If you are doing business in today’s marketplace, you already know the power of the analytics. The focus on big data and business intelligence has transformed the way business decisions are made, but the flip side is that it made us all feel like we need a PhD in math to understand what’s going on with the numbers. Between all the key performance indicators, short-term and long-term trends, cost-efficiency estimations and many other quantitative tools, there is so much information to be ingested that it makes the head of an average business owner spin in circles.
It really doesn’t have to be that complicated.
There is a simple way to make sense of all the incoming data and consume it in a format that can be processed intuitively. Most insights are much easier to understand when they are presented in a visual medium. The trick is to find smart ways for distilling the most important inputs and converting them into a format that can be grasped at a glance. That way, business people can continue thinking about business even while they are actively looking into key numbers.
When your data is accessed through integrated dashboards that draw raw input from many different platforms, you can quickly check key indicators for each area of business, from HR to sales and marketing. Visualised data is also very useful for internal presentations as well as lead generation, so it has an instant and very intensive impact on the entire organisation.
Prestanda’s approach to visual data crunching
Finding witty ways to summarise business data and turn it into eye-catching graphs and visual indicators can be a lot of fun, but at Prestanda this process is driven by a well-defined methodology. We don’t classify data based on its source or format, but try to capture the essence of your business philosophy and deliver a context-sensitive package that will provide you with the data you need, when you need it, where you need it.
Any visualisation technique that can help us deliver business value is a valuable tool, but the point of this step is to inform rather than impress. We often use simple graphs to show how the numbers are changing and use color-coding to immediately separate different kinds of information. Throughout the process, we keep in mind what the data we are processing really represents and try to award the optimal level of relevance to each variable.