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## Client Description
The Gap, Inc. is an international specialty retailer operating retail and outlet stores. The Company sells casual apparel, accessories, and personal care products for men, women, and children. The firm operates about 3,200 stores worldwide, including 450-plus franchise stores across Asia, Australia, Eastern Europe, Latin America, the Middle East and Africa.

## Challenge
The Gap wanted to retire a legacy data mart, named Mars, which had sales, inventory, promotions, and plan data.  They wanted an independent, technology-agnostic partner, who would help them craft a durable strategy, that would scale, with positive ROI.

## Solution
iOLAP carried out the evaluation of business benefit compared to the cost involved, requirements gathering, mapping, design, development, testing, deployment & warranty support.  As usual, the entire project was based on agile methodology. iOLAP built new semantic tables to meet the reporting needs and retrofitted outbound extracts.

## Benefit
The major highlight of this project was the PUB/SUB architecture, which reduced the load on Teradata, and thus resulted in savings of millions of dollars for The Gap.
  
![center-small](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/gap_logo_de34498ac1.png)



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Implementation of PUB/SUB architecture, which reduced the load on Teradata, and resulted in savings of millions of dollars.

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## Client Description
Lucia Health Guidelines is the first clinical decision support tool that leverages machine learning and the power of cloud computing to help maintain clinical guidelines at the point of care. Lucia’s mission is to create a future with better care, better outcomes, and better economics for our healthcare system.

## Challenge
American Heart Association and the American College of Cardiology have made clear-cut guidelines based on scientific evidence and outcomes data for the treatment of atrial fibrillation (‘AF’, ‘Afib’) and prevention of stroke.  Yet, Xian MD and colleagues at Duke University (March 2017) reported that most patients with atrial fibrillation are not being treated as per the guidelines in the community setting.  In fact, they reviewed the charts of ~94,000 strokes victims with atrial fibrillations and 84% (~80,000 patients) did not receive the recommended anticoagulation as per the guidelines prior to their strokes.  Xian believes that an estimated 58,000 to 88,000 strokes might be preventable per year if the Afib treatment guidelines are followed.   As a country we spend about $6Billion a year on Afib/prevention of stroke and ~$83Billion a year on the long-term care of stroke victims.

Here is our opportunity to save lives, prevents strokes, and save tens of billions healthcare dollars by helping clinicians maintain these Afib guidelines.

## Solution
iOLAP designed and implemented a well-architected, HIPAA-compliant, solution that captures and analyzes EKG images using custom computer vision code. These EKG results are compared with patient data to determine a personalized risk assessment.

## Benefits
Lucia can deliver a scalable, fully-automated solution which brings the equivalent expertise of highly skilled clinicians trained in AF to any point of care. This will allow patients to be discharged with optimal therapy and referral to a specialist for continued care.
  
![center-small](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/1597676328115_1d7141685f.jpeg)



Solution creation that captures and analyzes EKG images using custom computer vision code. These EKG results are compared with patient data to determine a personalized risk assessment.

Lucia Health Guidelines

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## Client Description
This undisclosed client is a major American manufacturer of plumbing fixtures under multiple brands, including bath and shower fixtures, faucets and toilets.  


## Challenge
The client needed to democratize enterprise data, which was hindered by legacy environments, hardware, and processes; omnipresent manual efforts and tribal knowledge within the organization. Also, the client wanted to modernize their data processing by developing a unified and accessible home for internal and 3rd-party data, as well as allowing future ingestion of data sources with minimal effort. 3rd-party data was especially problematic since schema changes were applied frequently and without advance notice.  


## Solution
iOLAP engaged with the client to address data needs throughout the organization and to modernize their experience with data by building a modern Analytics solution, following best practices and leveraging cutting-edge Azure services. The solution was responsible for extracting, shaping, validating, and processing input data to the Data Warehouse, Data Lake, and/or Delta Lake for more than 60 data sources in a variety of formats and velocity. A critical feature included the ability to detect and alert schema drift scenarios of key fields identified by the business stakeholders. The solution supported three different methods for data ingestion (SharePoint, API and e-mail attachment), while users were empowered by a self-service reporting and dashboarding functionality via Power BI, and discovery and governance via Azure Purview.  


## Benefits
The benefits of the new analytics solution were numerous - it is used across the organization, ready to support most critical analytical queries, while providing even greater value to our client’s customers. For a company that produces substantial amounts of data from a large number of sources, allowing minimal effort ingestion of data sources and gaining a deep insight into datasets, allows them to drive their business to the new level by making data-driven decisions while developing new and exciting products. In addition, by facilitating deprecation of outdated and manual processes as well as sunsetting legacy systems, significant cost savings were incurred.


Custom solution for extracting, shaping, validating, and processing input data for more than 60 data sources in a variety of formats and velocity.

Major US Plumbing Fixtures Manufacturer

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We leverage modern Microsoft products and Azure services to create cutting-edge solutions tailored to our clients' needs that drive innovation and differentiated business outcomes.

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Your Excel users do important work. Your Excel “Power-Users” do inspiring work. They may not know it yet, but they both are primed to elevate your business insight capabilities using what they already know. This is because Excel’s most powerful features have been re-imagined with scale in mind in Microsoft’s Power BI Service, and your Excel Power Users are surrounded with use cases ready to make the jump. You just have to ask them.

In this article we will first draw a direct line between Power BI’s feature set, and their Excel origins, as a way of explaining where your Excel-to-Power BI use cases begin. We will then share an iOLAP client story where interviewing one Excel Power-User led to an opportunity to eliminate hundreds of scheduled and ad-hoc reports with one Power BI Solution. We’ll close by examining what are the factors behind “Power BI Project Readiness” and why your Excel Power-Users are uniquely equipped to contribute.
  
If your Excel Power-Users are at all familiar with the Excel features known as Power Pivot and Get & Transform (also known as Power Query), then they already have experience with Power BI’s core feature set and the languages (DAX and M) that power them! Power BI, released to the public in 2015, was essentially spun off from these “Power” features in Excel 2013*, which added significant database and graphical capabilities to Excel’s ever-expanding footprint. Despite this humble beginning, Power BI has gone from being a catch-up software in the race for augmented analytics technology, to becoming the Microsoft’s keystone to realizing a full-fledged platform for Analytics and BI within the Office ecosystem. In fact, Microsoft has taken a commanding lead both in terms of vision and ability-to-execute in this space, as awarded by Gartner’s Magic Quadrant[1].
  
So where do Power BI’s strengths begin, and Excel’s end? It’s first important to recognize what role Excel plays to a Power User. A keen comparison could be drawn, of all places, from the professional kitchen; namely, the chef’s knife. Most cooks wouldn’t trade their chef’s knife for anything, because it’s an extension of themselves that has rewarded their mastery of technique. Try to sell them on a slice-and-dice mandolin, a complicated food processor, or even a handy garlic press, and they may respond with surprising methods to do the same thing with no more than a cutting board and a knife in hand. So too is the relationship between software and Power-User. For daily driving, rapid prototyping and small-to-medium scale data volumes, Excel can become great when applied with skill. Where Excel falls short is when those same features become fickle and easily broken, thus disrupting flow. A tool that dared to enter their daily routine must broaden their capabilities without snuffing out what makes them great in their original skillset. For our cook it might be a stand mixer that can knead bread and churn ice cream unattended, thus giving back their time and saving their shoulders from injury. For the Excel user, how about a beautiful mobile dashboard without gimmicks, that replaces the daily tedium of fixing a PivotChart on SharePoint Excel Services, thus saving them grief from their boss? What if they could leverage their core competencies into big accomplishments?
  
![2.jpg](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/2_18bb172508.jpg)
  
Where the real strength of Power BI comes from, is in those features that may not even occur to an Excel Power-User, who may be used to employing their ingenuity with VBA to solve complex problems. An iOLAP client story tells us about the value of consulting directly with Excel Power-Users and helping them to interpret use cases that exceed their original articulation of the root problem. A fast-casual restaurant chain contracted iOLAP to develop a Power BI solution to replace their previous system in a one-for-one feature parity in order to enjoy the benefits of improved visualizations and ease of use. The universe of users was small: VPs, District Managers (DMs), and one lone financial analyst. Upon iOLAP’s unveiling of the first iteration of the solution, the analyst spoke up with a previously undisclosed need: they needed the ability to export large amounts of data in a format identical to their prior system, in order to use a VBA script to generate individualized store-level reports catered to each General Manager (GM). That same VBA script would then email the reports to the GMs, one by one.

On further examination, the root cause emerged: GMs were expected to see only their own store’s data, not others. Similarly, the DMs were allowed only to see information for their own district; they frequently made ad-hoc requests as well, which had to be carried out manually by the analyst. Because Excel lacks any meaningful security (especially at the row level), no one report could be exposed to all of the Managers, even in a web context. Stated plainly, there’s no good way in Excel to filter data down to what one user is supposed to see, while preventing them from also seeing others’ data.

iOLAP would soon propose and develop a solution using Power BI’s RLS (Row-Level Security) feature. An administrator (the analyst) would be able to assign users and groups to roles in the Power BI service; they can then write DAX expressions (similar to Excel functions) that dynamically expose data based upon which user is active. Now the GMs could be brought on to Power BI services and presented with up-to-date dashboards with exactly the data they’re meant to see. Furthermore, the analyst could interpret ad-hoc requests from DMs into design-once/use-forever dashboards. And yes: Excel extracts would still be available to every user, only this time as a self-service feature in Power BI. All this meant that the client’s KPI distribution strategy was no longer tied to VBA scripts; instead a robust business intelligence framework would take its place, with an Excel Power-User-turned-Power BI Superhero analyst at the helm.
  
![factors.jpg](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/factors_f0accb84c9.jpg)
  
It’s no mistake that consulting with an Excel Power-User was so fruitful, and their transition to Power BI so promising. Because they forge their skills on the frontiers of their organization, where business logic is being codified and data wrangling is an artform, their work makes possible the determination of BI Project Readiness[2]. Their best workbooks are living case studies, complete with feasibility assessment with respect to data availability and quality. Most importantly, they are vital partners for championing progress because they can speak to what’s to be gained – time saved, risk reduced, and much more.

Power BI presents a clear on-ramp for your Excel Power-Users to level up their capabilities, not only in a hands-on sense but also as a stakeholder in broader analytics projects. Because so many of Excel’s features map directly forward to Power BI, they’ll often find their existing workbooks are prototypes for Power BI’s dashboards. And because Power BI provides a best-in-class analytics platform, the lessons learned from using it will make them better Excel users in turn.

* This is an abridged telling. Those “Power” features trace back to SSAS (SQL Server Analysis Services). In fact, Power Pivot is literally an embedded version of SSAS within Excel.

In this article we will first draw a direct line between Power BI’s feature set, and their Excel origins, as a way of explaining where your Excel-to-Power BI use cases begin.

Turn your Excel Power-Users into Power BI Superheroes

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One of the top innovative visualizations in use today is the stream graph, according to visme.co. A stream graph is a variation of a stacked area graph without a fixed axis, allowing the user to view the data as if it is a “stream” of data showing how large or small the data flows over time. Stream graphs can take a simple view of data and show at a glance when the largest and smallest flows occurred. When it is broken out by groups a user can determine how much of that flow is made up by one group as opposed to another.
  
The New York Times published an example of this visualization for box office receipts for movies from 1986 to 2008 which can be viewed here.
  
As an example of how this visualization can be viewed, we look at box office receipts by week for movies released in 2019 through Power BI below. We used the Stream Graph from the marketplace and loaded it to the Power BI service.
  
Below are the steps taken:
  
1. Source data from boxofficemojo.com and export to a .xlsx file
2. Start a new Power BI Desktop file and select Get Data
3. Select File ⇨ Excel ⇨ select the file from file explorer and select Open
4. In the Navigator window select the Sheet containing the data. We can either Load the data as is at this step or select Transform to make manipulations to the data
5. After loading the data, we can select Edit Queries in the menu ribbon to make modifications such as creating dates from datetime columns, transform columns, etc.
6. If not already available import the Steam Graph visualization from the marketplace or file if downloaded 
- Select the ellipses from the Visualizations section and select Import from marketplace/file and search for the visualization in the marketplace or the visualization file
7. Drag columns to the Category, Group, and Values areas for the visualization
  
From here, we can make any further manipulations such as titles, legends, and colors.
  
Through this visualization, we can spot trends immediately. From the end of April through May we can see how much Avengers: Endgame dominated the box office, and how high those box office totals sustained throughout the weeks after release. Similarly, at the end of the year Star Wars: The Rise of Skywalker dominated the box office. The view from this stream graph gives the user an easy analysis of the ups and downs of the year, from the lull throughout the first of the year and through the peaks and valleys of the major releases. With this type of analysis, studios can determine which months they want to release certain movies, whether indie movies needing room to breathe or blockbusters looking to dominate the mindshare of the viewing public.
  
What was interesting to find was that there are not any native Stream Graph visualizations for two of the other big data analysis players: MicroStrategy and Tableau. There are articles and D3 third party visualizations available, but nothing provided by the tools themselves. For basic needs in any other tools we would need three components: a date field, a value field, and a category field. Setting the dataset to that structure will allow us to produce a stream graph in any tool.
  
This type of visualization has many use cases across varying industries. Any companies looking for trending analysis over different time periods (hour, day, year, etc.) should look to implement stream graphs into their dashboard portfolios.
  
![ar0tnA.png](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/ar0tn_A_d2f4f7aa3e.png)
  
By comparison here is a stacked area graph of the same data. We can see the visually appealing nature of the stream graph compared to the stacked area graph.
  
![oDaN5z.png](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/o_Da_N5z_1f64c772a5.png)
  
Data: boxofficemojo.com
Stream graph: [link]https://appsource.microsoft.com/en-us/product/office/WA104380772?src=office&corrid=49b0ff90-4513-4193-aa04-c22fdaa3daea&omexanonuid=946405eb-c96c-4ef6-9349-155e62f3d5a5&referralurl=http%3A%2F%2Fdataap.org%2Fblog%2F2017%2F04%2F14%2Fstreamgraph-visual-in-power-bi-vs-r%2F(link)

Stream graphs can take a simple view of data and show at a glance when the largest and smallest flows occurred.

Stream Graph – 2019 Box Office

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#### Introduction
  
Data is the most valuable asset of the 21st century. Lately, collecting and analyzing data became not only desired, but mandatory for any business’ success. In order to answer relevant questions and evaluate outcomes, companies often reach out to data analysts and BI specialists who turn data into cognizable information.
The challenge of data rests in the speed of its generation. According to Forbes, every day we produce 2.5 quintillion bytes of data which corresponds to 2.5 million terabytes, and the number grows daily. Although not all data is compatible for analysis, companies still end up with millions of records in a single table within a data warehouse. A large data warehouse will probably be accessed by numerous employees from different departments, from different countries around the globe, for various business intelligence operations. It is crucial to enable those employees ready access while still maintaining data integrity and security.
  
#### Restricting User Access
  
Aside from the physical security of a data warehouse and data encryption, there is another significant method used when securing data warehouses (and information systems in general) – logical access control. The main purpose of logical access control is the protection of resources (hardware and software) from unauthorized remote access.
  
Many vendors of data warehouses include features in their products that simplify design of data warehouse logical access control. In this article, I will introduce you to Row-level Security, a feature that Microsoft has built in their database and data warehouse solutions.
  
#### Row-Level Security
  
As people in Microsoft say, Row-Level Security (or RLS) is designed to help database administrators implement restrictions on data row access. For example, ensuring that employees access only to those data rows that are relevant to their department. Since the restriction logic is located on the database side, rather than in the application that accesses the database, the security system is more reliable and robust.
  
Row-level Security can be applied to SQL Server, Azure SQL Database and Azure SQL Data Warehouse. It supports two types of filter conditions that define row-level access to records in a table, so called security predicates:
  
- Filter predicates – filter the rows that are available to read operations like SELECT, UPDATE and DELETE
- Block predicates – explicitly block write operations that violate the predicate like AFTER INSERT, AFTER UPDATE, BEFORE UPDATE and BEFORE DELETE
  
#### How does it work?
  
![rls.png](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/rls_9ce8245a39.png)
  
In RLS implementation, restricted access to data rows is controlled by a security predicate defined as a function that is invoked and enforced by a security policy. Depending on the security predicate, users can’t access the filtered rows, delete, or update them, and if the block predicate is defined, they are not able to use write operations.
The function that defines security predicate is table-valued function, meaning that it returns data of a table type.
  
#### Use Case Scenario
  
Imagine the following scenario: You work as a database developer for a hospital that provides inpatient and outpatient care. Among the tons of data that needs to be collected, the hospital’s staff wants to keep track of their patients’ records. Every time a patient visits the hospital, they want to record the valuable information such as the date he arrived, diagnosis, treatment, etc. Because of the practice of maintaining the security and confidentiality of patients’ records, you need to ensure that nurses only have access to the patients they care and restrict them access to other patients’ records.
For the sake of this demonstration, let’s keep it simple and say that there are two nurses and one doctor that admit patients on daily basis. Each of them can access the hospital database using their own username, because you have already created three corresponding users in the SQL Server database. Users are Doctor, Nurse1 and Nurse2.
  
![Page-3-Image-3.jpg](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/Page_3_Image_3_1c6dea8ff5.jpg)
  
![Page-3-Image-4.jpg](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/Page_3_Image_4_33223d3890.jpg)
  
You have also created the required table that holds patients’ records and you simply named it PatientRecords. For now, the doctor and two nurses can access all patients’ records since you have granted them select permission. Every patient is assigned to either Nurse1 or Nurse2 which is also documented in the table.
  
![Page-3-Image-5-1.jpg](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/Page_3_Image_5_1_54347bfd9b.jpg)
  
![Page-3-Image-6.jpg](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/Page_3_Image_6_d921eba49f.jpg)
  
Before Microsoft introduced Row-level Security feature in SQL Server 2016, the lowest security level of a database was table-level security, meaning that users could be restricted to access a whole table. The need of row-level restriction was met by writing custom stored procedures or more often by creating views for different users. Those approaches get the work done, but lately it has become difficult to maintain them because of the growing amount of data, and growing number of users that require large numbers of views and stored procedures.
Row-level Security, on the other hand, filters rows based on execution context using security policy, which makes the solution more flexible and dynamic.
As a database developer you like to keep your solution in order, so for easier maintenance you create a new schema that will contain the RLS function with security predicate.
  
![Page-4-Image-7.jpg](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/Page_4_Image_7_dd2f4a9d05.jpg)
  
This function is used to compare incoming @NurseResponsible argument with the username of user that is currently executing the query. The function will return 1 only if the current user is @NurseResponsible or Doctor. Since the same doctor is treating all patients, you need to make sure he has access to all of the patient records.
Next step is to create a security policy that will enforce created function with the security predicate. Since you need to restrict access to rows on a read level, you need to use filter predicate and call the function with security predicate on PatientRecords table. Don’t forget to parse the correct column to @NurseResponsible argument and activate the policy.
  
![Page-4-Image-8.jpg](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/Page_4_Image_8_4df9aec587.jpg)
  
Once you’re done, you want to test your solution, so you execute a simple SELECT statement three times, each time as different user.
  
![Page-4-Image-9.jpg](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/Page_4_Image_9_7d794433a9.jpg)
  
Doctor should see all patients’ records while Nurse1 and Nurse2 should have access to only those patients they are responsible for.
Congratulations! You have just implemented simple but powerful RLS solution.
  
#### Conclusion
  
Data security has become one of the greatest challenges in data-driven world. It is essential to find a compromise between user access and data integrity, security and privacy. This data-people-security balance is often achieved using access control features like RLS.
RLS enables more fine-grained access control execution process of a query. Since you can have multiple security predicates in a single security policy, the security logic is centralized, which benefits the application transparency and there are no changes needed at the application level. However, since RLS is using table-valued functions, it may negatively affect the performance.
Interesting fact: Microsoft also implemented RLS logic in their reporting solution, PowerBI!


Lately, collecting and analyzing data became not only desired, but mandatory for any business’ success.

Data, People and Security – Can They Work Together?

Comprehensive Microsoft partner with 20+ years of Microsoft SQL Server/BI experience and an early adopter of Microsoft Azure, ready to support you on your journey, regardless if it is on-prem, hybrid or in the cloud. We bring a broad expertise and delivery capabilities across entire project lifecycles, from strategy assessments and POCs, to implementation and Managed Services. Always eager to engage, regarless if it is in a single project phase or delivering an end-to-end to turnkey solution.

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