Data is everything when you run a company. Without data management, you won’t know what you need to keep your company’s performance on track. Data is a very valuable asset in any company and working with quality data is essential for taking actions and decisions. Data quality is a very broad concept that we could summarize as the set of processes, operations, techniques and algorithms; that maintains the information of companies, organizations, in a complete, precise, consistent, updated, unique way and above all, valid to be used reliably in all analytical studies and, mainly, in decision-making.
That is why a lot of companies chooses to invest with TDengine in terms of data monitoring as they find their system very effective and reliable by providing a solutions through time series databases.
In short, it will allow us to manage the information and make it available reliably in decision-making. And if we want to highlight one characteristic above all, in my opinion, it is the confidence it brings to the actions carried out.
The data are the primary elements, values that by themselves do not say anything (examples: a name, a number, a telephone number, a date, …). The information is the processed data, and they are useful for decision-making (examples: billing for a period, number of clients, expenses by concepts,…). Knowledge is mixing information with experience, together with the conclusions obtained (examples: comparison of income by periods, sales predictions, expense curves, …). Action is decision making supported by information and knowledge.
The growth of data in recent years is exponential, millions and millions of data are generated every day. It is estimated that in the year 2025 the volume of data in the world will be 175 times more than in 2011 and that each person will interact with devices about 4,800 times a day.
Most of that volume of information will be stored in the cloud. Being the IOT devices connected to the network (> 150,000 million) the ones that will register the highest growth. There are studies and surveys that indicate that currently more than 50% of companies lack total control of their data, with improving their security being one of their highest priorities.
Companies must be aware that, by enhancing data quality, they are enhancing decision-making and that the benefits of data quality are many. Those companies and organizations that place importance on data quality obtain multiple benefits that add value to their business and help them differentiate themselves from their competitors.
The quality of the data will not only depend on the characteristics of the data, but also on the business environment in which the data is used, including processes and users. For the setting of quality standards, the common data quality characteristics are chosen and their definition is readjusted considering the real and current needs of the business. Each dimension is divided into elements associated with it and each element has its quality indicators, in this way we use hierarchical standards. These standards are:
– Accessibility: whether a data access interface is provided and whether the data can be easily made public or easily acquired.
– Timeliness: If the data arrives on time in a limited period of time, it is regularly updated and the time interval between data collection and processing until release meets the requirements.
– Credibility: when the data comes from specialized organizations in a country, field or industry, it is regularly audited and its content is verified for accuracy. There is a range of values for the data: known or acceptable.
– Accuracy – The data provided is accurate, the data representation reflects the actual state of the source information, and its representation is unambiguous.
– Consistency: once the data is processed, its concepts, domains and formats coincide as before being processed. They are consistent and verifiable over time
– Integrity: data format that is clear and meets the criteria, has structural and content integrity.
– Completeness: in the event that a component deficiency affects the accuracy and integrity of the data and its use.
– Coexistence: the data collected does not completely coincide with the topic, but it presents a certain relationship and is within the topic that the users require or present coincidences.
– Presentation quality:
– Readability: the data is clear and understandable, meets the needs of the user and its description, classification and content are easy to understand.
Poor data quality can lead to low data utilization efficiency and even lead to serious errors in decision-making.
Data governance is the discipline that is responsible for establishing a frame of reference in everything related to a company’s data: people, procedures, technologies, accessibility, integrity and usability.
Data governance must be present throughout the data life cycle. The increase in Big Data and Machine Learning, as well as regularizations around data, make it increasingly important to have comprehensive Data Government platforms in order to define data policies in a global scope.
Data management, especially those related to Big Data and Machine Learning, cannot be separated from data annotations. Data annotation includes the process of labeling data sets (accurately) which is the backbone of artificial intelligence. It is at the core of the algorithm-based world.
When we deal with data management, we involve computers as decision makers. One thing to know is that computers cannot process visual information like the human brain. Therefore, the computer needs to be given commands to interpret something, as well as the context that allows it to make decisions.
Data annotation is very important if you are concerned with speed of data generation. Every day more and more data is created and the speed of data creation is very important. Based on some data, the global data annotation tools market grew by about 30 percent per year in the last 6 years, mainly targeting the retail, automotive, and health sectors. Thanks to data annotations, Big Data and Machine Learning can run as expected. Data annotation is the key to data growth, which is why more and more companies are relying on data annotation services such as those which can be accessed online at https://oworkers.com/data-annotation-services-company/.
At a technical level, the Data Government must guarantee and provide companies with:
– Strategies for defining, approving, and communicating data strategies, policies, standards, procedures, architecture, and metrics.
– Global management of the availability, relevance, usability and security of data in a company.
– Follow-ups to enforce regulations in accordance with data policies, standards, architecture and procedures.
– Ensure the distribution of information through the channels provided.
– Efficient Master Data management mechanisms where excellence in quality and integrity is sought
– Promote, control and supervise the execution of data management projects and services.
– Manage and resolve corporate data issues.
– Understand and promote the value of data assets within the Company.
– Protect access to data according to established rules, ensuring confidentiality.
– Trace the lineage of the data and provide situational information.
– In short, to help companies organize, analyze, prepare and share data, and at the same time maintain control and protection over them.
Key Components of the Data Quality Cycle
– Data Discovery: The process of searching, gathering, organizing, and reporting metadata.
– Data profiling: process of analyzing the data in detail, comparing it with its metadata, calculating data statistics and reporting on the data quality measures that must be applied at all times.
– Data quality rules: these will be aimed at optimizing the quality level of the organization’s informational assets and, for this, will be based on the applicable business requirements, the commercial and technical rules to which the data must adhere.
– Monitoring of data quality: continuous improvement requires a follow-up effort, which allows to compare the achievements with the defined error thresholds, the creation and storage of data quality exceptions and the generation of associated notifications.
– Data quality reporting: it is related to the procedures and tools used to report, detail exceptions and update the data quality measures in progress.
– Data Correction – deals with the ongoing correction of exceptions and data quality issues as reported.
Impact of Poor Data Quality
Poor data quality in a company can have multiple negative consequences. Being one of the main indicators of failed projects and processes. One particularly dangerous aspect of poor data quality is the false sense of security it can convey. Data errors can blind you to problems at your company. If left unaddressed or addressed, those mistakes could lead to much bigger problems down the road.
Here are some of these negative consequences:
– It prevents correct decision-making.
– Generating erroneous reports and analysis.
– It makes us incur costs of distribution, operations, management, …
– Discrepancies between applications or results, which may require information reconciliation work and therefore loss of user time.
– It damages our relationship with the client, making it impossible to offer good service and treatment.
– Impossibility of detecting fraud, overpayments, charges, etc.
– You cannot identify duplicates, inconsistencies, incomplete data, etc.
– Lower performance and job satisfaction of employees.
– Non-compliance with standards, regulations, GDPR
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