The data, the new oil or how to preserve data environment. (1/3)

This the first article in a series of 3 articles dealing with data, its governance to build the foundations of a real business transformation.

Like all of you, I read a lot of articles and interviews that talk about data as the new oil. An oil that must be extracted, refined, distributed and monetized.

Not being an oil specialist, I have some basic knowledge about oil:

  1. There are several sources for oil;
  2. There are several types of oil;
  3. There are different grades of oil.

Depending on the source, the type and the quality, the industry will produce petroleum derivatives, used in our daily life going from the car oil to all kind of plastics.

Thus, if the oil is of poor quality, poorly transported, stored or poorly refined, it will be a source of pollution for our environment.

And depending on the quality of refining, the petrochemical business will produce a bad quality of derivatives that will pollute, to varying degrees, our daily environment.

This same mindset also applies to data.

  1. There are several sources of data in a company. They are external and internal;
  2. There are several types of data: structured unstructured v / s, data related to diverse domains: product, customers, location, employees, etc …
  3. The quality of the data varies from one source to another.

As with oil, the data sources could be trusted or not.

The data provided will be of high quality, usable as is or will not be of quality and will require a treatment.

Poor quality data will pollute business operation, as well.

It will, if mixed with other data, make them unusable and unmanageable.

It will disrupt business operations by creating silos of unusable data.

One of the most visible results is the development of pieces of software for Excel files generation containing data to be reprocessed.

At the moment where you have more and more AI projects, covering chatbots and IoT, it is important to remember that, according to Gartner analyst firm, more than 75% of big data projects fail due to poor data quality.

Like oil, quality data can pay big dividends to the company. It will be a source of value, innovation and growth.

In a future article, we will see how to organize to ensure, with the governance of the data, the quality of the data.

How to ensure a good, secured distribution of oil? (life cycle of the data)

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