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5 Reasons Why Analytics Projects Fail

Read Time: 4 minutes

Analytics projects can bring tremendous insights to a business and its company leaders, but unfortunately, a large portion of analytics projects fail. In fact, some reports indicate that as many as 50% of these projects are considered failures. That is a shockingly large percentage. So why do these analytics projects fail and how can you avoid the same fate? 7T examines these common pitfalls and how to maximize your chances of getting useful analytics for your company. 

Reasons Why Data Analytics Projects Fail:

1. An Improper Starting Point

Analytics projects need a clear focus and a logical starting point. It is important to include data from the right timeframe and the right sources; otherwise, you might arrive at an inaccurate conclusion. 

To avoid this problem, it’s essential that your team takes the time to identify the central problem that must be solved or the question that you’re seeking to answer. Each analytics project (or each phase of a more complex, multi-phased project) should have a single goal to help you maintain focus and maximize your chances of success. 

Once you’ve identified this key issue, you’ll need to determine what metrics and data points will offer the most insight. What data will you need to arrive at an accurate conclusion? Also, consider how much data you’ll need to perform an analysis. You’ll need to analyze a statistically significant amount of data; otherwise, you risk a scenario where you arrive at a false conclusion because you didn’t analyze a sufficient data volume.

Multiple company timelines will be adversely affected if it’s ultimately determined that your analytics project must be extended in order to gather additional data. This underscores the importance of beginning the project with an accurate determination of how much data and time you’ll need to arrive at an accurate conclusion.

2. Lack of a Clear ROI

It’s vital that your team takes the time to establish a clear link between your analytics project and your bottom line. How is your central question or problem related to ROI? The link must be clear, strong and well-articulated. Otherwise, you’ll spend time, money and resources on an analytics project that doesn’t really bring a tangible benefit. It’s hard — if not impossible — to justify this sort of project to the company’s leaders.

In short, if your analytics project lacks a clear link to ROI, then it’s just an interesting pet project using technology that’s akin to a cool toy. This probably isn’t the best use of time and company resources, so focus on establishing a clear, measurable connection to ROI and your bottom line — only then can you effectively garner support from company leadership.

3. Forgetting to Involve Teams from All Areas of the Company

In most cases, an analytics project will have an effect on multiple divisions and teams within the company. So to maximize your chances of success, involve at least one representative from each affected team at the start of the project. This approach will ensure that all perspectives are taken into consideration and all teams are well-informed about the project details and timeline. 

You’ll need to gain an understanding of how the data (and the insights gleaned from the project) will impact various teams and departments of the company. This is especially critical if one or more teams are awaiting your conclusion before they take action or proceed to the next step. Be cognizant of affected team members’ timelines and how the analytics project will affect those timelines. Also, remember to consider your colleagues’ workflow and how they’ll be impacted by the project. 

Notably, communication is important throughout the project, particularly if other project timelines are going to be affected by delays. 

4. Poor Understanding of Data and its Sources

A typical data analytics project requires you to pull data from many different sources. However, it’s not uncommon for a large company to store data in multiple stand-alone reservoirs. This can make it challenging to access the data you need to succeed with your analysis. 

So once you’ve established the key business problems that you’re seeking to answer or address, it’s time to identify any and all data that will be useful to the project. This is another area where you’ll see a major benefit from involving representatives from various divisions or teams within the company. It is unlikely that any single individual will be aware of all the data that’s collected by the organization. 

Once all useful data sources are identified, develop a plan for moving this relevant data into a single data lake. By collecting all of your raw data in a single, centralized location, analysis becomes far easier from a technical standpoint. This data management plan will also maintain data quality while ensuring a high level of security.

5. Trying to “Boil the Ocean”

Your data analytics project needs to have a reasonable, achievable goal or objective. You can’t do it all in one fell swoop. Many analytics projects are multi-phased, with each phase focusing on a specific aspect of a problem or issue. Once all phases are complete, you’ll have the insights you need to perform a more comprehensive analysis. 

It’s also vital that you avoid a scenario where you misinterpret data or arrive at faulty conclusions in an attempt to answer a question — a question that really requires a more comprehensive, in-depth analysis. A faulty conclusion can be more harmful than no conclusion at all. 

A project is destined to fail if your objective isn’t realistic. If you’re seeking an answer to a complex question, opt for a multi-phase analytics project. Focus on answering a specific, narrow question with each phase. A broader, bigger question can then be explored at the end, once you’ve addressed all of the key issues along the way. With this strategy, it’s far easier to change course if you fail to see the quick wins that would suggest you’re headed down the right path. These quick wins also allow you to justify the project to company leaders while maintaining interest, support and — most importantly — funding. 

Evading the Reasons Why Data Analytics Projects Fail

To maximize your chances of success with a data analytics project, you’ll need the right analytics software and development partners. At 7T, our talented developers have created a scalable, user-friendly analytics platform called Sertics. Sertics allows users to perform comprehensive data analysis, data governance and data lake creation. Additionally, the platform even integrates with leading data visualization tools such as Tableau and PowerBl. 

Regarded as a top Dallas mobile app developer and software development company, 7T has gone beyond Sertics to work with clients nationwide as they strive to achieve a profitable digital transformation. From ERP and CRM development to one-of-a-kind mobile app and custom software development projects, we’re well-positioned to assist with a broad range of development projects. 7T has offices in Dallas, Houston, Chicago, and Austin, but our clientele spans the globe. If you’re in search of an innovative team to develop your next software solution, contact 7T today.

Reach out to our team today!

Alex Drozd

Alex Drozd served as 7T's Director of Business Development in Houston, Texas. Before joining 7T, Alex gained eight years of experience advising clients on cloud storage and data management solutions leveraging AWS, Google Cloud, Azure and other tools. He holds a Bachelor of Communications from Clemson University. When he’s not making magic happen for 7T’s clients, Alex can be found exploring the city with his wife, 2-year-old daughter, and their large fluffy dog, Sally.

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