Data, data, and more data. The excessive passion for data in the times we live in is no coincidence. Not only is it a differentiating factor from the competition, but it has also generated an entire ecosystem around it, which is partly possible due to the tension and duality generated by its exploitation: on the one hand, companies that want to use it in a more or less legitimate way to generate highly engaging offers for their market segments; on the other, an increasingly ingrained awareness of the phenomenon that "if the product is free, you are the business," which leads to growing social pressure on technology providers and legislators (is there anyone who hasn't heard of GDPR?) to impose all kinds of limits and fines.
It is the circus of business intelligence, better known by its English term, or by its corresponding acronym,BI; a phenomenon that, together with the omnipresent Digital Transformation, is part of the concerns of most executives in the most developed economies. Price competition is proving ineffective, and the classic recipes for increasing productivity by reducing margins have limits that cannot be stretchedindefinitely.
That being the case, competitiveness depends on knowing in detail all the possibilities for exploiting the business. And this is where we refer to a recent study by Tableau, a company specializing in data visualization forbusiness intelligence, on BI trends for 2019. This document can be consulted both online and in PDF format, from which we have borrowed some useful notes for those who do not want to get lost in the maze of business intelligence.
The first of these trends is the rise of Artificial Intelligence (AI), as a fully explainable phenomenon. The virtues of this technology and the tendency to rely on AI and machine learning models for data interpretation and analysis are no longer in dispute. What is at stake is the extent to which these models can be guaranteed to be reliable.
In this regard, Tableau's director of market intelligence, Josh Parenteau, believes that they will act by "helping to uncover ideas that have not been discovered before." This is a path in which chief information officers (CIOs) will play a key role, to the extent that, according to Gartner, by 2020, 17 out of 20 CIOs will be "test pilots." Gartner's research indicates that by 2020, "85% of CIOs will drive artificial intelligence programs through a combination of buying, building, and outsourcing efforts."
And yes, AI will need to be reliable because interactions with data, whether based on natural language or not, will not be. "Natural language represents a paradigm shift in the way people ask questions about their data," notes the Tableau report, such that when people can interact with a visualization as they would with a person, areas of analysis traditionally reserved for data scientists and advanced analysts are opened up. That is, business. Sales. Opportunities. "Users are not limited by their analytical skills, only by the breadth of their questions," so as natural language matures in the BI industry, barriers to the adoption of analytics across organizations will be broken down and data will be integrated into the core of workplace culture.
This also makes it possible to achieve one of the most sought-after goals in contemporary business culture: putting data into context. Which is tantamount to saying "where people want to act." Along with gathering knowledge on platforms and tools, it's about bringing new capabilities to users wherever they are physically located. We are talking about a consultant who can leverage customer data on the go, but also mechanics who can suggest repairs remotely by leveraging the vast and growing field of the Internet of Things (IoT). "The convergence of analytics and action will shorten the time and effort between perception and decision-making. It will also make data more available within business workflows, encouraging more people to incorporate data into everyday decisions," the report notes.
But in addition to personal data, which is irreplaceable and undeniably valuable, we also have social data. Or, to be more precise: collaborative data. And that is where we find a side effect that is already a trend among leading firms:focused efforts by public and private sector organizations are strengthening the "data for good" movement, according to this study. Datahas transformed the way organizations operate, including non-governmental organizations (NGOs) and non-profit organizations.
The "data for good" movement does not refer to a sudden philanthropic interest on the part of companies, but rather to two realities: generating parallel projects based on data intelligence creates greater empathy with markets; and it also fuels the possibilities of sharing data transparently and ensuring that its use is not only legitimate, but also benefits users and customers. In other words, using data for good leads to a general perception that data itself can have a positive impact on society. Therefore, it is likely to generate sympathy precisely for those companies that use it the most.
And as expected, when reality is overwhelming, it becomes necessary to establish patterns of behavior and codify them. That is the origin of data ethics codes, as found in professional fieldssuch as law, medicine, and accounting. "As data continues to proliferate in all areas of business, companies are beginning to evaluate how to apply these same principles to data analysis practices," the report states. If more and more companies are relying on data to shape business decisions within each department and function, it means that more and more people have a stake in how data is used and shared. If people are involved, whether we like it or not, there are also ethical considerations.
People who also face an increasingly complex ecosystem that raises new questions: what happens when the origin of data must be verified in order for it to be reliable? What if, in the course of that investigation, a component needs to be altered, how can this be done without structurally affecting the data set? And if it does affect it, what rules should have been followed to ensure that the entire structure is coherent, solid, and stable?
These questions are on the threshold of the necessary convergence with modern governed data curation platforms, knowing also that from a platform perspective, this data and its interpretation systems live for and by "the cloud." We are talking about "data democracy" systems, which have the virtue of raising the skills of data scientists, who are compelled to adopt "soft" skills that lead to the necessary cultural changes within organizations so that the adoption of data-based policies is a success.
Alongside them, new "storytellers" are emerging who are establishing a business language that is closer to the audiences of the new environments. Beyond capturing, understanding, interpreting the data, and acting accordingly, it is imperative to report in light of that data and to report on the data itself. This generates team dynamics where the least disruptive can be left out of the game. These dynamics exclude competitive egos in order to bring disciplines together, forcing leaders to focus less on the "adoption" of skills and more on organizational commitment.
There are many trends and significant challenges for those who truly want to venture into Business Intelligence. Of course, for those who don't want to, the challenge will be much more daunting: how to survive. It's not a matter of trends, it's a matter of anticipating the future of business in order to be able to live in it.
