They say, “Data never lies.” But sometimes, it might mislead you. This guide will help you understand your data and make informed decisions.

What can go wrong if we don’t understand data? Everything!

These days, business decisions are based on data rather than intuition. At the same time, the sheer amount of data businesses have to deal with is unimaginable. Every time a customer interacts with a brand, there is new data to interpret. The bigger the organisation, the more data to store and analyse. No wonder there are now a number of platforms and services aimed specifically at assisting clients to keep their data in check. 

Ignoring, misunderstanding, or mishandling data leads to problems. Resources may be wasted, and opportunities may be missed. 

The bankruptcy of online grocer company Webvan in 2001 is a classic example. They had a great business idea but overestimated demand and targeted the wrong audience. Ignorance led to losses.

But what does it mean exactly, “understanding data”? How do we go about it?

Here is our five-step guide which explains the process in every detail. 

Five steps to understanding data

Step 1: Data collection – tools and techniques

Data collection is the first step. Focus on quality, not just quantity. You need to choose reliable tools and those that are up to the task. Google Analytics and SQL databases help you collect and manage data efficiently. APIs fetch specific data quickly. Web scraping collects public data. IoT sensors provide real-time information. Choose the right tool and the right format to store the data:

• CSV is best for simple, tabular data. It is ideal for straightforward data needs,
• JSON supports hierarchical and nested data,
• SQL databases manage complex queries effectively and offer advanced data management,
• API sources provide structured data from various platforms, like social media, financial markets, or weather services, and enable you to retrieve real-time or historical data for analysis.

Match the format to your exact needs for optimal results.

Storage is essential, too. Cloud storage offers flexibility, while on-premise solutions give control. Whatever you choose, ensure your data is accurate from the start.

Step 2: Data cleaning

Once collected, your data needs cleaning. Dirty data leads to wrong ideas. Data cleaning has three steps: handling missing data, removing outliers, and standardising formats.

Missing data can skew results. Address it with imputation or deletion. Outliers distort the analysis. They can be identified and removed using statistics.

Consistent data is easier to analyse, so standardise your data formats. 

Step 3: Data exploration

You have clean data – go on and explore it. Data exploration helps you understand what your data is trying to tell you. Use summary statistics for an overview. Look at means, medians and modes.

Correlations identify relationships between variables, and these reveal hidden patterns. Use Power BI or Tableau for exploration as visualisation tools always make trends clearer.

Exploratory Data Analysis (EDA) is essential. It helps spot anomalies and/or confirms initial hypotheses. This step sets the stage for deeper analysis.

Step 4: Data analysis – turning data into actionable insights

Now, analyse your data. This turns it into actionable insights. Choose your method based on goals. Regression models predict outcomes. Classification categorises data.

Advanced tools like SAS and SPSS are invaluable for proper analysis. These platforms handle complex datasets. Once you get your results, interpret them. Make sure they’re clear and actionable.

For example, a store finds that discounts boost sales. Great, now this insight can be used to plan promotions. Right analysis reveals strategies that lead companies to success. 

Step 5: Data visualisation

Visualisation is critical for presenting findings to other teams, senior management or clients. It’s more than just making things look good. Visualisation helps stakeholders understand complex data.

Choosing the right chart type is essential but ultimately, it’s down to your choice. Bar charts compare categories. Line graphs show trends over time. Scatter plots reveal relationships. 

Again, we recommend using tools such as Tableau or Power BI for effective visualisation.

Good visualisation is the final stage of analysis. It provides clarity of understanding. It makes it easier to make informed decisions.

Infrastructure setup: How not to waste money

Building a data infrastructure is very important. But you don’t want to overspend. Over-provisioning is a common mistake. It’s like buying a sports car to drive around town.

Plan for a scalable infrastructure. Cloud solutions offer flexibility and often make much more sense than on-premises solutions. Cloud solutions scale up or down based on demand. Open-source tools are cost-effective.

A word to the wise: don’t underestimate storage needs. Data grows fast. Plan ahead to avoid extra expenses and allocate resources wisely. Focus on areas with the best ROI.

For example, Airbnb had scaling issues initially, and cloud-based infrastructure solved this.

Always follow best practices! But what are they?

Start with data governance. Set policies for data quality and security. Ensure your data is reliable and accessible. 

Data privacy is critical. Follow regulations like GDPR. Compliance isn’t optional. Non-compliance leads to fines and huge reputational damage. Secure your data. Respect the user and respect user privacy. 

Remember that only regular audits help to maintain standards. Continuous training is vital as data changes rapidly. Remain competitive by keeping your skills at a high level.

Case study

There was a retailer who had inventory issues. Analysing sales data revealed customer demand patterns.

They stocked the right products at the right time and won: it reduced storage costs and increased customer satisfaction.

They started by collecting sales data and cleaning and analysing it. Visualisation tools helped present findings to management, and dashboards made it easy to adjust inventory levels.

The result? A 15% cost reduction and increased customer satisfaction. The key takeaway here was understanding and utilising data.

Importance of understanding your data

Even with correct data, decisions need to be made carefully. Understanding the data is not just about collecting it. It is about interpreting the results and using them wisely.

Consumers expect their data to be used responsibly. An example of how data can be mishandled is a well-known case from 2012. The company Target used data analysis to predict customer behavior. It particularly focused on identifying pregnant customers based on their purchasing patterns. The company then sent targeted advertisements, including baby product coupons, to these customers. However, in doing so, sensitive information was exposed. A father discovered his teenage daughter’s pregnancy through these ads. This exposure caused a public backlash, trust issues and financial losses for Target due to negative publicity. 

Conclusion

Understanding your data is the foundation of smart business decisions. 

From efficiency to cost savings, the benefits of data-driven decisions are clear. But approach data carefully. Follow the five steps—collection, cleaning, exploration, analysis, and visualisation. The cost of poor data understanding is high.

Invest in your data processes, improve your data literacy and let your business live long and prosper!