The maturity level of an organization to face Big Data / Analytics projects is an element that we must always keep in mind. A project with the best technology does not have to be successful if we do not add other elements that contribute to the project’s overall result.

In these years, some organizations like the data science coaching in Hyderabad have dedicated themselves to obtaining frameworks to measure an organization’s level of maturity. One of the ones we like the most is this one you see below, the Analytics Maturity Quotient (AMQ):

As can be seen, five factors add up and contribute to that level of maturity to face these projects in an organization:

  • Data quality: it all starts with data quality. We are so in agreement on this that our first module deals precisely with the importance of having good data quality. If an organization has a sound data storage system, a good data infrastructure, the project has started well. The “GIGO” paradigm is also often cited here. If we put insufficient data, no matter how much we have good analytical models, we will not get good results from our Big Data project.

This factor, the data quality factor, in turn, affects four others. As can be seen in its formal representation, it is the most important and representative of all of them. We must have good data.

    • Data-driven leadership: 40% of the remaining success (once we have “good data”) depends on institutional and organizational leadership that genuinely believes that data and its analysis are an excellent lever for improving decision-making within the company. In the article that opened everyone’s mouth with this Big Data (“Big Data: the management revolution”) from the Harvard Business Review, this idea of ​​changing the decision-making paradigm of the “person who earned the most” was illustrated. We thus need leaders, CEOs, managers, line managers who “buy” this discourse and the value of data as a lever to support decision-making.
    • People with analytical skills: 30% of success will depend on having a good team. This is right now the significant handicap in Spain, without going any further. There is a lack of “Big Data professionals” in all the roles that this may require: Data Science to properly interrogate Big Data technologists with infrastructure deployment capabilities, statisticians and mathematicians, data “viewers”, and so on. We must add the importance of having a particular orientation to business or market processes in general since the data is objective per se; where value is extracted is from its interpretation, questioning, and application to different business needs. Right now, companies are solving this handicap with the training of the people in their organization.
    • Decision-making process «data-driven»: with Big Data, we will obtain «insights. » Key ideas that will allow us to improve our decision-making process. An orientation towards data analysis is the lever on which decisions will be made within the company. And the decisions are made once the orientation to the data has gotten into the processes. How will we decide to invest in marketing? Based on the efficiency of investments and the ability to convert to sales? Or based on an increase over last year’s budget? The data is there to make decisions, not to be “just another project “. 20% is this critical success factor.
    • Technological infrastructure: Finally, obviously, it is challenging to undertake a project of this caliber without technological infrastructure. For Big Data technology, it will not be. We also dedicate a good number of hours of another module to it. The technological landscape is growing. But, you see the previous elements that we must take into consideration before reaching this point.

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