When referring to data analysis, the term big data is frequently used. It refers to the collection, management and analysis of a large volume of data which, due to its size and complexity, exceeds the processing capabilities of traditional tools.

When mastered, DA offers a competitive advantage to organizations by allowing them to identify new opportunities and capitalize on their knowledge to make the best possible strategic decisions.

Data analytics solutions are evolving as the digital transformation of businesses progresses.

Despite their apparent complexity, any company can benefit from their advantages by following the correct methodology.

In this article, we share some tips that will allow you to benefit from the advantages of data analysis in the concrete case of business decision-making.

What Is Data Analytics?

Data analytics consists of analyzing data to detect trends and draw conclusions on available information.

This analysis is carried out using specialized software that transcribes the information analyzed on dashboards to optimize strategic decision-making.

The final objective sought by DA is to boost a company’s performance. According to Austin math tutoring experts, people most eligible for work in data analytics are those with degrees in computer science, economics, finance, statistics, and applied mathematics. Math knowledge is crucial for understanding algorithms, statistical methods, and data analysis techniques, which are foundational for extracting meaningful insights from complex data sets.

Decision-Making Based On Data Analysis

To facilitate decision-making processes based on data analysis, it is necessary to ensure that the information available is appropriately organized, accurate and easily interpretable.

The first essential step in structuring these processes is creating a standard procedure for integrating data from different sources, internal or external, into the organization.

At the end of this automation comes the supervision stage and analysis of the data presented.
This analysis is done through interactive dashboards designed to make data analysis visual and intuitive, allowing you to understand the information presented quickly.

This system extracts data in real-time, allowing for more accurate analysis.

The use of data to drive decision-making in business strategy is known as data-driven decision-making.

Let’s Take a Look At The Different Steps of This Method.

Definition of The Problem

Initially, it is necessary to know the initial status of the situation to be analyzed and to identify the possible problem to be solved. 

To do this, open questions can help identify the problem to be solved: What is the ideal analysis scenario? What is the current situation? 

Data Preparation

Once the problem has been identified, it is necessary to understand what data must be analyzed to improve the initial situation or to solve the problem.

In this case, the following questions may be relevant: What data should be collected to solve this problem? How can this data be obtained?

Data Processing

Once the data has been collected, the next step is to process and prepare it for further analysis. During this step, it is essential to consider which information is relevant and which should be deleted. This database cleaning is necessary to obtain the information that is really useful for the final objective. 

Data Analysis To Draw Lessons

Finally, we move on to the data analysis phase to study the problem and find possible solutions. In this phase, we must answer the following questions: what information does this data bring to the initial problem? How does this knowledge help us solve this problem? 

Implementation Of Analysis And Modeling

At the end of the steps described above, all the conditions are met to apply the analysis carried out to decision-making based on the results obtained.

Concretely, all the steps consist of defining an objective (what must be solved), designing the strategy (how to solve it), determining the strategies (actions to be taken) and choosing the key indicators that will be used to analyze the results.

Data Archiving

Finally, the last step consists of electronically archiving all the valuable information resulting from the processing and analyzing of the data.

This archiving makes it possible to access the information necessary during or at the end of this analysis to meet the conservation and data protection standards. 

Conclusion

The high level of competition in the market forces large companies to turn to data analytics to improve their decision-making.

Today, a large amount of information is archived, allowing the use of artificial intelligence to generate reports and dashboards that facilitate the search for solutions, with the ultimate goal of optimizing the profitability of the company. ‘company.

Using data analysis techniques, it is possible to interpret basic information to detect trends or uncover insights that will aid decision-making for business success.

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This Eliminates Slowdowns And Inefficiencies

Business intelligence, therefore, allows you to focus on a complete view concerning your organization. Slowdowns and inefficiencies can be easily eradicated by correctly interpreting new market trends. This concept was born in the 1960s to indicate a system to share information within organizations. Then, from the Eighties onwards, it developed in the field of information technology up to the boom of today: now modern business intelligence focuses on information speed, data monitored thanks to ad hoc platforms and flexible analyzes. The key concept, which can be said to be the basis of business intelligence itself, is business management. For the related objectives to be easily achieved, always having up-to-date, clear and relevant information is necessary. And it is business intelligence that provides the necessary tools for both the organization and the interpretation of data.

The Size of The Company And The Strategic Steps

Regardless of the company’s size – whether it is a large company or an SME, it does not matter – the very fact of aiming for a virtuous increase in the business represents an absolute indication of the need to develop a solid business strategy. How? Using business intelligence tools is a mix of processes that aim to collect, analyze, and interpret data relating to the company. The latter must be transformed into useful information to lay the foundations for making various decisions and growing the business. In this context, graphs, tables and reports are available but not only: all information that will allow managers to act for the best in terms of strategic steps. It is thanks to the analyzes and reports that, for example, it becomes possible to estimate the ROI (an acronym for Return on investment): a critical step above all in terms of digital marketing.

The concept of business intelligence is constantly expanding, continually exposed to market fluctuations, technological innovations and – above all – the company’s needs. The need to share data will always increase, so there will be increased collaboration between different sectors. At the center, as always, teamwork.

Also Read: What Advantages Does Business Intelligence Bring To Your Business?

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A professor in the area of ​​Computer Science and Artificial Intelligence could contribute to public confidence in AI. In the first part of the interview, he tells us it is essential to have explainable algorithms and how they are technically developed.

What is Reliable and Explainable Artificial Intelligence?

Today, Artificial Intelligence is a technology that is practically in any area, helping us make decisions and obtain patterns, usually using large amounts of data.

So it is essential, for learning and this information to be helpful, that humans can understand it. The European Union has opted for this ethical, reliable, responsible, and explainable Artificial Intelligence.

How Can We Get Fair and explainable Algorithms?

There are several cases that we can investigate. Still, the general idea is that the algorithms stop being as a black box as they are today and allow some explanation, auditability, maintenance of privacy, or guarantee of sustainability. Efforts are being made on many models, and we need more research. It is a boiling field.
In my research group, we work in different areas. For example, we have developed algorithms that work in distributed environments, and what they try is to guarantee the privacy of the data of each node. So we can learn from the data of all the nodes simultaneously, but without sharing it and without sending it over the network to gather it in the cloud or any central node. What is communicated are the parameters of the algorithm.

We have also worked on the explanatory part of the algorithms. We have introduced in the evaluation metric of the algorithm the number of variables that we are using to develop the explanation. So we try to maintain the performance of complex algorithms but having an understandable algorithm above them so that a human can interpret that result.

What Profiles are Needed to Address The Explainability of AI?

We would need to be able to work in more diverse teams. this is not very common. Still, it is interesting that we learn to work with personnel from other areas, such as Sociology of Law, to address more ethical aspects or those that have to do with the development of suitable algorithms but with the developing algorithms that are good for people. They can help us integrate this change from a more social perspective.

How Does Explainable Artificial Intelligence Benefit Us?

There are areas where most of the algorithms we are using, such as deep learning ones, are pretty opaque. Very powerful from an accuracy point of view, but not very playable. I believe that the subject of the explicability of Artificial Intelligence is a particular term that perhaps for some areas is not necessary, while in others it is.
We must learn to develop more transparent algorithms, or at least that they can be auditable. From knowing if the data we are entering is partial to understanding the entire algorithm or the algorithm’s output.

Transparency can have different degrees. The explicability of the algorithms would be the highest degree, in which we would need that algorithm or its results to be easy for a person on the street to see.

It is one of the issues included in the General Data Protection and Regulation Law. It is said that a person has the right to receive an understandable explanation when they are affected by a decision of an Artificial Intelligence algorithm.

In Which Areas is Explainable AI Most Necessary?

We speak of sensitive or high-risk areas, and this has yet to be defined precisely because we do not want to put restrictions on the research and development of algorithms that can be very exact. I think the issue is not that we are going to sacrifice accuracy for the sake of explainability but rather in trying to balance.
A sensitive area can be healthy if a person is affected by a decision determining what treatment they obtain or what diagnosis they face. Also, areas that have to do with Fintech, insurance issues, loan concessions, legal issues, etc.

Probably in other areas much less sensitive for people and in which we use Artificial Intelligence every day, as it is not so important. As I said, I think that we will try to achieve a balance and try to explain the explanatory nature of Artificial Intelligence. as much as possible in susceptible areas.

Would These Measures Help Society To Accept and Incorporate AI?

It is essential to build trust. Sometimes the media or movies are broadcasting Artificial Intelligence topics that make citizens distrust them. Some of the actions that we have seen, mostly related to data privacy, have created confusion and a specific need to protect themselves. And so we see that some tools, such as Radar COVID, are not being adopted by the population, perhaps a little because of that mistrust.

Citizens must understand that Artificial Intelligence is at their service, and for that, it is essential that it be. So, we need to modernize the Public Administration and convey this idea of ​​a much more reliable AI, and I think this is catching on in Europe little by little. And probably in other areas such as the US, where we have witnessed scandals that have to do with the transfer of data, privacy, companies that backtrack on a project, etc.

I think it is essential that we create a citizen conscience. The more educated we are about the capabilities and limitations of current Artificial Intelligence, the more we will trust technology, and I believe that we can offer better tools.

Also Read: Does A Chatbot Need Artificial Intelligence?

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More and more technologies and programs process these mountains of data and allow companies to benefit from advanced and data-based analyses.

What is Big Data?

Big data essentially describes two aspects: on the one hand, the term describes faster-growing amounts of data ever. On the other hand, it is also about new and compelling IT solutions and systems to work profitably. Especially data from z. B social networks make up a large part of this amount of data.

Big data is often used as a collective term for modern digital technology. In digitization, big data is increasingly influencing the collection, management, utilization, and, above all, digital data analysis.

Why is Big Data So Important?

Big data starts where conventional approaches to information processing reach their limits. Big data is, of course, also about the amount of data a company collects, but much more about what is ultimately done with it.

One of the primary goals is to discover reproducible business patterns. The correct use of data enables costs to be reduced, time to be saved, new products to be developed, offers to be optimized, and well-founded business decisions to be made. Users can do the following tasks more efficiently, among others:

• recognize fraudulent behavior in good time,
• Recalculate risk portfolios within a few minutes,
• foresee possible future events and results,
• Determine causes of problems, defects, and malfunctions almost in real-time,
• create vouchers based on customer buying habits.

Data Mining

As already described, the real added value of big data lies in the evaluation – also known as data mining. This term summarizes the entire process of preprocessing, searching, and evaluating data – i.e., the systematic use of methods for analysis with the aim of pattern recognition.

From a technical point of view, algorithms are used in data mining that helps to establish connections between the amounts of data and thus to discover cross-connections and trends. It is essential here: Data mining is only about the evaluation and not about the generation of new data.

Competitive Advantage With The Help of Big Data

Due to the enormous amounts of data that arise within a company, those responsible have entirely new insights into customers’ interests and behavior. Companies use analysis methods to filter and process this information. These methods provide insightful information about the customer – hidden patterns, unknown correlations. In this way, companies generate competitive advantages over their competitors.

Therefore, advanced data management is the prerequisite for process optimization since providing information in real-time is the basis for entirely new business processes. In addition, the growing technical possibilities offer companies the opportunity to use big data projects to simplify the system infrastructure and reduce the number of databases.

Software Tools For Analysis

There are several different software tools for analysis. One example of this is predictive analytics from SAP. The software uses big data for predictive analysis. Users get a forecast of future events and likely results based on the analysis of historical data. The software thus creates added value for companies, as decisions in every conceivable area can be made more well-founded based on forecast models.

This method is used for. B. in maintenance. To make predictions, large amounts of data are collected, stored, and analyzed. Storage and analysis technologies from big data, such as data lakes and analytical databases, are used.

The basis for predictive maintenance are systems that are equipped with sensors and networked, embedded systems.

Also Read: How Long Does It Take To Make Money From Blogging?

Big Data Use Cases

As described in the previous example, there are already areas in which big data is used. We have put together a selection of other fields of application here:

• Retail: In retail, using big data is key to building customer relationships. Retailers need to know what is most likely to appeal to their customers, which products which target group is buying, and what effective customer acquisition strategies are. Big data analyzes forms a reasonable basis for implementing this.
• Health care: In the health care system, accuracy, but also speed, are the top priorities – and in many cases also sufficient transparency to meet the strict legal requirements. With the help of efficient, extensive data management, healthcare providers can gain the knowledge they need to improve patient care.
• Banking: Because of the large amounts of information, banks have to find new, innovative ways of handling large amounts of data. Minimizing risk and fraud in compliance with legal regulations is as important as a positive customer experience and satisfied customers.
• Education: With the help of big data analytics, educational institutions can improve systems and curricula. This means that better systems for evaluating and supporting teachers and managers can also be implemented.
• Sales and Marketing: Big Data makes it easier for the sales and marketing departments to tailor product offerings to customer segments or individual customers and thus minimize losses in the customer base. The observation of the market and competition can therefore be expanded significantly with big data analyzes.

Conclusion

Big data will develop into a critical business success factor in the future. Therefore, it is already crucial for companies to take the time to collect, store, and process big data. Do you have any questions about big data or possible big data tools? Then we can help you. The mind square consultants are experts in the field – contact us, and we will be happy to advise you.

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