Tag: Data

Develop a Research Proposal – Methodology – Data Analysis #longitudinal #data #analysis #course


In your research proposal, you will also discuss how you will conduct an analysis of your data. By the time you get to the analysis of your data, most of the really difficult work has been done. It’s much more difficult to define the research problem, develop and implement a sampling plan, develop a design structure, and determine your measures. If you have done this work well, the analysis of the data is usually a fairly straightforward affair.

Before you look at the various ways of analyzing and discussing data, you need to review the differences between qualitative research/quantitative research and qualitative data/quantitative data.

Why do I have to analyze data?

The purpose of analyzing data is to obtain usable and useful information. The analysis, regardless of whether the data is qualitative or quantitative, may:

  • describe and summarize the data.
  • identify relationships between variables.
  • compare variables.
  • identify the difference between variables.
  • forecast outcomes.

Earlier, you distinguished between qualitative and quantitative research. It is highly unlikely that your research will be purely one or the other – it will probably be a mixture of the two approaches.

For example, you may have decided to ethnographic research, which is qualitative. In your first step, you may have taken a small sample (normally associated with qualitative research) but then conducted a structured interview or used a questionnaire (normally associated with quantitative research) to determine people’s attitudes to a particular phenomenon (qualitative research). It is therefore likely that your mixed approach will take a qualitative approach some of the time, and a quantitative approach at others depending on the needs of your investigation.

A source of confusion for many people is the belief that qualitative research generates just qualitative data (text, words, opinions, etc) and that quantitative research generates just quantitative data (numbers). Sometimes this is the case, but both types of data can be generated by each approach. For instance, a questionnaire (quantitative research) will often gather factual information like age, salary, length of service (quantitative data) – but may also collect opinions and attitudes (qualitative data).

When it comes to data analysis. some believe that statistical techniques are only applicable for quantitative data. This is not so. There are many statistical techniques that can be applied to qualitative data, such as ratings scales, that has been generated by a quantitative research approach. Even if a qualitative study uses no quantitative data, there are many ways of analyzing qualitative data. For example, having conducted an interview, transcription and organization of data are the first stages of analysis. This would then be continued by systematically analyzing the transcripts, grouping together comments on similar themes and attempting to interpret them, and draw conclusions.

1. Manchester Metropolitan University (Department of Information and Communications) and Learn Higher offer a clear introductory tutorial to qualitative and quantitative data analysis through their Analyze This. site. In additional to teaching about strategies for both approaches to data analysis, the tutorial is peppered with short quizzes to test your understanding. The site also links out to further reading.

Complete this tutorial and use your new knowledge to complete your planning guide for your data analysis.

There are many computer- and technology-related resources available to assist you in your data analysis.

Online General Resources

Quantitative Data Analysis Resources

  • The Rice Virtual Lab in Statistics also houses an online textbook, Hyperstat. This textbook introduces univariate and bivariate analysis, probability, distribution and hypothesis testing. The site also includes a really useful section of case studies, which use real life examples to illustrate various statistical tests.
  • Not sure which statistical test to use with your data? The University of the West of England has a really helpful tree diagram to help you decide which is the best one for you. The diagram is housed within another good introduction to data analysis.
  • Free Statistical Analysis and Data Management software

Common Computer-Aided Qualitative Data Analysis Packages

There are many computer packages that can support your qualitative data analysis. The following site offers a comprehensive overview of many of them: Online QDA

Another package that allows you analyze textual, graphical, audio and video data. It also has the functionality to produce tag clouds to represent the codes you have created. No free demo, but there is a student price.

This has add-ons which allow you to analyze vocabulary and carry out content analysis. There is a visual MAPS add-on which produces visual representations of your data. There is a free demo.

This was one of the first packages for qualitative data analysis. It allows you to create code trees and search your data easily. There is a free demo.

Transana, HyperRESEARCH and HyperTRANSCRIBE, and N-Vivo are other products.

2. When you are done, you will also need to address concerns about the reliability and validity of your possible results. Use these questions and explanations for ideas as you complete your planning guide for this section.

Some common worries amongst researchers are:

  • Will the research I’ve done stand up to outside scrutiny?
  • Will anyone believe my findings?

These questions are addressed by researchers by assessing the data collection method (the research instrument) for its reliability and its validity .

Reliability is the extent to which the same finding will be obtained if the research was repeated at another time by another researcher. If the same finding can be obtained again, the instrument is consistent or reliable.

Validity is understood best by the question: ‘Are we measuring what we think we are measuring?’ This is very difficult to assess. The following questions are typical of those asked to assess validity issues:

  • Has the researcher gained full access to the knowledge and meanings of data?
  • Would experienced researchers use the same questions or methods?

No procedure is perfectly reliable, but if a data collection procedure is unreliable then it is also invalid. The other problem is that even if it is reliable, then that does not mean it is necessarily valid.

Triangulation is crosschecking of data using multiple data sources or using two or more methods of data collection. There are different types of triangulation, including:

  • time triangulation – longitudinal studies
  • methodological triangulation – same method at different times or different methods on same object of study
  • investigator triangulation – uses more than one researcher.

Sampling error is a measure of the difference between the sample results and the population parameters being measured. It can never be eliminated, but if random sampling is used, sampling error occurs by chance but is reduced as the sample size increases. When non-random sampling is used this is not the case.

Basic questions we need to ask to assess a sample are:

  • Is the sample random and representative of the population?
  • Is the sample small or large?

All errors, other than sampling errors, are non-sampling errors and can never be eliminated. The many sources of non-sampling errors include the following:

  • Researcher error – unclear definitions; reliability and validity issues; data analysis problems, for example, missing data.
  • Interviewer error – general approach; personal interview techniques; recording responses.
  • Respondent error – inability to answer; unwilling; cheating; not available; low response rate.

This section was discussed in Elements of the Proposal. where there are many online resources, and you have reflective journal entries that will support you as you develop your ideas for reliability and validity in your planning guide. In addition this writing tutorial specifically addresses the ways in which this can be explained in your research proposal.

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Observatorio ARVAL: Solar System Data #solar #system #data


ARVAL: Solar System Data

Given that the orbits are elliptical (but of low Eccentricity ),
the Radius figures show the semi-mayor axis, which is also the average distance from the planet to the Sun.
An orbit’s Inclination. is measured with respect to the Earth’s orbital plane (the “Ecliptic Plane”).
Sidereal Period is the time taken by a planet to complete an orbit around the Sun, measured relative to the stars.
Synodic Period is the time taken by a planet to complete an orbit around the Sun, measured relative to the Earth. It is the time taken by a planet to complete a cyle of its phases, the interval of time between two succesive equal phases.

Given that the planets present some Polar flattening, Diameters are equatorial.
The Earth’s Mass is 5.9740 * 10 24 Kgr.
The Earth’s Gravity is 9.8 m/seg 2.

A comma has been used as a thousands separator, and a dot as a decimal separator.

Data from the “Field Guide to the Night Sky”, The Audubon Society 1991.

All of the above 9 Solar System objects were commonly considered to be “Planets”, but there was no formal, scientific, definition. Pluto, discovered by Clyde Tombaugh (1906 – 1997) in 1930, was the smallest and farthest planet. See Hubble Views the Pluto System (also in ARVAL ).

This changed, as in August 24 ’06 the International Astronomical Union (IAU) approved this definition:

RESOLUTION 5A: Definition of “planet”

The IAU therefore resolves that planets and other bodies in our Solar System be defined into three distinct categories in the following way:

(1) A planet 1 is a celestial body that (a) is in orbit around the Sun,
(b) has sufficient mass for its self-gravity to overcome rigid body forces so that it assumes a hydrostatic equilibrium (nearly round) shape, and
(c) has cleared the neighbourhood around its orbit.

(2) A dwarf planet is a celestial body that (a) is in orbit around the Sun,
(b) has sufficient mass for its self-gravity to overcome rigid body forces so that it assumes a hydrostatic equilibrium (nearly round) shape 2 ,
(c) has not cleared the neighbourhood around its orbit, and (d) is not a satellite.

(3) All other objects 3 orbiting the Sun shall be referred to collectively as “Small Solar System Bodies”.

1 The eight planets are: Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, and Neptune.
2 An IAU process will be established to assign borderline objects into either dwarf planet and other categories.
3 These currently include most of the Solar System asteroids, most Trans-Neptunian Objects (TNOs), comets, and other small bodies.

RESOLUTION 6A: Definition of Pluto-class objects

The IAU further resolves:

Pluto is a “dwarf planet” by the above definition and is recognized as the prototype of a new category of trans-Neptunian objects.

On 13 September ’06 the IAU Minor Planet Center assigned to Pluto the asteroid number 134340, and to 2003 UB313 the name “Eris” and the asteroid number 136199. The one known moon of Eris is now named Dysnomia.

Ceres and Eris are now “Dwarf Planets”, Charon continues to be a satellite of the now Dwarf Planet Pluto, the second largest known member of the Kuiper Belt. Eris is the largest known, some 5% bigger than Pluto.
This membership is the reason why Pluto was demoted (2-c above).

The name 2003 UB313 was provisional, as a “real” name had not yet been assigned to this object. A decision and announcement of a new name were not made during the IAU General Assembly in Prague, but at a later time.
Pluto and Eris are “Trans-Neptunian Objects”, but not Ceres.

The orbit of Eris is even more eccentric than that of Pluto. Pluto moves from 30 to 50 times the Sun-Earth distance over its nearly 250 year orbit, the new planet moves from 38 to 97 times the Sun-Earth distance over its 560 year orbit, and its orbit inclination is 44�.

This page was updated in: September 16 ’06, June 24 ’14

Best seen with Font Verdana .
See About the Web Pages of Observatorio ARVAL .

Back: Observatorio ARVAL – Home Page (ARVAL Catalog of Bright Objects)

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Industrial Flooring #data #center #flooring #system


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Silikal is unlike other floor used for commercial or industrial locations. These inferior products will allow penetration of bacteria, dirt and water to name a few everyday flooring threats because of their porous nature. This in turn creates a cleaning and longevity nightmare. Silikal on the other hand will give you a totally seamless floor surface that is completely pinhole and porous free in addition to being an extremely strong industrial floor!

Silikal Flooring in Further Detail

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Data Scientists in Demand: Salaries Rise as Talent Shortage Looms #business #plans #examples

#business week


Help Wanted: Black Belts in Data

A new species of techie is in demand these days—not only in Silicon Valley, but also in company headquarters around the world. “Data scientists are the new superheroes,” says Pascal Clement, the head of Amadeus Travel Intelligence in Madrid. The description isn’t exactly hyperbolic: The qualifications for the job include the strength to tunnel through mountains of information and the vision to discern patterns where others see none. Clement’s outfit is part of Amadeus IT Holding, the world’s largest manager of flight bookings for airlines, which has more than 40 data scientists on its payroll, including some with a background in astrophysics. The company recently launched Schedule Recovery, a product that tracks delays and automatically rebooks all affected passengers.

A study by McKinsey projects that “by 2018, the U.S. alone may face a 50 percent to 60 percent gap between supply and requisite demand of deep analytic talent.” The shortage is already being felt across a broad spectrum of industries, including aerospace, insurance, pharmaceuticals, and finance. When the consulting firm Accenture surveyed its clients on their big-data strategies in April 2014, more than 90 percent said they planned to hire more employees with expertise in data science—most within a year. However, 41 percent of the more than 1,000 respondents cited a lack of talent as a chief obstacle. “It will get worse before it gets better,” says Narendra Mulani, senior managing director at Accenture Analytics.

Many data scientists have Ph.D.s or postdoctorates and a background in academic research, says Marco Bressan, president for data and analytics at BBVA, a Spanish bank that operates in 31 countries and has a team of more than 20 data scientists. “We have nanotechnologists, physicists, mathematicians, specialists in robotics,” he says. “It’s people who can explore large volumes of data that aren’t structured.”

So-called unstructured data can include e-mails, videos, photos, social media, and other user-generated content. Data scientists write algorithms to extract insights from these troves of information. But “true data scientists are rare,” says Ricard Benjamins, head of business intelligence and big data at Telefónica, Europe’s second-largest phone company, which employs more than 200 of them. Says Stan Humphries, chief economist at Zillow, the real estate listings site: “You can find a great developer and a great researcher who has a background in statistics, and maybe you can find a great problem solver, but to find that in the same person is hard.”

Universities are taking note. MIT, where graduate students in physics, astronomy, and biology are fielding offers from outside their chosen fields, is in the process of setting up a dedicated data-science institute. Marilyn Wilson, the university’s associate director for career development, says the center will begin enrolling graduate degree candidates in 2016.

In the U.K. the University of Warwick introduced a three-year undergraduate data-science program last year, which David Firth, the program’s mastermind, says may well be the first of its kind. “Big Business was complaining about the lack of people,” he says. “Finance is a major employer, but also large-scale insurers, large online commercial retailers, high-tech startups, and government, which has huge data sets.”

Accenture’s Mulani says he’s tallied some 30 new data-science programs in North America, either up and running or in the works. The University of Virginia began offering a master’s in 2014, as did Stanford. Many of those students may be tempted to drop out before collecting their degree. “Companies are scrambling,” says Margot Gerritsen, director of Stanford’s Institute for Computational Mathematical Engineering. “We have second- and third-year students getting offered salaries much higher than what I get.” Starting pay for some full-time jobs is above $200,000, she reports. Summer internships, meanwhile, pay anywhere from $6,000 to $10,000 a month. To make these stints memorable, many employers offer perks such as free meals, complimentary gym memberships, and occasionally temporary housing. “Sometimes you read about students getting abused in internships and working like slaves,” Gerritsen says. “We don’t see that.”

The bottom line: McKinsey projects that by 2018 demand for data scientists may be as much as 60 percent greater than the supply.

Before it’s here, it’s on the Bloomberg Terminal. LEARN MORE

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Hard Drive Recovery Software #hard #drive #recovery, #recover #hard #drive, #drive #recovery, #data #recovery,


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Returning Oracle Stored Procedure Resultset in SSRS @ Joel Lipman # #quot, #parameter, #ssrs,


Reporting Services

Posted on Jan 19, 12 at 12:01 AM

So I’ve spent a fun time googling and binging but still haven’t found a simple and complete example of getting a resultset from an Oracle stored procedure and displaying this in SQL Server Reporting Services (SSRS). Well “non-productive” more than “fun” as most of the examples on the net are either half-complete or partially documented. So here goes.

  • Using Business Intelligence Development Studio v2008 (BIDS)
  • SQL Server Reporting Services v2008 R2 (SSRS)
  • Oracle SQL Developer v3 (you can use any equivalent, eg. SQL*Plus)

I think this is one of those very rare occasions that Microsoft people can say “it’s so much easier using a Microsoft product to work with another Microsoft product” (ie “Seamless integration”). Yes, I’m trying to get an SSRS report to display the results from an Oracle stored procedure. I have a previous article describing a basic stored procedure in Oracle, this article aims to outline how to apply this to an SSRS 2008 R2 report.

If you’ve googled this topic as much as I have, you’ll note that everyone seems to be recommending using a cursor. I went through about 10 pages of google results without working out how to use these in SSRS and then resorted to my old methods of debugging. For those of you who don’t know what a cursor is, you can find this info on another site as this article focuses on getting an Oracle stored procedure to work with SSRS .

Main Example:
My stored procedure will do the following:

  1. Get a student ID number based on the student username
  2. Get a student’s names and date-of-birth based on the student ID number

Agenda Outline
Note: As simple as this sounds, this is not a five-minute job. Even with all your SQL scripts with backups of your datasets, each of these will need tweaking.

  1. Create your stored procedure in Oracle (and grant execute for your SSRS user)
  2. Add a data source to your SSRS report
  3. Add an SSRS input parameter
  4. Add a dataset
    1. Query type: Stored Procedure
    2. Refresh fields button
    3. Parameter name (oracle input sp variable) and Parameter value (ssrs input parameter)
    4. Fields (field name=ssrs reference, field source=oracle sp field name)

1. Create a stored procedure using a cursor for SSRSPermissions on this SP
Might need to give permission for your SSRS user (specified credentials in data source) to run this stored procedure. Notes:

  • No “SELECT. INTO. ” on the second query which is the one that will return my columns.
  • Two output fields are expected to be returned: “p_STUDENT_DETAILS” and “p_STUDENT_REF”.

2. Add a datasource
Nothing different to your usual method of connecting to a data source, I just added this section to say that my data source is type “Oracle” and NOT ODBC or OLEDB. Unicode=true and my user is a specific read-only user.

3. Add an SSRS parameter
Idem as in just specify a parameter as you would for any usual SSRS report. (Ignore my 2nd and 3rd SSRS parameters in my example as I didn’t end up using them)

4. Add a dataset
Follow these steps carefully. I’ve added some screenshot images to help and highlighted areas which you should ensure are correct (or which applies to your report)

4a. Add a dataset: query type “Stored Procedure”
Click on the radio button when adding your dataset and then select the stored procedure from the dropdown list.

4b. Add a dataset: the “Refresh fields. ” button
Click on the “Refresh fields. ” button so that you can define the query parameters.

Add comment


0 David S Wednesday, 6th February 2013, 9:47 pm

create or replace
PROCEDURE sp_RunMissingCh ecks (
l_CURSOR OUT sys_refcursor
DECLARE RECNT number(8);
–Divide each section into begin-end blocks
–Since my procedure has an output of the record count
–a variable needs to be placed.
— opening cursor for the select query to get the output
— that the SSRS DI is looking for.
— In this case a record count from the refreshed table.
Count(AGENT_NUMBER) as RecordCnt
–Simple once you get it done but the learning curve is nasty.
END sp_RunMissingCh ecks;

0 David S Tuesday, 5th February 2013, 10:24 pm

Okay, I keep finding this type of answer on how to run an oracle sp from SSRS all over the net. Yours is the best so far as detail and clarity goes. What I need is a bit more simple. How to kick off a sp with out parameters and with little or no output, single field at worst. The only output is a record count from the sp. This has stumped our Oracle guru and the SQL Server gurus are being real slow in getting back to me on it. sad:

-2 Joel L Wednesday, 6th February 2013, 11:28 am

I would think the most basic query would be to open a cursor. The following in theory should just return a table:
l_CURSOR OUT sys_refcursor

— opening cursor for the select query that I want to display in SSRS
my_table a;

Your report should return the columns “my_column_1, my_column_2, and my_column_3” See picture above on point 4d.

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Cobalt Data Centers – Las Vegas Data Center (Sahara) – Cloud and Colocation #las


Cobalt Data Centers Las Vegas Data Center (Sahara)

Cobalt s Sahara data center is located at East Sahara Avenue, Las Vegas, NV. The data center is located in an area of 2.4 Acres and has an extremely low instance of natural catastrophes and is considered a safe-zone from natural disasters. The area faces neither the earthquake risks associated with coastal California nor the winter storm problems that afflict the Mountain West and the northern US. The data center is well suited to provide disaster recovery and business continuity services.

Cobalt s Sahara data center is a Tier III facility infrastructure. The data center is a 60,000 square feet facility and is SSAE 16 SOC I Type II certified with HIPPA, PCI DSS, and IRS 1075 compliant. The security of data center is maintained through 24 x 7 x 365 presence of security guards, bullet proof glass and surfaces, and CCTV surveillance. The access to data center resources is multi factor authentication system including biometric security scanners, retina and fingerprint access system, and multiple mantraps. The facility is installed with double interlock, pre action dry pipe fire suppression with VESDA control for the life and equipment safety.

The Cobalt Sahara data center is the first multi-tenant data center in Nevada, and one of the most environmentally friendly, technology advance, energy efficient data centers that has received the LEED Silver certification. The data center has dedicated 10 MVA utility circuit, and lighting protection. 4 x 2500 kVA, 480 Volt electrical service, DC power plant, 120V/ 208 V single and three phase power, 200 watts per square feet of power density, and circuit level load monitoring are the key power features. The power failure is protected by tri redundant UPS system with N+1 configured backup power provided by 5 x 2.5 MW diesel generators. 2N configured centralized chilled water plant utilizing both water and air for cooling, and redundant CRAC units in N+ 2 configurations are utilized to automate the temperature of the data center.

Sahara data center is a carrier neutral facility. The facility is designed to be network access point (NAP) for the region with presence of more than 20 carriers. The data center is located in rich fiber district of US, and serviced by such a significant amount of long haul carrier fiber. The facility has four fiber entrances, two meetme rooms, and loop network. Sahara data center has 24 x 7 x 365 NOC for the technical assistance, information security consulting, and also offer remote hands services as and when required.

Cobalt Data Centers specializes in secure, high-density, network-rich colocation. Cobalt provides next-generation environmental systems, security, redundant power systems, and robust fiber networks. The company offers connectivity services that include IP transit, local and national transport services, and wireless connectivity. Cobalt offers a full suites of services to its customers including managed services, professional services, disaster recovery, hybrid cloud computing, and network operations. The disaster recovery design start from pre disaster business activity to post disaster business continuity plan with measures including automated incremental backups of physical and virtual resources, onsite and offsite recovery procedures, full system image backups to offsite, and to select from public and private cloud DR backup solution options. Cobalt has roughly a dozen customers including those in the cloud computing, voice over IP and healthcare industries. Cobalt Data Centers was founded in 2011, and is based in Las Vegas, NV.

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Cobalt Data Centers Las Vegas Data Center (Sahara) Highlights

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  • 60,000 Square Feet of Data Center
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  • HIPPA, PCI, and IRS 1075 Compliant
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Canada Data Recovery Services – Hard Drive Recovery, RAID Data Recovery, hard disk data


hard disk data recovery

Hard disk data recovery

Data Recovery Canada offers the fastest, most efficient computer data recovery in the nation, usually within 3 to 5 business days.

Hard disk data recovery

Hard disk data recovery

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Hard disk data recovery

Hard disk data recovery

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Because of their size, portable media are prone to failure. Fortunately, Data Recovery Canada is prone to retrieving lost data.

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Best Data Visualization Tools – 2017 Reviews & Pricing, visualization data.#Visualization #data


Data Visualization Tools

Price Range




Sage Live


IBM Cognos Analytics

Call us for a free FastStart Consultation: (844) 680-2046

SAP – Business Intelligence


Call us for a free FastStart Consultation: (844) 680-2046



Related Software Guides

Market Research Associate

Last Updated: August 31, 2017

Data visualization software helps companies make sense of their vast data stores by providing graphical representations of key information. These tools make it easy for ordinary, non-IT users to quickly view data in an easy-to-understand format and assess it so they can make better, more informed decisions. Data visualization solutions are particularly helpful for illustrating data that can be presented with executives or clients who want to quickly understand important insights and trends without having to perform deep data dives themselves.

Many data visualization tools also offer the ability to download, share and embed visualizations, which gives organizations a way to provide internal and external parties greater visibility into performance.

There are a wide range of data visualization solutions on the market available to buyers. We’ve created this guide to help you better understand what data visualization solutions are, the benefits they can offer your organization and what important considerations to keep in mind when selecting a system to purchase.

Here’s what we’ll cover:

What Is Data Visualization Software?

Data visualization software processes information from an organization’s databases and presents it in graphical form. These visualizations range from simple charts and graphs to complex flow charts and geographical maps. Many of these visualizations are interactive, allowing users to adjust certain settings and see the impact in real-time.

Visualization data

Geographical data mapping using Tableau

Features of Data Visualization Software

The breadth and depth of data visualization tools will vary depending on the type of solution. A simple stand-alone data visualization tool, for example, will likely offer fewer and less robust features than a tool that is part of a fully integrated business intelligence solution that includes multiple other applications for data reporting, predictive analytics, data mining, and so on. Regardless, most data visualization software usually offers some or all of the following functionality:

Benefits of Data Visualization Software

Data visualizations enable users to more readily analyze information, gain insights and discover trends. This makes users more efficient because they spend more time asking questions and getting answers, and less time interpreting numbers and creating graphics.

Here are a few ways that data visualization software can assist users in gaining insights from their data:

Geographical maps. Mapping data can help users discover geographical trends that may have gone unnoticed in traditionally analyzed data sets. Users can utilize these visualizations to pinpoint geographic strengths and weaknesses and find ways to address them.

Heat maps. Heat maps are 2D data visualizations that use colors to represent different values in order to provide users with a quick and easy way to understand how these values are performing relative to one another. They are similar in nature to fractal maps and tree maps, all of which use different colors to represent a hierarchy of variables. They can be particularly helpful when analyzing how users interact with web pages. For example, a heat map that analyzes web page engagement might show areas of the page that users spent the most time on in colors such as red, orange and yellow, while the areas of the page that had less user interaction with might appear in blue, green and purple shades.

Timelines. Plotting data in a time series is a common way for businesses to track success. With data visualization software, users can constantly adjust parameters to adjust both the metrics considered (e.g., revenue, profit or margins) as well as the relevant timeframe.

Workflow representations. These visualizations allow users to quickly understand the progress of a particular item within a project. By nesting individual workflows under multiple levels of a larger project, both frontline managers and executives can understand at a glance where particular activities stand.

Key Considerations for Buyers

You should consider the following when evaluating data visualization applications:

Do you need a stand-alone data visualization tool or an integrated BI application? Is more advanced BI functionality such as that included in predictive analytics and forecasting systems a necessity? If so, you should consider a BI application with integrated visualization, reporting and analysis functionality.

Is mobile BI on your roadmap? Mobile BI is one of the biggest trends in business analytics, and most vendors currently offer at least some functionality on mobile devices. Visualization applications will often require the larger tablet screen to be effective. If you plan on using mobile devices to access visualization applications, ensure that the vendors you evaluate provide support for popular tablets (e.g., Apple iPad and Android-based tablets).

Pricing: Web-Based vs. On-Premise

Buyers of data visualization software have the option to choose between web-based (also known as cloud-based) deployment or on-premise deployment.

Systems using a cloud-based deployment model deliver Software-as-a-Service (SaaS); this is also known as web-based software. In this model, the software is hosted (meaning housed, served, maintained and delivered) remotely, on the vendor s or service provider s own servers. It is then accessed by users through any device that is both compatible with the system and has an internet connection, which gives users the ability to use the tool anywhere, anytime.

With on-premise deployment, the software system is hosted in-house, on the user s own servers. Data in the database and all associated applications are housed and run locally, and users may or may not be able to access the system remotely (depending on whether or not the system is compatible with mobile devices and remote computers).

While larger companies will often have the IT resources to host a solution in-house, this can be a costly option for smaller companies. Smaller organizations may thus want to consider cloud-based solutions, which are usually faster and easier to implement and are less costly to manage and update. However, cloud-based solutions may not include the type of robust functionality that a more comprehensive, on-premise BI solution might.

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