What are the most common data analytics project pitfalls and how can you avoid them?

Data analytics projects are iterative in nature, however the final deliverables must be discussed and clearly articulated by the top managers because after all they are the ones who are going to benefit from the insights you glean from the data , one thing I learned over the last couple years working in the data science field is to avoid self imposed deadlines , always give yourself a wider room time to maneuver through the various phases of the projects , as I mentioned deliverables are fixed but the other aspects of your data analysis project tend to get progressively refined as managers often realize the need for additional metrics to evaluate some aspects of the business performance

One of the most critical pitfalls in data analysis is a lack of familiarity with the business context. It's essential to have a solid grasp of the business, which includes understanding the data sources, data collection methods, and potential data biases. Another common pitfall is not clearly defining the problem at the outset and neglecting data cleaning and preparation. Selecting the appropriate tools and techniques is equally vital. Lastly, correctly interpreting the results is paramount. This involves recognizing the limitations of your analysis and understanding how the results will impact your stakeholders. Clear and accurate interpretation is key to making informed decisions.

The most common pitfalls in Data analytics is to not read the organization job function and ignore the needs and requirements of industry trends and patterns.

Common pitfalls: inadequate planning, limited stakeholder involvement, poor data quality, scope creep, and weak data governance. - Avoid pitfalls with a clear project plan. - Involve critical stakeholders from the beginning. - Prioritize data quality assurance. - Maintain a well-defined project scope. - Implement robust data governance. - Ensure continuous communication and alignment with business goals.

Data analysis has always been the bone for business acumen and proper management of all the aspects of smooth run. We need to have a keen eye on collection of and analysis of the data.Common data analytics pitfalls include: 1. Incomplete or Dirty Data 2. Overfitting 3. Confirmation Bias 4. Ignoring Outliers 5. Lack of Context 6.Not Defining Clear Objectives 7. Data Privacy and Security 8. Unexplained or Black-Box Models 9.Ignoring Data Visualization 10. Failure to Communicate Findings 11. Not Adapting to New Information

Data quality is the make or break case for any data analytics project. If the data infrastructure of an organization is all around the place, it's difficult to trust the data and build solutions on top off them. New age companies have a good grasp of this issue. But legacy companies have a lot of tech debt and find it difficult to organize their historical data and align it with their current systems and processes. Hence, data quality needs to be taken utmost care before starting a data analytics project. Remember that garbage in (poor quality data) will lead to garbage out (poor business insights).

Rubbish in, rubbish out. If the data isn't there, or the quality of the data is terrible, not even the best data scientist in the world can do any analysis. It's critical to be aware that process of data science begins at the data collection stage - all analysis are possible only after ensuring that good and unbiased data is being collected.

It is garbage in garbage out approach. Efficient and clean data pipeline is essential for effective data analysis and accurate decision making.

I would say that insufficient or poor quality data is one of the biggest challenges facing data analytics professionals today. It's a problem that can lead to misleading results, wasted time and resources, and poor decision-making. But there are a few things that can be done to avoid this problem. First, it's important to plan your data collection carefully. Make sure you have a clear understanding of what data you need and how you will collect it. Second, use a variety of data sources to get a more complete picture. Third, clean and prepare your data before you start analyzing it. This includes removing errors, inconsistencies, and duplicates. And fourth, check the quality of your data once you have cleaned and prepared it.

Once you set clear objectives, identifying the appropriate sources of data is critical. Collect only the required data and keep the necessary attributes. There are multiple cloud/onpremise options to store huge volume of data but defining and refining the data paves ways for directed and goal oriented analytics.

Falling in love with your analysis. That's a big problem. You should use the simplest analysis that helps solve the problem. Don't go into unnecessary complexity just because you can or find it interesting. Data analysis is about the problem, not the methods.

This is a very common pitfall especially when you are starting a career in data analytics. You can get carried away thinking that you can use a certain analysis method that would look "cool" but fails to answer/address the business problem. Always remember that your business problem should define the analytical method and not the other way around. This is the same for using the right tool as well. Few ways to mitigate this pitfall are - Ask yourself if a certain analytical technique will answer the problem? - Will this method help me gather insights and is there an easier or more straightforward way in getting these and in shorter time - How will I explain the analytical technique to stakeholders in order to tell a story?

Avoid falling down the rabbit hole: It's not uncommon to become so engrossed in a particular problem or question that we lose sight of the bigger picture. While deep dives can sometimes yield valuable insights, it's essential to periodically assess whether the time and effort being invested are proportional to the potential value of the insights gained.

Once my stakeholders asked for some data related to the project. To be clear, I asked him why he needed that data. With further communication, we found that those analyses were inappropriate and would divert us from our project analysis. I always use simple tools to not show any complex charts or data. To add only relevant analysis, I use questions of WHY in the communications with stakeholders.

Inappropriate or complex analysis- While it is rewarding to be abreast with different types of data analysis techniques, this could actually be a pitfall when you do not select the right method that's suitable for your objectives and data sets collected. Of course, having to validate any data analysis method and techniques you have decided to adopt is key to ensuring that the insights you are getting align with the objective and stakeholder's expectation of the project

"Let the data speak for itself" is a quote I always keep in mind, from my Statistics uni classes and throughout my analytics career. Too often there is a tendency to cherry-pick data findings to support a desired narrative - what I find more useful is to come in with an open mind: let the data tell its own story, and see how that supports or challenge the hypothesis. It's more truthful, more interesting that way.

"Data don't lie", but it can be biased. It is so important to actively question the methods with which the data were collected, how the data cleaning process occurs (which also sheds light on the variables/information that is prioritized for the study), and the type of answers you seek out through the data analysis process. Some helpful ways to do this are to keep track of your .do files to keep your work transparent, intentionally invite people, both scholars and not, to lay eyes on the analysis, and to understand that the perspective we take on the data gives us the narrative, not the other way around. Breaking down the gatekeeping barriers surrounding data analysis must be a constant focus in your mind as you engage in this work.

May I also add that most transaction data are "engineered" data. The data is a result of our business actions. Hence they do not happen randomly. E.g. sales promotions will drive higher transaction volumes. Therefore, data analysts and scientists must be sensitive to the internal promotion/ festive schedules to avoid producing the wrong insights or models. There will always be effects from the actions by outside actors, e.g. the effects of your competitors' promotions on sales. So we must do what we possibly can within time and budget to prepare our data prior to analysis.

Explain thoroughly all the set of assumptions that are beneath your analysis normally helps getting everyone aligned while always looking for a second opinion, especially by people not directly involved in the analysis, can improve our limits/biases and above all convince/challenge ourselves to rethink from a different perspective, where applicable.

One of the intrinsic qualities of a great data analyst is 'inquisitiveness and curiosity'. Being alert of your preconceptions and explore wide range and variants alternatives will help you to avoid cognitive bias. Also, testing your hypothesis with different kind of results is a sure way to go.

Analytics is a team sport - Poor Data + Poor Analysis = Bad Result - Poor Analysis + Poor Communication = Bad Result - Poor Communication + Poor Data = Bad Result => Good Data + Good Analysis + Good Communication = Making a difference!! All 3 need to work together without ego to take the raw data to business outcomes!!

Story telling with data is an art, and is a must skill for most of the data analytics project leads. Just presenting with numbers and charts may not be enough to tell the story. The context of data and how its presented can change the outcome of what the stakeholders would have understood. Selection of the data sets, explanation of the outliers and its impacts, range of data for the charts and graphs, and the highlights will affect the understanding. Does it resonate with the business outcome and aligns to what problem it is trying to solve? Elaborate it with a walk through, step by step, and build upon the story to finally get to the finishing line with an impact.

When presenting insights one must keep in mind that the audience won't remember many figures or graphs at all. In fact, if they want to know them they will just look back at the presentation. Therefore, why do have the presentation? Well, it's a great opportunity to convey a message. We must then choose wisely what our message is, and use all available resources (graphs, figures, etc.) to support this message. In doing so, we must also keep our audience engaged, entertained. Otherwise, why would they bother listening to us when they could read it? Prompt your audience, ask questions, crank up a joke or two and make sure that you keep them entertained.

While it might be tempting to showcase all the data points you've gathered, overwhelming your audience with too much information will be counterproductive. The key is to prioritize and present only the most relevant and impactful data that aligns with your audience's interests and needs.

Keep it simple! A lot of analysts and analytics leaders feel that building a dashboard is the best way to present data and insights to all audiences. Majority of the executives that I have worked with in the past, do not need fancy presentations or dashboards, they need a summary of the data/insights story, recommendations, answers to “what ifs”, etc.

I poured my heart into creating an amazing dashboard, but it's disheartening when it seems to lose traction after a few weeks. ???? To make it more enduring and impactful, I actively collect user feedback, ensure regular updates, and, more importantly: promote awareness. you got to keep the momentum going! ??

A data analytics project is often a "new ways of working project" with an integral analytics component. Many businesses have not fully embraced data-driven decision making, and those that have often struggle with the last mile challenge of converting insights into action and business benefit. Early in a project it's important to review how the results of the analytics will be used by its recipients to drive business impact. We do this at the same time we begin analysis of data availability, sufficiency, and quality. We maintain a small squad of change experts to engage as necessary with stakeholders to help them strengthen weak spots and bridge the gaps between intelligence/insights from analytics and action to create business value.

Actionable business activity/ tasks with data driven outcome usually would lead to a successful project implementation. Having a well defined metrics driven success criteria will go a long way. As the quality of data is continuously being monitored, so is the follow up required with the stakeholders of the usage and the utility of project for them. Monitor the usage, review the challenges faced, address the concerns and review the change in business needs. Incorporate this important step in the project plan, and keep revising the data source, rules, business needs and problem statement it tries to solve.

The most common pitfall is the lack of consistent support from top-level management which results in poor user adoption. Several organisations fall into this trap when top-level management disengages from the project after approving the budget. The following support is needed from top-level management: 1. Top-level management needs to consume the insights from deliverables to make informed decisions - only this can ensure user adoption. 2. To enable the above, top-level management needs to be data literate. They need to spend time to understand the data concepts as consumers.

Speak the Same Analytics Language or Risk Confusion. In any complex analytics project, nailing down terminology is a must. Take 'Active User Retention' chart for a fitness app, for example. If one team member thinks an 'Active User' just signed up, and another thinks it's someone with an active subscription, you're in for chaos. A shared analytics glossary fixes this. Keep it visible and updated, and make sure your team actually uses it. Tools like DBT can lock in definitions at the system level, while data catalogs keep everyone on the same page across the organization.

Failing to involve key stakeholders throughout the project can result in misalignment and lead to solutions that don't meet their needs. If the project is followed by low or minimal adoption of the tool or platform, that too could be considered as a pitfall. As an example, if people continue to download data into excel post implementation, something was missed out from addressing from a people, process and/or technology standpoint.

Focus on UX! The concept of analytics dashboards evolved from car dashboards, which served as the central location for operators to monitor major functions at a glance via an instrument panel. Thus, digital dashboard design must start with the customers/stakeholders. Understand their goals, needs and wants for data analytics dashboards in order to define the highest value workflows and metrics to focus on. Finally, UX will be increasingly critical as we start incorporating AI into dashboards to create conversational dashboards.

In today's data-driven business landscape, data analytics projects are pivotal for securing a competitive edge. However, they often come with challenges like vague objectives, data quality woes, privacy lapses, and communication gaps. To triumph, initiate your project with crystal-clear objectives that align with your strategic vision. Prioritize stringent data quality checks and robust security measures to safeguard data integrity. Foster transparent, efficient communication within your team, ensuring insights are shared and understood. By proactively addressing these hurdles, you set the stage for data analytics to power your organization's growth and competitiveness. #DataAnalytics #BusinessInsights