What are the disciplines of data management?

What are the disciplines of data management?

Enterprise Data Management

Data science encompasses the preparation of data for analysis, including cleaning, aggregating and manipulating data for advanced analytics. Analytic applications and data scientists can review the results to uncover patterns and enable business leaders to gain informed insights.

The vast amount of data collected and stored by these technologies can generate transformative benefits for organizations and societies around the world, but only if we know how to interpret it. That’s where data science comes in.

Data science reveals trends and generates information that companies can use to make better decisions and create more innovative products and services. Perhaps most importantly, it allows autonomous learning (ML) models to learn from the vast amounts of data fed to them rather than relying primarily on business analysts to see what they can discover from the data.

Mathematical data management

When talking about data systems, it is common to hear the terms data governance and data management used interchangeably, almost as if they were the same function. In reality this is not the case, and confusions of this type can affect the ability to understand the reality of the business, determine the value of information and make the right decisions.

Information management is the management of people, processes and technology in a company, seeking to maximize control over the structure, processing, delivery and use of data needed for management and business intelligence purposes. But, this area encompasses both electronic and physical information.

The DAMA Dictionary of Data Management defines data governance as “the exercise of authority, control and shared decision making (planning, monitoring and implementation) through the management of data assets”.

DAMA has identified ten functions of data management, of which data governance stands out as a major component. Data governance is necessary in order to exercise the authority that enables, among other things:

Data Governance

The world’s top companies don’t do things like everyone else. They are different; and we are obsessed with finding out exactly what those differences are. After working with more than 15,000 of the world’s top companies, we’ve been able to identify the key points that seem to differentiate those that succeed from those that fail. And this boils down to a deliberate investment in four disciplines that drive companies forward: planning, process, collaboration and visibility.

They may seem obvious, but in today’s on-demand economy these concepts need to be rethought. We are in a new era where virtually everything is constantly changing, forged in a perfect storm of sky-high customer expectations, rapidly advancing technology and ever-increasing global competition. By embracing these fundamentals and adapting them with methods for agility, empowerment and automation, leading companies have been able to achieve operational excellence and operate at ever-increasing levels of efficiency.

What is data science

In a world where we are increasingly surrounded by information, structured and unstructured, it is necessary to group these features in the most optimal way, through systems that facilitate their understanding. This requires a variety of multidisciplinary skills and techniques, including communication and task execution.

In data science, programming is essential in information processing. For Professor Casafranca, “relational database management systems (RDBMS) are indispensable; as well as the use of SQL (structured query language), NoSQL (database management systems) and NewSQL (new trend of database engines), together with programs such as MySQL, Redshift or MongoDB”.

These tools are aligned with the most widely used development software (R, Python and SQL) and facilitate the creation, definition and updating of databases. According to Casafranca, these programs offer cleaner integration with other tools, so they are certainly essential for data scientists.