4 Must-Haves in Setting Up a Business Email Like an IT Pro

Email haters, beware! A recent survey has proven that email is the top tool used for communication at work and will increase in importance during the next five years. The survey also showed that employees spend 3.2 hours each day checking work-related emails. A separate study by The Radicati Group declared that an average of 122 business emails were sent and received per user per day last year.

What does all this data mean? It’s a good reminder that email is, and will continue to be, a mission-critical application for any business—whether small or large. When setting up a business email solution, there are many factors to consider, especially if yours is a small or midsized company targeted to grow in the next few years.

Setting up a business email like an IT pro

To find the best email service for your business, only consider those that meet the following must-have criteria:

  1. Hosted email solution—There are two main options for business email setup: in-house and hosted email. For many businesses, the total cost of ownership related to in-house email does not make good financial sense. It might mean imposed storage limits, which are inconvenient to your employees, and it translates into your company being responsible for upgrades and backups.
    However, when you use a hosted email solution, email hosting services take the burden of operation from your IT team’s shoulders. Hosted email service for business users also can provide dynamic storage options and scale to meet your company where it is in its growth cycle.
  2. Adequate server space—Depending on how large your company is and how much email storage it needs, you will need to choose between shared and dedicated hosting. Many small companies start with shared hosting—which means your physical server is used by multiple tenants—because it is extremely affordable and still provides all the benefits of email hosting services, including security, reliability and privacy. When that shared server space is no longer enough, companies can scale up to dedicated email servers for business. There is often more customization available with a dedicated solution too.
  3. Mobile access—In today’s fast-paced business world, compatibility between your email solution and your workers’ mobile devices is crucial. Not only is it necessary for your employees to be able to access their business emails when away from their desks, it’s also important that you consider an email solution with mobile calendar and document sharing capabilities. In addition, make a selection that is consistently upgraded for mobile technology and allows for syncing with other devices.
  4. A solid provider—When selecting an email service for business purposes, the “who” is just as important as the “what.” Choose a company you can trust, with years of proven experience providing superb email hosting services. Specifically, look for a provider with a strong uptime guarantee, high reliability and fantastic security. The provider’s email solution should make your employees’ jobs easier, including features such as integration between the calendar, contacts and files; a shared calendar feature; and 24/7 support from experts.

It’s fairly evident that email isn’t going anywhere anytime soon. Organizations should take great care when setting up a business email solution in order to increase efficiency and have a positive effect on the bottom line.

Let Atidan help your business flourish in the email world. For more information, contact us at office365@atidan.com.

Reference: https://blogs.office.com/2016/09/08/4-must-haves-in-setting-up-a-business-email-like-an-it-pro/

Microsoft Power BI Launched – Bring Your Data to Life

Atidan is pleased to be a part of the business intelligence revolution – for over ten years we have worked with powerful visualization tools starting with the early versions of SQL Server and many third party tools. Now, it is even easier than ever to collect data from almost ‘anywhere’, create ‘stories’ from your data, create insights with simple drag-and-drop gestures, and share your dashboards and metrics internally and externally. Contact us today to see the latest release of Microsoft Power BI.
Microsoft Power BI is a collection of online services and features that enables you to find and visualize data, share discoveries, and collaborate in intuitive new ways. There are two experiences now available for Power BI: the new experience, generally referred to as Power BI, and the previous experience which is referred to as Power BI for Office 365.Power BI

The new experience is centered on PowerBI.com, an online service where you can quickly create dashboards, share reports, and directly connect to (and incorporate) all the data that’s important to you. The new experience also introduces the Power BI Desktop, a dedicated report authoring tool that enables you to transform data, create powerful reports and visualizations, and easily publish to Power BI. The new experience extends to all your mobile devices, too.

Note   If you were using the previous experience and want to migrate to the new experience, you can download this migration guide to understand and prepare for the migration process.

The following sections provide an overview of the new experience, along with introductions and links to learn more about the details and capabilities of each offering.

Power BI – the new experience

In the new experience, Power BI displays dashboards on the Power BI service that are interactive, and can be created and updated from many different data sources. Three elements are primary to the new experience:

In the new experience, you create dashboards that keep you informed about what’s most important about your business. Just like a dashboard in your car displays important information about your vehicle, such as its speed, its fuel level, or how healthy the engine is, dashboard in Power BI display important information about your business.

Dashboard in Power BIIn Power BI, dashboards display tiles that represent important information about your business. Tiles are based on reports (just like gauges are based on vehicle data from the engine, the fuel tank, or the battery). You can select a tile to explore more information from the underlying report.

Power BI - dashboard to reportThe new experience also introduces a dedicated report authoring tool:

With Power BI Desktop, you get a powerful and dedicated report authoring tool that enables you to connect to and combine data from lots of different sources, using Power BI Desktop’s Query Editor. From the datasets you build with Query Editor you can create rich reports and visualization within Power BI Desktop. And when you’re done, publishing to the Power BI service is easy.

Power BI DesktopThere’s a lot more to learn about the new experience, and plenty of content to help you . The following links provide information about the new experience, and start with overview information (to get your familiar, oriented, and comfortable) then move into specifics. A separate section is dedicated to the Power BI Desktop, and those links also go from overview to specifics.

Getting Started – the new experience

The following links provide guidance on the new experience in Power BI.

Using Power BI Desktop

The following links start with an overview of Power BI Desktop, then describe common tasks and capabilities that Power BI Desktop provides.

Fuzzy LookUp for Excel – Prelease Download – Identify Textually Similar Data

Microsoft Fuzzy Lookup Add-In for Excel

A challenging problem in data management is that the same entity may be represented in multiple ways throughout the dataset. For instance, customer “Andy Hill” might also be present as “Mr. Andrew Hill” or “Hill, Andrew R.”. Variations can result from merging independent data sources, spelling mistakes, inconsistent naming conventions and abbreviations, or records with additional/missing information.

Microsoft Fuzzy Lookup technology, developed by Microsoft Research, allows you to quickly identify data records which are textually similar. You can identify fuzzy duplicates within a single table or perform a fuzzy join between two different tables. The default configuration works well for a wide variety of data, but the matching may also be customized for specific domains.

Fuzzy Lookup for Excel Beta - Atidan


System Requirements

The add-in works on any Microsoft Windows operating system with Microsoft Excel 2010 or newer installed.

Installation Steps

Close any open instances of Excel. Uninstall any existing versions of Microsoft Fuzzy Lookup Add-in For Excel using the Windows control panel. Navigate to http://www.microsoft.com/en-us/download/details.aspx?id=15011, then download and run Setup.exe to launch the installation wizard.

If Setup.exe is run with administrator privileges, it will be installed for all users on the machine. It will be installed only for the current user otherwise.

Once successfully installed, launch Excel and you should see a Fuzzy Lookup tab in the Excel Ribbon bar.

See Portfolio Sample section for an introduction on how to use the add-in.

Installation Folder

The add-in, documentation and samples will be installed to the folder:

If the installer is run without administrator privileges,

%LOCALAPPDATA%\Microsoft\Fuzzy Lookup Add-In For Excel\         %LOCALAPPDATA% is typically C:\Users\%USERNAME%\AppData\Local

If the installer is run as administrator on a 32-bit operating system,

%ProgramFiles%\Microsoft\Fuzzy Lookup Add-In For Excel\         %ProgramFiles% is typically C:\Program Files

If the installer is run as administrator on a 64-bit operating system,

%ProgramFiles(x86)%\Microsoft\Fuzzy Lookup Add-In For Excel\         %ProgramFiles(x86)% is typically C:\Program Files (x86)

Portfolio Sample

This section describes how to use the Fuzzy Lookup Add-In for Excel with the spreadsheet Portfolio.xlsx which is located in the installation folder.

Imagine you have a stock portfolio described by two columns Company and Shares and that you are interested in computing the average price/earnings (P/E) ratio of the companies in the portfolio. To do this, you need to join your portfolio table with another table containing P/E ratios. The spreadsheet contains a second tab called SP500 which contains company data imported from the stock screener at the http://finviz.com website. Looking at the data, one can see that an exact join on the Company columns of the two tables would fail as the string representations of the companies differ (e.g., “AMAZON COM INC STK” and “Amazon.com Inc.”).

A fuzzy join of the two tables can be performed as follows:

  1. Turn the each data range into an Excel table by selecting a region and pressing CTRL-L. You can assign a name to the table clicking on it and selecting the Design tab in the Excel ribbon.
  2. Open the Fuzzy Lookup pane by clicking on the Fuzzy Lookup button in the Fuzzy Lookup tab of the Excel ribbon.
  3. Pick the left and right tables from the drop down menus. Matching rows from the right table will be returned for each row in the left table.
  4. Select the columns to match on. If the two tables share one or more column names in common, a default join will already have been added. If you wish to match on different columns, first delete the existing join by pressing the “X” button on the join row in the Match Columns table. To create a new column binding, select one or more columns from each table (multiple columns may be selected by holding down SHIFT or CTRL and click on the column names). Next, press the button in between the two lists of columns to add a row to the Match Columns table.
  5. Select one or more output columns to be output for each match.
  6. Select the maximum number of matches to be returned for each left row.
  7. Set the similarity threshold. All matches returned must have a similarity greater than or equal to this value.
  8. Move the current cell selected in the Excel spreadsheet to an empty cell which has empty space to the right and below it. The Fuzzy Lookup matches will be output starting at this cell.
  9. Press the “Go” button to perform the match.

One should see the results as indicated in the screenshot above. Notice that each returned match includes a similarity score indicating how close the two records are. 1.0 means an exact match while lower scores indicate less similarity.

Note that Fuzzy Lookup can also be used to identify matches in a single table by setting the left and right tables to be the same.

Advanced Concepts

Fuzzy Lookup technology is based upon a very simple, yet flexible measure of similarity between two records.

Jaccard similarity

Fuzzy Lookup uses Jaccard similarity, which is defined as the size of the set intersection divided by the size of the set union for two sets of objects. For example, the sets {a, b, c} and {a, c, d} have a Jaccard similarity of 2/4 = 0.5 because the intersection is {a, c} and the union is {a, b, c, d}. The more that the two sets have in common, the closer the Jaccard similarity will be to 1.0.

Weighted Jaccard similarity and tokenization of records

With Fuzzy Lookup, you can assign weights to each item in a set and define the weighted Jaccard similarity as the total weight of the intersection divided by the total weight of the union. For the weighted sets {(a, 2), (b, 5), (c, 3)}, {(a, 2), (c, 3), (d, 7)}, the weighted Jaccard similariyt is (2 + 3)/(2 + 3 + 5 +7) = 5/17 = .294.

Because Jaccard similarity is defined over sets, Fuzzy Lookup must first convert data records to sets before it calculates the Jaccard similarity. Fuzzy Lookup converts the data to sets using a Tokenizer. For example, the record {“Jesper Aaberg”, “4567 Main Street”} might be tokenized into the set, {“ Jesper”, “Aaberg”, “4567”, “Main”, “Street”}. The default tokenizer is for English text, but one may change the LocaleId property in Configure=>Global Settings to specify tokenizers for other languages.

Token weighting

Because not all tokens are of equal importance, Fuzzy Lookup assigns weights to tokens. Tokens are assigned high weights if they occur infrequently in a sample of records and low weights if they occur frequently. For example, frequent words such as “Corporation” might be given lower weight, while less frequent words such as “Abracadabra” might be given a higher weight. One may override the default token weights by supplying their own table of token weights.


Transformations greatly increase the power of Jaccard similarity by allowing tokens to be converted from one string to another. For instance, one might know that the name “Bob” can be converted to “Robert”; that “USA” is the same as “United States”; or that “Missispi” is a misspelling of “Mississippi”. There are many classes of such transformations that Fuzzy Lookup handles automatically such as spelling mistakes (using Edit Transformations described below), string prefixes, and string merge/split operations. You can also specify a table containing your own custom transformations.

Jaccard similarity under transformations

The Jaccard similarity under transformations is the maximum Jaccard similarity between any two transformations of each set. Given a set of transformation rules, all possible transformations of the set are considered. For example, for the sets {a, b, c} and {a, c, d} and the transformation rules {b=>d, d=>e}, the Jaccard similarity is computed as follows: Variations of {a, b, c}: {a, b, c}, {a, d, c} Variations of {a, c, d}: {a, c, d}, {a, c, e} Maximum Jaccard similarity between all pairs: J({a, b, c}, {a, c, d}) = 2/4 = 0.5 J({a, b, c}, {a, c, e}) = 2/4 = 0.5 J({a, d, c}, {a, c, d}) = 3/3 = 1.0 J({a, d, c}, {a, c, e}) = 2/4 = 0.5 The maximum is 1.0. Note: Weghted Jaccard similiary under transformations is simply the maximum weighted Jaccard similarity across all pairs of transformed sets.

Edit distance

Edit distance is the total number of character insertions, deletions, or substitutions that it takes to convert one string to another. For example, the edit distance between “misissipi” and “mississippi” is 2 because two character insertions are required. One of the transformation providers that’s included with Fuzzy Lookup is the EditTransformationProvider, which generates specific transformations for each input record and creates a transformation from the token to all words in its dictionary that are within a given edit distance. The normalized edit distance is the edit distance divided by the length of the input string. In the previous example, the normalized edit distance is 2/9 = .222.

Technical resources

For more technical details on Fuzzy Lookup, see the following resources:

Microsoft Research Data Cleaning Project

Transformation-based Framework for Record Matching

Efficient Exact Set-Similarity Joins

Help and Support

The Fuzzy Lookup Add-in for Excel is pre-release software and no support is officially provided by Microsoft.

A forum for questions about the add-in is available here: http://blogs.msdn.com/b/business_intelligence_labs/archive/2011/04/27/fuzzy-lookup-add-in-for-excel.aspx

Questions and comments may also be sent to dcrt@microsoft.com

This document is provided “as-is”.  Information and views expressed in this document, including URL and other Internet Web site references, may change without notice. Some examples depicted herein are provided for illustration only and are fictitious.  No real association or connection is intended or should be inferred.  This document does not provide you with any legal rights to any intellectual property in any Microsoft product. You may copy and use this document for your internal, reference purposes.

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Fuzzy Lookup Add In for Excel

Fuzzy Lookup Add-In for Excel

The Fuzzy Lookup Add-In for Excel was developed by Microsoft Research and performs fuzzy matching of textual data in Microsoft Excel. It can be used to identify fuzzy duplicate rows within a single table or to fuzzy join similar rows between two different tables. The matching is robust to a wide variety of errors including spelling mistakes, abbreviations, synonyms and added/missing data. For instance, it might detect that the rows “Mr. Andrew Hill”, “Hill, Andrew R.” and “Andy Hill” all refer to the same underlying entity, returning a similarity score along with each match. While the default configuration works well for a wide variety of textual data, such as product names or customer addresses, the matching may also be customized for specific domains or languages.