How you prepare data for analysis has a direct impact on the quality of your results. Data analysis, like most other business-critical practices, requires a solid foundation to succeed. Effective data preparation provides that a foundation that significantly enhances and enriches the process so that your data is relevant, accurate, reliable, and easy to process for your data analysis and BI tools. The importance of the preparation stage cannot be overstated.
Important Steps In Data Preparation
A recent study from Harvard Business Review Analytic Services shows that there is a resounding gap between the expectations and realities of businesses when it comes to using data to achieve business outcomes. While more than 90% of businesses believe that data is essential for innovation and progress, less than 25% believe that their organizations implement effective use of data.
Moreover, large organizations rely on data from multiple disparate sources which are often inconsistent and fragmented. This dictates a renewed focus on data preparation techniques if you want solid results from your data analysis.
Let’s look at the most important steps in data preparation:
Define the Parameters
At the very outset, it’s crucial to remember that any data project should be driven by specific business goals and requirements. You would need to define those requirements first in order to create successful data analysis projects.
Consider these important questions first:
- What are the main business questions that you want to answer through data analysis?
- What are the important KPIs you need to measure?
- Who would need access to the data? At what technical level? How will they use the data?
- What analytical applications will you need to bring into the data once it has been prepared?
For example, if you are looking to analyze data to figure out customer perception about a product, you cannot limit your analysis just to transactional data. You also need to examine other forms of data that indicate things like product preference, product awareness, etc.. Whenever working with unstructured data, clearly defining the parameters of the ultimate analysis at the very outset can spare you a lot of unnecessary complications. It presents you with a more accurate picture of the data and informs better decision-making. It can also help you become better prepared for the entire data analysis process with the right amounts of time and resources devoted to it.
Identify the Data
Once you have a concrete idea about your needs, you can start identifying the data. You need to figure out where you can get the exact data you need. Your data might be stored in multiple places, including data lakes and warehouses, NoSQL databases, auto-generated databases coming from CRM solutions or external partners like ad networks. You might need to use a tool like Hadoop to handle native formats.
When dealing with unstructured, complex data from multiple sources, it’s critical to identify the exact length and breadth of the data you would need. Choosing to bring in all your data sources at once can impact performance and efficiency.
Once you have identified your sources, start looking for Relevance and Relations. Understand the schema, note the relationships between different sources and sub-sources, consider the relative sizes and details of sources compared with one another, and decide on a level of granularity you need for the analysis.
Next, you need to map out the data in a way that everything fits together well and remains relevant for the eventual analysis. You need to keep the possibility of ad-hoc queries open while modeling the data. Find common fields that can make it easier for you to merge two or more datasets together. Find ways to combine different data fields and tables, create key tables, and summaries.
At this stage, it is also important to map out relationships between the data. These can be of three kinds. One-to-one Scenarios dictate that there are unique values for every data row for both sides of the relationship. One-to-many Scenarios dictate that one particular side of the relationship would contain unique elements in every data row while the other would carry duplicates of the corresponding values in the first table. Many-to-many Scenarios dictate that both sides of the relationship will have duplicates. This is something that you would need to resolve and bypass.
If you are using many different data platforms, you might have to navigate many different APIs to create the internal connections. Look for a standardized solution to make this step easier.
This very important step is all about problem-solving; you sort out problems now that can lead to errors later. For this, everything needs to be consolidated using the right format and structure. Any errors or inconsistencies need to be ironed out. If you want to combine fields that are related, or standardize fields for data uniformity, you do that at this stage.
Data wrangling involves checking each and every source for accuracy, recency, and completion. This can be especially complicated and time-consuming if you want to manually manipulate or transform the data or group the data in some new way. Many-to-many relations in particular need to be sorted out at this stage as they can lead to inaccuracies in analysis and also become major resource hogs while the analysis is taking place.
Different techniques, like running specific SQL commands, can be put to good use to find and neutralize many-to-many relationships in the data. You can also program custom alerts that can proactively show you when the analysis runs into issues due to these relationships.
Load the Data
When you have completed the cleaning and wrangling, it is time to load the data into a single, centralized location for easy access during analysis. This location can be a data lake, data warehouse, or any manner of analytical database solution.
Consider the volume of the data and the frequency at which you plan to run queries while choosing the right place to load the data. You need to consider if you have the right hardware capacity, choose the right cloud solution to store the data, or go for a single-stack BI platform. The final option can present some advantages, such as letting you dip into only the amount of data you need for a specific analysis. These parameters can help you analyze larger datasets.
You can also create some general definitions regarding how to deal with the data. You can choose to replace all data during the time of the build, which is particularly helpful for dimension tables. You can choose to accumulate, or add data to existing data tables, which is a better option for detailed fact tables. Or, you can choose to ignore when unchanged, which does not import data unless there are changes in the table schema. This is a salient option for smaller data marts and summary or snapshot fact tables.
Verify the Data
The final step verifies the entire process and identifies any problems along the way. For this, you can use a simple query that has an expected answer and query the data to see if the result matches your expectations. You can also run automated algorithms using pre-defined parameters to verify the integrity and completeness of the data.
The Main Objective
If you want fast, efficient, and accurate insights from your data analysis, it is crucial to take data preparation seriously from the very outset. These steps of data preparation for analysis can apply to a large number of use cases and several different technical setups.
While it might be convenient to use different solutions and approaches for each stage of the process, it can always be worth the extra effort to look for a single-stack solution that can add more speed, functionality, and better error-tracking. That way, you can manage all of this from a single platform.