The importance of strategic management of your company’s data

Sep 9, 2021 by

The need for a good database is no longer news to companies. Online presence is a great data generator and an increasingly important aspect, as well as benchmarking and monitoring market trends. For this reason, the management of this data has become a new challenge for any company.

The difficulty of having legitimate data

The main concern of brands is not being able to take advantage of their data to create solutions that improve their relationship with consumers. One of the reasons for this is the quality and legitimacy of the data they have. Excessive inaccurate or disposable data prevents companies from gaining valuable insight. With a large amount of unusable information, brands can lose control over them.

The use of a well-built database is essential to meet customers’ digital service expectations. The companies consulted stated that close to ⅓ of their data is inaccurate and 70% stated that they did not have control over their data to generate objective actions, such as customer experience insights, for example.

Almost 95% of those surveyed believe that poor data quality is bad for any company. This means that, for brands, it is a bigger problem than the constant changes in customer demands.

Therefore, it is necessary for brands to have a critical view of their own database, analyzing whether it is really usable.

Technological limitation

There is a lack of technologies that can offer the flexibility and scale necessary for companies when managing data. Many companies are beaten by quantity and fail to organize and take advantage of everything they have at their disposal. This also affects the perception of new demands from the target audience. Consumers increasingly have an expectation of exclusive and personalized attention.

Organization of data management

The management of legitimate data allows not only the improvement of the relationship with the client, but also to make decisions based on real information, promote the innovation of the company and gain an advantage over the competitors.

There are three possibilities for companies to solve their database problems:

– Process analysis – It is necessary to understand where the data comes from, what technologies are used to manage it and mainly how it is used: By tracing the entire journey of the use of your data, we would be able to identify how and when possible failures occur (whether they are collection , data storage or analysis).

– Develop a data strategy – The use of data cannot be based solely on the technology used for collection. It must involve collaborators who analyze what has been collected and who know how to apply them in each area of ​​the company, creating solutions based on real customer demands, not only directing automatic and unsuccessful reports.

– Identify practical measures – It is necessary to identify small “wins” in the short term, which motivate the company to continue investing in data-driven strategies. This is important because large changes based on the information in the analyzed data take time, such as testing customer experience strategies, for example. For this reason, it is important to create simple and objective applications – such as changing the email sending schedule and increasing the open rate – to present practical results and stimulate the brand to work with the information collected.

Share data and troubleshoot

Almost 90% of companies said they had difficulties in managing their data. One of the main problems when extracting value and content from that data is defining who is responsible for analyzing it. About 85% of companies leave this task to the IT team, which has other responsibilities and priorities.

Artificial intelligence for the benefit of companies

Artificial intelligence (AI) is a term that brings together a set of techniques that allows machines to perform actions in a rational way, that is, they make sense, can intervene in the world and have the same or better capabilities of the world.

For example, autonomous cars perceive their surroundings, identify if they have a person or another vehicle next to them and that the traffic light is red; He plans what he will do to avoid an accident or run over anyone and executes the action, that is, he moves at a calculated speed and interacts with his environment in such a way that he does not make mistakes. Artificial Intelligence enables companies to understand customer needs, suggest the perfect product to the right person, and streamline the sales process.

What is artificial intelligence used for?

Today artificial intelligence is used to automate processes, program machines to do high-volume tasks, in less time than human beings; also, to capture and analyze information, including for accurate and timely medical diagnoses. This area of ​​knowledge aims at the benefit of the consumer, they were created from the algorithms that recommend products in e-commerce, to the virtual assistants, which interact with the user.

Apple’s Siri is one of the most famous virtual assistants in the world, through voice recognition, it is capable of making calls, sending messages, searching for information and contacts. He even sings if a human asks him to. Among the virtual assistants are also chatbots, incorporated into web pages and social networks, capable of answering questions and helping people 24 hours a day and in real time. Self-driving cars are capable of mimicking human capabilities without committing traffic offenses or causing accidents. These vehicles, through laser, radar, global positioning systems and computerized vision, are capable of perceiving the environment and driving in the city or on highways. For an emblematic case in the world about artificial intelligence, read Sophia, the most famous humanoid in the world, who describes herself as a robot with artificial intelligence, which helps people.

AI in e-commerce

Artificial intelligence is combined with Big Data to analyze data on customer profiles in real time, which optimizes the user experience and helps sellers improve engagement with them. The more data there is, the better the algorithms will work. For example, Netflix feeds its artificial intelligence algorithms with the conglomeration of all the interactions that its users have historically had with its content, characterizing their preferences and generating good recommendations.

The client receives the right message at the right time, even the algorithm is able to predict the future. Artificial intelligence puts the consumer at the center because their experience is personalized. Virtual stores can capture information that helps them to know their users, know what they buy, know their tastes and have their demographic data, to influence their advertising campaigns in a correct and intelligent way, instead of spending resources targeting people who do not need nor do they look for their products.

Any e-commerce has three artificial intelligence techniques that occur in different stages: understanding customers, suggesting products, and streamlining the sales process. Thanks to AI, Some e-commerce platforms have a chatbot with which they can respond to all kinds of doubts, complaints and claims, as well as guide the customer throughout the purchase process.

A few years ago, this service could only be offered through a call center, which needed many employees, trained to respond to all customer needs and questions. However, today it is possible to train chatbots, technological tools that serve to hold automated conversations with a natural language, that is, people do not notice that they are talking to a machine. These improve response times, scalability, and lower operating costs.

Today, AI and machine learning are very important

In modern industry, AI and machine learning are very important. Therefore, these two aspects continue to experience rapid development, and as a result, there are great innovations that can provide many advantages in every field that involves AI and machine learning in its “operational processes.”

In machine learning, it is very important for an object to be recognized by computer vision. Therefore, image annotation techniques should be used to make an object recognizable. Not only able to provide benefits to the AI ​​sector, image annotation techniques (data annotation techniques) are also able to provide benefits to other sectors.

Data Annotation: What is it?

Basically, data annotation is a process of labeling data in various formats such as text, images, and videos. However, a labeled data set is needed to ensure the machine can recognize the input pattern, thereby recognizing the object properly. In this context, companies need professional data annotation services such as those provided by Oworkers.

Multiple types of data annotations

As mentioned above, data annotations include text, images, and videos. For example, in image annotation, some popular annotations used are polygon annotation, bounding box annotation, polylines annotation, landmark annotation, and 3D point cloud annotation. There are many software on the market that can help with the annotation process.

Benefits of data annotation

Directly, the data annotation process can provide benefits to machine learning algorithms, including increased output accuracy. Another benefit is that the user experience can be significantly improved. Each user can experience something completely different and certainly better.

In the end, data management with the support of AI, machine learning, and data annotation is crucial for the modern industrial climate, enabling a company to continue to compete in a competitive path.

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