The Three I’s of Data Analytics
Data analytics involves the extraction of insights from unprocessed data.
Even though many data analytics tactics and processes have been automated, it still takes an expert eye to give humans meaningful insights from raw data.
In my experience, the entire process of analyzing data can be broken down into the Three I’s:
- Identify — What data do you need to answer questions? How will you collect the data and then use it in your analysis?
One example to illustrate the identification phase of data analysis is deciding on which social media platforms to invest your time in.
You can do this by looking at your current data and determining which platforms most of your followers are from.
If you’re in the initial stages of your business, identifying your target market will also help you know which platforms to be on simply by finding out which platform your target market usually hangs out.
Keep in mind that no matter how complex the question you want to answer or problem you want to solve, everything starts with figuring out which data you need to analyze to arrive at the answer or solution.
2. Inventory — This involves putting your data into a specific structure so that you can use it over and over again.
Microsoft Excel and Google Sheets are two of the most popular ways many people inventory data but you can use any tool or app that allows you to sort and analyze data in a particular structure.
Ensuring the data is clean is the key element of the Inventory phase. Data with typos, extra spaces, etc. needs to be cleaned before analysis. You also want to make sure your 1st row is always headers so you can easily run pivot tables or queries.
3. Integrate — After collecting, sorting, and analyzing the data, the next step is integration.
This phase largely involves communicating the data and using the information in such a way that people in your organization will be able to make informed decisions.
To be really effective with this phase, you have to use data visualization and storytelling to help the end-user make sense of the numbers. Boiling down massive amounts of data into 3 bullet points that a decision-maker can use to run their business is something the best analysts can do easily.
Which phase of data analytics are you having difficulties with? Do you have trouble finding the right data, is your data messy or are you having trouble getting the point across?
Let me know in the comments! We are here to help.
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Dan Meyer heads Sonic Analytics, an analytics firm with offices in Manila, the San Francisco Bay Area and Ocala, FL. With over 20 years in Big Data, Dan is an author, public speaker and business expert in virtual staffing and process automation.
Dan brings big data analytics solutions like business intelligence and data storytelling to small businesses looking to level up their use of data, virtual staffing and technology. An advocate for the use of analytics for civic responsibility through training, consulting and education, Dan loves volunteering his expertise with schools and non-profits.
