5 day-to-day biggest issues for data analysts, and how they affect organisational performance

by | Feb 28, 2022 | #BrijjtheGap, Data & Insight | 0 comments

5 day-to-day biggest issues for data analysts, and how they affect organisational performance

by | Feb 28, 2022 | #BrijjtheGap, Data & Insight | 0 comments

Whether you’re aiming to improve your organisational data culture or adjust your data strategy, our experts have you covered. Understand 5 day-to-day biggest issues in a data analyst job and their affect on overall organisational performance. 

Last week, we’ve covered the 5 things that really matter to data professionals and what steps to take to improve your data teams‘s job and career satisfaction. This week, we’re moving onto 5 biggest day-t0-day issues and how those affect overall organisational performance.


Organisational data is your greatest asset

All businesses should know the value of their organisational data as well as individuals in data analyst jobs managing it, no matter the industry or their size. Let’s get one thing abundantly clear: next to your services, products and people, data is your greatest asset! You don’t have to take my word for it. You can take the word of experts at Forbes or News York Times. – They keep banging on about that too. No matter where you look, there is someone there talking about the importance of being a data-driven business.

So how is it that despite the hype and the heavy investment in data processes and management, many organisations still struggle with so many data work-related issues and frustrations? How is it that despite knowing the importance of data, many struggle to derive real business value from it?

Well, it could be a case of organisations being stuck in their ‘old ways’ of doing things. It could be a lack of data literacy among organisational leaders and across other departments. Maybe ‘Big data’ and ‘data strategy’ are merely buzzwords to them. Or it could be the confusion surrounding best data practices and tools. It could be one of these, or it could be all of them.

Regardless of whether it’s one or more of those for your organisation, these issues need addressing if your business is to thrive in today’s competitive, fast-paced environment.


Data: past vs present

The importance of data to business has grown significantly in the last 20 years. So have changed the attitudes towards it. Despite that, few businesses have adjusted their strategies accordingly. Practices concerning data capturing, sharing and management have been the same for years. And unfortunately, they’re no longer relevant in today’s world.

Once upon a time, data used to be considered the by-product of business actions and activities. It was only considered relevant to tech specialists and businesses. Others, not so much. Consumers have only started using iPhones and apps available to them 13 years ago. As time went by and they started generating bigger and bigger amounts of data, business leaders started seeing its wider potential. As organisations began setting up their own processes for data capture, they soon got swamped with information overload. Missteps in managing data led many to customer and financial losses. There was no technology in place to maximise the potential of data and its impact on business outcomes.

A decade later and the world has changed completely. Data is incorporated into every business department and considered integral to organisational performance.  And its value will only grow.

But companies still struggle with culture surrounding data capturing, analysing, storage and management. To really harness the power of data, they should investigate where issues lay for them. If you’re not sure where to start with the process of doing so, this article is for you!


5 day-to-day biggest issues for data professionals

We’ve recently released our first report titled ‘Emotional Intelligence Report 2022: Data & Insight job satisfaction and its impact on successful data projects.’ To gain insight into the industry trends, we spoke to 120 data professionals across different industries. The survey we ran covered many questions. Among others, we asked our respondents what day-to-day issues they faced in their current jobs.

Responses revealed the most common 5 day-to-day issues for those in data analyst jobs. These can easily be translated into challenges standing on organisations’ way to truly harness the value of modern data.

Without further ado, let’s dive in!

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Discover what really matters to your data teams and increase the chances of your data project success!

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  • Are data professionals happy in their careers?
  • Do data leaders know what matters to their teams?
  • What are the biggest day-to-day issues data professionals are facing – and do those affect their data project outcomes?
Brijj answers these and many more questions so you can start closing gaps in understanding within your data teams.
Please provide your work email.
You will automatically start receiving Brijj weekly newsletter with Data & Insight news, advice and opinion. Unsubscribe anytime.

1. Bad processes

“There is plenty of overhyped technologies out there that people want to be seen using. Too many varied tools don’t solve the problems we’re facing. They add to them.” – Data Analyst


Data is the currency of the business world. Good data can really evolve your business in the best way possible. But bad data can do the complete opposite to it…The quality of data collected ties closely with business actions and their outcomes.  Making internal and external business decisions based on false, irrelevant, or incomplete data, leads businesses to lose time and financial resources.

Organisational leadership needs to make sure that the margin of error is minimised as much as possible. They can do so by investigating and standardising every step of their data process. Everything, from taking requests, through gathering requirements and cleaning data should be standardised and systemised to improve the entire process of delivering insight.

To give you an example of what I mean:

Request submission and management, as well as requirements gathering are a vital for the success of any data project. 90% of business customers still use email to submit their work requests through to data teams. This makes it considerably harder for those in data analyst jobs to manage their work. Chances of potential error grow as email chains multiply and workload increases. To deliver business impact, data teams need to be able to oversee and prioritise tasks efficiently to ensure they focus their efforts on the right places.

The best way to reduce error and increase data analyst job and project stakeholders’ satisfaction is to systemise and standardise your processes like request submission, requirements gathering, and feedback.  

2. Lack of/bad strategy

“Organisations need to strategise their approach to data. We need
good talent and the right technology so we can advance the data to its absolutely potential” – Data Analyst

“Reliance on other business functions like engineering and IT. These can become serious blockers and really get in the way of delivering.” – Senior Data Analyst

Many organisations don’t utilise data to its full potential. In fact, research shows that over 40% of businesses don’t have a person responsible for data strategy and management!

While you don’t need data strategy to keep the business running, you certainly need a clear strategy to maximise the value of your organisational data. When implemented correctly and communicated clearly across your organisation, a data strategy presents businesses with competitive advantages and opportunities for growth and financial gain. Leaders shouldn’t neglect the importance of it.

The concept of a business strategy is well understood across organisations regarding assets like people and products or services. So, going back to the beginning of our article: if you want to treat data like the business asset it is, data strategy isn’t optional. Modern businesses rely on data. Having a set of long-term objectives for data management, aligning with your overall business strategy, is a must! Week data management causes numerous issues, including weakened customer relationships and lost revenue.

A successful data strategy should cover the following:

  • Business requirements
  • Data – what you have, where it’s stored and who can access it.
  • Tools – what works for ALL data & insight project stakeholders.
  • Analytics techniques – best ways to extract business-critical insights.
  • Collaboration – within data teams and between them and their business stakeholders.
  • People – ensuring data literacy across all departments.
  • Roadmap – how you get where you want to be.


3. Unrealistic expectations

“Lots of parts of the business areas think data grows on trees and everything is clean and ready to use.” – Data Analyst


Managing data users’ expectations can often be a very frustrating part of a data analyst’s job. Business customers are often dissatisfied with project timeframes or the insights delivered. The common statistic is that 80% of all data & insight projects are a failure. But what causes the dissatisfaction and how can we prevent it?

There are numerous possible reasons for ‘unrealistic expectations’. One of them could be weak or lack of data literacy across other departments of the organisation. Another that comes to mind is poor collaboration or insight delivery not meeting the needs of the target audience. Whatever the reason, lack of clear alignment between data teams and other project stakeholders is likely to lead to wasted effort and dissatisfaction.

The key to managing data end-user expectations lies in agile and transparent collaboration. Too often, data business users aren’t involved in the data project from question through to outcome. Business aspiring to become truly data-driven, should change that to strengthen their data culture.

Data project leaders should be sure to make all project stakeholders a part of the entire process. Implementation of the right data project management tool working for both data teams and their customers, as well as clear communication and feedback on project outcomes is also essential.


4. Bad people management

“I’m always the last to find out data structures have changed!” – Data Analyst

Respondents in data analyst jobs indicated ‘bad people management’ as one of the most common day-to-day issues in their current jobs. When asked to elaborate, they specified poor workload management, poor communication, and inadequate workload distribution within the team.

We’ve already established that agile collaboration between data teams and other departments across the organisation is essential to successful project outcomes. What’s equally essential is communication and collaboration within data teams themselves.

Data team leaders, to improve your data team’s performance, you must improve data team management. Managing a data team requires skills, practice, and commitment. Here are a few factors to consider:

  • Structure. Choosing how to structure your data team according to your organisational needs encourages efficiency and creates a network of accountability for all stakeholders.
  • Job roles. Data team leaders should assign specific roles to all team members. Those should be chosen in alignment with their skills to highlight how they’d be the most effective within the team.
  • Stakeholder engagement. Encouraging a positive relationship and effective collaboration between the data team and their stakeholders is essential for project success.
  • Positive work environment is proven to contribute to creating aligned, more professional, and supportive workforce.
  • Development. It’s important for team leaders to promote educational culture within their teams and encourage learning and skills development.
  • Leadership. It’s important that those managing data teams develop their own leadership skills to truly connect with their teams and become more people focused.


5. Feeling unappreciated

“Seems as though a lot of companies have a data/insight team and then do not use the recommendations coming out of that team.” – Senior Data Analyst

Have you heard the phrase “people don’t leave jobs, they leave bosses?”

Often, people do not leave jobs even when they feel overworked or underpaid. They leave because they don’t feel valued. Yes…when we feel that our work isn’t appreciated or our efforts go unnoticed, we stop feeling motivated. This, consequently, leads to decreased productivity, longer turnaround times, and yes, dissatisfied stakeholders!

It is the organisational leadership’s job to make sure their people feel like their work matters. Wondering whether hours of your hard work resulted in any business outcomes or whether the insight provided was ignored can drive one mad. It’s important to provide feedback to data teams on the quality & utility of their work. Incorporating work follow-up into your data project processes (better yet, automating it!) allows your data analysts to focus on future value.


Organisations must start from mastering the basics of data analysis

Modern businesses seem to understand the importance of data-driven decision-making. They’re also aware of opportunities coming from improving their data management processes and overall organisational data culture. So, it’s surprising that so many are still catching up to make the most out of their data.

Soon, advanced data strategies will be crucial for business survival. They enable organisations to connect with their customers, spot new opportunities for growth, and manage risks. Get amongst it or get left behind…Now is the time for organisational leadership to get the fundamentals of data management right. That means investigating data teams and their stakeholders, dedicating time and financial resources to data management, and investing in technology.

It can be an overwhelming process, but getting the basics right and developing an ambitious, long-term data strategy is vital for business success. If you’re not sure where to start, start from your data teams. We spend millions in hours and money on keeping our data assets in order. Organisational leadership, whether responsible for data or not, should spend more on the most valuable data asset of all – people.





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