The level of maturity of organizations in data analysis
If we assume that studies indicate that 90% of the world's data have been generated in the last two years, and it is estimated that they will grow 44 times over the next decade, we can come to the following conclusion: DATA (in capital letters), has become for several years now the new "raw material" of organizations and why?
- It is inexhaustible: as reflected in the exponential growth studies of data that are created at all levels.
- It is manageable: tools are currently available to work with large volumes of information
- It is almost unexplored: it is now when we are starting to talk more often about Big Data projects, predictive analytics, and so on.
In addition, we currently have many tools that allow us to store, process and analyze data. And with the advantage that the cost of these tools has been decreasing until they have self-consumption Business Intelligence tools: many companies can start to consider BI projects to analyze their data, practically without cost.
However, there is a reality, and that is that the greater the value and benefit for organizations of using Business Intelligence tools, the greater the complexity of their implementation. We can see this in the following graph:
Mainly, Business Intelligence projects implemented in organizations analyze existing data and describe what has happened: how much I have produced this month, how much I have sold compared to last year, etc..
The next step for improvement would be to perform a diagnostic analysis and ask ourselves: Why did it happen? It is important to know not only that I have sold 5% less than the previous year in the same period, but also to find the causes of this decrease.
In both cases we are talking about retrospective analysis, always analyzing past information. However, if we want to optimise our organisations much more, we have to talk about forecasts. Having the tools to carry out predictive analysis will allow us to improve our organization. For example, it will be vital for a transport company to predict what a rise or fall in fuel prices will mean.
And the highest level of profit for a company is to get a prescriptive analytics: what can I do to make something happen (for example, increase my sales by 20%). This type of predictive analysis is based on mathematical algorithms that have been invented for more than 50 years, but it is now, thanks to current technology, that these algorithms can make business objectives a reality.
Therefore, and in conclusion, as we have in our organizations huge amounts of data (both internal and external), we must use tools to analyze them and, in this way, get to provide the greatest possible value to achieve our business objectives.
If we want to reach the highest level of forecasting and optimization, we will have to start with the base, analyzing in a descriptive way, and then improve and evolve our analysis in the different phases to reach that maximum level.