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Our team of specialists wanted to share some articles on technologies, services, trends and news of our industry in the era of digital transformation.

Customer Data Management

Customer Data Management

Did you know that every day we generate around 2.5 quintillion bytes of data in the world? That’s a lot! This data is very valuable to organizations like yours because it helps answer important questions about customers, such as how they make purchasing decisions. Therefore, it is key that companies can collect, store, analyze, and use this customer data in an organized way, a process that is called Customer Data Management (CDM).

 

Lack of control over your customer data creates inefficiency and problems when making decisions. Customer Data Management represents a strategy to regulate how your company can use its customer information. Using CDM ensures a consistent way to generate your most valuable insights and maintain data quality. It has become essential to accompany the day-to-day of your organization with a process and policies to govern the use of our clients’ data. This represents a safe and reliable way for companies to collect, analyze and use their customer data.

 

A CDM system is made up of three stages, which guide companies in managing data, information and insights about their customers:

    1. Type of customer data.
  1. Data platform.
  2. Data management process and its use.

 

Let’s analyze each of them in detail…

 

1. Type of customer data.

Structured and unstructured data.

Only 20% of the data you generate in your organization is structured and easy to analyze. The remaining 80% is unstructured, complex data that represents a difficulty in analysis and standardization. It is important for a customer data management system to be able to identify between the different sources and types of data based on their structure, in order to anticipate the standardizations that will be needed to make the most of them.

Identity and behavioral data.

You also have to consider the types of data that your company can count on based on the information they provide. Identity data represents personal information about the customer; The importance of these data lies in the possibility of understanding the different profiles of a company’s clients, taking advantage of psychographic information to characterize them. They are generally obtained from direct customer transactions with your organization.

Behavioral data represents information collected from any customer interaction with the organization, as well as the actions they took to communicate with you. This data is used to better understand the different points of contact and intentions of the customer during their journey with your company. Clear examples are the data obtained from browser cookies.

Quantitative and qualitative data.

Last but not least, the data that your organization obtains can be quantitative or qualitative. Quantitative data allows obtaining specific metrics. Clear examples are internet metrics, offline interaction metrics, campaign metrics, etc. These hard pieces of information are gathered from helpdesk solutions, digital platforms, CRM systems, and marketing automation tools.

On the other hand, qualitative data is characterized by not being structured and by containing a greater level of depth, which makes it difficult to represent in conventional numbers, statistics or graphs. Some examples are customer comments, reviews, opinions, and complaints. Any type of information that cannot be directly translated into specific numbers falls into this category. Qualitative data can also be obtained from feedback tools and processes, such as social platforms, customer chats, etc.

As a practical example of quantitative and qualitative data obtained directly from clients, you can read about the General Service Survey we developed for Aeropuertos Argentinos.

 

customer data management - qualitative and quantitative data

Illustration by Optimal Workshop

 

2. Data platform.

When identifying the types of data that your company will use for the study and understanding of your customers, you need to define which platforms can help you.

Companies typically use two main types of platforms. On the one hand, Customer Data Platforms (CDPs) allow the study of your own customers, offering detailed information on each and every one of them. A very common example of CDPs are the famous Customer Relationship Management (CRM) systems. At Huenei we have extensive experience in the retail sector, where CRMs are very important, and we can help your company develop one that is tailored to its specific needs.

On the other hand, Data Management Platforms (DMPs) handle third-party data, so the user profiles that are created are completely anonymous. There are several companies that provide this type of services and databases to carry out an in-depth analysis of the segment in general, beyond the specific customers of your company.

 

3. Data management process and its use.

To manage the data with the help of the defined platform, a process common to other systems that use data must be considered, which consists of the following steps:

  1. Data collection, that is, having procedures to obtain customer data: online behavior, market research, among others, as well as a structure that allows an ETL process to be carried out.
  2. Preparation of the data, to have them organized and ready for later analysis. In these instances, it is very useful to have a person in the company who knows how to use database management languages, such as SQL.
  3. Data analysis, to generate actionable and useful statistics, reports and visualizations for decision making.
  4. Validation and use of data, that is, taking these results and reports to the platforms used by the team in order to take advantage of them and support decision-making.

 

As you can understand after reading our brief introduction to the world of customer data management, this process is essential for organizations to understand consumers and provide them with a better service. These systems can help you stand out from your competitors, and at Huenei we will be happy to help you in their development.

Data Lifecycle Management: Understand its Importance for your Business

Data Lifecycle Management: Understand its Importance for your Business

Since the last decade of the 20th century, Decision Support Systems (DSS) have gained popularity and their use has increased in companies and organizations of different areas. Over the years, DSS have evolved and mutated into comprehensive Business Intelligence (BI) systems, in the context of Data Science Strategies. Since the origins of DSS and the growth of BI, business data lifecycle management, analysis and knowledge have been key to successful strategies.

Data is a fundamental component in the architecture of Business Intelligence systems. The incorporation of large volumes of data in the daily operations of companies (the trendy Big Data) helped Business Intelligence systems evolve, focusing their scope on data analysis and obtaining valuable information and knowledge for decision-making.

In today’s article we intend to understand the life cycle of data in an organization, in order to study how data “lives” from when we collect it until we use it for decision-making. This framework of data lifecycle management is useful to understand the importance of data and its correct management and administration in each of the parts of its life cycle.

 

Data Lifecycle Management

 

The data lifecycle management process is closely related to the information systems process to support strategic and business decision-making. The data flow enters the company from certain internal and external sources, and needs to go through certain stages and components until it reaches its final stage of data visualization and presentation of results.

 

Step 1: Collection.

This is the stage where we obtain access to the data. We try to extract and collect data from various databases or sources internal and external to the organization. An interesting example that we want to tell you about is the case of the Voice Assistant Platform that we developed for SoundHound, which consisted on a voice recognition product for the Spanish-speaking market. In this case, we can see that the data is obtained through the recognition of the voice of the users.

We can talk about different types of software for data collection management. An alternative is on-premises software, that is, software that is installed on the local server without the need for access to the cloud, such as the Oracle and SAP platforms. On the other hand, there are also alternatives that are offered through cloud services, such as Microsoft Azure, Amazon Web Services, Google’s BigQuery, and Salesforce (SaaS).

The data collection methods, which we can take advantage of from the aforementioned platforms, are the following:

  • Batch: When the software periodically connects to the data source in search of changes or updates since the last connection.
  • Streaming: When the software is constantly connected to the data source, so that information, changes and updates impact the moment they are made.

 

Step 2: Storage.

Storage consists of saving the data that has been collected and keeping it protected until the moment it needs to be analyzed. The basic elements of the storage layer are databases and files. The former we use with relational database management systems (such as MySQL and SQL Server) since they allow us to store structured data. While the latter allows us to store unstructured data and, therefore, are used with non-relational database systems, also known as NoSQL.

 

Step 3: Processing and Analysis.

Processing refers to the moment of data transformation, preparation and enrichment, where the main objective is to clean and order the data to align what may bias or hinder subsequent analysis.

When we move on to the analysis, it is time for action! It is at this stage when the exploitation and analysis of the data allow us to find solutions to problems that may arise in the company.

The data is processed and analyzed using statistical analysis, software and programming languages.

 

Step 4. Visualization.

Through data visualization tools and correct communication, value is given to the data, allowing its understanding by those who need to make decisions based on it. In this stage we generate graphic visualizations of the important information to communicate solutions that have an impact. This last step represents the impact on the business, our goal is to generate a team with the business stakeholders to explain the solution.

 

Data Lifecycle Management - Data Visualization

 

For this, in addition to the use of visualization tools such as Power BI and Tableau, or visualization libraries such as Plotly or Seaborn in Python, there are tailor-made developments that can add extra value to decision-making. This is the case of the development of the General Service Survey that we carried out in Huenei for YPF, which consisted in a platform for conducting surveys in Aeropuertos Argentina, which automatically summarizes, analyzes and visualizes the information in an accessible and easy to understand way.

In the world of BI, the importance of Data Storytelling is growing as a practice focused on building a narrative for data and its visualizations. Its purpose is helping to convey the meaning of the findings to decision makers. This is a very interesting and exciting topic. We’ll talk in depth about it in an upcoming article!

What is Data Science All About?

What is Data Science All About?

Nowadays, data takes an important role in the operation and context of all organizations. Data allows us to make informed decisions and take advantage of the past to learn and get to know our customers, users, employees, processes, competitors, and more. To be successful a company must extract value from its own data and that of its environment, pursuing the objective of becoming more and more competitive. When a company decides to enter the world of data and data science, the opportunities are immense.

But the question that often arises is: “what is data science all about?” The answer may seem simple, however, many times we fall into misconceptions that do not help us understand the concept. Let’s work it out together.

 

What is Data Science?

If we look for the formal definition, we could say that it is a discipline that focuses on uncovering insights (i.e. discovering hidden knowledge) from large amounts of data, both structured and unstructured. In fact, this is how IBM defines it, adding the idea that Data Science uses statistics, machine learning, data mining, and predictive analytics to achieve its goals.

From a practical point of view, we could say that by implementing Data Science processes in our organization, we are seeking to develop an approach that allows us to better understand our environment, our problems, our opportunities and, mainly, our business. Data science allows us to understand and explain past events and generate models to estimate or predict situations or behaviors that will generate impact in the future. So, it is necessary to understand that it is an interdisciplinary field, which combines formal sciences (i.e. mathematical modeling and statistical analysis), computing (i.e. computer science and software development), and the specific domain over the knowledge of a certain business or industry.

At Huenei, we take advantage of advances in data science and technology to enhance our software developments from a comprehensive business vision.

 

What is Data Science All About - Venn Diagram

Data Science as an Interdisciplinary Field

 

What About BI and BA?

It is very common to confuse Data Science with Business Intelligence and Business Analytics. It is important to know the differences to be able to evaluate what elements can help your company become competitive.

Business Intelligence is a business and strategy process that incorporates technology and software solutions for data analysis and information communication in a timely manner, with the aim of supporting decision-making. If correctly implemented, Business Intelligence can be weighed as a major organizational advantage these days.

On the other hand, Business Analytics represents an integral part of the Business Intelligence structure of an organization, which focuses on building statistical models and analyzing databases to predict scenarios and understand potential future states.

In short, both of them take advantage of available data to improve decision making. But their differences lie in the fact that Business Intelligence represents a general business strategy, which uses historical information in order to learn and boost decision-making. Whereas Business Analytics is a key component within the Business Intelligence strategy that focuses on using the organization’s historical data to predict future events. So, we have a global strategic business vision and advanced management of analysis tools and methodologies; by adding the boost of computational power, we enter the world of Data Science.

 

How Can Data Science Help your Business?

Data science can help you solve business problems, get to know your consumers or users, understand your employees and your competitors. Technologies available are varied and each professional will determine the right ones for their business.

 

What is Data Science - Technologies Available

Technologies Available for Data Science | Illustration by Berkeley

 

We suggest you read our article “What’s the most important programming language in 2021?”  to learn more about the leading programming language in 2021.

Regardless of the technology, languages or tools used, there are various applications of Data Science –and their integration with Business Intelligence and Analytics– in a company, that you could take advantage of depending on your needs. There are a large number of methodologies, analysis and strategies, and it would be impossible to name them all. But we want to take advantage of this space to introduce you to the world of data science through some examples:

1. Segmentation of customers, employees, suppliers, etc. Statistics and machine learning allow you to naturally group people, companies, or any unit of analysis, counting on variables of our interest. Thus you can create groups that are heterogeneous amongst each other but internally homogeneous. In the marketing environment, for example, this can help you recommend personalized products, display customized ads, among others.

2. Churn. Churn analysis can help you understand when a customer will stop buying from you, when a user will uninstall your app, or when an employee will quit. In addition, you can evaluate the probability of “abandonment” (churn) of each individual.

3. A/B Testing. This methodology is about conducting experiments where two groups are randomly created: A and B (the control group and the experimental group). For example, you could compare the typical production of an industrial plant with an alternative production by implementing a different methodology. You could also compare two versions of an advertisement, or two different website designs. The objective is, through comparison, to determine which one offers better performance.

4. Sentiment analysis. Through the use of Natural Language Processing, it is possible to extract subjective information from texts and speeches, to standardize it in variables and quantitative indicators. The classic example is the social media sentiment analysis, where Tweets, comments on Instagram posts, and so on, can be summarized in an indicator of sentiment towards the brand (i.e. positive, neutral or negative sentiments).

 

In a nut shell, it is essential for a company that wants to increase its competitiveness to get involved in the world of Data Science. For this, the strategy must be planned and it is important to understand the field of action of the different components of a Data Science strategy in an organization. The applications are varied and the ways in which your organization can benefit are endless.