Big Data
Business leaders are increasingly looking for data analytics to guide their decision making, yet many struggle with how to get started. In order to derive value from it, they must first understand what data analytics is, along with different approaches.

Data analytics is the process of extracting data from heterogeneous sources, processing the data on predefined conditions and parameters, and identifying patterns in independent and integrated data sets that would be impossible to obtain otherwise. Data Analytics is classified into four types:
  • Descriptive analytics
  • Diagnostic analytics
  • Predictive analytics
  • Prescriptive analytics

Descriptive analytics is a process of applying logic and math to a volume of data to find what is currently happening or what has happened. By analyzing the data, it provides valuable insight for business leaders and sometimes-even leads to additional questions that add value in identifying the root causes of outcomes.

Most enterprises still have limited experience with, and staff expertise in, decision management. Business analysts must learn where to use it more effectively. They must also understand when to automate decision-making and when to use decision support approaches. W2S Solutions helps clients to identify algorithms and implement software tools in an appropriate way for their business needs.

A sample set of projects that W2S Solutions has worked on is below. These projects exhibit a scenario where a business was not utilizing its data effectively. We were able to work with the businesses to enhance their understanding of their data, analyze and organize it efficiently, and produce a positive outcome for the business and/or its clients – all through web and mobile applications.
Case 1: Environmental Services Analysis App for Saskatoon Health Region (SHR)
saskatoon_health_region

Problem: SHR was challenged with ever increasing Patient expectations related to hygiene and quality. Regulators were intervening often to ensure compliance with standards of care. SHR has an Environment Hygiene Quality Audit program that gathers cleanliness data by Fluorescent Marking, Visual Assessment, ATP Monitoring, Patient Survey and Process Observation. All of this data is collected for every healthcare unit, room, piece of equipment and washroom across the region of Saskatoon. When we were engaged, their process was manual. Data was collected and stored in a massive spreadsheet on a daily basis. Since the volume of data is so large, and there were no diagnostic analytics tools available to convert the spreadsheet into useful reports. Hospital Management was not able to analyze their hygienic and quality standards effectively. The inefficient system was producing a high amount of pressure from Regulators, and stress on the team at SHR who were responsible for the data collection and reporting.
Solution: Step one was to analyze the business process that was currently taking place and observe the process first hand. This ranged from observing cleaners recording data to staff inputting data into Excel. We put heavy emphasis on understanding their reporting objectives/ requirements and their expected outcomes. Our team had recurring discussions to understand their complex spreadsheets and internal algorithms and calculations that have been built over the years by various people. Our solution was to build a Diagnostic Data Analytics Tool that utilized a smarter algorithm to convert the data into to a much more versatile and comprehensive analytical reporting system. Business leaders are now able to provide any search criteria to generate a quality real time audit report, and even look at specific items in more detail. The reports have been effectively used by hospital management to help them to achieve the standard they have set out for the Quality and Hygiene Audit for 5 hospitals in Saskatoon.
Technology Stack: Microsoft .Net, MVC, SQL Server, Spreadsheet data reader.
Predictive Analysis: In building this solution, the application had to utilize data to anticipate future behavior and figure out unknown outcomes. The SHR solution used predictive analysis to find out the latest trends and predict behaviors.
Prescriptive Analytics: Prescriptive Analytics were used to ensure that data was able to specify a preferred course of action. This type of analytics provides what would be the next action item to achieve a goal or a vision of the organization. It’s mainly used in Employee Performance Management and evaluation systems.
Case 2: Converted Three Years of Research into an Online Management Tool
saskatoon_health_region

Problem: Chris Preston, Director at the Culture builders ( www.theculturebuilders.com ) and Jane Sparrow, author of the book “The Culture Builders; Leadership Strategies for Employee Performance” determined that their three years of research for their book should be converted into an online tool quickly in order to seize an opportunity in the employee assessment market. Their previous research was focused on individual managers and leaders, and helped them to derive a spreadsheet of data that classified employees into 4 unique categories. They had a complex algorithm dealing with human behaviors. Chris and Jane wanted to build a flexible system to easily allow new enhancements and provide customized reporting with a 360 degree evaluation component for clients.
Solution: Chris Preston engaged Madhu Kesavan at W2S Solutions, to help him better understand the challenge of converting the set of highly complex excel sheet algorithms into an online performance management tool. W2S Solutions built a tool that could scale enormously and adapt new business logic with short notice. In the initial stage of the project, we received the previous algorithm and excel spreadsheets from the client, and an overall project concept. We worked with the client to compile a complete list of all the requirements and features of the tool they wanted to build – including those that the client did not initially think of. The app was built quickly as per the client’s needs, and is successfully and is being used by large corporations such as Bank of America, Sony and British Telecom at the present time.
Technology Stack: Microsoft .Net framework, SQL Server, Spreadsheet data reader.
Case 3: AGS Shopping Mall
saskatoon_health_region

Problem: AGS Shopping Mall is your typical bricks and mortar-shopping center. Their usual revenue is $500,000 per month on an average. More than 30,000 products are being sold to customers per month. However, Management was not able to integrate all of the front line representative sales data to analyze the most popular products being sold on daily basis, calculate revenue figures, and weekly purchase quantities. This resulted in poor store management and a heavy loss in its business from inefficient business practices.
Solution: W2S Solutions built a complex wholesale management system to address the client’s issues. The system we build now manages product inventory, inventory purchases, sales, customers, and accounting data. It is capable to handling multiple sales counters interconnected with a centralized server. A high volume of data resides in a Microsoft SQL Server. The solution was built in two parts. The first is a web interface for Mall staff to sell products through, and the second is a mobile app for stores to buy and manage products using a mobile interface.
The software is built using the Microsoft .Net framework and deployed in IIS dedicated servers.
The interactive web dashboard helps businesses in the Mall to:
  • Determine fast moving/most popular products (top sellers vs. quantity)
  • Products Purchased for Sale vs. Actual Sales Charts and Reports
  • Mall/Store Membership Information – Contact and demographic details
  • Payments Made to Directly Suppliers – Charts and Reports (Day, Month and Quarterly)
  • Revenue Charts and Reports (Daily, Weekly and Monthly)
Diagnostic Analytics were utilized: Diagnostic Analytics helped to drill deeper by asking more questions around the data. We applied logic and analytics to the data to find why an event had happened and what drove the outcome. Industrial knowledge is mandatory in achieving a diagnostic analytics, which provides useful trends and contextual data to business leaders.

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