Check out these real-life examples of business intelligence from the RTS Labs client archives. These are business problems real clients have used BI to solve. Which ones sound similar to what you might be going through right now? And yet, there are business owners who still ask us if BI is worth the investment. Well, we chose these seven case studies specifically because they tell some of the best stories of how business intelligence has significantly improved business processes and bottom lines for a variety of businesses.
Want real BI results? Keep reading to discover not only some of the best uses of business intelligence but also the potential power of BI.
For our first example of business intelligence, our client, a key player in the retail business, was dealing with erratic sales performance with most reps not meeting targets consistently. Their strategies were driven by guts instead of data, and they were faced with dwindling sales rep morale. The sales director wanted to correct this course by attaining a granular level of understanding of the sales process and performance.
The Opportunity: Come up with a data-driven strategy to streamline their sales process. Due to a better organized target-setting process and refined sales strategies backed by data, the reps were not only meeting but surpassing their targets. Almost there! Please complete this form and then click the button below for the download to begin. Their studies depended on collecting clinical information from their internal lab information system, as well as from external sites.
The problem was that the process was negatively impacting the quality of the data and productivity. The Opportunity: RTS Labs was brought in to help the Clinical Trials team scale up on research by streamlining their data management process through the use of business intelligence. By streamlining the data gathering, data management , and analysis processes, RTS Labs enabled the Clinical Trials team to scale their research without having to scale their resources. Our client, a fast-growing company in the bio-sciences industry, wanted to develop a data-driven marketing plan for the coming year.
While the strategy was designed to increase market share, the company lacked the processes and systems necessary to capture and analyze the customer insights that were needed. Solution: RTS Labs implemented a unified business intelligence platform, integrating multiple data sources into a single data mart.
Customer case studies
Ithaca College IC is a four-year residential private college in Ithaca, New York, with 6, undergraduates, graduate students, and faculty members. IC began as a conservatory of music in and now has four professional schools Music, Business, Health Sciences, and Communications and a liberal arts school.
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IC is primarily tuition-driven and is challenged with declining high school graduate demographics and intense competition for Northeastern-based prospective students; 85 percent of the student body is from the Northeast. Other challenges include diversification of the student body and the need to improve its student success rate.
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IC is increasingly aware of its need for analytics to help address these issues. The BI-developed operational data store ODS is in silos according to administrative areas and is overwritten nightly without preserving changes.
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IC used a single vendor for the entire technical stack, resulting in complicated and expensive bundled contracts. Additional components for authentication, single sign-on, and a portal product separate from other IT products have added to the environmental complexity and the ability to remain current on upgrades; indeed, since , the environment has been in a constant upgrade cycle. In addition, the BI team has had little time to develop the integrated longitudinal data structures needed to answer critical institutional questions. Despite multiple attempts, IC has been unable to gain traction on developing an enterprise data warehouse.
This is in part because IC lacks a strong data management culture, and also because a series of large IT projects have been competing for funding. Further, AIR began working with an external company to meet specific data science needs around student success. This venture was cloud-based and helped push BI to explore the possibilities of cloud services and a move away from a single-vendor environment. Ultimately, IC needs a solution that requires BI to spend less time maintaining complex infrastructure and offers more time for developing useful data structures.
This will allow AIR to spend less time on manual data collection, cleansing, and manipulation and more time actually exploring and analyzing the new data structures that BI created. IC's need for a solid BI and analytics roadmap is not in question, nor is the observation that the majority of BI efforts have failed to provide successful outcomes.
Like many colleges and universities, IC is big enough to have big-data problems but not large enough to be able to invest the often-inordinate resources needed for traditional—and risky—data warehousing and BI projects. The plans to realize these goals—which were inspired by common enterprise data lake, data warehouse, and data mart topographies—have been somewhat simplified to allow for quicker progress and increased flexibility. Learning from its previous failures, IC is adopting a new approach: rather than rely on monolithic vendor solutions, the focus will return to supporting the tool, not the data.
If the work requires costly vendor or consultant engagements, it will again fail to secure the funding needed to see it through to completion. In the past, this approach to building a BI environment clearly would have been impractical. The infrastructure costs alone would have prohibited exploration and experimentation—imagine, for example, asking IT to create an entire server environment just to try out an idea. Enterprise-level ETL tools, databases, and reporting environments were heavy things, demanding significant resources to implement, understand, and keep alive.
Successful IT team members were encouraged to build their skills around specific platforms, not general tools and concepts. The basis of the new approach, and the formation of its three principles, started with the realization that there was no off-the-shelf solution for a college like IC—one that is still in the early stages of its data analytics journey yet which also has a large and varied data landscape.
So, while IC's internally focused, experimental approach may seem like a return to an earlier time, it is in fact made possible by the current landscape of data science and IT tools. This is also not simply a build-versus-buy discussion; some vendor products and off-the-shelf solutions will smartly fit into the project. Looked at simply, the BI and analytical warehousing work has two fundamental, intertwined streams of effort: designing powerful representative data structures and doing the technical work of building an infrastructure to bring forth those structures and manage data collection and transformation.
The technical work should exist only to enable the work with the data. The leading data science and exploration tools—such as R and Python —are languages, not application platforms, and they've been overlooked in the past precisely because they do not offer a turnkey BI solution. With the realization that no turnkey solution exists, these and other tools become more of an imperative.
Building capability in open toolsets also drives the adoption of open standards. Whereas a vendor-provided platform often leads to development of a proprietary implementation, tools such as R and Python can work with almost any data source. Thus, avoiding vendor lock-in is not just a financial move—it's a fundamental way to ensure that IC's data remain open and accessible to the largest possible toolset, now and in the future.
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The agile methodology was born of the need to solve fundamental problems in software development. The agile approach advocates daily collaboration between users and builders and the frequent delivery of functionality, and it emphasizes the importance of embracing change and simplicity. While the effectiveness of various agile implementations has been—and will continue to be—debated, the foundational principles have a fundamental validity. It boils down to this: IC and most institutions like it have strongly believed that the best path forward is to take direct internal hands-on responsibility for building the BI and analytics environment by building capability within the teams.
As a result, institutions have been hesitant to take on these projects due to the risk of failure, perceived cost, and lack of skills investment.
In reality, however, IC has been supporting costly vendor tools for decades—a significant outlay that has not achieved the desired result. Now the tool landscape has evolved: the most widely used data science and analytics tools, which are free and open source, have immense power, while the cloud negates many traditional infrastructure issues.
By taking an experimental agile approach, IC believes it can both reduce cost and the risk of failure while simultaneously increasing engagement across IT, BI, and AIR teams, fostering an even greater sense of data ownership and data-informed decision-making. OSU is one of two land-, sea-, space-, and sun-grant universities in the United States. It draws students from all 50 states and more than countries to its two campuses, 11 colleges, 15 experiment stations, 35 extension offices, and plus academic programs.
Among the OSU academic programs that are often ranked among the world's top programs are forestry, oceanography, mycology, marine biology, agriculture, robotics, and natural resources; its Ecampus online program is among the top eight in the country.
Asked how to make Chinese potstickers, an amazing home cook once offered the following lesson: He pulled together the key ingredients, and then whipped together the meat filling in a bowl. When asked how much of which ingredient to put in, he just shrugged and guessed. He then offered up the aromatic bowl, noting that, "You put in the ingredients until it smells like this.
For some rare and talented people, that is sufficient guidance to replicate results. For others, a defined and well-documented process is absolutely needed. For example, the student of that home cook who might be one of the coauthors of this case study… tried—hundreds of times—to replicate that potsticker recipe, and the only thing he got right were the ingredients.
In higher education, business processes can, over time, grow to be historically driven and complex. As you stretch those processes over years and decades, it becomes even more challenging to understand or remember why we do things a certain way. An oft-heard refrain— because it has always been done that way —is all but a secret passphrase for maintaining historic processes.
When these business processes are built into our enterprise systems, we may end up with numerous local modifications that are expensive to maintain and that impede innovation. The increasing demand for business efficiency to support institutional decision-making, as well as research and teaching activities, is causing us to rethink our assumptions about why we do things the way we do.
As these new software-as-a-service SaaS solutions shift computing from on-premise to cloud computing, we are forced to reevaluate the business processes that have been in place for dozens of technology generations. When we faced the challenge of a major ERP system upgrade, the need to upgrade hundreds of historic modifications added months of time, thousands of hours of extra work, and additional complexity to the project. Rather than blindly reinstalling modifications as we had done so many times in the past , we decided to use this opportunity to rethink and modernize our business processes.
Using the BI techniques, we first focused on the cost and benefit of the modifications and used these insights to justify a focused effort to move toward baseline functionality.