“Data-driven” means decisions/actions that are driven by data. In other words, a person can use a set of acquired data as evidence to aid in his/her decision-making process, instead of relying on “gut feeling”.
The concept of “Data-driven” has long started since the 90s, when large corporations used it for more specialized purposes to aid in their management and operations decisions. Fast forward to today with the introduction of big data, Internet of Things (iOT), and tons of free analytic tools, this concept has evolved to be crucial for improving efficiency, not only for businesses, but also for mass consumers and startups as well. Essentially it is possible to acquire data for anything today, even the status of a tyre or the least favourite TV channel, allowing any Tom, Dick, and Harry to adopt this concept in their lives.
As we speak of data, most people expect “Data-driven” techniques to require a huge amount of data, and more often than not, many freak out at even hearing the word data. While it is true that “Data-driven” requires a certain amount of information to be effective, understanding how this concept works can actually simplify the amount of data and effort needed.
1. Identify a goal and the information needed for it
To gather the correct data as evidence, we’ll first have to ask ourselves “what is the problem that I’m facing and what do I need to know?” Understanding this allows us to better set a goal and understand the metrics needed (information needed to drive a decision), thus filtering off all the unnecessary information while only focusing on acquiring the right one.
Example → Restaurant A has a decline of sales for the past 1 year, and the owner would like to understand why. Instead of gathering every information about why sales have declined, the problem could be more focused on “Is this affected by internal or external factors?” and “if it’s internal/external, how can i improve it?” Thus, the owner will only need to focus on understanding the value that the restaurant brings to its customers (e.g. asking clients for their feedback) and the understanding the local eating trends (e.g. conduct an online research or survey on the local eating trends).
2. Identify the most effective way of acquiring data
More often than not, people tend to make the mistake of assuming that data appears in digital form only. Truth is, data can appear in digital or non-digital formats, quantitative or qualitative. While it might be easier to manage data digitally due to all the automation tools available, data acquired offline can sometimes be more effective, and cost less to acquire too.
At the end of the day, identifying the causes for your problem is all that you need. If it’s an open-ended paper survey that’s expected to give you the best results, so be it.
3. Data Mining & Segmentation
After acquiring information, you’re expected to have a big load of raw data, and it’ll take a lot of time and effort for a mere human brain to process all of it. Fortunately, technology today allows us to filter out any useful data from the big load (data mining), and segment it into smaller chunks (data segmentation) for our brains to analyse it easily.
The most common method today is to use the help of artificial intelligence (AI). However, AI is mostly limited to digital and quantitative formats, and can also be quite costly to implement one.
As an alternative, businesses can also leverage on crowdsourcing methods to solve this. Basically this method allows businesses to outsource these big load of data to a big crowd of “freelancers”. Each of them will work on a small chunks at a time, allowing it to be completed much faster and at a cheaper cost.
4. Analyse Data
By implementing the above steps, you should now have a summary of data information, as evidence to your previously identified problem. All you have to do next is to implement a change. Good luck!
*We are Supahands, a data-focused crowdsourcing company that utilizes automation technology, to solve any repetitive data work problems (e.g. content moderation, data mining & segmentation, data management etc), with better speed, quality, and security.