Good use of data increases business productivity

Data management has not always been valued by data scientists, who often see it as an obstacle to obtaining information. But nevertheless, la experiencia de clientes de diversas industrias muestra que una buena gestión de datos favorece el éxito de los proyectos de analyticsAnalytics refers to the process of collecting, Measure and analyze data to gain valuable insights that facilitate decision-making. In various fields, like business, Health and sport, Analytics Can Identify Patterns and Trends, Optimize processes and improve results. The use of advanced tools and statistical techniques is essential to transform data into applicable and strategic knowledge...., improving business productivity.

Information, both structured and unstructured, is the basis for improving business productivity, since it allows to make better decisions, anticipating risks and finding areas for improvement in processes, among many other benefits.

CIOs and CEOs no longer question the importance of Big data and increasingly they understand it as essential in order not to be left behind in the market and achieve what is already called “analytical advantage”.

What is it about?

To set out on this path, the first step is to work with the data. This way, the governance data facilitates the work of data scientists and analysts, enabling business leaders to obtain relevant information for decision-making faster.

Data increases productivity:

• Accelerate continuous performance improvement processes.

• Promote smart decision making, with information in real time.

• They allow greater efficiency in the selection of the ideal personnel.

• They offer an analytical advantage.

• Drive higher revenue, reducing risks and costs at the same time.

5 Ways Good Data Governance Increases Business Productivity

1. The quality of business metadata is critical to the business. Metadata governed effectively, namely, with data that labels or categorizes other data, facilitate the discovery process for data scientists, helping them find the information they need, when they need it. Labeling and cataloging data at the time of ingestion helps the organization maintain its data lake clean while giving data scientists a better understanding of what is available to them.

   

   It will interest you: Data quality and analytics to improve teaching

   

2. Effective schema management saves time and money, especially in a Big Data environment. Schemas define how data should be read. It is critical that data consumers know which schema to use when searching for specific files. But nevertheless, schema management can be difficult, especially in a big data environment. The discovery of technical and commercial programmatic schemes alleviates the problem. When a new dataset is entered into a data lake, an open source tool can help determine the schema automatically. Y, in a mature environment, match newly discovered data with existing business metadata, providing business and technical metadata immediately. To post, reviewing and governing all known schemas will save data scientists and analysts considerable time, that they can invest in other tasks where their expertise is essential.
3. Good data quality and profiling can speed delivery of ideas.. Poor data quality is one of the key reasons why the 40% of business initiatives do not achieve expected benefits, according to a Gartner Report Inc., which also points out that data quality affects overall labor productivity by up to a 20%. Developing strong architecture and effective data quality protocols will help prevent the data lake from becoming a data swamp. It will also be useful to establish data use agreements between producers and consumers of data., as these agreements give everyone a better idea of ​​the level of quality of the data that is expected and how it will be documented. Creating data profiles and storing the profiles with metadata is also a useful practice, as it enables data scientists to better understand the types of data contained in the system and enables them to formulate hypotheses more quickly.
4. Data lineage can help avoid lawsuits or layoffs. In an age of data breaches, data governance can provide important protections for the company and its employees. Data management will not prevent certain hackers from accessing secure data, but in the case of a violation, help to understand what has and has not been violated. Data governance provides particular protections for people working in regulated industries, like financial services and healthcare. In an audit, data governance allows you to show exactly where the data came from and how specific calculations were performed.
5. Models and analyzes will run correctly in production. If the data governance program includes the measures discussed up to this point, the data will be of a high enough quality that they will experience fewer problems with models and analysis in production.

In summary, data enables business leaders to understand, analyze and improve your companies. Y, before any data analysis project, start first: data governance and management are essential to your success and overall productivity impact.