Big Data Analytics, the keys to a smart company

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Big Data Analytics is the process of working with huge volumes of data that result in relevant information for decision-making in companies.
If there is one thing that 2020 left us with, it was an unprecedented push on the path of digital transformation for companies. This, coupled with the economic crisis that resulted from the pandemic, means that companies have to be as efficient and resilient as possible.

Those that use data to make decisions, i.e. are data-driven organizations , will be the ones that survive unscathed and will be better positioned to face future crises. Why?




Businesses have realized that their big data warehouses office 365 database a largely untapped goldmine that could help them become more efficient and agile in the face of changing circumstances.

As a result, business leaders are looking to data for answers to becoming smart businesses.





70 % of managers and directors surveyed confirm that in the last 12 months their executives have explicitly asked them to become more data-driven. In addition, 87% of CXOs say that becoming smarter companies is one of their priorities for the next 5 years.

Source: IDC



What is Big Data Analytics?
The term big data refers to the digital storage of information that has a high volume, velocity, and variety. Big Data Analytics is the process of discovering trends, patterns, correlations, or other useful insights in those large stores of data .

Data analytics is not new. It has been around for decades in the form of business intelligence and data mining. Over the years it has improved dramatically, so it can handle much larger volumes of data, execute queries faster, and run more advanced algorithms.

Market research firm Gartner categorizes big data and analytics tools into four different categories :


Descriptive analytics: These tools tell companies what happened. They create simple reports and visualizations that show what happened at a particular point in time or over a period of time. These are the least advanced analytics tools.

Analytical Diagnostics: Diagnostic tools explain why something happened. More advanced than descriptive reporting tools, they allow analysts to dig deeper into the data and determine the root causes for a given situation.

Predictive Analytics: Among the most popular big data analytics tools available today, predictive analytics tools use highly advanced algorithms to forecast what might happen next. Often, these tools make use of artificial intelligence and machine learning technology .

Prescriptive analytics: A step above predictive analytics, prescriptive analytics tells organizations what they need to do to achieve a desired outcome. These tools require highly advanced machine learning capabilities, and few solutions on the market today offer true prescriptive capabilities.