With the democratization of the action of turn data into insights, we also see a correlation with the type of problem that is being solved. Without the need to justify a big data scientist salary, business leaders can focus on small problems, using existing data to gain insights, and leveraging existing staff to gradually build information.
This, combined with the right tools and data, allows anyone with a problem to solve to benefit from data science.
However, there remains a significant disconnect in perception, where people simultaneously believe that data science is unattainable and highly valuable at the same time. There are a number of alleged obstacles to the use of data that urgently need to be dispelled.
With this in mind, there are four key areas of any data project – areas that can be easily scaled up or down regardless of the size of the company or the challenge:
1. Identify the problems
correct
to solve
Any business leader on the analytics path will undoubtedly have a problem in mind to solve. Just as the automated telephone exchange was invented because misdirected calls caused annoyance, your business must also begin the change process by asking, “What are we most concerned about?”
The problem itself may not have an immediate solution, but with the right data and analysis tools, it becomes much more feasible. As with any process, the early stages of data analysis have a series of steps. The key is to start small and increase the challenges.
2. Evaluate the data and tools you have and how you want to use them
A key challenge in the early stages of any business looking to begin its analytics journey is determining what data and tools it already has. All companies – in one form or another – have data sets that can be used to gain insight and have a significant impact on business decisions. Most companies are likely already using some form of analytics.
In the early stages, it is wise to start small and build a bank of replicable successes. The fundamental thing is quality of the data, not the quantity. With a small set of high-quality data in place, even legacy systems are usually enough to get you started.
As the information needed from the data becomes more complex, more processes and easier-to-use tools can be added when a specific need arises.