The importance of quality data in data driven companies
There are two expressions that are, lately, on everyone’s lips: Big Data and Data Driven. The two are closely correlated, since both refer to large volumes of data processing in the search of valuable predictions for organizations.
Is all the data we store useful?
Data-oriented companies base their decision-making on data and not on the intuition of those who run the company. In an era where storage is ridiculously cheap, we find that many organizations store the maximum amount of data possible even without knowing why it is useful for or what they might need it for.
Is this Big Data? Don’t you think it’s a useless effort? In my opinion, this hurried storage does not meet the goals of Big Data, as it speaks more of the storage capabilities of a company than of what really matters: the value of such data and the use that it can be given to meet the goals of the organization. Big Data, in essence, should have a purpose: if the data we are oriented towards does not help us meet the purpose of the organizations nor helps us make strategic business decisions, what good is it for?
What kind of data should we store?
Data collected with Big Data technologies meets three characteristics: it’s extensive, complex and fast growing (and therefore, impossible to process with conventional databases).
To these characteristics I would add two points – in my opinion- fundamental:
• and valuable,
Because, without data that is true to reality and valuable for the compliance of the strategic goals of organizations, we can fall into the error of storing unimportant data that, instead of contributing, hinders.
We must always remember that the key is not the technology we use to store data, whether it’s Big Data, conventional databases or even Excel files: the difference is made by the quality of the data and our wisdom to make it useful for our strategic decisions.
Needs and goals, the key
When deciding to collect data, it is important that the organization reflects on their needs and goals. There will be companies whose KPIs and analysis require large volumes and types of data for which Big Data technologies will be truly useful; on the other hand, there will be others who, due to their characteristics and needs, will not.
Organizations must be aware, then, that storage capacity and massive prediction are only means, but must never be the ultimate goal. At the same time, organizations must seek to store only quality data: if data of no use is stored, it can serve as a distraction from truly useful data that remains hidden among the rest. Finally, it is important that organizations provide access to their data and predictions, and put them at the service of their employees, allowing them a transparent access to the organization’s information and goals.