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Sheet Metal Solutions
Sheet Metal Solutions
But how do we do it?The answer lies in analysis and Business Intelligence (BI). This technology has enabled the development of tools that facilitate the task of drawing practical information from the data that a company generates and handles, helping us to understand the figures and the reason behind them, to display and share results and to obtain responses in a simple language that is accessible to the whole company.
Facing more demanding environments
The evolution of the market and the technological innovations have made the environment more complex, demanding and fragmented. The industry, in order to carry on making progress, has to make an extra effort to improve its products and satisfy the client by implementing the use of BI and analysis in an omnipresent way, relying on DataOps, AI and other options that are revolutionizing the way that companies work. This way, they have an appropriate approach in order to make the most of big data and operate with these large amounts of information. Choosing the right technology is key when it comes to guaranteeing that users get the most out of the content that they create while operating inside a managed BI ecosystem.
For this reason, and at risk of repeating myself, BI has become more intelligent, using that magic that means that the industry can anticipate the market, maximizing the information and having a strategic vision in order to understand the current state of the organization and raise the levels of competitiveness. And, with all of this, helping the managers in decision-making along with the rest of the company’s workforce, including plant operators, to push the company towards its business objectives. In fact, different investigations reveal that the companies that use data for decision-making tend to make better decisions, which has a repercussion in terms of greater productivity and profitability.
To make all of this possible, and in a very schematic manner, the BI tools must, on a daily basis, gather and analyze master and transaction data stored in relational databases (created with OLTP, Online Transactional Processing), or which come from Excel files, XML, from web services, social networks or other types of sources.
In a highly schematic way, the information contained in this type of storage can be used to generate so-called OLAP cubes (Online Analytical Processing), which are abstractions that give us the metrics of interest – or the facts – in different scenarios which are configured using whichever parameters we want – which we call the dimensions. This allows us to analyze and compare, for example, sales (Facts) according to employees, products, business units or certain periods of time (Dimensions).
We’ve already discussed two of the BI pillars: the OLTP processes, which are used to gather the data, and OLAP which allows us to access the information and analyze it. But a third element is required which transforms the data into information and uploads it into the OLAP storage: this process is known as ETL (Extract, Transform and Load). These tools make it possible to add the new data generated by the OLTP process and to the OLAP storage, that is, collect all of the data relating to investments, sales and times in order to, subsequently, establish commercial strategies, resolve possible problems and extend the advantage compared to other competitors.
Finally, there are the analysis tools that show the OLAP data on dashboards through controls that display the facts (tables, graphs and indicators) and controls that structure and filter the dimensions. Intuitive, fast and very dynamic, the dashboards allow the information to be visualized, categorized, grouped and compared from different angles.
This entire process has changed a lot since the origins of BI and, for better, because now analytics can use powerful techniques in order to analyze big data and, in a matter of seconds, make value data more tangible so that it can be transformed into information and, subsequently, knowledge, so that business decision-making can be optimized to achieve the organization’s objectives along with economic transformation.
