Incorta

Forterra, a manufacturing company with 1,001-5,000 employees, was facing significant challenges with its finance department's data reporting and analytics. The department was heavily reliant on the IT team to pull even the most basic reports, leading to slow and inefficient processes. The time taken by the IT department to extract, transform, and load data from Oracle EBS and other systems could span weeks or even months. This delay created massive inefficiencies within the team. Furthermore, their existing BI and reporting tools, including Sisense, Cognos, and SAP Business Objects, were generating hundreds of reports across 30 financial dashboards. This made it extremely difficult to simplify, clarify, or consolidate critical data across various sources.

Redstone Federal Credit Union, a financial institution with 1,001-5,000 employees, was facing a significant challenge in leveraging data to gain more visibility and control over banking operations. The credit union aimed to deliver more value to its members through data-driven insights. However, fragmented data spread across a myriad of systems and applications was a major obstacle. This fragmentation was holding back the credit union's Business Intelligence (BI) and data science initiatives, preventing them from making the progress they desired. The data science team was heavily reliant on the IT team, which was slowing down their ability to innovate with data.

Al-Watania, a manufacturing company with over 10,001 employees, was facing significant challenges with their previous Business Intelligence (BI) solution. The system was slow, often delivered inconsistent data, and made reporting unreliable. The process of accessing data and creating reports and dashboards was crucial for the company to monitor its operations effectively. However, the slow data loading and query execution times made real-time dashboard running virtually impossible. The IT team had to rely on multiple SAP applications to develop the requested dashboards, resulting in complicated user interfaces. This complexity made it difficult for business users to navigate the system, further straining the already costly IT resources.

The world's largest coffee retailer was facing a significant challenge in gaining comprehensive, timely access to data across all its 32,000 locations. The company needed to understand the cost of goods sold at a granular, transactional level, by product, region, store, and week, to improve operations and profitability. The retailer also wanted to enhance visibility into its multiple lines of business to optimize the supply chain and make informed decisions about product placement. Additionally, the company required a solution that could integrate seamlessly with Microsoft, one of their primary technology providers.

SeaLink, a company with 24 brands in its portfolio, was struggling with slow and inefficient data analysis and reporting due to outdated tools and a lack of support. The company relied heavily on accurate analysis and reporting to set prices for its various products. However, with numerous products and data sources, the process was slow, with one monthly report taking over 100 hours to compile. The company was using a legacy Business Intelligence (BI) system that was nearing its end of life and showing signs of failure. With no support available, the company was exposed to significant risk. Users found the tool difficult to use, making them dependent on IT for tasks. Analysts also lacked control over its configuration, making it even more challenging to implement logic.

Shutterfly, a manufacturing and retail company with over 10,000 employees, was facing significant challenges with its inventory management. The supply chain management and procurement team were spending hours manually compiling inventory data to identify part numbers that required attention. This inefficient process often led to urgent issues, such as stockouts, which negatively impacted Shutterfly's customers and put undue pressure on the buyers and planners. The lack of access to accurate inventory data also resulted in E&O expenses, with unnecessary stock costing the company both money and space. The data lag and lack of visibility were also problematic for Shutterfly's leadership, as rigid legacy reports hindered any real understanding of how the organization was performing.

A Fortune 5 company was grappling with the challenge of managing and analyzing data from multiple systems across the organization. The company was heavily reliant on static, homegrown reports that took hours to run, often forcing users to run these reports overnight or during weekends to avoid slowing down critical applications. The process of building new reports using the company’s legacy reporting tools, including SAP Business Objects and Informatica, was time-consuming, taking between 8-12 weeks. These tools only delivered static, graphical reports at specific times to specific people and required considerable maintenance and special skill sets. Furthermore, they did not provide access to queries, drill down or data filtering capabilities for non-technical users.

In 2018, a Fortune 50 media and technology company was grappling with an outdated financial data analytics environment that was nearing its end-of-life. The company's key objectives were to reduce the time taken to run financial reports, increase data availability and refresh rates, speed up development time, and enhance the user-friendliness of the data analytics experience. Upgrading the existing systems would necessitate the addition of 29 new servers, a solution that was not only costly but also insufficient to truly advance the company. The legacy BI tool, primarily used as an extract and pivot tool, was not providing enough value and was even referred to as “the million dollar download tool” among employees, indicating the urgent need for a more effective change.

GC Services, a business process outsourcing company with 30 offices across the US, faced significant challenges in mining their vast data for actionable insights into client and internal staffing needs. Their initial attempt at business intelligence (BI) using traditional data warehousing failed due to the extensive data transformation required, poor performance on large table joins, and the inability to combine data from SQL Server, Microsoft Excel, and text files. The company needed a solution that could quickly aggregate data with near real-time updates and be user-friendly enough for non-technical users to drill down at multiple levels.

Smiths Medical, a global medical device manufacturer, was facing challenges with its legacy data pipelines and processes. The company desired more granular visibility into its business systems to improve its bottom line and identify new growth opportunities. However, the existing data pipelines and processes were not able to deliver timely and actionable insights. The IT team was unable to provide the necessary data-driven insights due to the limitations of the legacy systems. This lack of timely and granular insight was hindering the company's ability to make agile and data-driven decisions.

The Sales Comp, Finance, and IT teams at a Fortune 100 technology company were struggling with the accumulation, synthesis, and understanding of data needed to support specific queries and manual processes relating to sales commissions. The data-intensive activities were time-consuming and prone to error, with various systems including SFDC, Oracle, Snowflake, and Kafka serving as data sources. The company was heavily dependent on IT resources and a few core developers, leading to weeks of delays in preparing and summarizing data. Reports took minutes or even hours to run due to manual data refreshes and unwieldy SQL Server data schemas. Low replication with the company’s Snowflake data warehouse often resulted in unreliable data. In 2020, a change in the company’s commission data led to an explosion of transactions, exacerbating the situation. The time required to transform data to insights increased to 4-6 weeks, and the average time needed to resolve approximately 3,000 commission disputes ballooned to 40 days. Traditional approaches like increasing staffing were no longer viable solutions, necessitating a fundamental transformation.

Nortek, a global manufacturing and technology company with over 10,000 products and millions of connected systems, faced significant challenges due to its complex business structure and multiple new acquisitions. The rapid advances in IoT and smart home technologies added market pressure to deliver value, which required real-time insights. However, to analyze their Oracle NetSuite ERP data, they had to dump it into Microsoft Excel and then manually apply pivot tables and lookup functions. They also used business intelligence (BI) tools like Tableau or Microsoft Power BI, which required keeping the data warehouse up to date and structured correctly. This process was time-consuming and diverted IT resources from delivering business analytics. The company was in dire need of a more efficient solution.

Solutions by stc, a technology company with 1,001-5,000 employees, was facing significant challenges due to siloed data and systems. The company was using multiple data sources to monitor client-side network nodes, each containing millions of records. When a client reported a network issue, agents had to investigate five separate systems to identify the problem, a process that could take hours. This delay often led to exceeding KPIs for response and resolution time. Additionally, the company was using Microsoft SQL Server Reporting Services (SSRS) to develop weekly and monthly reports about each client's nodes and network status. However, each report took two to three days to complete, making it nearly impossible to provide timely updates while clients were on the line.