Data business is the huge business. Analyst is yet to find the aspect of human life that data hasn’t ensued. Since data is almost everywhere, that creates a precedence. Which means, you already have the being there, done that of all things.
For instance, as a newbie in sports, you need to get an acumen on a concept. Exploring sport related data will be a good way to start. Somebody somewhere already took the path. If none, be the pioneer.
So, regardless of the business sector to want to venture into. You already have a step to begin your journey into adding value. Source for data, then jump on the datasets and move on.
But getting these datasets isn’t as important as drawing insights from it.
Also, data collection is expensive.
Yes, there are different analytic tools that help us collect and get insights from datasets. But, cost effective, quality and timely analytic tools are uncommon.
These following tips will help you turn data into quality insights in four quick steps.
Start with the end
With data analytics, you’re about to thread a direction to solve a particular problem. But how do you know when you get to the actual solution?
You’ll get to a destination only when you know the destination.
Else, you’ll get there without knowing.
Before you start any analytics, it’s important you’ve an idea of the end result. This doesn’t mean that you should alter the voice of the numbers and or influence what it reveals.
Once you’ve this in mind, you’ll save time, effort and money. You’ll immediately identify the insight once it uncovers.
Understand the data
How do you know the actual data that suits your study or search? This should prompt almost immediately you foresee the problem set.
First, you’ll need to know the parameters attached to your quest, these parameters may include boundary conditions or controlled environment. Be aware that variations in these boundary conditions will reduce the accuracy of your measurement.
Secondly, what’re the sources of your data? You’ll also need to break that huge chunk of data into demographics, geographic and content to further highlight the details.
This is the first step towards understanding your data. Once you understand your data, you belong to the 360 degrees directional view of doing everything relevant with it. It may also raise further questions and views.
Having said, possibly it would be appropriate to validate the data or get clarifications regarding any lapse that you perceive.
Determine the data strength
Every data has its state.
Those in the confidential level have access and usage limitation. A data is said to be strong or good enough for analytics when the quality is intact.
Some data don’t have the necessary parameters or response that suits the study. They’re not full enough.
For instance, a company wants to know the top five quality drug stores in a geographic location so that they can map and simplify product delivery and better serve the community due to the fragile nature of the product.
Unfortunately, the survey questions don't cut across why the respondents mentioned the drugstores. Which creates a bias, because there is every possibility that someone could mention a particular drugstore because a family member has used it before or the drugstore is located on his street.
This data strength is weak due to an incomplete type. You should also note that data could decay which means that data is timely.
Process the data
Data only becomes insights when properly processed. This creates meaning and helps in quality decision making, smoother operations, outstanding customer satisfaction, employee retention and the overall drive of the business.
Most times this analysis is accomplished by a specialized personnel who knows the data management practices, detailed and can effortlessly model and interpret data sense in an excellent format.
The expertise of this personnel will be lame if the appropriate analytical tools are not employed. So, to better achieve repeated, real-time and long lasting insights, analytics solution such as alteryx will help you deliver quality insights within hours.
Data analytics wouldn’t have been better. Ensure you utilize the above steps and become entirely data driven.