Sisense Case Studies EDA Transforms Data Management and Analysis with Sisense

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	EDA Transforms Data Management and Analysis with Sisense Sisense

EDA Transforms Data Management and Analysis with Sisense

Sisense

Technology Category	Analytics & Modeling - Big Data Analytics Application Infrastructure & Middleware - Data Visualization Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries	Agriculture Construction & Infrastructure Equipment & Machinery
Applicable Functions	Business Operation Product Research & Development
Services	Software Design & Engineering Services System Integration
Challenge	Sonny explains that before Sisense, company resources would be invested in reigning in data and wrestling with the complicated process of aggregating, processing and delivering it to the client. “We would build a bunch of pivot tables in Excel on numerous tabs, and then we would give people an import function that would import the raw data so that they could see the dynamic reports in Excel. But there were a number of problems, for example Excel would limit the amount of rows in a report, or the report was slow, or people just didn’t know how to use it.” Sonny also mentions that another significant problem was the task of distributing the data reports to thousands of different clients. To keep the reports current and updated, clients had to manually re-import the data, and eventually customization requests demanded even more time and resources, per client. “Overall, there just wasn’t control over what was happening. On top of that, if I had to update the report configuration, I had to send out thousands of new Excel files that had all the pivot tables defined in them. And ultimately every customer would need us to modify their pivot tables. It was just a nightmare.” Read More
About Customer	Equipment Data Associates (EDA) serves approximately 12 different industries by collecting, aggregating, and analyzing industry data for the purposes of extracting market insights. The company was founded in 1998 and today is a wholly owned subsidiary of Randall-Reilly. EDA serves industries that require financing heavy capital purchases like construction, machine tools, and agriculture. Its products service three major sectors in the industry: OEM (original equipment manufacturers) which are the companies building the equipment like John Deere or Caterpillar; the dealer network which includes companies who have exclusive rights within specific territories to sell the equipment; and “after-market” companies which are businesses that sell complementary services or products to the main industry (such as parts). Sonny Rivera, VP of Information Technology, has been working in the industry for nearly 25 years. When he started working at EDA about five years ago, data analysis was handled by one person who managed it all with an Excel spreadsheet. Throughout his time at EDA, Sonny transformed a 1-person technology team into a software product development team of about 15. Read More
Solution	EDA decided to build a web-based application that would allow users to access EDA’s products and define their own queries and filtering conditions. This solved the issue of updating and distributing the reports, but there was still something missing: insightful analysis. “Once the data was aggregated we asked ourselves, ‘What do we do with it?’ That was our problem. We needed a tool that would help us analyze the data. Our pain was going from being a data provider to an insight provider. Our clients wanted to know not only lists of data regarding the market, but also the trends and tendencies.” In addition to Sisense, Sonny investigated a number of major BI vendors, including Birst, QlikView, Tableau and Logi Analytics. The evaluation process included running the same test with each solution, side by side. A number of issues were explored, including the ability and speed of the tool to aggregate and process the data, the user interface, and the value and insights each managed to extract from the data. “We took our existing data and told ourselves that we need to build the tests based on that, running a full POC implementation using our own data. That was the litmus test. If we couldn’t outperform our current solution, then there wasn’t an advantage. We ran the same test on all the solutions so that we could see and compare how hard or easy it was to perform or how technical we needed to be and whether our consumers could potentially to it.” Each solution had its pros and cons, but none except Sisense had the “full package”. “Birst was too immature and costly; QlikView had memory and performance problems; Tableau’s user interface was great, but the analytics weren’t useful whatsoever. Tableau is pretty, but there’s no substance.” Sonny narrowed down the options to Logi XML and Sisense. While Logi XML worked well, the user interface and user experience were awkward, requiring a more technically savvy user to navigate through the system. That wouldn’t work within the company, or with EDA’s clients. After comparing all the tools, Sisense was the clear choice. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Big Data Analytics

Application Infrastructure & Middleware - Data Visualization

Platform as a Service (PaaS) - Data Management Platforms

Applicable Industries

Agriculture

Construction & Infrastructure

Equipment & Machinery

Applicable Functions

Business Operation

Product Research & Development

Services

Software Design & Engineering Services

System Integration

Challenge

Sonny explains that before Sisense, company resources would be invested in reigning in data and wrestling with the complicated process of aggregating, processing and delivering it to the client. “We would build a bunch of pivot tables in Excel on numerous tabs, and then we would give people an import function that would import the raw data so that they could see the dynamic reports in Excel. But there were a number of problems, for example Excel would limit the amount of rows in a report, or the report was slow, or people just didn’t know how to use it.” Sonny also mentions that another significant problem was the task of distributing the data reports to thousands of different clients. To keep the reports current and updated, clients had to manually re-import the data, and eventually customization requests demanded even more time and resources, per client. “Overall, there just wasn’t control over what was happening. On top of that, if I had to update the report configuration, I had to send out thousands of new Excel files that had all the pivot tables defined in them. And ultimately every customer would need us to modify their pivot tables. It was just a nightmare.”

About Customer

Equipment Data Associates (EDA) serves approximately 12 different industries by collecting, aggregating, and analyzing industry data for the purposes of extracting market insights. The company was founded in 1998 and today is a wholly owned subsidiary of Randall-Reilly. EDA serves industries that require financing heavy capital purchases like construction, machine tools, and agriculture. Its products service three major sectors in the industry: OEM (original equipment manufacturers) which are the companies building the equipment like John Deere or Caterpillar; the dealer network which includes companies who have exclusive rights within specific territories to sell the equipment; and “after-market” companies which are businesses that sell complementary services or products to the main industry (such as parts). Sonny Rivera, VP of Information Technology, has been working in the industry for nearly 25 years. When he started working at EDA about five years ago, data analysis was handled by one person who managed it all with an Excel spreadsheet. Throughout his time at EDA, Sonny transformed a 1-person technology team into a software product development team of about 15.

Solution

EDA decided to build a web-based application that would allow users to access EDA’s products and define their own queries and filtering conditions. This solved the issue of updating and distributing the reports, but there was still something missing: insightful analysis. “Once the data was aggregated we asked ourselves, ‘What do we do with it?’ That was our problem. We needed a tool that would help us analyze the data. Our pain was going from being a data provider to an insight provider. Our clients wanted to know not only lists of data regarding the market, but also the trends and tendencies.” In addition to Sisense, Sonny investigated a number of major BI vendors, including Birst, QlikView, Tableau and Logi Analytics. The evaluation process included running the same test with each solution, side by side. A number of issues were explored, including the ability and speed of the tool to aggregate and process the data, the user interface, and the value and insights each managed to extract from the data. “We took our existing data and told ourselves that we need to build the tests based on that, running a full POC implementation using our own data. That was the litmus test. If we couldn’t outperform our current solution, then there wasn’t an advantage. We ran the same test on all the solutions so that we could see and compare how hard or easy it was to perform or how technical we needed to be and whether our consumers could potentially to it.” Each solution had its pros and cons, but none except Sisense had the “full package”. “Birst was too immature and costly; QlikView had memory and performance problems; Tableau’s user interface was great, but the analytics weren’t useful whatsoever. Tableau is pretty, but there’s no substance.” Sonny narrowed down the options to Logi XML and Sisense. While Logi XML worked well, the user interface and user experience were awkward, requiring a more technically savvy user to navigate through the system. That wouldn’t work within the company, or with EDA’s clients. After comparing all the tools, Sisense was the clear choice.

Impact #1	EDA uses Sisense to monetize its aggregated data by creating industry-specific products.
Impact #2	EDA has productized its data analysis, making it possible to offer free product trials to prospective customers.
Impact #3	EDA has been able to bring their product to market fast and enjoy significant returns.

Impact #1

EDA uses Sisense to monetize its aggregated data by creating industry-specific products.

Impact #2

EDA has productized its data analysis, making it possible to offer free product trials to prospective customers.

Impact #3

EDA has been able to bring their product to market fast and enjoy significant returns.

Benefit #1	Within 90 days from purchasing Sisense, EDA launched a new product for the trucking industry called “Weekly Business Intelligence”.
Benefit #2	In a 9-month period, EDA generated 7 figures of revenue from just this product.
Benefit #3	In 2013, in less than a year, Sisense was used to generate 7 figures of revenue and in 2014 these revenues are expected to double.

Benefit #1

Within 90 days from purchasing Sisense, EDA launched a new product for the trucking industry called “Weekly Business Intelligence”.

Benefit #2

In a 9-month period, EDA generated 7 figures of revenue from just this product.

Benefit #3

In 2013, in less than a year, Sisense was used to generate 7 figures of revenue and in 2014 these revenues are expected to double.

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Overview

EDA Transforms Data Management and Analysis with Sisense

Operational Impact

Quantitative Benefit