In our cutthroat digital age, the importance of setting the right data analysis questions can define the overall success of a business. It is not just important to gather all the existing information, but to consider the preparation of data and utilize it in the proper way, has become an indispensable value in developing a successful business strategy.
That being said, it seems like we’re in the midst of a data analysis crisis. Although organizations spend millions of dollars on collecting and analyzing data with various data analysis tools, it seems like most people have trouble actually using that data in actionable, profitable ways.
However, the truth is that no matter how advanced your IT infrastructure is, your data will not provide you with a ready-made solution unless you ask it specific questions regarding data analysis.
To help transform data into business decisions, you should start preparing the pain points you want to gain insights into before you even start the data gathering process.
1. What exactly do you want to find out?
It’s good to evaluate the well-being of your business first. Agree company-wide what KPIs are most relevant for your business, consider what your goal is and what decision-making it will facilitate. What outcome from the analysis you would deem a success? These introductory data analysis questions are necessary to guide you through the process and help focus on key insights. You can start broad, by brainstorming and drafting a guideline for specific questions about data you want to uncover. This framework can help you to delve deeper into the more specific insights you want to achieve.
2. How will this data be used?
Once you have your data analytics questions, you need to have some standard KPIs that you can use to measure them. For example, let’s say you want to see which of your PPC campaigns last quarter did the best. “Did the best” is too vague to be useful. Did the best according to what? Driving revenue? Driving profit? Giving the most ROI? Giving the cheapest email subscribers?
All of these KPI examples can be valid choices. You just need to pick the right ones first and have them in agreement company-wide (or at least within your department).
3. Where will your data come from?
Identify data sources you need to dig into all your data, pick the fields that you’ll need, leaving some space for data you might potentially need in the future, and gather all the information into one place. Be open-minded about your data sources in this step – all departments in your company, sales, finance, IT, etc., have the potential to provide insights.
You can use CRM data, data from things like Facebook and Google Analytics, financial data from your company, as long as the data source is relevant to the questions.
4. How can you ensure data quality?
If your data is incorrect, you’re going to be seeing a distorted view of reality. That’s why your next step is to “clean” your data sets in order to discard wrong or outdated information. This is also an appropriate time to add more fields to your data to make it more complete and useful. That can be done by a data scientist or individually, depends on the size of the company.
When you ensure your data quality, you’ll have built yourself the useful asset of accurate data sets that can be transformed, joined, and measured with statistical methods.
5. What ETL procedures need to be developed, if any?
ETL tools pulls your data from external sources, conform it to demanded standard and convert it into a destination data warehouse. These tools provide an effective solution since IT departments or data scientists don’t have to manually extract information from various sources, or you don’t have to become an IT specialist to perform complex tasks.
If you have large data sets, and today most businesses do, it would be wise to set up an ETL service which brings all the information your organization is using and can optimize the handling of data.
6. Who are the final users of your analysis results?
Another significant of your data analytics questions refers to the end users of our analysis. Who are they? How will they apply your reports? You must get to know your final users, including:
- What they expect to learn from the data
- What their needs are
- Their technical skills
- How much time they can spend analyzing data?
Knowing the answers will help you to decide how detailed your data report will be and what data you should focus on.
Your final users should be able to read and understand them independently, with little or no IT support needed. Are they junior members of the staff or part Management? Every type of user has diverse needs and expectations.
7. What data visualizations should you choose?
You can have the most valuable insights in the world, but if they’re presented poorly, your target audience won’t receive the impact from them that you’re hoping for.
You have to convince other decision makers within your company that this data is:
- Correct
- Important
- Urgent to act upon
Effective presentation aids in all of these areas. There are dozens of data charts to choose from and you can either thwart all your data-crunching efforts by picking the wrong data visualization or give it an additional boost by choosing the right data visualization type.
8. What kind of software will help?
With the expansion of business intelligence solutions, data analytics questions to ask have never been easier. Powerful features such as basic and advanced analysis, countless chart types, quick and easy data source connection, and endless possibilities to interact with the data as questions arise, enable users to simplify oftentimes complex processes. No matter the analysis type you need to perform, the designated software will play an essential part to make your data alive and “able to speak.”
Moreover, modern software will not require continuous manual updates of the data but it will automatically provide real-time insights that will help you answer critical questions and provide a stable foundation and prerequisites for good analysis.
9. What else do I need to know?
Your end result is equally important as your process beforehand. You need to be certain that the results are accurate, verify the data, and ensure that there is no space for big mistakes.
Ensure to test your analytical environment against manual calculations and compare the results. If there are extreme discrepancies, there is something clearly wrong, but if the results turn accurate, then you have established a healthy data-environment. Doing such a full-sweep check is definitely not easy, but in the long term, it will bring only positive results. Additionally, if you never stop questioning the integrity of your data, your analytical audits will be much healthier in the long run.
10. How can you create a data-driven culture?
Whether you are a small business or large enterprise, the data tell its story, and you should be able to listen. Preparing questions to ask about data analytics will provide a valuable resource and a roadmap to improved business strategies. It will also enable employees to make better departmental decisions, and, consequently, create a cost-effective business environment that can help your company grow. Dashboards are a great way to establish such a culture, make it prominent across all departments. They need to understand why it is important to conduct data analysis in the first place.
Data analysis isn’t a means to discipline your employees and find who is responsible for failures, but to empower them to improve their performance and self-improve.