One of the critical goals of any entrepreneur when starting out, is to make his business sustainable. Strategies and performance indicators are put into place to systematically steer the ship to the desired direction.
Data has become the commodity that reflects the performance of adopted ideas. Big data currently seems all the rage. The necessity to view data in its highest definition, has spawned different genres of information dissection, Web Analysis, Marketing Analysis, Sales Analysis, and Campaign Analysis to name a few. The apparent point being that each of the mentioned types of analysis are associated with a function in a business. By making each cog in the business a data point, allowing each data source to interface with each other eventually creates a synergy, even if the systems are not all the same. The systems in place to gather and manage the data will mimic the real world interactions between each department that is involved in the processes to output a quality product. One must always remember, the system(s) is not the process, systems are subordinate to the process.
Success and data interpretation is relative, and it is making analytics imperative to any business starter but, like with anything in life, it comes with its risks. Data input/output mechanisms are only as good as the data it carries and the minds that decipher it. Data quality is non-negotiable and should be of the highest priority. Without any significant best practices in place, companies risk chasing the wrong dream. These quality principles are expected to be upheld at each point in the habitat of systems, by people who have decided to join a field that The Harvard Business Review called the sexiest job of the 21st century.
The technology involved in creating and maintaining a data/analytical system is an ongoing responsibility. As companies grow and evolve, so must its systems. When any change is executed to a specific part of the system habitat, consideration must be given to how the change will affect the interfacing of all other parts. Don’t forget, if we, by now realise that data is a commodity, then we must appreciate that it is valuable and valuables need to be protected.
From all the noise that you will experience from the data you collect, it is a good idea to measure only what matters. Focusing on the irrelevant is as good as not having the data at all.
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