Basic Data Analytics Principles

Today, data and analytics are undeniably key to any organization’s digital transformation initiatives however without guidelines and frameworks in place, it becomes very easy to get lost in a sea of data by not knowing what to look for or why to look for it. Below are 6 important principles to look out for during data analysis:

1. Have a purpose

“Data is like garbage. You’d better know what you are going to do with it before you collect it.”- Mark Twain

It’s one thing to collect masses of data, but as Mr Twain says, unless you have a plan for that data, it won’t really be of much use. Before you collect the data, you need to have a specific question you want to answer or a challenge you want to overcome. Based on this, then you can determine the kind of data you need and where to source it. Data collection with no purpose is Garbage!

You need to know what you want to achieve, how you will achieve it and when you will achieve it

2. It should be meaningful & actionable - this is what matters

Data should actually say something and allow you to act based on what it tells you. You should be able to understand WHAT is happening and proactively look into WHY it is happening.

This relates to point #1 above, where the objective is to look for problems you can actually fix, rather than just interesting pieces of information. A simple way to avoid the latter is to set analytic standards, run reports to monitor those standards and then find the discrepancies. This will ensure you are looking at specifics rather than just aimlessly scanning through data.

3. Provide the right interfaces for users to consume the data

Your data is as good as the interface being displayed to the final consumer of that data. If it is too complex to understand, then that data will not make sense. So, there is a need for dashboards and displays that present data in an understandable form for those who aren’t well versed in extracting insights from strange charts and symbols. The tool of choice should allow for varying displays to meet the demands and skillsets of the various people interpreting the data.

4. Choose Data Tools Wisely

Not everyone using a data analysis tool has the time or experience to interpret tones of information so the tool itself should process and present datasets in an easy-to-digest format. It should be fast, and user-friendly with real-time reporting capabilities so as to save time, effort and resources.

5. Analysis is for everyone, not just the specialists!

Everyone has to interpret data to some extent in the day-to-day dealings at any workplace. For example, running a report to find anomalies in how voucher numbers or invoice numbers are entered is a form of data analysis. Such a task will not be assigned to a data specialist, but to someone in the accounts department. So, what really matters is not your credentials, but rather, the relevance of data to one’s specific position. After all, your company’s data presents information that virtually all members of your organization can pick insights from.

6. Collect data now and as often as possible

“With data collection, ‘the sooner the better’ is always the best answer.” - Marissa Mayer.

The sooner you start collecting data, the sooner you can benefit from its insights—and the sooner you can use those insights to make smart decisions. So don’t wait to start tomorrow, start now!

Running regular reports proactively clears the backlog that needs to be processed so that you’re looking at current information – not the additional burden of data from prior weeks or months.

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At Astonish Learning, our team of data analysts & scientists are ready to support you to get started on this journey. Visit our website or get in touch with us on social media.

By

Timothy Kibirige