How to prevent organizations from drowning in an ocean of data

Terca-Feira, 30 de Maio de 2017 às 13:23:18

The challenges are many, but rewarding. To work with the high volume of information so that the impact is positive for the business of businesses, it means that we should focus on its strategy, preventing its employees from analyzing unnecessary data and standardizing the way to make decisions.

Walter Sanches and Marcelo Salles, superintendent and it coordinator

WARNING: This article was translated by an automatic translation system.

by Walter Sanches and Marcelo Salles

Much is already said about the importance of storing and analyzing immense volumes of information in an organization. However, start any big date initiative without first identifying what are the correct questions to be made about your business, may cause you to drown in an ocean of data. Often, the correlations between different variables will make no sense whatsoever. For example, the ratio of the increase in the consumption of copper tubes and the increased rainfall rate is nothing but a simple hypothesis. So, crossing information without first identifying all variables could be, apart from the loss of time, a risk.

Isolated information shows what happened in the past and what was the result, but when we relate actions, targets and performance, we can measure the effectiveness of what was done and predict scenarios. Making an analogy: it's like a fuel tank of a full vehicle. Alone does not allow to identify much beyond the volume in liters. However, when combined with the motorisation of the vehicle, distance traveled, average consumption, route and average speed, this information enables us to provide an analytical vision, exploring patterns and drawing conclusions with concrete bases.

The great prerequisite to immerse yourself in this ocean of information is to have a profound knowledge about the organisation. By having a clear view of your company's strategy – essentially involves a charter guideline and a business plan – you can identify what data we need to measure the results. Such data can come from various systems, such as crms, public information from reliable websites of the internet (such as ratings and IBGE), spreadsheets, among others. Additionally, it is possible to combine this information using diverse technologies such as storage, analytics, and methodologies, including BSc (Balance score card), decision trees, predictive algorithms, and machine learning.

With this exponential growth of information and the ease of creating new systems using different methodologies, it is easy to get lost in the way, as it is necessary an analytical vision and a lot of agility. In this way, much of the companies end up generating only reports with descriptive data about the past, but when we elevate the level of analysis to predictive and prescriptive, people change their mental models and look at the data thinking in the future.

In short, organizations are still learning to work on an uninterrupted cycle, in which systems are able to generate information and which, in turn, are exploited to create new systems with new information and so forth.

The business intelligence process is continuous. We need to turn back and follow the results of our actions, but at the same time have a preemptive look at predictive analyses that allow us to make decisions and act before problems occur. When this job is done, we can avoid many headaches, such as stock breakage and customer inactivation.

Although challenging, investing in big data can pose a great opportunity for any business. To illustrate a positive experience, it is worth mentioning a case which I attended by Termomecanica, industry leader in the copper sector and its alloys. We had a high volume of data with great complexity of analysis, so we developed a decision tree of the stock of finished products that, through it, allowed our tool to standardize the decision and inform the developer the action to be taken for that product. This process was accomplished manually and it took weeks, when finished some information was already irrelevant.

For many, working with big date is synonymous with high investment, however, there is no rule that relate to work with information at a given cost. It will depend much on the scenario of each organisation, such as the established goal and the starting point: if there is already information and if it is necessary to create new systems to obtain them. Regardless of the scenario, if we know our point of departure and arrival, the return of this investment will certainly come. In cases like this, working information is a competitive differential and the value will no longer be measured by "how much it costs to do", but by "how much it costs not to do".

Although financial investment is considered one of the main impediments when working with big data, little is talking about the implications of a change in organizational culture. As we discussed earlier, having at the disposal a high volume of information, which can be obtained from different fronts, requires an exchange of behaviour of the individual. In short, there is no point in creating a strategic planning, establishing a base of indicators and investing in systems, if people are not trained to analyze the data and, more than that, if they did not incorporate the interest in the analytical vision.

The challenges are many, but rewarding. To work with the high volume of information so that the impact is positive for the business of businesses, it means that we should focus on its strategy, preventing its employees from analyzing unnecessary data and standardizing the way to make decisions.

* Walter Sanches and Marcelo Salles are respectively, superintendent and it coordinator of Termomecanica, leader in the copper transformation sector and its alloys

Note: Automatic Translation Disclaimer: This article was translated by an automated system, without human intervention. Termomecanica São Paulo S.A. uses these automatic translations to help people who speak English and Spanish to take advantage of company news. Thus it is possible that it contains errors in grammar, vocabulary or syntax.


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