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  • Writer's pictureDominic Cincotta

DEBACLES IN THE DECISION MAKING PROCESS AROUND TECHNOLOGY IMPLEMENTATION

Updated: Aug 31, 2020

DEBACLES IN THE DECISION MAKING PROCESS AROUND TECHNOLOGY IMPLEMENTATION: A CASE STUDY OF COMPANY ACME DURING A DATA ANALYSIS AND WAREHOUSING PROJECT



Dominic Cincotta, Robert Morris University, ddcst4@mail.rmu.edu


Abstract


This paper seeks to create an understanding of data, information, and knowledge as it relates to strategic decision making in the Consumer Packaged Goods industry (CGP). This paper relates a case study of a technology implementation project by company ACME in order to store, house, and analyze data around their shopper marketing and marketing finance departments. What is shown is that a data-warehousing project without purpose is data for data’s sake and does not create information or knowledge. At company ACME, a single person proceeded to implement a piece-meal solution to force fit a strategic decision making model, therefore sacrificing context and data connection due to technological limitations. This decision ends up costing ACME a great deal of money and inhibits the greater decision-making ability of both groups by improper resource allocation. The conclusion can be drawn that a data-warehousing project without a proper purpose and corresponding data analytics tools and models is fraught with potential errors.


Keywords: SharePoint, Data Warehouse, Decision-Making, Technology Implementation


Introduction


Technology and data management is a key business process for any successful consumer packaged goods company (CPG). These processes allow companies to make strategic course decisions. The more efficient, nimble, and comprehensive this process, the higher quality and more frequent these decisions can be made and evaluated. Large CPG companies use large data warehouses to store consumer, consumption, financial, and sales data. This data is called upon in order to plan for future events, product introductions, or the scaling of sales activity through various analytic tools and presented through a refined dash board presentation.


The most efficient set up for this quantity of data is use data marts within a data warehouse. This data can then be called on by classification in order to cross-reference with data in other data marts and make strategic decisions. This data warehouse would also have open access to all organizational functions so that cross-functional decisions can be evaluated on a more broad organizational level. The organizations that implement this process most effectively use highly advanced and technical dashboards to pull regular data reports and present them in useful graphical interfaces in a real time basis.


What this article presents is a case study of an organization that had a desire to execute such a data project, but lacked the financial resources to employ the proper systems. This organization pieces together parts of systems that are already in place, attempts to create a bridge from system to system, and expects the results to match those of larger and more complex systems. In the end, the failure of this project can be traced back to a poor understanding of the size of the data pool, the necessary flexibility in data analysis to create knowledge, and the bandwidth needed to execute such a system.


Discussion


Company ACME is a CPG, which produces food goods and interacts with a very nimble marketplace. ACME makes budget and funding decisions on a daily basis, constantly evaluating spends, balancing budgets, and taking advantage of unforeseen opportunities. One of the most active departments in this process is the Customer Marketing department. This department interacts directly with the end retailer in negotiating and executing feature pricing, display vehicles, and promotions in order to maximize volume and retailer relations.


In ACME, the budgets for customer marketing are set by the brand marketing teams by month. These brands have expectations of their traditional marketing investments to actualize and become real at a high level of certainty each month. The nature of customer marketing funds is to fluctuate throughout the year and be fluid due to retailers cancelling and moving investment opportunities. These changes make it very difficult for the brands to manage and balance their monthly budget through their marketing finance position. In turn this created a great deal of frustration for the person in the marketing finance role.


The customer marketing execution, budget management, and reporting to the finance department has traditionally been managed by agency A. Agency A manages all data, workflow, calculation, and data transfer through a series of Microsoft Excel Spreadsheets. All of these spreadsheets are manually manipulated, changed, and morphed based on programming and reporting needs. This was a point of contention for the marketing finance role.


Without a focus on the true work that Agency A performs, the marketing finance role pinpointed that the fluctuations in budget were due to human error and not due to the natural flow of the customer marketing function. In this decision, the marketing finance role decided to explore how to remove the work that Agency A does behind the scenes make it more apparent, tangible, and automated. With this goal, the marketing finance role decided to engage and outside consultant to devise a solution at minimal cost that would accomplish his goals of standardization, visibility, and accurate reporting. At the time that the marketing finance role undertook this project, Agency A produced a report that their estimation and reporting work is accurate within +/-5% error rate. Company ACME deemed this margin of error acceptable.


After a few weeks of closed door meetings, the marketing finance role and the consultant announced their solution. Company ACME was already invested and using SharePoint software. The designated solution was to use SharePoint as a data warehouse and apply a Microsoft Excel worksheet with multiple Macros to pull data through as a data entry and reporting tool. Agency A would log onto SharePoint and pull off the latest estimating worksheet. They would enter all of the data and at the end of the day, upload to dump all of the new data in SharePoint. This reduced the reporting of this system a maximum of once per day due to the daily batch upload schedule of data. For reporting, the consultant would work over the next few weeks to develop multiple Excel templates with Macros that would pull data from SharePoint for reference.


Ultimately the consultant was never able to make the input Excel sheets as nimble as the manual work Agency A was performing. This resulted in a standardized set of inputs that did not correspond to specific retailers and reduced the vibrancy of the data. As a result of this Agency A was required to continue their manual work and then fit it to the macro Excel program the consultant had written. This created double work and reduced the timeliness of the available data. Finally, it was discovered that a single Excel worksheet with macros did not contain enough data space for all of the entries that certain retailers required. Therefore, Agency A had to narrow their data and track certain retailers outside of the system. All of this ended up making a very clumsy and inefficient system.


Conclusion


The implications of the decisions of the marketing finance role are far reaching. The conclusions that other organizations can learn from fall into two categories, Technology Implementation and Decision Making.


Technology Implementation


In proper technology implementation, a clear understanding of the available expertise should be obtained. In combination with the desired metrics of success, a project manager is able to ensure a functional and efficient end tool. In the organization being examined here, the marketing finance manager jumped into the project with limited knowledge of the interface being redesigned. This manager was always on the output end of the system and had no interaction with the input process. This led him/her to making assumptions about what went into the data entry method, creating conflict when a new process was presented to the workforce. They rejected the system on the basis that it did not meet their input, tuning, and experimentation needs at this level. They also rejected due to the fact they did not have any input on the validity of the new tool. In reality, they had been making adjustments to their process in order to prevent errors, which had been effective over the past 12 months. A manager with limited technological skill and minimal knowledge of available technology initiated the process. The goal was set to develop a system using Microsoft Excel interface with SharePoint data warehouse. This was evidence that the proper consideration of why technology should be implemented was not properly considered. The manager had basic knowledge that systems that could perform the desired functions were in existence; however the details of these systems remained an unknown. This knowledge led this manager to decide to try and implement this data warehouse with a very basic set of tools and minimal budget. The technology was not being introduced because the latest tool was available. A vision was never established for the base of this project. Meetings were never conducted to survey team expertise, needs, and proficiencies to gain support for the project. A clear background of the project lacked proper examples and facts to support its development. In failing to outline this beginning point, the manager of this project failed to properly establish reasoning for this technology. In an ideal situation, the managers of this project would have engaged multiple systems, experts, and users to gauge and rate which system is appropriate in order to improve organizational functionality. Proper implementation would be set in stages to properly achieve buy-in from users and managers, training, and trouble-shooting.


In the case presented here, implementation was planned through limited meetings and full dictation to the end users. This was justified by the desired end result of getting this departmental tool to generate an outcome that would correspond with an organizational wide tool, and now in a needed process or report.How a manager should implement technology touches on the enterprise culture. Some organizations are highly committed to training and coaching. The organization that is examined here, conducts an extensive marketing training academy signaling that it does have an interest in pacing and progressing its employees. This corporate culture seems to dictate that a very methodological process be used for development of this technology.


Decision Making


Nutt (2002, pg. 117) states, “Decision makers who become fixated on an idea fail to ask “reframing” questions.” This manager was fixated on the idea that the implementation of this project was the key to his/her success. Had they stepped back and asked, “how can I make the best decision for the business?” instead of, “how can I make this work by the deadline?” they had the opportunity to put themselves in a true leadership position. In other words, a simple reframing of the “how” approach to this project had the potential for great acclaim instead of conflict. Williams (2002, pg 58) concludes, “To combat possible framing effects, it helps if we are able to frame problems in terms of what it is we are trying to accomplish. Looking at a situation from the conclusion backward often allows us to frame our problems more objectively.”


In the end, this project is a good example of a debacle of technology introduction and implementation. It provides a learning opportunity for other technology manager and employees looking to take a leadership role in their organizations. This case shows how the considerations of why, when, and how in projects of this type and if not considered properly, how they can doom a project from the start.


References


1. Nutt, Paul C. (2002). Why Decisions Fail: Avoiding the Blunders and Traps That Lead to Debacles. San Francisco: BerrettKoehler Publishers, Inc.

2. Stenzel, Joe. (2011). CIO Best Practices. Hoboken: John Wiley & Sons.

3. Williams, Steve W. (2002). Making Better Business Decisions: Understanding and Improving Critical Thinking and Problem Solving Skills. Thousand Oaks: Sage Publications.

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