SAP Business Objects Business Intelligence 4.2 Service Pack 05 BOBJ

SAP Business Objects Business Intelligence 4.2 Service Pack 05  is due to be released at the end of 2o17.

SAP BusinessObjects BI (also known as BO or BOBJ) is a suite of front-end applications that allow business users to view, sort and analyze business intelligence data. The suite includes the following key applications:

  • Crystal Reports — Enables users to design and generate reports
  • Xcelsius/Dashboards — Allows users to create interactive dashboards that contain charts and graphs for visualising data
  • Web Intelligence — Provides a self-service environment for creating ad hoc queries and analysis of data both online and offline
  • Explorer — Allows users to search through BI data sources using a GUI interface. Users do not have to create queries to search the data and results are shown with a chart that indicates the best information match.

This version is expected to offer a number of innovations to users and the key focus of this release is around

  • Enterprise – scalability, multiple users
  • Agility
  • Smart pillar – not just look at preformatted reports, integrating with simple predictive capabilities
  • Big data – HANA Vora
  • Cloud – investing in “all in data” in the cloud
  • Hana integration with Hadoop

SAP Business Objects Business Intelligence 4.2 Service Pack 05

SAP Business Objects Business Intelligence 4.2 Service Pack 05

SAP S4 Hana BI Big Data Enterprise Cloud Architecture

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.

 

YellowFin BI Latest Versions

YellowFin BI Latest Versions

YellowFin BI Latest Versions – 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 Yellowfin BI is 7.3+plus  – build 20170608 – released on 30 June 2017

Yellowfin BI Latest Versions

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 Engine Release Date Review

Tableau Hyper

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 Tableau 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, and SQL 2017 server.

Tableau Hyper
Tableau Hyper Release Date

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 Tableau Version 11. Hyper will  replace the Tableau Data Engine (TDE) by end of 2017.

Magic Quadrant BI 2018 – Gartners Business Intelligence

Magic Quadrant BI 2018 – Gartners Business Intelligence

Before we examine the maturity model of BI solutions and the guidelines for its development, it is necessary to perform an analysis of the capabilities of BI solutions, as well as the market for these solutions.
As a starting point for this research we take Gartner’s “Magic Quadrants for BI
platforms”,

Magic Quadrant BI 2018 – Gartners Business Intelligence

In it Gartner defines BI platforms as software
platforms providing 12 capabilities, divided into 3 basic categories:
✓ Delivery of information;
✓ Integration;
✓ Analytics
Information technology is a highly dynamic field of research. As part of it, business
intelligence systems (BIS) also develop very quickly. In this paper we shall adhere to
the following definition of BIS: “BIS combine the activities of data mining and data
processing and knowledge management through analytical means in order to present
complex competitive information to consumers who draw plans and make decisions.”1
1. Analysis of today’s capabilities of BI platforms
Before we examine the maturity model of BI solutions and the guidelines for its
development, it is necessary to perform an analysis of the capabilities of BI solutions,
as well as the market for these solutions.
As a starting point for this research we take Gartner’s “Magic Quadrants for BI
platforms”, published in 2007 (fig. 1). In it Gartner defines BI platforms as software
platforms providing 12 capabilities, divided into 3 basic categories:
✓ Delivery of information;
✓ Integration;
✓ Analytics.
1
Articles
To the first category belong the capacities for:
✓ Generating reports;
✓ Navigation panes;
✓ Ad hoc queries;
✓ Integration with MS Office.
The second group includes:
✓ BI infrastructure;
✓ Metadata management;
✓ Developing environments;
✓ Workflows;
✓ Cooperation.
The third category covers the capabilities for:
✓ Online analytical processing;
✓ Visualization;
✓ Delivery of knowledge and forecasting;
✓ Maps of results.
Five years later Gartner published a new “Magic Quadrant for BI platforms”,
that is still current for 2012. In this document BI platforms continue to be viewed as
software platforms, delivering the capabilities described above. But two more features
have been added to the “delivery of knowledge” category, namely search-based BI
and mobile BI.
The basic capabilities of BI platforms have been widely discussed and studied,
so for this reason they will not be considered in detail in this paper. More attention will
be paid to the two new characteristics, offered by Gartner.
The first opportunity is search-based BI. Essentially it is an application of a
search-index in structured and unstructured data sources and their division (organization)
into a classification structure of measures and dimensions, which consumers can easily
navigate and explore, using a Google-like interface.
The basic difference between search engines and data warehouses is that search
engines are very flexible and support any kind of format and type of information – be
it structured or unstructured. Thus search engines can cope with increasingly evolving
data structures. The indexing of both existing and new data (unknown so far) does not
require additional data modeling. Conventional data warehousing architecture has limited
capabilities for dealing with unstructured data that are necessary for facilitating decision-
making and search engines “fill this gap”. In comparison, data warehouses require
time not only for creating the warehouse model, but also for adding new data. Another
positive feature of search engines is their ease of “navigation” through contents. At
each step of the navigation, search engines provide different opportunities for filtering
the results according to contents into the multitude of data that have been indexed and
analyzed in nearly real time. Relational database management systems (RDBMS)
have no capacity for data analysis unless they possess some knowledge about different
type of data. That is, a search engine can easily follow any event that happened at a
certain moment in time, while using conventional RDBMS a search can be performed
only within strictly defined data fields

Qlikview Version 13 Release Date

Qlikview 13

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, etc.

 

Ease of Learning Qlikview Version 13

Qlikview Version 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. In short, 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.

 

Predictive Analytics

Predictive 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:

  1. Prediction (rather than description, classification or clustering),
  2. Agile and rapid analysis measured in hours or days
  3. Highly business relevant e.g. why did we sell x many widgets in New York (no complex ivory tower analyses)
  4. Easy to use
  5. Highly visual analytical results (no complex tables / data)

 

Predictive Analytics

In our experience the way to succeed with Predictive Analysis is to Empower a C-Level Predictive 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.

 

TIBCO Spotfire Cost – Options and Pricing

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, ..

tibco spotfire costs pricing

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, Oracle, 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 Spotfire® Cloud TIBCO Spotfire® Platform
Pricing $200/month OR $2000/annual
subscription pricing
Subscription, Perpetual and Term Licenses
Licenses 1 Authoring Seat (includes online and offline authoring) Per Customer Order
Cloud Data Storage 250GB 0GB

TIBCO Spotfire version 6 Review

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.

During our Spotfire Review we were able to create a variety of colorful visualizations in the form of motion charts, bar charts, cross tables, scatter plots and many more that

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

What is a Data Scientist?

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. However, 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.