Category: Big Data

The use of spreadsheets in business today is essential for the vast majority of people. From quick calculations and generating reports to basic data entry, spreadsheets have a way of working themselves into our daily tasks. What’s not to love about spreadsheets? They’re quick, easy to use, require little to no training, and are quite powerful tools overall. However, developing too much of a dependency on them can be problematic, especially when used as a workaround for other solutions due to convenience.

This dependency problem is commonly referred to as spreadsheet addiction. While referring to this phenomenon as an addiction might seem a bit extreme, many organizations find themselves heavily reliant on the use of individual spreadsheets to perform core functions. This high usage rate causes many problems and can ultimately hinder a company’s growth. Let’s explore the potential causes of this addiction as well as review possible treatment plans of action.

What’s Wrong with Using Spreadsheets?

While Excel and Google Sheets can be quite effective in their own right, heavy reliance on spreadsheets can create risk and cause a number of negative effects. 

A few examples of potential problems they create are:

• Things Start to Break – As the size of the dataset increases, things within your spreadsheet inevitably start to break. Once this starts to occur, it can be seemingly impossible to identify the source of the problem. You’ll likely drain more time and resources into finding and fixing the issue than on your actual analysis. These breaking points also create the risk for errors and other data corruption.

• Static and Outdated Information – Exporting data from your CRM or ERP system instantly causes the information to become outdated. Spreadsheets don’t allow you to work with your data in real-time, additionally, it’s also extremely difficult to implement any form of automation within your sheets. This creates more work for users as well as poses the problem of inaccuracy.

• Impedes Decision Making – Spreadsheets are notoriously riddled with errors, which can be costly when it comes to decision making. You wouldn’t want to base any kind of decision on a forecast that is more likely to be inaccurate than not. Reducing discrepancies, specifically human error will improve decision making overall.

Treatment Method to Spreadsheet Addiction

Regardless of the severity of your company’s spreadsheet dependency, effective treatment is no small feat. Change doesn’t happen overnight and you should approach your treatment plan as an iterative process. While this list is not exhaustive, here are a few pillars to consider when promoting change.

Evaluation of Symptoms

First, you must identify how spreadsheets are currently being used. It’s important to start with a comprehensive overview of your situation in order to form an effective plan of action. 

To access your addiction level, start by asking yourself questions such as:

• How are spreadsheets used by an individual user and across departments?
• What company processes involve the use of spreadsheets?
• How are spreadsheets used as a method of collaboration?

Drill down how spreadsheets are being used from the company level down to the individual users. Determine not only how they are being used by departments but also their frequency, purpose, and role in daily operations.

Assign Ownership

Next, assign an individual to lead or build a small team to take ownership of the project. If no one feels they are directly responsible for driving change, the bystander effect will inevitably rear its ugly head on the situation. Assigning responsibility for the transition away from spreadsheets will facilitate the flow of change.

New Solutions and Systems

A new solution requires a widespread change to business processes and will ultimately impact day to day operations. This is the main reason spreadsheet addiction is prolonged in businesses, everyone is more comfortable with their usual way of doing things. But implementing the proper tools and systems is vital to decreasing dependency on spreadsheets. 

Use your evaluation to identify your company’s business intelligence needs and acquire tools accordingly. While this transition might require an initial upfront cost, investing in the proper data and analytics tools will help reduce costs in terms of efficiency and labor in the long run.

Promote User Buy-In 

Buy-in from employees across all levels is crucial to the acceptance of new solutions and business processes. Spreadsheet addiction will prevail if users aren’t comfortable with using the new systems put into place. Learning is required when it comes to any change, it’s essential to offer training and available support resources to aid the shift.

In the end, accept that there will always be some tasks and projects done through Excel or Google Sheets. The important thing is that not everything or even the majority of work will be done through these platforms. Though beating spreadsheet addiction might come with some withdrawals, driving change now will foster greater efficiency in the long run. 

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There’s No Clear Big Picture

In most cases, these projects don’t fail due to technical challenges. While there might be some obstacles when it comes to loading and connecting data, the leading pitfalls of project failure are predominantly organizational. Stakeholders commonly feel that there is a lack of clarity surrounding the warehouses’ goals and primary objectives.

Companies often see this most prevalently in the division between technical teams and the ultimate end user. You don’t want your architect or engineers to be on a different page than your analysts. Therefore, it’s important to establish the high-level goals behind why you are undertaking this project to all members of your team before putting processes into place. 

Before beginning, the team should have definitive answers to questions like:

• What are our data goals?
• What insights are we looking for to satisfy our business needs?
• What types of questions do we need the data to answer?

Developing a clear understanding of the big picture early on will help you avoid uncertainty around strategy, resource selection, and designing processes. Knowing the company’s “why” behind taking on the initiative will also allow those involved to recognize the purpose of their efforts.

The Heavy Load of Actually Loading the Data 

Despite the organizational obstacles, there are also many hurdles on the technical side of things. Before data can be loaded into the warehouse, it has to be prepped and properly cleaned. This poses an initial challenge as cleaning data is notoriously a time-consuming task. IT leaders are often frustrated by the wasted hours spent preparing data to be loaded.

The primary main concern is the ability of organizations to easily move and integrate their data. Movement and ease of access to data are crucial in order to generate any kind of insights or business value. According to a recent study conducted by Vanson Bourne and SnapLogic, 88% of IT decision-makers experience problems when it comes to loading data into their data warehouse. 

The most common data loading inhibitors were found to be:

1. Legacy Systems – Migrating data from legacy technology can be time-consuming. However, the primary issue here is that these systems can be difficult to access, making any kind of data movement restrictive.
2. Unstructured and Semi-Structured Data – Complex data types are tough to manage in any situation. Inconsistencies surrounding structure and formatting drains time and technical resources, preventing effective loading.
3. Data Siloed in Different Infrastructures – Disconnection of data sources prevents integration across the organization. Many companies have hundreds of separate data sources as they continually grow across departments and with the addition of various projects. 
4. Resistance to Sharing Data Across Departments – Oftentimes departments act as their own separate entities and aren’t willing to share. The sales team may not want finance to have access to their customer data due to misaligned goals. 

All of these warehouse factors drain an organization’s time and resources, contributing to a lengthier and more costly project overall. Additionally, improperly loading data can cause a number of problems in itself such as errors and data duplication.

Low End User Acceptance

So you’ve successfully moved your data into the warehouse, now what? Another issue that commonly contributes to the failure of data warehouse projects is end user acceptance. As much as new technologies can be exciting, people are inevitably creatures of habit and might not always delve into acceptance. This is where education and training come into play. Onboarding users is vital to the success of any project. 

Establishing a data-driven culture is the first step to promoting user acceptance and engagement. End users should be encouraged to indulge in their data curiosities. Implementing a form of self-service analytics will increase the ease of use for non-technical users and help them quickly gain access to information. These transitional efforts will not only help with the success and use of your data warehouse but also drive better decision making throughout the organization in the long run.

Conclusion

Overall, there are a variety of reasons that contribute to the failure of data warehouse projects. Whether those pitfalls are organizational or on the technical side of things, there are proven ways to properly address them in order to maximize investment and foster successful insights. 

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