NIQ and GfK have recently announced and confirmed their strategic combination to create the world's leading consumer intelligence company.

The alliance between these two industry-leading companies consolidates their strengths and establishes a complementary partnership to offer customers new, innovative solutions that deliver results.

With advanced technological tools, companies gain a more detailed understanding of consumer purchasing behavior. This allows them to provide valuable insights for decision-making and sustainable growth to major retailers and brands.

The symbiotic relationship between NIQ and GfK is beneficial from all angles. NIQ contributes unique information on fast-moving consumer goods, while GfK provides insights on durable goods and consumer technology. This combination grants access to comprehensive and practical store-level information.

Full View approach

Their offering, "The Full View," provides a complete global perspective on consumers, making this combination a unique option in consumer intelligence.

CEO Jim Peck summarizes it best: "Together, we have tremendous potential with greater global reach, omnichannel coverage, granular data, consumer insights, and predictive analytics, resulting in practical analysis and growth for our clients. Importantly, we bring together industry innovation experts and complementary cultures driven by integrity and an unwavering commitment to delivering the highest quality ever."

Furthermore, the companies boast nearly 200 years of collective experience in the industry, combining the expertise of both organizations. Present and future clients can trust that their experience with the tools and technological offerings will yield great results.

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The dual usage of Universal Analytics (UA) and Google Analytics 4 (GA4) is no longer possible, except for companies using Analytics 360 with premium features until July 1, 2024.

As of July 1st this year, standard Universal Analytics properties have stopped processing data. However, according to Google Support, UA 360 properties will continue processing this data until July 1st of the following year.

This implies that all businesses using this platform to improve their performance must stay updated on the planned transformation and the new features and capabilities that GA4 will bring.

Google Analytics 4 collects data generated by website or app users to provide relevant information to businesses, enabling them to make better and more informed decisions. It can also integrate with tools such as Customer Relationship Management (CRM) systems to further enhance results.

GA 4 brings new functionalities

Google Analytics 4 has been designed with new functionalities and improvements compared to Universal Analytics to serve businesses better and leverage Google's analytical potential.

Some differences in GA4 compared to UA include the ability to separate data collection from website users and app users, machine learning, and AI to fill in data gaps from users who do not share data, in compliance with the General Data Protection Regulation (GDPR).

Other features include removing duplicate IDs, integrating indicators such as Gmail accounts to identify users across different devices, customizable reporting, a lifecycle view with options for acquisition, engagement, and monetization, and many other functionalities that can be crucial for strategic business planning. Google offers us a window full of possibilities for data analysis.

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Google Cloud is integrating all its existing data analysis tools into Looker, which will now be part of Looker Suite and also include its own Locker Pro, encompassing all artificial intelligence and machine learning tools under its umbrella.

The new version of Looker has been launched in Cloud Preview, featuring integrated cloud infrastructure services. In addition, a preview of Looker with Google Sheets has been introduced, and there are plans to support visualization-focused tools like Salesforce's Tableau, which is also crucial in the world of data analytics.

A key aspect of the Looker acquisition has been Looker's semantic data modeling capability, which allows for defining data assets and analytics to ensure consistent interpretations and meaning of the data.

Furthermore, enabling users to combine different data sources is highly beneficial for business analysts as it allows them to have multiple tools in one place, saving time and enabling them to perform more effective work. Overall, the consolidation aims to empower businesses to bring in more data and different applications to make better daily decisions.

Looker Studio Pro will play a critical role at the enterprise level

Looker Studio Pro will incorporate various features that will enable companies to differentiate themselves as advanced in their analytics. These include the aggregation of different management functions and multiple collaboration tools, among others.

The enterprise version would be integrated with Dataplex, the intelligent data fabric that focuses on data governance and monitoring, providing visibility into both data and metadata lineage. This integration enables powerful analytics and project scalability.

The post Google Data Studio becomes Looker Studio and consolidates its business intelligence tools appeared first on Cross-Border Magazine.

By Sandra Galiano - Business Analytics is fast becoming a fundamental tool for any entrepreneur wishing to be successful. This is because nowadays, we generate data practically every moment in every single second of every day without even noticing. 

“There were 5 Exabytes of information created between the dawn of civilization through 2003 (...), but that much information is now created every two days, and the pace is increasing.” - Eric Schmidt, CEO at Google. 

Taking into account the relevance of social media and our significant ‘daily life usage’ contribution to these technologies, we have - as a direct result - a hyperconnected world in which information appears to be power, now more than ever. 

“Data analytics is the future, and the future is NOW! Every mouse click, keyboard button press, swipe or tap is used to shape business decisions. Everything is about data these days. Data is information, and information is power.”- Radi, Data Analyst at CENTOGENE.

In this world - thanks to current technologies - we can have a totally different lifestyle from the one our parents or even older siblings experienced. 

Our communication, buying, selling, investing, planning trips, and even entrepreneurship undertaking - among many other facets of human activity - had changed due to the profound implications of the current data technologies. 

So, it is only logical to imagine that, for specific jobs, the result will be to outgrow, transform, or even die. One of the areas impacted in this fashion is the one we aim to focus on in this article: Business Analyst. 

What is Business Analytics?

Business Analytics is the discipline focused on analyzing data with the objective of providing the company with favorable results and increasing the decision-making process. 

Thus, a Business Analyst is a person whose firm grasp of both worlds - technology and business - allows the contribution and proper team working between the CEO and the programmer since he can deeply understand the two parts involved. 

The three main types of analytics to master 

What should we do? What's the next step? How can I solve this problem? These and many more questions can be answered with business analytics, having data as the centerpiece of the whole project.  

The decision-making process is based on three main types of analytics encompassed in the business analytics area, those are: 

• Descriptive Analytics: uses the data to see what is happening or has happened in the company to create a bigger picture of the business and its situation. For instance, the sales certain businesses had of a specific product over the last two years, ordered by months. We could see the relationships with things such as the festivities, and temperatures, compared with the sales of its substitute or complementary product, among others. 

In this analytics, we use techniques such as sentiment analysis, statistics, averages, graphic visualization, dashboards, etc. 

• Predictive Analytics: answers the question of what could happen. Its foundation is the analysis of the future based on the relationship between business variables. We could use for this analysis data mining techniques like linear regression, classification, clusterization, statistical analysis, or machine learning.  

• Prescriptive Analytics: what should we do? Using the previously analyzed analytics, we conclude with a decision for the business, considering the rules, characteristics, vision, and objectives of the company involved. In this phase, we use optimization, simulation, or multi-criteria decision analysis tools. 

“Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.” - Geoffrey Moore, management consultant and author of Crossing the Chasm

Every Business Analytics project follows certain well-structured steps that include the tools to use and how to proceed. This is what data scientists or analysts use professionally in a company. However, all of this is scalable, it can be shaped for a small business or an entrepreneur from the most elemental, and still, without having the greatest techniques or tools, results can be improved considerably. 

7 steps to succeed in a Business Analytics Project

1. Data Sources: the basis of any project, data. By working with it, we will achieve our project objectives.

“Data are just summaries of thousands of stories—tell a few of those stories to help make the data meaningful.” - Dan Heath, bestselling author.

The data is there, everywhere. However, not all have the same quality. It is important, when using business techniques based on data analysis, to find reliable sources of information and, above all, useful ones.

There are many tools for this; I recommend doing two procedures when working with sources: go to secure pages of high-quality information and differentiate between data, information, knowledge, and insight.

“We are surrounded by data, but starved for insights.”- Jay Baer, marketing and customer experience expert.

It is very important to follow the steps of data, information, knowledge, and insight to discern what we have, where we come from, and where we are heading.

• Data: representation of an attribute or variable, that is, the data as we find it. Example: 2,700
• Information: organized data set. Example: 2700 blue sneakers sold this last month.
• Knowledge: conclusion. Example: Blue sneakers have sold very well this month.
• Insight: intelligent part, the objective.  Example: improving the production of blue sneakers for the following month.

By interpreting the data and giving it meaning, we find the information, and when we draw conclusions from this, we have the knowledge, being able to obtain valuable insight.

What kind of data do we have? We could mainly divide them into 3

categories according to their format:

1. Structured Data: Have a fixed format and field. For example, everything you can put in a table, such as numbers.
2. Semi-structured Data: This is data that does not have a fixed format but does have some internal organization system that facilitates its treatment, such as labels, markers, etc. (XML, HTML, CSV).
3. Unstructured Data: They do not have any type of structure, for example, images, audio, or text, generally abundant in social networks.

And what about sources? We can divide them into sources internal to the company and external. 

Regarding the internal ones, we find CRM (customer relationship management) and ERP (enterprise resource planning). The first is associated with the management of the relationship with customers and commercial contracts, and the second one is with the company's own finances, sales, purchases, etc. In general, any data that the company itself gives us comes from an internal source.

In external sources to the company, we will therefore have all the data that we obtain from abroad but that we need or that is convenient for the company. Examples: social networks, open data from governments, and official sources such as the National Statistics Institute, among others. 

2. Data Ingestion: The process by which data is extracted from different sources to dump them into the data repository for further analysis. The main part of this is another process called ETL, which stands for Extraction, Transformation, and Load, although it's actually five major steps:

1.  Data extraction

2.  Cleaning: Duplications, correct errors, complete empty values…

3. Transformation: recovers the clean data and summarizes it in the analysis models.

4. Integration: Validation that the data that we are going to load is consistent with the necessary definitions and formats.

5. Update: A process that allows us to add new data to the repository.

3. Storage:  where are we going to store this data? It is generally a database, a set of related information whose treatment is facilitated using a database management system.

4. Processing: what do we do with the data? specific techniques used to answer the questions. How do we process this data? What do we want to do with them? This is the most technical part of the project and in which programming skills, data mining, etc., are involved.

5. Visualization: we will understand what we are doing and the results obtained, but we have to consider that this has to be understood by someone completely unrelated to us, who, in most cases, will not have the technical knowledge or skills that we have. That is why this phase focuses on synthesizing the information, putting it in graphs and dashboards, and showing the data and processes in the clearest way possible.

6. Consumption: It is time to present these dashboards and those results to others, perhaps to the CEO or those responsible for making the last decisions of the company. This is where the analyst has to demonstrate communication and problem-solving oratory skills and where he has to narrate that story with data from the moment the problem or objective was raised until he found the final proposal, justifying the entire process.

7. Action: decision making. Based on all the previous steps, what will the company do? This will be a very rich decision since it will have hard work behind it that confirms that it will be the best possible. 

To make the long story short, data analysis can transform your business, not only making it more profitable, solving problems, or anticipating what the future holds, but also fulfilling the obligation to adapt to the constantly changing society that reigns in these times and avoiding being left behind, or stagnate; and as it is printed on the vinyl of my university class, “to transform a world of data into a world of intelligence”.

About the author: Sandra is a tech-savvy young entrepreneur currently President of Generación Empresarial, a youth-oriented university association committed to creating the next generation of successful European business leaders.

🔥 Check out more about Sandra here!🔥

The post What is Business Analytics? 7 steps to make the right call! appeared first on Cross-Border Magazine.