The current business landscape is changing by leaps and bounds with the irruption of new technologies in all phases of the value chain. Big Data is currently one of the buzzwords in the business world. We now have a wealth of articles and information available, and in many surveys of managers and executives, the need to implement a Big Data system is at the top of the list.
According to a report by the consulting firm Gartner, around 30% of companies have already implemented and are using Big Data technologies. IDC states that more and more organizations are including big data technologies to improve the decision-making process. Depending on the industry, it is estimated that each organization today has between 5 and 20 million gigabytes of data. Most of this data (80-90%) is unstructured or in systems that are difficult to manage: emails, paper documents, social networks, etc.
In short, we are talking about the most precious asset of all: data. If we manage it properly, we will obtain information, and with the managed information, we will be able to make decisions. A McKinsey study states that 64% of executives surveyed confirmed that big data has changed the way they make decisions.
However, there is still a lot of confusion about what Big Data really is.
We could call Big Data the management and analysis of huge volumes of structured and unstructured data (messages on social networks, audio files, sensors, digital images, form data, emails, survey data, logs, etc.) that cannot be processed in a conventional way. These quantities are so enormous that they exceed the limits and capabilities of standard software tools used for data capture, management, and processing. This concept encompasses infrastructures, technologies, and services. The goal of Big Data is to convert all this data into information that facilitates decision-making, even in real time.
Two decades ago, sectors such as banking already managed millions of data, and even established algorithms to identify needs and better understand their customers, in what was known as “data mining”. Now, the terms have changed: we talk about Customer Experience or 360º vision of the customer, expert systems that learn cognitively, architectures oriented towards machine learning, and analytical systems that are crucial for Industry 4.0, capable of detecting manufacturing defects or halting a production line. A whole range of cases, closely related to deep learning of neural networks and AI (artificial intelligence) systems.
After this summary and reflection on Big Data, what surprises me is that the adoption of the concept of Data Governance by companies is not as exponential as Big Data. Sincerely, these are two concepts that should go hand in hand to establish an effective and efficient data management model.
As organizations become increasingly interested in the intensive use of Big Data technologies, questions arise, such as: where does the data come from? Does it have the necessary quality? What do we know about our information, and is this data aligned with our company policy? It is time for Data Governance.
In essence, organizations soon discover that the viability of their information analysis projects depends on efficient data management. It is not just a matter of analyzing the data, nor is it just a matter of storing or processing it.
Data Governance provides a holistic approach to managing, improving, and leveraging information.
Data Governance is of strategic importance within organizations of all sizes and industries, as it is becoming increasingly evident that information management issues affect decision-making. Processes and policies must be in place to ensure data reliability and consistency.
Without a defined Data Governance and information accountability structure, data is often unverified, redundant, incomplete, and dangerously out of date. In response to this situation, it is necessary to implement a systemic model (methodology and tools) of Data Governance. What is Data Governance, though?
The four most important initiatives in the field of Data Governance are the following: Master Data Management, Information Lifecycle Management, and Security and Privacy.
To help us in the implementation of effective Data Governance, there is, for example, the American company Erwin Inc, a world reference manufacturer in the design of CASE tools for data and process modeling, with a strategic approach: Data Governance.
According to Erwin, “Data Governance is a strategic and ongoing practice to ensure that organizations can discover and track their data, understand what it means within a business context, and maximize its security, quality, and value.
Erwin states that “Organizations view Data Governance as important or critically important from a business perspective, with compliance as the primary driver. While it supports data privacy, security, and compliance, that’s not all it does. Data Governance also drives digital transformation, customer satisfaction and trust, better decision-making, reputation management, and analytics.”
At Mediterranean Consulting, after almost 20 years of history, we have successfully assisted companies of diverse types, sectors, and sizes in achieving higher effectiveness and efficiency. We accomplish this by optimizing operational processes, implementing systems and structures, ensuring swift productivity gains, and establishing efficient organizational models that eliminate cyclical processes and fragmented functions while promoting seamless process integration. In this sense, it is a pleasure to announce our strategic alliance with Erwin, Inc, becoming an Authorized Partner for Spain. To address a scenario such as the one described in this article, we must equip ourselves with the best methodologies and the best tools to achieve a good result; that is why Erwin is, without a doubt, a good choice.
In short, any Big Data strategy should be based on a solvent, stable, and robust Data Governance model that enables the successful implementation of the decision-making process based on information management.
