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The Biggest Frustrations that Data Analysts Face

William Frierson AvatarWilliam Frierson
January 23, 2013

Group of analysts reviewing data

Group of analysts reviewing data. Photo courtesy of Shutterstock.

Data analysis and business intelligence have become booming fields, with a huge growth in demand for talented analysts and business professionals. The vast majority of companies and corporations maintain some sort of data analysis initiative, but it can be a highly frustrating field to work within. More than half of business intelligence projects fail or are never completed, mostly due to a lack of resources or management trust.

Despite the frustration, it can also be a highly rewarding field for people who can manage the stress of working in a growing, dynamic industry. If you’re considering a career in data analysis, there are a number of challenges that you should be prepared to face in addition to the challenge of crunching a database full of numbers.

Data Integrity

Naturally, the data you’re working will be the source of your largest headaches. For many companies or clients, data will be fragmented in different sources, full of holes, or simply noisy. Larger databases will be full of attributes values that are incorrect or missing due to faulty measuring or human error when registering data. Since many business databases are dynamic and changing daily, it can be a serious challenge to always stay on top of your system.

Faulty Hypotheses

Much of data analysis requires a scientific approach to cleaning up and interpreting data. When you approach a new project for an internal or external client, you’ll typically have a set of goals to achieve or to support with quality data. As any scientist can tell you, any faulty experiment comes at the price of a handful of failures. It is important to realize that even if your data does not support a specific hypothesis, you still learned something.

One of the largest pitfalls of data analysis is ignoring data that doesn’t support a preconceived notion or drawing conclusions from insufficient data. People may say that you can prove anything with statistics, but that is only true if you’re using them improperly. Know when you may not have enough data to support your conclusions. Without solid facts, your conclusions won’t have any value to your client or to your company.

Role Confusion

The role of data analysts will be very different from company to company. Some positions will require heavy programming knowledge, while others will be more of a business and marketing role. The role of a data or business analyst can sometimes be caught between the tech world and the business world, creating tension between your IT department and your marketing department. Being able to effectively and knowledgably communicate with both groups will make your life much easier.

Talent Gap

McKinsey Global forecasted a 50-60 percent gap in the supply and demand of people with analytical talent. While this may seem like an overarching business problem, it will have a profound effect on the daily function of current data analysts and business intelligence professionals. Data gathering and interpretation is a serious undertaking and requires a lot of manpower and brain power. From technical engineers to build viable data systems to people to fill them with usable data, people with data skills are much harder to find. As the role of big data in business increases and the numbers of qualified analysts shrinks, data analysis and data discovery will become a more difficult task.

Gut Feelings

While data analysts are hopefully approaching their data without prejudice regarding their project, that is often not the case for upper management or client representatives. Many decisions for your team or analysis can be swayed by the “certainty” of gut feelings held by company leadership. Unfortunately, your data has no room for emotions, only facts. While this may be a common problem, it is easy to include the gut feeling in your test hypotheses, but don’t let it dictate the entire flow of your work.

Shooting the Messenger

When delivering the data to people who are already sure of the outcome, unpopular results can be met with hostility or rejection. But as any seasoned business intelligence professional can tell you, unpopular evaluations will often be accurate, and ultimately valuable. Luckily, accurate analysis will stand on its own merits, supported by facts. If you can back up your observations with clean statistics, your numbers will speak for themselves.

Cara Barone is the social media marketing manager for Kforce, a provider of staffing and workforce solutions including data analyst jobs. Cara also manages Knowledge Employed, a career advice blog for job hunters, seasoned employees and hiring managers.

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