Strong Demand for Excel Skills

Source: Indeed.com

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Tabular versus Multidimensional Modeling

According to Microsoft TechNet (2012), the distinction between tabular versus multidimensional modeling is operationally significant for analysts:

Multidimensional modeling, introduced with SQL Server 7.0 OLAP Services and continuing through SQL Server 2012 Analysis Services, enables BI professionals to create sophisticated multidimensional cubes using traditional online analytical processing (OLAP).

Tabular modeling, introduced with PowerPivot for Microsoft Excel 2010, provides self-service data modelling capabilities to business and data analysts. The tabular modeling experience is more accessible to these users, many who have spent years working with data in desktop productivity tools like Excel and Microsoft Access. In SQL Server 2012, tabular modeling has been extended to enable BI professionals to create tabular models in Analysis Services or to import a tabular model from PowerPivot into Analysis Services. Note that a PowerPivot model cannot be imported into an Analysis Services multidimensional model.

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Business intelligence (BI) analysts who have not yet done so will want to become conversant about PowerPivot for Excel, PowerPivot for SharePoint Services, Analysis Services Tabular, and Analysis Services Multidimensional. Follow the link below to learn more.

Source: Raja, N (2012, May 3). Choosing a Tabular or Multidimensional Modeling Experience in SQL Server 2012 Analysis Services. Microsoft TechNet.

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Tatsuo Horiuchi: Excel Spreadsheet Artist

Meet Tatsuo Horiuchi, the 73-year old computer artist who uses Excel (Microsoft) as his palette to create world-class artworks.

Tatsuo Horiuchi (1940- ) standing behind some of his many Excel creations

Follow the link below to learn more about Tatsuo Horiuchi's life and artistic endeavors, as well as to download original Excel files of his acclaimed works.

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Below is an example from Tatsuo Horiuchi's Excel files.

"Cherry Blossoms at Jogo Castle" by Tatsuo Horiuchi (2006)

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Quandl Has Arrived

Looking for "big data" sources? Quandl enables searches of over 5,000,000 financial, economic, and social datasets made available by data contributors worldwide. Morover, every Quandl dataset is available for direct download via Python, Stata, Excel, R, as well as embeddable as a graph on any website. According to Quandl's creators:

Quandl has indexed over 5 million time-series datasets from over 400 sources. All of Quandl's datasets are open and free. You can download any Quandl dataset in any format that you want. You can also visualize, save, share, authenticate, validate, upload, index, merge and transform data. Our long-term goal is to make all the numerical data on the internet easy to find and easy to use.

Establishing a Quandl user account is free and easy. I tested downloading Quandl datasets using the Excel add-in and R package accessories readily available for download from the Quandl website -- both worked perfectly.

Quandl has arrived...

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Regarding Analytics Software

According to Dr Abhijit Dasgupta of R-bloggers (2013, March 26):

The truth is, very few of us data geeks (data scientists, data analysts, statisticians, or what ever we call ourselves use only a single tool for all of our work. We will often extract data from a SQL database, munge it using Perl or Python, and then do statistical analysis using R or SAS, reporting the results using Word or, increasingly, the web. Specially for data analysis, there is often no single tool that can do the end-to-end workflow well, however much we would like to believe that there is. Each tool has its strengths and weaknesses, and often a mixture works best. The trick is in finding the right “glue” that can string our workflow together.

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RStudio console on my computer

The analytics applications I personally use most include Excel (Microsoft), Decision Tools Suite (Palisade), ModelRisk (Vose), Crystal Ball (Oracle), XLStat (Addinsoft), R (Project R), RStudio (RStudio), Visual Basic for Applications (Microsoft), and Python (Python Software Foundation).

Source: Dasgupta, A (2013, March 2013), Python vs R vs SPSS -- Can’t All Programmers Just Get Along? R-bloggers.

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Business Intelligence Process Integration via Knime

The business intelligence (BI) production path integrates data access, transformation, analysis, visualization, and exploitation into a unified process. Knime (Konstanz Information Miner) is a professional open-source software package that integrates all of these functions onto a single platform. According to the Knime website:

Knime, pronounced [naim], is a modern data analytics platform that allows you to perform sophisticated statistics and data mining on your data to analyze trends and predict potential results. Its visual workbench combines data access, data transformation, initial investigation, powerful predictive analytics and visualization. Knime also provides the ability to develop reports based on your information or automate the application of new insight back into production systems.

Knime is supported by an expanding number of third-party extensions that enable interfacing with Excel (Microsoft), R (Project R), BIRT (Eclipse), WEKA (University of Waikato), and more. Expand the graphic below to see how Knime integrates various functions and processes via its discrete process simulation features.


Knime Function Nodes [click to enlarge]

Follow the link below to learn more about Knime and its powerful BI production features for enterprise.

Knime

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Knime for Business Intelligence

For business analysts and firms seeking to expand their skills and capabilities from localized analytics into the broader realm of business intelligence processes and solutions, check out Knime. According to Knime's website:

Knime (Konstanz Information Miner) is a user-friendly and comprehensive open-source data integration, processing, analysis, and exploration platform. From day one, Knime has been developed using rigorous software engineering practices and is used by professionals in both industry and academia in over 60 countries.

Knime delivers robust features that encompass the full spectrum of business intelligence production requirements, including tools for: a) integrating multi-source data via open database connectivity (ODBC) and real-time processes; b) diverse analytic tools for data mining such as clustering, decision trees, rule induction, neural networks, association rules, scoring, meta-analysis, and more; and c) state-of-the-art presentation tools that easily integrate with existing ad-hoc reporting, automated dashboard, and systems actuation platforms. Knime is also actively supported by third-party extensions that integate Knime with R (Project R), Excel (Microsoft), and other widely-used integration, analytics, and presentation platforms.

Knime is the missing application that analysts have long-sought to enable self-service production of business intelligence. Anyone seeking to understand and manage the entire business intelligence production process will find Knime to be didactically useful.

Follow the link below to learn more.

Knime

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A Primer on PowerPivot Topology and Configurations

Analysts who use Excel (Microsoft) with PowerPivot (Microsoft) will find the presentation below interesting.



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NodeXL: Open-Source Network Graphing for Excel

Introducing NodeXL, an open-source template for Excel (Microsoft) 2007 and 2010 that makes it easy to explore network graphs. With NodeXL, you can enter a network edge list in a worksheet, click a button and see your graph, all in the familiar environment of the Excel window. Follow the link below to download a copy and learn more.

NodeXL Download Page

The business intelligence tools for Excel just keep getting better. The NodeXL project is sponsored by the Social Media Research Foundation, a group of researchers dedicated to creating open tools, generating and hosting open data, and supporting open scholarship related to social media.

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Subject Matter Experts Prefer Excel

Charley Kyd of ExcelUser writes that subject matter experts (SME's) prefer Excel (Microsoft) over third-party alternatives for the following reasons:

1. Excel allows SME's to begin with their own assumptions, and then build on them. Excel doesn’t force SME's to adapt to a system created by anonymous programmers on a tight schedule.
2. Excel offers the ability to create more sophisticated models than most third-party packages offer.
3. SME's typically help their careers more by learning Excel well than by occasionally using one of dozens of competing budgeting and analytical packages.
4. Small companies and divisions only can afford Excel. And that represents the majority of locations where budgeting happens.
5. Several excellent tools exist for enhancing Excel for budgeting and forecasting, rather than replacing it.
6. In this challenging economy, forecasts and budgets often should include data from beyond a company’s four walls. But most third-party tools rely on data controlled by the IT Department, which typically prohibits most third-party data.
7. Excel is more flexible than dedicated budgeting and forecasting software.

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For the record, this SME has no intention of abandoning Excel anytime soon, if ever...

Source: Kyd, C (2012, February 18), Will Your Company Replace Excel with a Dedicated Budgeting Program? ExcelUser.

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Death of the Star Schema?

by Tom Gleeson of Gobán Saor

With the release of the next version of PowerPivot (Microsoft) around the corner (mid March I think), I’ve been re-acquainting myself with its new features. Most of the current version’s annoyances have been remedied (no drill-thru, no hierarchy support for example); and the additional enhancements to the DAX language (crossjoins, alternate relationships etc) make modeling most any problem possible (and generally easy).

Death of a Star

The more I come to know PowerPivot, the more I believe that modeled data warehouses' days are numbered. I didn’t say data warehouses per se, rather those that attempt to centrally model end user reporting structures (usually as star-schemas).

There will continue to be a need for centrally controlled data warehouses (or at least simplified data views (and/or copies) of operational datasets, either provided by system vendors of by in-house IT) to bridge the raw-to-actionable data gap. But I suspect the emphasis will change from providing finished goods to providing semi-processed raw materials.

So, will the star-schema become redundant? No, as it’s still a valid method of modelling a reporting requirement in order to make many queries simpler to phrase (this obviously applies to SQL , but also to DAX queries). But, those who build them will be doing so closer to the problem at hand, and specific to that problem (I’ve discussed this before in Slowly Changing Dimensions: Time to Stop Worrying).

For many reports the barely modified operational data model will be all that’s required (for example, DAX doesn’t require “fact” header/detail tables to be flattened to detail level, as would be the case with a classic star).

“Good Enough” models will become the norm; classic “Everything You Ever Wanted to Know” centralised models a luxury for most (especially as such models tend to “age” very quickly).

If you’re about to invest or re-furbish your data warehouse or your reporting data sub-systems, don’t do so without first taking a serious look at PowerPivot. This is a game-changer, not just for full-stack Microsoft BI shops, but for any business that finds that their reporting datasets invariably end-up in Excel.

If you need any help evaluating PowerPivot or modeling your reporting needs in PowerPivot, I’m for hire.

Source: Gleeson, T (2012, January 19), Death of the Star Schema? Gobán Saor.

Republished with kind permission of Tom Gleeson of Gobán Saor

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Understanding US Debt via Excel + PowerPivot

For those interested in gaining a deeper understanding of the US national debt, I commend Tyler Chessman's new book, Understanding the United States Debt (2011) and its companion website. Excel (Microsoft) and PowerPivot (Microsoft) users in particular will want to follow the link below to download a free copy of the PowerPivot-enabled Excel workbook (requires Excel 2010 with PowerPivot installed).

Companion Website

To order a copy of the book, follow the link below.

Order Book

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Five Attractions of OLAP for Excel Users

Online analytic processing (OLAP) is attractive to Excel (Microsoft) users for five major reasons:

1. OLAP offers one version of the truth
2. Fast report development
3. Immediate spreadsheet updating
4. Independence from IT
5. Reduced errors

Follow the link below to learn more about why Excel users are attracted to OLAP.

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Self-Service Business Intelligence with Excel 2010

For those who have not yet taken a moment to experience the new self-service business intelligence features in Excel (Microsoft) 2010, here's a great overview of those capabilities in action.



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Federal Reserve Economic Data (FRED) for Excel

The Federal Reserve Bank of St Louis has released a new Federal Reserve Economic Data (FRED) add-in accessory for Excel (Microsoft). The FRED add-in provides direct access to over 30,000 free data series from a variety of sources (e.g., BEA, BLS, Census, & OECD) directly from Excel. Other key features include:

• One-click instant download of economic time series.
• Browse the most popular data and search the FRED database.
• Quick and easy data frequency conversion and growth rate calculations.
• Instantly refresh and update spreadsheets with newly released data.
• Create graphs with NBER recession shading and an auto update feature.

The video below provides a quick summary of features.



Follow the link below to download a copy of the FRED add-in software or to learn more.

FRED

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History Reimagined: Apollo 11 (in Excel)

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Excel + ModelRisk: Interpolation Calculations

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Excel + ModelRisk: Subject Matter Expert Time Series Modeling

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Excel + ModelRisk: Wilkie Models

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Excel + ModelRisk: Multivariate Time Series Modeling

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