Data preparation is what you think and more
Data preparation is now a necessary and crucial part of making business decisions using enterprise data. It is also often the most tedious one. Ask any data scientist, data analyst and IT person and they will tell you that data preparation is time consuming, taking 50 to 80 percent of a data professional’s time. However cumbersome it is, data preparation is the process that guarantees good data is pumped into the analytics process.
What is data preparation? It involves harmonising, enriching or standardising the messy, inconsistent and unstandardised data of an enterprise, as well as collecting, cleaning and consolidating all the data from multiple unstructured sources or business functions within the organisation into one file or data table for analysis. The process includes data acquisition, data storage, data handling and data cleaning.
Business functions generate enormous amounts of data stored in different data lakes and warehouses. As an enterprise grows, it becomes more and more urgent to tame the extreme heterogeneity and volume of an organisation’s data before they become expensive and unproductive swamps. With data preparation, the data becomes clean, organised, detailed and easily understood – the basis for accurate and insightful analyses – a formula for successfully turning enterprise data into better business strategies. Furthermore, good data preparation acts as a foundation from which analytics can be executed repeatedly and at scale.
Data preparation is not just about turning messy data into good data. It’s about reshaping data into suitable material for insight-delivering analysis. As such, it’s more than an initial step in analyses. Data preparation will take place at any point as needed in the analytics process.
Here are some tips
Given the cumbersome yet crucial role of data preparation, here are tips to make the process more efficient:
- Have a clear idea of the problem or question. Data preparation always starts with a problem to solve or a question to answer. Working backwards from a problem to deal with already prepared data will only multiply an already time-consuming process.
- Clean data is not the end goal. Analytics is always the goal. Data should always add value to your organisation through analysis. Strive for analysis or data preparation is a waste of time and resources.
- Never re-key data if you can. This is a source of errors and a time sink. One option is to find a unique identifier among all sources and then use a data join to combine data into one table.
- Find the best tool for the job. Legacy tools like Excel may not be the most efficient solution. An integrated platform that allows access to hundreds of data sources and a data quality suite is ideal.
- Automate data preparation when possible. Cutting down on manual data preparation tasks, means cutting down on time.
- Use natural language processing to prepare unstructured or raw data such as social media feeds. This means increased accuracy of analytics for customer focus applications.
- Realise that preparation goes on throughout the analytic workflow. Data preparation is continuous throughout the analytics process. It doesn’t end at the initial stages of the analytics process or before analysis.
- Actually look at your data. Before machine learning work, look at the data and ask if a human could understand how this goes together.
- Every project is unusual and requires customised steps for preparation and possibly different tools.
As businesses become more and more digitised, they’ve become more data-centric and data-reliant, competing on who will best be able to leverage the vast amount enterprise data within the organisation as a performing asset. Analytics is that tool turning the extreme diversity and amount of data into significant business value. Data preparation, an essential part of this process, is what takes the organisation beyond merely having the data to making it possible for analytics to transform data into a business edge.
For questions on the business importance of data preparation and data management, contact ADEC Philippines Managed Services on +63 2 775 0632 loc 8187
