Getting started with Data Analytics
Data is garage in, garage out, good data and bad data, useless data and data with value. With digitalization, data is everywhere, raw data give contexts to help derive Information. Information gives meanings to become Knowledge and Knowledge gives insights to finally become Wisdom.
In simple words, we all probably have some data in different ways but how can we derive insights and value from Data? Before that, let’s understand the common problems of Data.
Common problems of “Data”
- Offline – Many data are in physical manual hardcopy forms
- Limited – Many useful and valuable data points are not collected
- Not integrated – Data that are available are in some files like Excel in different workstations
- Not usable – Data stored are also inaccurate, not logged properly or not updated
- Data hidden somewhere secure – Request to IT for data and reports to be generated. This often takes days or weeks and every time you need it.
With such common data challenges, the outcome is mostly basic charts and reports that requires heavy manual work and often results that are inaccurate, incomplete and not 100% trustable.
So, what can be done? Our 2-simple approach to get things started.
1. Set the “Data” foundation so data are available and fit for use
- Convert offline data to online, in other words digitalized hardcopy forms to online whether in database, Google forms or Excel files
- Collect data points that are of value: Plan, design and implement data collection process that are useful
- Integrated and consolidate them to a database management system such as Microsoft Access or MySQL or Data Lake/Warehouse.
- Put in place Data Quality checks by focusing on process, monitoring, profiling and governance for Data to be trusted.
- Schedule and automate data pipelines, refresh and alerts so it eliminates manual request or actions and get value from data isolated sitting somewhere.
2. Get actionable insights from trusted data
- Operational Report – Most reports are descriptive in nature and can only tell you what has happened such as sales last week. This is also reactive and operational in nature for measuring efficiency or performance.
This is the first and important step to understanding and getting value and use from your data.
- Advanced Reporting – As more data are available and integrated, you can explore data that are connected across multi-dimensions for analysis such as sales by locations by transaction volume and number of transactions over time.
More questions will be answered but also asked next. Reports will evolve to dashboard for exploration, benchmarking and decision-making.
- Actionable Insights – With multi-dimension analysis, apart from “What Happened”, you will be able to answer, “Why did it happen”. For example, why did sales drop in quarter, is it due to the products, stores, existing customers or new customers conversion. Actionable insights can now be derived, and proactive actions can be taken.
