Magic Quadrant BI 2018 – Gartners Business Intelligence

Magic Quadrant BI 2018 – Gartners Business Intelligence

Før vi undersøke modenhet modell av BI-løsninger og retningslinjer for sin utvikling, det er nødvendig å utføre en analyse av egenskapene til BI-løsninger, samt markedet for disse løsningene.
Som et utgangspunkt for denne forskningen tar vi Gartners “magiske kvadranter for BI-plattformer”,

Magic Quadrant BI 2018 – Gartners Business Intelligence

I det Gartner definerer BI plattformer som programvareplattformer gi 12 evner, delt i 3 grunnleggende kategorier:
✓ Levering av informasjon;
✓ Integrasjon;
✓ Analytics
Informasjonsteknologi er et svært dynamisk forskningsfelt. Som en del av det, virksomhet
intelligens systemer (TIL) også utvikle seg svært raskt. I denne artikkelen skal vi følge
den følgende definisjonen av BIS: “BIS kombinere aktivitetene til data mining og data
behandling og kunnskapsforvaltning gjennom analytiske virkemidler for å presentere
komplekse konkurranseinformasjon til forbrukere som trekker planer og ta beslutninger.”1
1. Analyse av dagens egenskapene BI plattformer
Før vi undersøke modenhet modell av BI-løsninger og retningslinjer for sitt
utvikling, det er nødvendig å utføre en analyse av egenskapene til BI-løsninger,
samt markedet for disse løsningene.
Som et utgangspunkt for denne forskningen tar vi Gartners “magiske kvadranter for BI
plattformer”, publisert i 2007 (fig. 1). I det Gartner definerer BI plattformer som programvare
plattformer som gir 12 evner, delt i 3 grunnleggende kategorier:
✓ Levering av informasjon;
✓ Integrasjon;
✓ Analytics.
1
artikler
Til den første kategorien hører de kapasiteter for:
✓ generere rapporter;
✓ Navigasjons ruter;
✓ Ad hoc-spørringer;
✓ Integrasjon med MS Office.
Den andre gruppen består:
✓ BI infrastruktur;
✓ Metadata administrasjon;
✓ Utvikling miljøer;
✓ arbeidsflyter;
✓ Samarbeid.
Den tredje kategorien omfatter mulighetene for:
✓ Online analytisk prosessering;
✓ Visualisering;
✓ Levering av kunnskap og prognoser;
✓ Maps resultater.
Fem år senere Gartner publiserte en ny “Magic Quadrant for BI-plattformer”,
som fortsatt er aktuell for 2012. I dette dokumentet BI plattformer fortsetter å bli sett på som
programvareplattformer, å levere egenskapene beskrevet ovenfor. Men to flere funksjoner
har blitt lagt til i kategorien “levering av kunnskap”, nemlig søkebasert BI
og mobil BI.
De grunnleggende egenskapene til BI plattformer har vært mye diskutert og studert, så derfor vil de ikke bli vurdert nærmere i denne artikkelen. Mer vil oppmerksomhet
utbetales til de to nye egenskaper, tilbys av Gartner.
Den første muligheten er søkebasert BI. I hovedsak er det en anvendelse av en
søk-indeksen i strukturerte og ustrukturerte datakilder og deres divisjon (organisasjon)
inn i en klassifiserings struktur av mål og dimensjoner, som forbrukerne kan enkelt
navigere og utforske, ved hjelp av en Google-lignende grensesnitt.
Den grunnleggende forskjellen mellom søkemotorer og datavarehus er at søk
motorer er svært fleksibel og støtte noen slags format og type informasjon - det være
den strukturerte eller ustrukturerte. søkemotorer kan således håndtere stadig utvikler seg
datastrukturer. Indeksering av både eksisterende og nye data (ukjent så langt) gjør ikke
krever ekstra datamodellering. Konvensjonelle datalagerarkitektur har begrenset
muligheter for å håndtere ustrukturerte data som er nødvendige for å tilrettelegge beslutnings-
making og søkemotorer “fylle dette gapet”. Til sammenligning, datavarehus krever
tid ikke bare for å skape lageret modellen, men også for å legge til nye data. En annen
positiv funksjon av søkemotorer er deres enkle “navigasjon” gjennom innholdet. På
hvert trinn av navigasjons, søkemotorer gir ulike muligheter for filtrering
resultatene i henhold til innholdet i en mengde av data som er indeksert og
analysert i nesten sanntid. Relasjonsdatabase styringssystemer (RDBMS)
har ikke mulighet for dataanalyse med mindre de har noe kunnskap om ulike
type data. Det er, en søkemotor kan følge enhver hendelse som skjedde på en
visst tidspunkt, mens ved anvendelse av konvensjonelle RDBMS et søk kan utføres
bare innenfor strengt definerte datafelter

SAP Business Objects Business Intelligence 4.2 Service pakke 05 BOBJ

SAP Business Objects Business Intelligence 4.2 Service pakke 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 ReportsEnables users to design and generate reports
  • Xcelsius/DashboardsAllows users to create interactive dashboards that contain charts and graphs for visualising data
  • Web IntelligenceProvides a self-service environment for creating ad hoc queries and analysis of data both online and offline
  • ExplorerAllows 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.

Denne versjonen er ventet å gi en rekke nyvinninger til brukere og nøkkelen fokus for denne utgivelsen er rundt

  • Enterprise - skalerbarhet, flere brukere
  • Smidighet
  • Smart søyle - ikke bare se på forhåndsformaterte rapporter, integrere med enkle prediktive evner
  • Big data - HANA Vora
  • Cloud - investere i “all in data” i skyen
  • Hana integration with Hadoop

SAP Business Objects Business Intelligence 4.2 Service pakke 05

SAP Business Objects Business Intelligence 4.2 Service pakke 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 Nyeste versjoner

Yellowfin BI Nyeste versjoner

Yellowfin BI Nyeste versjoner – 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 juni 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 Utgivelsesdato omtale

Tableau Hyper

Tableau HyperTableau 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 bord 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, og 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 versjon Table 11. Hyper will replace the Tableau Data Engine (TDE) by end of 2017.

QlikView versjon 13 Utgivelsesdato

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 versjon 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. Kort oppsummert, 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.

prediktiv analyse 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 Prediktiv analyse 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 Costalong 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 Serviceapprox. $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 versjon 6 Gjennomgang

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 buzzordData 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 beSales 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 formatthe old adage of aa picture is worth a thousand wordsand nowhere is this more true than in the case of Data Analytics.

Under vår 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 Spotfireclearly 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

Hva er en Data Scientist?

data vitenskap er studiet av generaliseres utvinning av kunnskap fra data, men stikkordet er vitenskap. Den inneholder varierende elementer og bygger på teknikker og teorier fra mange felt, innbefattende signal-behandlings, matematikk, sannsynlighetsmodeller, maskinlæring, dataprogramering, statistikk, datateknologi, mønstergjenkjenning og læring, visualisering, usikkerhet modellering, datavarehus, og krevende databehandling med det formål å trekke ut mening fra data og data fra pasientens produktene. Data vitenskap er et moteord, ofte brukes om hverandre med analyser eller store data, som ofte misbrukt for å markedsføre noe som involverer databehandling, spesielt for å re-brand eksisterende konkurransedyktige etterretning og Business Analytics tilnærminger. Data Science trenger ikke alltid være for stor data, derimot, det faktum at data oppskalering gjør store data en viktig del av data vitenskap.

En utøver av data vitenskapen kalles en dataforsker. Data forskere løse komplekse dataproblemer gjennom å ansette dyp kompetanse innen noen vitenskapelig disiplin. Det er generelt forventet at data forskere er i stand til å arbeide med ulike elementer av matematikk, statistikk og informatikk, selv om kompetanse i disse fagene ikke er nødvendig. derimot, en datavitenskapsmann er mest sannsynlig å være en ekspert på bare en eller to av disse disiplinene og dyktig i ytterligere to eller tre. Dette betyr at data vitenskap må praktiseres som et team, der over medlemstall på teamet er det kompetanse og ferdigheter på tvers av alle disipliner.

Gode ​​data forskere er i stand til å bruke sine ferdigheter for å oppnå et bredt spekter av sluttresultatet. Noen av disse inkluderer muligheten til å finne og tolke rike datakilder, håndtere store mengder data til tross for maskinvare, programvare og båndbredde begrensninger, flette datakilder sammen, sikre konsistens av datasett, lage visualiseringer til hjelp i forståelsen av data, bygge matematiske modeller ved hjelp av data, nåtid og kommunisere data innsikt / funn til spesialister og forskere i laget sitt og om nødvendig til en naiv publikum. Ferdigheter-sett og kompetanse som data forskere benytter varierer mye. Data forskere er en integrert del av konkurransedyktige etterretning, en nye felt som omfatter en rekke aktiviteter, for eksempel data mining og analyse, som kan hjelpe bedrifter få et konkurransefortrinn.

Data vitenskap teknikker innvirkning på hvordan vi tilgang til data og drive forskning på tvers av ulike domener, inkludert de biologiske vitenskaper, medisinsk informatikk, samfunnsfag og humaniora.