο και offline
- Εξερευνητής — Επιτρέπει στους χρήστες να αναζητούν μέσα από πηγές δεδομένων BI χρησιμοποιώντας μια διεπαφή GUI. Οι χρήστες δεν χρειάζεται να δημιουργήσετε ερωτήματα για να αναζητήσετε τα δεδομένα και τα αποτελέσματα εμφανίζονται με ένα γράφημα που δείχνει την καλύτερη αντιστοιχία πληροφορίες.
Αυτή η έκδοση αναμένεται να προσφέρει μια σειρά από καινοτομίες για τους χρήστες και το επίκεντρο της αυτή η έκδοση είναι περίπου
- Επιχείρηση - επεκτασιμότητα, πολλαπλούς χρήστες
- Smart πυλώνα - και όχι μόνο δούμε διαμορφωμένες αναφορές, ενσωμάτωση με απλές δυνατότητες πρόβλεψης
- Big δεδομένων - HANA Βόρα
- Cloud - επενδύοντας σε «όλα σε δεδομένα» στο σύννεφο
- ενσωμάτωση Hana με Hadoop
SAP Business Objects Business Intelligence 4.2 Πακέτο υπηρεσίας 05
SAP HANA platform has been available since 2010, and SAP applications like SAP ERP and the SAP Business Suite have been able to run on the SAP HANA database and/or any other database since launch.
The SAP S4 HANA platform was released on in February 2015 and SAP S4HANA was billed as being SAP’s biggest update to its ERP strategy and platform in over two decades.
The feedback from analysts was that it was perceived as a transformational shift but raised questions about the functionality, availability, pricing and migration surrounding SAP S4 HANA.
SAP S4 HANA
By the end of 2016, SAP announced that 5,400 customers had implemented SAP S4 HANA but other analysts disputed the viabiltiy of some of these figures given that it included many customers who were actually running Proof of Concept / trial projects rather than customers that were actually live.
Although many SAP customers have heard of HANA SAP still faces challenges getting the user base to understand what the various options are for migration and implementation.
SAP S4 HANA is basically SAP’s well known ERP in the cloud and is powered by the HANA in-memory database and the cloud version of S/4Hana is designed for hybrid scenarios combining on-premises and cloud software.
Κιτρινόπτερου BI τελευταίες εκδόσεις
Κιτρινόπτερου BI τελευταίες εκδόσεις – Yellowfin BI is a business intelligence software application that provides a range of Business intelligence dashboard reporting and data analysis functionality. Yellowfin BI allows reporting from data stored in relational databases, multi-dimensional cubes or in-memory analytical databases.
YellowFin BI is based in Headquartered in Melbourne, Australia,
The latest version of κιτρινόπτερος BI is 7.3+plus – build 20170608 – released on 30 Ιούνιος 2017
YellowFin BI Version History
Yellowfin BI Version 6.2
Yellowfin BI Version 6.3
Yellowfin BI Version 7.0
Yellowfin BI Version 7.1
Yellowfin BI Version 7.2
Yellowfin BI Version 7.3
Yellowfin BI Version 7.3 Plus
Tableau Hyper – Tableau acquired Hyper in March 2016. Hyper was a German, academic based startup developing a high performance, in memory optimal database engine.
Hyper’s high-performance database system is being integrated into Tableau’s product offerings and will bring a range of of new capabilities to Tableau customer base. Hyper will replace the ageing τραπέζι Data Engine (TDE).
Tableau Hyper Data Engine
This new functionality will enable existing Tableau users to undertake
- Faster analysis of Tableau data-sets
- Improving Tableau’s big data strategy by providing support for large unstructured data sets
- Improved data integration, data transformation, data aggregation and data blending
- Richer analytics, such as k-means clustering and window functions
- Tableau Hyper will also extend the hybrid data model.
- Unification of analysis and transactional systems
- Hyper will also provide tools for the harmonisation, cleansing and transforming complex and large data sets
- The aspiration is that Hyper provides a so-called “instant analytics” capability that will automatically display various contextual details as users interact with their data. This will be served by the in memory processing database engine.
Hyper will also retain connectivity to the 50 or so data sources that Tableau supports in version 10 – this covers disparate data source such as Amazon Redshift, Google BigQuery, Snowflake, και SQL 2017 server.
The beta for Hyper is already underway (early 2017) and the Tableau Hyper Release Date is expected to be Q4 2017 and be released with έκδοση Πίνακας 11. Hyper will replace the Tableau Data Engine (TDE) by end of 2017.
Business across the globe are evolving at a breathtaking pace and the need to make decisions instantly has also grown multi-fold. Decision making at the Top Executive level is no more an intuitive or hunch-driven thought process. It has to be backed with data and based on data and thorough information. To quickly assimilate a huge amount of data, there is now huge demand for Business Intelligence tools that can help the Top Executives give a quick snapshot of the complete picture of Business. These tools are nowadays an utmost necessity as they help the Management keep an eye on the pulse of the business.
There is a huge gamut of BI tools that are there in the market today which help Business in one or other way. Gartner’s Magic Quadrant has ranked Qlikview Version 13 in the Leader’s segment in the BI products category and Qlikview has been able to maintain its stance for past several quarters which is enough of a reflection of Qlikview’s popularity among the major CIOs of the world from almost all the domains including Finance, Banking, Insurance/Actuaries, Automobiles, Pharma, FMCG, Retails, CPG, Manufacturing, Utilities, και τα λοιπα.
Ease of Learning Qlikview Version 13
QlikView Έκδοση 13 popularity can be attributed to the large number of features that it offers. Not only it has a low deployment time its TCO (Total Cost of Ownership) is also lower compared to several other BI tools. It is easy to learn Qlikview as it has a low Learning curve and also is very easy to be followed and understood for the end user. Since most of the companies/organizations are deploying Qlikview to serve their Data Visualization and Business Analysis needs there is huge Demand Supply gap in terms of required manpower with the desired skillset. Qlikview Developers are having a very absorption rate in the Analytics industry which is no more a strong hold of the big IT firms as it was the case earlier. Every kind of Business be it small, medium or a large enterprise is looking for manpower which can take care of Qlikview setup, prepare dashboards, and prepare business reports for them. Εν συντομία, the demand for Qlikview developers is at its peek at the moment and is a promising field for people who want to enter the Data Visualization arena today.
Προγνωστικών analytics will help your organisation reveal and predict trends, anticipate business change, and drive moire empirical strategic decision making with using a range of predictive analysis software.
predictive analytics can be used to describe any approach to data mining with five attributes:
- Prediction (rather than description, classification or clustering),
- Agile and rapid analysis measured in hours or days
- Highly business relevant e.g. why did we sell x many widgets in New York (no complex ivory tower analyses)
- Easy to use
- Highly visual analytical results (no complex tables / δεδομένα)
In our experience the way to succeed with Προγνωστική ανάλυση is to Empower a C-Level Προγνωστικών analytics Champion. Recently we have worked with a large retail organisation with a CFO who was hugely keen on predictive analysis to help drive business growth and spot new market opportunities.
Spotfire Cost – along with many other software applications it can be quite difficult to find out the cost of the Spotfire Business Intelligence tool.
TIBCO Spotfire designs, develops and distributes in-memory analytics software for use in business intelligence and analytics and provides users with executive dashboards, data analytics, data visualization, ..
Spotfire has been around since the early 90s but didn’t really take off until 2007, when the brand was acquired by TIBCO Software. An exact customer count is unavailable, and has around $1 billion in revenue and a growing market share of the BI Tools market.
If you are a corporate user then it’s likely you will be able to negotiate a tailored pricing plan depending on the pleothora of different options to choose from.
However to give a flavour for the cost of TIBCO Spotfire Cost there are two potential options
1. Spotfire Cloud Personal Service – approx. $300/year, 100GB storage, 1 author seat (slightly limited functionality in that the desktop software has limited connectivity to local data and can upload only local DXP files).
2. Spotfire Cloud Work Group ($2000/year, 250GB storage, 1 business author/1 analyst/5 consumer seats) and gives the single author the ability to read 17 different types of local files (dxp, stdf, sbdf, sfs, xls, xlsx, xlsm, xlsb, csv, txt, mdb, mde, accdb, accde, sas7bdat,udl, log, shp), connectivity to standard Data Sources (ODBC, OleDb, Μαντείο, Microsoft SQL Server Compact Data Provider 4.0, .NET Data Provider for Teradata, ADS Composite Information Server Connection, Microsoft SQL Server (including Analysis Services), Teradata and TIBCO Spotfire Maps. It also enables author to do predictive analytics, forecasting, and local language scripting).
TIBCO Spotfireis a Data visualisation and anayltics tool that enables users to access, analyze and create dynamic reports on a variety of data sources.
Spotfire, in my opinion is a tool best aimed at those users who are true Data Analysts or using the new buzzord “Data Scientists“.
Spotfire also keeps the Total Cost of Ownership low by allowing users to build once and publish to many (non licensed) users over internet/intranet, as PDF or as MS PowerPoint reports.
If used correctly with a good all round understanding of the data content Spotfire can deliver immediate value whether you are a market researcher, a sales representative, a scientist or a process engineer by letting you quickly identify trends and patterns in your critical business data.
Spotfire can access data in a number of places such as on your desktop or in a network file system. It can even access your data if it is located in remote databases via the Information Link feature, without you having to involve your IT department each time you wish to ask a new question. However for typical business users most will need input from their IT Departments to make the underlying database tables and fields understanddable. For example making Table 123_xyx / Field 4455gt to be “Sales Quantity”.
Spotfire lets you filter your data interactively, and helps the business user delve into the data to provide answers instantly and in a visual and understable format – the old adage of a “a picture is worth a thousand words” and nowhere is this more true than in the case of Data Analytics.
Spotfire also has a number of nifty features and dashboards with street-level mapping very similaur to Google Maps.
In a recent presentation I was in to the CFO of a Global 100 retailer he stated that he now wanted his team going forward to ditch the Powerpoint presentation and present data to him using Spotfire – clearly an enthusiast for this type of tool!
Static reports can be limiting to business users hence Spotfire Version 6 allows you to create dynamic reports that aid the user in posing further business questions and data dissemination. Data visuations can be easily turned into your reports to show to colleagues and customers.
The new features available in Spotfire v6 are:
- Advanced level Google maps style mapping and integration
- The ability to interactively select a data subset on a chart and then drill down / through into the data
- A range of new charts features
- Text can easily be placed on top of images
- Improved integration and ability to push data onto mobile devices
- Visualize, explore and analyze data in the context of location
- Expand situational understanding with multi-layered geo-analytics
- Mashup new data sources to provide precise geo-coding across the enterprise
- Improved Web Based authoring
The Data sources that Spotfire Version 6 can connect to are:
• Cloudera Hive CDH4, CDH5
• Cloudera Impala CDH4, CDH5
• Composite Information Server (ADS) 6.1, 6.2
• Hortonworks Data Platform 1.3, 2.0
• HP Vertica 6.1
• IBM Netezza 6.1, 7.0
• Microsoft Analysis Services 2008, 2012
• Microsoft SQL Server 2005, 2008 R2, 2012
• MySQL 5.1, 5.5, 5.6
• Oracle and Oracle Exadata (Oracle 11gR1 and R2)
• Oracle Hyperion Essbase 9.3, 11.1
• Pivotal Greenplum 4.1, 4.2, 4.3
• Pivotal HAWQ
• PostgreSQL 8.4, 9.0, 9.1, 9.2
• SAP HANA SPS6
• SAP NetWeaver Business Warehouse 7.0.1
• Teradata Aster 5.0, 5.11
• Teradata 12.10, 13.00, 13.10, 14.00, 14.10
Data science is the study of the generalizable extraction of knowledge from data, yet the key word is science. It incorporates varying elements and builds on techniques and theories from many fields, including signal processing, mathematics, probability models, machine learning, computer programming, statistics, data engineering, pattern recognition and learning, visualization, uncertainty modeling, data warehousing, and high performance computing with the goal of extracting meaning from data and creating data products. Data science is a buzzword, often used interchangeably with analytics or big data, that is often abused for marketing anything involving data processing, in particular to re-brand existing competitive intelligence and business analytics approaches. Data Science need not be always for big data, however, the fact that data is scaling up makes big data an important aspect of data science.
A practitioner of data science is called a data scientist. Data scientists solve complex data problems through employing deep expertise in some scientific discipline. It is generally expected that data scientists are able to work with various elements of mathematics, statistics and computer science, although expertise in these subjects are not required. Ωστόσο, a data scientist is most likely to be an expert in only one or two of these disciplines and proficient in another two or three. This means that data science must be practiced as a team, where across the membership of the team there is expertise and proficiency across all the disciplines.
Good data scientists are able to apply their skills to achieve a broad spectrum of end results. Some of these include the ability to find and interpret rich data sources, manage large amounts of data despite hardware, software and bandwidth constraints, merge data sources together, ensure consistency of data-sets, create visualizations to aid in understanding data, build mathematical models using the data, present and communicate the data insights/findings to specialists and scientists in their team and if required to a naive audience. The skill-sets and competencies that data scientists employ vary widely. Data scientists are an integral part of competitive intelligence, a newly emerging field that encompasses a number of activities, such as data mining and analysis, that can help businesses gain a competitive edge.
Data science techniques impact how we access data and conduct research across various domains, including the biological sciences, medical informatics, social sciences and the humanities.