More and more companies are turning to tools from Business intelligence And with that they choose digital transformation to stay ahead of current trends. Moreover, as applications become more sophisticated, they big data Organizations have increased their efforts to treat data Businesslike as a valuable asset.
The ability to leverage the business information stored in data marts or in a data lake, However, it is not the strong point of all companies. Sometimes, appropriate tools are needed to transform data into other forms that can be interpreted. Therefore, it is important to know the most common errors in information management that can harm business strategies.
Most common errors in management data business-like
Currently, companies from different segments are trying to increase their efforts to achieve a corporate culture Data Driven. Although it’s not an easy task, if you pay attention to these common mistakes you can make a difference and Capitalize on all the potential of your data, Through strategies such as Business Intelligence, Predictive Analytics, Data Science, Big Data, among others.
1. Treat it Data Governance as a project
Many companies often treat Data Governance as any other project, when the reality is that information changes very quickly and this approach does not allow organizations to manage it in line with the changes of the organization. To avoid this error in information management, the most appropriate approach is to apply a program-based approach, as this allows managing several projects associated with a main one and is more appropriate for the constant changes in data and the organization.
2. Putting into practice a poor quality of data
Data profiling is mandatory for creating professional data integration applications. Through them, ETL developers can take the current data, analyze it, and define business rules in a tool for implementation. data cleansing o data scrubbing on the boards.
For this reason, current and future data sets must be kept in mind to avoid errors when inserting or extracting information from the database. This means that data profiling must be developed to spend less time on cleaning up the data. data in the future.
3. Focusing solely on meeting the goal
Sometimes, when a data scientist When trying to meet only the goals of an organization, you may take shortcuts to facilitate the data governance process. This should not be done, as mistakes are likely to be made along the way. It is ideal to consider future goals and corporate profitability.
4. Working without a proper data governance process
Every company must ensure an effective Cat Government Data Warehouse. It is the best way to harness the potential of utility business intelligence, business analytics and allow one data scientist Work correctly with strategy data science.
5. Seeing the Big Data as the solution to all problems
With the arrival of massive data, many companies focus on implementing only strategies of Artificial intelligence with machine learning to solve your problems. The reality is that this usually does not solve the management difficulties, which is why you should try options such as modernizing the Data Warehouse with Microsoft Azure, Snowflake Data Cloud, or something else; that allows you to have reliable information before you apply it to AI strategies.
6. Forgetting to take care of the data architecture
In any business environment, attention should be paid to investments in data architecture and utilities that can improve the management and analysis of these. For example, it is possible to create plans for periodic reviews, develop specific architectural methodologies, verify the integration of data, etc.
7. Ignoring the Quality of the Data
The quality of data is fundamental to drive proper management of the data, whether by using it dashboards, Artificial intelligence with neural networks, Among other things, you need to investigate the potential data governance issues, train the work team, and ensure that the review of the data be a priority, etc.
8. Based on intuition
Even with the technology available today, many companies base their decisions on intuition. This is a mistake, as the data provided by the strategies must be studied in order to make informed decisions. big data, As long as they are of good quality.
9. Acting without leadership
Many times the projects related to the data Business intelligence is led by various areas of the company. It is important that there is always a person who specializes in business, data analysis, and more, able to digest all the information and distribute it to each department.
10. Collecting unnecessary data
At some point in the data management process, information must be recycled to avoid the accumulation of data. data redundant and obsolete. However, when companies do not follow this approach, they extend the maintenance process even further to ensure that it is kept in order.
Avoid these mistakes and manage them data successfully
As we have seen, there are errors in the management of relatively simple information and others that are quite complex, which make us question our overall data strategy. Sometimes it is necessary to take a step back and root out the challenge that most impacts information acquisition and decision-making.
The key to success lies in achieving a continuous process and generating an effective data management system that saves time and resources. Ideally, you should be able to solve all problems using a software appropriate to meet the company's demands in a timely manner.
At Intus, we can support you at all levels of information management, from integration to data governance, to improve decision-making with BI tools such as Microsoft Power BI.
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