How To Articulate Data Like A Pro
In the year 500BC, Greek philosopher Heraclitus said ‘the only constant in life is change’. Heraclitus was right, of course, and the human race has spent the next 2500 years proving the accuracy of that statement.
However, nowhere has change been more prevalent than in the last 5 years. Large corporations have shifted their focus from attempting to create demand for their products to using data to predict which products will be in demand and when. This has led to companies wanting to hire people with skill levels that can only be described as somewhat unrealistic. Case in point is when Sebastian Ramirez, the founder of FastAPI saw a job advert requiring 4 years experience in FastAPI when the program itself had only been around for 1.5 years. While this became a joke, it is indicative of the expectations of employers; they want people who can have an immediate impact on their company.
To that effect, candidates and employees alike need to show that they have a, finger on the pulse and that they are the right people to get the job done. This is often achieved by being armed with facts and figures to add heft and credibility to what you have to say. These data points have changed from being a wow factor to the price of admission into a conversation. As such, the way that data is presented can be a major difference-maker. The importance of the ability to articulate data is increasing in today’s business world and its prominence will only continue to grow as time goes on.
Dicorm prides itself on being a company fueled by our ability to monitor data and provide actionable insights. We, just like Mando, believe that ‘This is the way’. Here are some of the most effective ways to articulate data.
Correlating data is a skill that many understand but few truly use to their advantage. Correlating data means showing how multiple data points are connected with each other and in doing so, enables a problem solver to identify and improve on any structural weakness.
For example; an online clothing store has observed that their monthly sales are down 14% from March to April. This comparative analysis paints a bleak, albeit incomplete picture.
What the company should do is look for other data points that could explain why there was a drop in sales. Factors that could impact sales could be:
social media engagement rates
An analyst should then be looking at these data points and similar decreases.
For example, if that website traffic had fallen by 2%, it would not account for the 14% drop in revenue. However, the analyst might realise that in April, social media engagement had fallen 20%, meaning the content create was not as appealing/attractive to prospective customers resulting in a fall in sales.
To effectively use data correlation, a user must first possess a basic understanding of the data points available to them and the impact each data point has on each other. This would enable the analyst to seek out connections between the data points which could be the key to solving the company’s problems.
Manipulating data is not as sinister as the terminology might suggest. Rather, this refers to instances where the data must be processed and run through the wringer to reveal information that cannot be ascertained from the surface.
For example; a company with customer support agents noticed that in June, the amount of time each agent took with a customer (handle time) had increased by 30 seconds or 10% higher than the previous month. The department head realises that in June, the number of customers who had their issues resolved during their first call had also increased by 18%. The data correlation seems to indicate that agents spending more time with customers ensured that issues were resolved, thereby improving service and reducing repeat calls.
The department head would then need to explain to his superiors why monthly call targets may not have been met. By manipulating the data, he can prove that the extra 30 seconds per call resulted in an 18% increase in first call resolution. This, in turn, would bring about an overall decrease in the number of calls. Fewer calls translates to lower personnel numbers, allowing the company to save money. The method is simple:
This data manipulation hinges on the user first correlating the data before performing calculations designed to unravel the truth about the information presented.
Data interaction allows the data consumer to ‘touch and feel’ the data rather than solely relying on the words and graphs the data presenter might utilize.
To that end, the data presenter will need to use specialised software designed to enable the consumer to interact with the data
The software allows data presenters to:
create tables and dashboards with some customisation
interact with graphs, charts and tables
help draw own conclusions
The true beauty of these services is that they can be installed on a company’s servers. The tools can then have direct access to an organisation’s data source OR the data can be uploaded to a web service, enabling the user to interact with the data fully.
These tools are powerful and can reduce the time spent by an analyst or for senior management combing over the data. It also has an added benefit of giving the end-user the ability to play around with the data, making it tangible and real.
Of course, there is a shortcut that will help organizations elevate their data management game with minimal fuss and bother. That is to use Dicorm and one of our services designed to help you achieve your end goal. Contact us today to find out how we can help you get the most out of your data.