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The insurance industry requires extensive data collection and compliance with security guidelines. Celtic Insurance faced challenges with their existing document-based workflow system, which was unreliable and unsupported. This led to employees circumventing the system, risking regulatory compliance. Celtic needed a solution to streamline data collection, reduce redundant data entry, ensure procedural adherence, and create a paperless office.

Ferrero faced three significant challenges due to their decentralized system: lack of executive visibility, inability to take real-time action, and poor customer risk management. Finance executives had limited visibility across business units, making it difficult to monitor important accounts or have a holistic view of customer data for smarter decision-making. The A/R team struggled to take real-time actions due to siloed systems, leading to a lack of information flow between systems and departments. Additionally, the lack of inter-team visibility hindered better decision-making and enforcement of customer rules for various risk segments.

Over the years, Zurich struggled with its Cash Application process due to two core challenges - a 100% manual end-to-end process and a decentralized processing system. Zurich took several initiatives to maintain control and improve the efficiency of the existing cash application process in the past, but continued to face multiple roadblocks. The Credit and A/R team struggled to manually process the bulk of electronic payments and receipts. They wanted to further reduce the time and resources spent on posting payments, minimize errors made during the cash application, match the payments faster and accurately while minimizing manual intervention. The delayed cash application process led to erroneous status reports for accounts/invoices. Even if the customer had paid for an invoice, they might wrongfully receive DNOC or dunning letters, resulting in a disparate status of accounts and dissatisfied customers. In order to resolve the errors in the system, the Credit and A/R team was frequently compelled to have multiple internal calls or meetings with brokers, underwriters, account executives, and Finance Operations managers. This led to further delay and dispute in the process.

Danone faced significant challenges in its Accounts Receivable (A/R) processes due to a decentralized system and a diverse customer base. The company had to manage multiple payments and remittance formats manually, leading to increased process complexity and a high number of short pays. The A/R team spent a considerable amount of time processing electronic and check payments, which delayed dispute identification and resolution. Additionally, the manual deductions management process caused delays in dispute management, increasing the Days Deductions Outstanding (DDO) to over 45 days. The lack of automation in the collections process further affected cash flow and working capital, and the inability to identify the right customers for collections hindered customer-client relationships.

The BlackHawk A/R team faced significant challenges due to the lack of a standardized workflow. They had critically high deduction aging with over 2000+ line items that were more than two years old, and over 100+ line items per deduction. The team also faced slow cash application for prepayments, difficulties with payments received with non-invoice reference numbers, and payments with incomplete or missing remittances. Additionally, the deductions team struggled with high volume per deduction, no workflow for deductions management, and lack of visibility into performance.

Mosaic faced significant challenges in their credit process within the order to cash cycle. Administering from North America and Brazil, they had a complex credit approval hierarchy with 7 to 10 layers of approval, which slowed down the process and affected customer experience. Additionally, they had to manually fetch credit data from various sources, which was time-consuming and labor-intensive. Their rigid credit scoring model did not account for country-specific risk factors, making it inefficient. Furthermore, the lack of real-time visibility into negative payment trends and macroeconomic fluctuations hindered their ability to proactively manage credit risk.

Duracell faced significant challenges in their Accounts Receivable (A/R) processes, particularly in cash application, collections, and deductions. The manual processing of remittances and payments was time-consuming, and the deduction coding process was heavily reliant on manual labor. Additionally, there was low visibility into the payment status of customers, leading to confusion among teams. The lack of ability to add extra customer information into invoices and the manual data extraction from web portals further complicated the process. Duracell also struggled with visibility into the resolution time for deductions, and the manual collaboration and aggregation of backup for trade deductions caused delays. The company sought to free up their analysts, increase process visibility, and establish a better workflow to target customers more effectively.

The existing cash application process at Sanofi was plagued by extreme differences in the process flow and structure all around the company; low uniformity led to disorganized work and confusion. With a presence across the globe, even though some of the branches they had did use automation, others were completely manual and did not have any form of automated solution in place for pulling remittances or invoice data from emails and portals. A lack of clarity among teams into their goal setting and visibility into end to end processes also led to dumbing down of the workflow. They also wanted to reduce their A/R overdue workload as it clogged up the entire cycle and also led to delays.

A significant challenge with their existing stack was the disparate systems with master customer data across different platforms. Different components worked independently of each other and thus had little interaction with each other. Their collectors had a vendor automation solution in place and the credit team had a non-integrated in-house system. These worked separately with no overlap and this caused significant problems for them: Fragmented Credit Analysis: Micro Focus’s credit analysis was faulty at best due to its inability to predict customers’ risk and behavior patterns. Improper data storage and logging also meant much of their time was wasted just on finding said data. Limited Data Visibility: The company’s analysts had little visibility into their operations due to the way it was stored and spread out. This lack of clarity led to duplication of efforts as analysts had no idea whether a customer had been contacted or not or which ones were higher risk. Separate Reporting and Tracking across Different Systems: Having separate systems for storage and data tracking meant that anyone looking for a customer status or report would have no means of knowing where to look for it. Disjointed storage also meant that errors, if made, were seldom rectified as there was no way of cross-referencing data. No Centralized Repository for Data from Acquisitions: The company lacked a way to allow teams to access data freely. This caused delays in their operations as information could not be shared or moved around easily. This problem also added to the company’s issue of transparency because it hindered inter-team communication. Data Migration for Storage and Reference: Micro Focus faced issues with integrating data in their system; they wanted a way to do this easily. Credit exposure also wasn’t being reflected correctly due to issues with their data feed.

Plastic Jungle’s business success is predicated by its ability to move faster than the competition. When the company brought in a new CFO in 2012, he aimed to deploy a data solution that would match the company culture. Speed, accuracy and agility were the key tenants for the company data approach. “We wanted to make sure we didn’t paint ourselves into corner by taking a traditional approach to data warehousing.” That meant a solution that could grow to massive amounts of data, and allow regular business users to work with data quickly without requiring a huge investment that would leave them beholden to the product.\n\nPlastic Jungle's BI requirements included the ability to:\n• Manage and sustain the entire operations aspect of Plastic Jungle’s data warehouse with little or no operating support from the engineering and IT staff\n• Allow business users to create any ad-hoc reports they required\n• Provide the abstraction layer between the schema and the metrics that the business sought\n• Refresh in close to real-time – a minimum of once per day, and ideally every couple of hours\n• Not incur a significant capital outlay

ProMarket required timely and accurate reporting and analysis of key metrics, such as sales, inventory, profit by product category, spoilage, and optimal order quantities for each store. The company was struggling to process very large amounts of data (over 40 million rows) from its centralized database. The data processing, transformation, and analytics were extremely time- and labor-consuming, making it impossible to generate some of the analytics required by management. Business partners of two leading BI vendors demonstrated their solutions to ProMarket and provided implementation proposals. However, ProMarket selected Sisense due to its ability to fully meet their requirements, faster customization of reports and dashboards, impressive data processing speed, and lower total cost of ownership.

Before implementing Sisense, alpharooms.com faced significant challenges in providing detailed and interactive reports and dashboards to various business functions. The existing tools, such as Excel and OLAP cubes, were not effective in building user-friendly dashboards or offering easy drill-down interfaces. Additionally, IT involvement was required for any modifications, making the process cumbersome and time-consuming. Lee Eckersley, Head of Business Analysis, had experience with other BI tools like Cognos, Hyperion, and Business Objects, but found them too expensive and resource-intensive for the company's needs. The team needed a more efficient and cost-effective solution to handle their data analytics and reporting requirements.

Fiverr needed quick insights on growing data. The company wanted to connect data from MySQL with data from Google Docs, Spreadsheets, and Analytics to better track user actions on their website and mobile app. As users increased, so did Fiverr’s data needs, making it larger and more complex with millions of rows a day from various sources. Despite using an internal big data system based on Hadoop, the data complexity made it difficult for the team to build reports and dashboards quickly. Fiverr’s senior BI director, Slava Borodovsky, emphasized the need for real-time results due to the dynamic nature of their data. The product department relied heavily on BI to determine their product roadmap, making the need for data more urgent as departments began understanding its impact on their success.

Trupanion faced challenges in managing and analyzing large volumes of data across multiple departments. The company needed a solution to track real-time performance, optimize marketing opportunities, and build accurate financial reports. Existing in-house solutions were time-consuming and prone to inaccuracies, leading to a need for a robust BI tool that could be easily used by non-technical users and deployed quickly.

GOAL Academy, an online charter school, faced challenges in effectively utilizing data to keep students on track and align staff with key performance indicators (KPIs). The school relied on various data resources like Google Docs, Excel spreadsheets, and different servers, which made it difficult to create a consistent format accessible online. The institution needed a modern tool to better visualize and understand the data, and to merge multiple platforms into one place. The goal was to find a web-based tool capable of processing data from different sources to help teachers make quick decisions in the classrooms and improve student retention and graduation rates.

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.”

In addition to the custom software that Businessolver had developed internally, they were also using several off-the-shelf technologies such as inContact for their service center; Salesforce.com for sales team and Sage for the accounting department. There was no unified method to tie those disparate data sources together and get an overall picture of the customer interaction. Users would get various exported files from various other users and load them up into MS Excel and analyze in a single location. Due to this lack of uniformity to data access, team members were constantly providing different results to the same question. The problem was not the volume of data but rather the disparate sources of it. It was critical to Businessolver to find and acquire a tool that would allow them to connect all their data sources across the enterprise. That way they would be able to have full confidence that their results were accurate no matter who was collecting the information, and that data could drive decision making based on facts and not based on feelings. Sony Sung-Chu is the Director of Applied Data Science at Businessolver and it was his task to find and test potential solutions. With his years working as a Business and IT Analyst, he came at the question from a very technical perspective. Sony also brought in Sara Johnson, the lead BI analyst for Businessolver, whose background is in economics, math and Business Intelligence. Johnson brought in the first data warehouse at Businessolver and was to be tasked as the internal BI expert, responsible for training and maintenance.

Casting Network’s data was being stored but not really seen, leaving the Sales and Business Development departments tracking KPIs manually. As the company started to expand internationally, the need to aggregate different data sets and evaluate the business on a global scale became even greater. Needing to access Google Drive documents, multiple Quickbook files and 35 SQL databases comprising over a billion rows of data, Nitika had her work cut out for her in pulling together a solution that met Glen’s directive.

Ignite Media processes massive amounts of data, maintaining approximately 3 TB of transaction, demographic, and media performance data. They had been building all their reporting internally using PHP and .Net, but it was becoming increasingly difficult to scale. Writing new reports from scratch to follow a 'hunch' could take weeks, making it impractical to test new ideas. The company had valuable data but lacked the resources to fully leverage it. Mazda Ebrahimi, the VP of Application Development, sought a solution that would allow them to produce results faster and more easily without sacrificing their intellectual property.

Integrous Marketing faced significant challenges in integrating data from multiple disconnected sources such as Adwords, Analytics, email marketing tools, and Salesforce. The process of manually gathering, cleaning, and joining datasets was labor-intensive and prone to errors. Additionally, generating visualizations from the data in Excel was time-consuming and often resulted in outdated and inaccurate information. The company needed a method to automate these steps and provide a better user experience, especially given the limitations in the types of reports and views that some tools provided.

Many companies today often have data that is so complex or distributed, it needs a dedicated team of specialists to clean and maintain it, and only then can a BI tool be useful. There’s a lot of work preparing complicated and dirty data, and many businesses need a simple way to prepare data that most BI tools on the market cannot provide. Mike saw that even after implementing a BI solution for customers. There was often such a steep learning curve, the customer would still be unable to build reports and dashboards independently. Mike’s customers typically come to him after they've failed with another BI solution. His company then takes on managing the client's internal data, some of which as been around for 10+ years, and is big, disparate, and dirty. His team would then wrangle the data together into a BI solution for the client. Mike saw that even after implementing a BI solution for customers, it often had such a steep learning curve, the customer would still be unable to build reports and dashboards independently. It was important that his customers could be self-sufficient, so he wanted to standardize on a tool that he knew would be a win for them.

Quentin Villon, a non-technical data analyst at Qualifa, faced significant challenges in managing and utilizing the vast amount of data collected by the company. Internally, he relied on Excel to manually build activity reports, a process that took two hours daily and resulted in a cumbersome 20MB spreadsheet. This method was prone to errors, with broken formulas and other issues, making it a nightmare to manage. The reports were also delayed, going out a day after the activity took place, which meant they could only acknowledge performance rather than influence it. Generating commission reports was even more time-consuming, taking two days each month. Externally, creating campaign insight reports for potential clients was a labor-intensive process that required several days of manual effort. These reports were crucial for client acquisition but were not reusable, adding to the inefficiency.

foodpanda faced significant challenges with their existing data warehouse, which was unable to efficiently handle terabytes of complex data from multiple sources. The limitations included a lack of data mining functions and the inability to affordably process large volumes of data. Additionally, foodpanda aimed to centralize data to promote transparency and democratization, reducing employee reliance on the BI department. They needed an intuitive, self-service BI solution to shift the focus from data collection to insights and strategy.

The decision to evaluate BI solutions came in the wake of what could be defined as a “cultural revolution” taking place in JDC: from siloed data, with each stakeholder holding on to his or her own data and only sharing it with others on a “need to know” basis, to a corporate culture of complete transparency of information that aims to make data available to everyone, and challenge them to make good use of it. The data itself was being stored in a Cobalt character-based CRM system running on the IBM RS/6000 platform, as well as Excel spreadsheets. This data consisted of sales, operational, utilization, financial and other data. Prior to Sisense, reporting was done with Excel and Access. JDC has been looking for some sort of dashboard software for roughly two years but did not find any product that could suit their needs, until they ran into Sisense in a user group in which two Sisense customers had presented it. The VP of Parts and Business Systems downloaded the trial version right there at the convention, and within two days was already publishing dashboards.

Pantechnik International faced significant challenges in managing and utilizing their vast and scattered data sources. The company struggled with a 'black hole' of largely untapped client data, making it difficult to gain a holistic view of overall performance, cost-effectiveness, and service level agreements (SLAs) of their carriers. Senior management lacked visibility and control over these metrics, which was further compounded by the data preparation nightmare of the ETL process. The company evaluated various solutions, including Qlikview, but found the 'buy before you try' business model unappealing. They needed an embedded BI software with a powerful backend capable of handling thousands of large, scattered datasets.

Blueline’s biggest problem was figuring out which budget allocations were connected to which sources of revenue. A single marketing campaign could bundle together various lines of business, making it hard to get a clear picture of the bottom line of promotional activities or to break down the revenue of product bundles by product and sub-product. The company lacked a way to combine data sources from different departments, leading to standalone reports that didn’t match up. This confusion resulted in inappropriate investments, reactive problem-solving, and missed business opportunities. As Blueline prepared to launch their mobile initiative, Nacer decided it was time to address the problem once and for all, anticipating high volumes of transactions that could cause enormous problems if not managed properly.

EEG had to structure the data to have the functionality and flexibility at an interface level so that they could easily deploy it to any event type and use consistent tools to report on the data. That is where the problem arose: though the data was being collected, the data created data dumps that were cumbersome to understand and not structured to produce easy or accurate data analysis. After Adam sifted through the data dumps of a number of their clients, he realized that some data just couldn’t be connected in a traditional reporting environment - creating gaps of what they could report. After talking to a longtime client, he found that many of their clients were forced to manually pull data and reports together - and were spending a significant amount of time doing it. Adam attempted to solve the problem by doing data analysis for his clients internally and sending out the reports. But, for Adam to do that internally, he had to pull the data out of Salesforce, cobble it together, create reports and charts in Excel and then email them out. Every report would have taken weeks at least, and the clients wanted to see this data frequently and quickly, so it wasn’t feasible.

Amlin, a leading underwriter of specialist insurance and reinsurance, faced several challenges in its operations. The company needed to provide BI availability to all divisions and job functions across the organization worldwide. They required access to real-time data for decision-makers via reports tailored to their needs and aimed to consolidate group systems around standard business processes. Additionally, Amlin needed to integrate data from various external sources and deliver it via web technologies using a single intuitive user interface. The company also sought to develop a system to provide corporate performance management (CPM), optimize business processes, and directly align IT strategy to corporate strategy. Before investing in MicroStrategy, Amlin had no corporate performance management system, inefficient business processes, and misalignment between IT and corporate strategy.

Komar, a global apparel distribution company, faced significant challenges in managing cash application and deductions on a massive scale. The A/R teams worked in silos, leading to blocked orders and negative customer experiences. Inefficient deductions processes caused bottom-line erosion and reduced employee productivity. The manual cash application process increased costs and delayed cash processing, resulting in higher DSO and negatively impacting downstream processes.

Ardent Mills faced several challenges in their cash application, collections, and deductions processes. The cash application process was entirely manual, leading to slow workflows and increased risk of errors. High lockbox fees were also a significant issue. The collections process lacked a standardized strategy for work list prioritization and a central location for customer communications, leading to inefficiencies. Additionally, the deductions process suffered from a lack of visibility into dispute volume and patterns, with data being saved via email, complicating correspondence and resolution.