One third of the workers have a remote connection with a portable device for professional use, which allows them to work away from the company's headquarters. Although unevenly, this situation makes it possible to better adapt to the work scenario that has arisen in this health crisis.
It is also noteworthy that the gap in the level of technological equipment between large and small companies is narrowing year after year. This is due to a twofold trend: on the one hand, large companies are reaching the maximum level of equipment in the main technologies and, on the other hand, companies with fewer employees have an increasingly higher level of technological equipment.
In this "ICT breeding ground", the proliferation of Business Intelligence (BI) tools, together with the improvement in Internet speed, the reduction in memory costs and the increase in computing capacity, among other factors, have meant that BI is now being extended to all types of companies, and is no longer exclusive to large corporations. That's good!
The functional evolution of BI tools, increasingly oriented towards self-consumption and therefore friendly and easy to use by users, not necessarily computer users, as well as the availability of access to free BI tools, is also causing that within the same company their use is occurring spontaneously and autonomously by staff in different departments. Is that right or wrong?
The contracting of projects under a purely IT approach, buying software licenses and subcontracting a few hours of technical services/programming for the implementation of a BI tool, is causing a proliferation of dashboards, increasing the volume of indicators, graphs of all kinds, etc., without a proper perspective and business logic, which can lead to paralysis of the analysis. This is wrong.......
If we look for the definition of BI, as it usually happens there are many and for all tastes, but we will find a common denominator around its description as: the set of processes, technology, methodology and even skills, for decision making based on data. And then the key is, or at least it should be, on what issues do I need to make decisions and what information do I need to analyze to make them, and not the other way around, what data do I have and how do I analyze it? However, many times we embark on projects and "start the house from the roof" defining dimensions, metrics, indicators, types of graphs, etc, etc, etc, etc, to process data and have dashboards for commercial, logistic, production, financial analysis, ....... But, to decide about what? That is something that is not defined at the beginning of the projects, and we start generating dashboards without really knowing what they should respond to. And multiple people in different departments are dedicated to the analysis based on these scorecards, reaching conclusions in different directions, "running around like headless chickens".
On the other hand, all these types of projects revolve around "data". It is assumed that it exists, that it is complete, that it is correct, and that it is somehow accessible. But, to paraphrase a historical moment, sometimes the data "is not there, nor is it expected", and this in itself can be the key that frustrates the project. As a consequence, in the first analyses made with BI, we discover that the information it shows us is wrong. In these circumstances, the easy thing to do is to blame the BI, but the fault usually lies in the quality of the data. In the data itself and/or in the criteria for its processing.
Continuing with the simile of "starting the house from the roof", not only have we not thought about what I need the house for according to the needs of life to be covered (decisions to be made), but we have also not bothered to analyze the land where to build it and the foundations I need (data to be managed). If we add to this that in a city (company) each citizen (user) at his own risk starts to build his own house (scorecard), who knows what shantytown (BI) we may find after a while. Therefore, if under these conditions it is possible to obtain a BI that is useful for decision making in the company, it would be as fortuitous as winning the lottery.
Some would say that the above conclusion is a bit exaggerated, and they would be right, but in any case what is certain is that there are many projects in which the above circumstances and adverse effects of the implementation of BI solutions occur, to a greater or lesser degree. And all this for not stopping to think, and investing time and money in analyzing and defining the BI project before rolling up our sleeves and starting to build anything. All this by ignoring the data and not analyzing its quality, nor investing in its prior debugging before its extraction. It is true that doing all this is a previous stage in a BI project, a stage that lengthens deadlines and involves higher costs, so some people prefer to start building their BI directly. Another thing will be the time and cost overrun that will come at the end when what is obtained does not meet the decision making needs of the company, the information provided by the BI is questioned as incorrect, etc., something that is not normally taken into consideration, but that is what the application of Business Intelligence in an unintelligent way has.
Opinion article written by Iñaki Arrieta, partner of the Consulting and Information Technology area at PKF Attest.
Published in the printed edition of Expansión País Vasco.
