At the same time, it is necessary to highlight the need for the Management Control Function to be increasingly closer to the business priorities and manage to develop a multidisciplinary role in which it is capable of: becoming the internal advisor in key aspects such as setting objectives, preparing financial and operational forecasts and participating in any transformation project undertaken by the company.

If we analyze the current opinion of CEOs, different studies that analyze their priorities for the future coincide in highlighting, among other pillars, the application of big data and data analytics to identify opportunities that support and facilitate the profitable growth of the business or the need to take advantage of technological change as an opportunity to be more competitive.

These and other levers are undoubtedly fertile ground for controllers to play a new role, closer to the business, taking a step forward in organizations, but clearly not without difficulties and representing a great challenge.

The 2nd Radiography of the Controller in Spain revealed a series of behaviors that give us a picture of the starting point in our country in aspects related to managerial skills and the style of leadership and negotiation of the professionals who are dedicated to the functions of Controlling. It is still surprising that currently, only 41% of Spanish companies have a Department exclusively for Management Control, although over time, it gains weight.

The Finance and Management Control Functions manage a highly complex environment where it is necessary to operate on a global scale, strengthen financial analyzes to question and facilitate business strategies and take advantage of an environment of regulatory changes such as the current one.

In this new context, the figure of the Controller is acquiring growing strategic importance since control systems and strategic planning become essential resources for decision-making and value generation.

As a consequence of the above, one of the most developed technical skills, according to the 2nd Radiography of the Controller, is reporting and planning, with a focus on business management control, rather than financial control itself.

An area for improvement identified in this study is the existing imbalance between the high knowledge of reporting tools and the low existing expertise in Business Intelligence and ERP tools. It was also revealed that the Controller must improve their negotiation and innovation skills since the vast majority do not consider these to be their strongest points when, however, they are the ones that bring together the most knowledge about the business, in addition to having a strategic vision developed.

In other aspects, such as team management, the Controllers are good communicators and have a clear orientation toward the internal customer, although 47% of those surveyed consider that they should improve their leadership skills to have more influence in the organization and become agents of the change.

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Companies can access Big Data Analytics services offered by large technology companies in the cloud, but many times the cost of these services are not affordable by small and medium-sized companies and they opt for on-premise solutions that allow computing resources to be exploited—owned by the company itself.

In addition, another factor to take into account when deciding where to deploy a Big Data service is data privacy. By its nature, there are many data sets with sensitive information that companies need to protect from outside eyes and companies that offer Big Data Analytics services in the cloud can guarantee that no one else will access your data once you start processing it. But the truth is that the user has no control over what is really happening in the computing nodes in the cloud.

When a company decides to deploy on-premise Big Data Analytics services on its own computing resources, certain complexities arise. On the one hand, the complexity of installing, configuring, maintaining and updating each one of the technologies that make up each service, and, on the other hand, how to get the most out of computing resources that are divided into different clusters with different communication networks communication.

The Radiatus project emerged as a response to all these problems. Radiatus is a Big Data Analytics platform as a service that allows the deployment of technologies for data analysis in a very easy and intuitive way. The project is in its fourth year and already has more than twenty integrated community technologies such as Jupyter, Zeppelin, Spark, Flink, Cassandra, Kafka, MySQL, HDFS, MinIO,… In addition, we have developed our own technologies such as DistributedML, a framework for training Machine Learning models. radiatusIt also has a multi-tenancy user management system, which allows users to be organized into groups and assigned dedicated computing resources for the deployment of their services. Likewise, Radiatus can also make use of GPU resources for the execution of high-performance computing.

One of the latest developments of Radiatus has been the federation system that allows different platforms to be interconnected in order to share the computational resources of all of them with the users. This functionality is very useful, for example, to link different Radiatus platforms at the Edge, Fog, and Cloud levels, thus allowing the execution of data flow processing services with a Data Continuum model, or also for the use of computational resources hosted in different clusters for the deployment of Big Data services.

For the future, we continue to investigate new technologies that add value to Radiatus and we continue to integrate new services and update existing ones to offer the complete platform possible.

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The Situation Of Companies Before Hackers

However, only a lack of business culture means that we are not more aware of the risk that our company data entails being exposed, in one way or another, to planned and executed attacks, in many cases, with the aim of extorting us through a ransomware. Four years ago, a shocking study by Kaspersky Lab and Ponemon Institute showed that 60% of small and medium-sized businesses that experience attacks end up disappearing within six months.

Some Examples Of Attacks On Companies

In 2016, Yahoo confessed that it had been attacked three years ago (2013) and 3,000 million profile data had been leaked: email addresses, passwords, names and surnames, birthday dates, telephone numbers, … One of the biggest hacks of history.

The cyberattack on SONY in 2014 resulted in cancellations of filming and premieres, unpublished revelations from the industry, and losses that reached 200 million dollars.

In 2015, Ashley Madison, a website dedicated to offering relationships with lovers to already committed people, suffered the theft of data from 39 million profiles. The case was serious, because data from users, celebrities and politicians among others were published.

A technology company with important security measures, could not prevent a WannaCry attack in 2018, like many other international companies and organizations.

This year, the hack of the hypermarket chain TESCO led to theft of money from the bank accounts of 20,000 customers.

Creepy, Right?

Cybersecurity Recommendations

To avoid these problems, the experts make us some recommendations:

• Install only programs from solvent sources.
• Regularly perform system and program updates.
• Carry out regular awareness and training campaigns for employees.
• Control external devices introduced into the company (security policy for BYOD -Bring your own device-)
• Have a good antivirus and antimalware.
• Continuous monitoring of the network and company traffic.
• Ensure the perimeter security of your company and have cyberprotection on your own data.
• Back up to two different locations, one of them outside the company.

A Professional Cloud Backup Solution

Of course, there are many Cybersecurity providers. At Extra Software, we have opted for cyber protection and backup systems in the Acronis cloud. We have partnered with a first-rate manufacturer, because we are convinced by the technology they use.

• AES-256 military grade encryption for data in storage or transit.
• SSAE-18 certified security, the best disaster prevention technology.
• Tier IV designed data centers.
• Artificial Intelligence Technology vs. Ransomware.

The other reason why we have chosen it is because it has two different licensing systems: Per-GB Model, in which you only pay for the data you save, and Per-Workload Model, in which you pay for the load of work that protects itself. In this way, you make sure that the information you keep that you consider really valuable in your company. Lastly, the backup system is also flexible. You can work with cloud backup with full, differential or incremental copies, or a combination of them.

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However, data has a time value, and this is often discussed in circles of big data analytics or sales information. Over a period, most kinds of data could lose their value. For instance, contact credentials, addresses and the like could gradually lose significance as people move from one place to another, change careers or pass away. Besides, certain financial products such as credit cards have a life of value and could expire after some years or could change due to financial fraud or being misplaced. And while medical records are considered valuable to help better predict therapies, these too could become outdated when new therapies and treatment options begin to emerge.

So, when it comes to data security, data decay can be an ideal thing. For instance, if intellectual property dated from the 1970s or the 1980s is stolen today, it could probably be worthless. Long-established companies sometimes keep records that date back to their origin, simply because they may not have a specific decree to get rid of the information. But with the rise of data breach laws and liabilities, it can result in tremendous risk. If organizations begin to perceive the natural decay in the value of information, this can help to balance some of the risk associated with regulations.

When organizations prepare for a data breach, it is crucial that they accurately assess the amount of data stored in their systems and networks and the kind of data present. In addition, employing a proactive data and document security solution such as DRM (digital rights management) can ensure that relevant, sensitive information and confidential data that is pertinent to the organization stays secure. In this scenario, companies must also consider the rate of data decay for the kinds of data that it stores. Some information that holds value, such as intellectual property, products and services information and the like, can present a higher risk to the company as cybercriminals may look to steal this kind of valuable data. It is essential to compare the rate of data decay with the overall practicality it offers to the company. This can help to prioritize the efforts in disposing of unneeded information.

To address this issue, most companies anonymize data sets and get rid of explicit identifiers by replacing them with numeric codes and individual credentials. This practice is regarded as re-identification. The main objective, here, is to cut down on the dangers connected to data exposure, while ensuring that valuable data remains stored safely. In getting rid of specific characteristics, organizations consider that such data may not be exposed to a breach. Although data breach regulations and other laws take anonymization into account, in most cases, security and breach notification conditions are not applied to such anonymized information. And hence, when companies assume that a specific data set is anonymized, they believe it becomes safe to disseminate or share without the data being breached. But unfortunately, this is not true.

In many cases, anonymization has been seen to be reversed. To the lay man, a data set that has been anonymized could appear impossible to route back to the person, but is it worth trivializing such a task? This is because even anonymized information contains content that can be distinctive to a person, for instance, the prescription of a specific medication, lifestyle habits and symptoms. When such individual details to other information sources are mapped, say in the case of a voter enrolment list, customer buying profiles, marketing information and other information sets, it becomes possible to associate databases and eventually recognize people. This all boils down to the point that even anonymized information carries a massive amount of risk if a data breach were to take place.

Rather than anonymizing data, employing digital rights management (DRM) can prevent access and use of your protected data from all users, except the ones that you allow. You can control document access and use, including the number of times a document can be viewed and the length of time a protected document can be accessed for. Further, DRM can prevent the content of your protected document or PDF file from being altered, shared, copied, printed and saved into unprotected formats or used in unprotected applications. Further, it can also prevent the anonymous consumption of your protected content as document use can be monitored and logged. As the only proactive solution to prevent the unauthorized alteration and copying of digital content, DRM technology offers complete control on how a company’s documents can be viewed and used.

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Information technology (IT) is an area in constant innovation thanks to the emergence of new sciences, disciplines, fields of development and research. All particularly focused on companies. Let’s see then, what is the impact of IT science applied to business.

The Impact Of IT Science

Although, until not too long ago, the systems engineer or the database architect were the most specialized profiles of the IT sector of the company, today the evolution of IT science has allowed new professionals such as the big data analyst or data scientist.

In fact, these IT professionals in Spain have salaries up to 24% higher than the average.

Expanding and increasing the capabilities of this Department are not the only changes that data science is promoting. According to Gartner, the impact of IT science on the business is noticeable in 5 areas:

1. Innovation: Drive new ideas and business disruptions based on data science.
2. Exploration – allows you to explore unknown transformation patterns in data.
3. Prototypes – makes it possible to challenge the status quo with radical new solutions.
4. Accuracy: IT science focuses on continuously and iteratively improving existing processes and products.
5. Prevention and compliance: allows you to identify the drivers of certain situations that could negatively affect your business.

The cloud is the environment for accelerating the development of IT science and expanding the scope of its discovery, enabling disruption in markets. For this reason, cloud computing and data science are considered inseparable.

Limitations To The Potential Of IT Science

However, despite the great benefits it represents for the digital company and the changes for the better that it promotes, sometimes IT science does not bring the expected value to the business. This can be due to the following reasons:

• The gap between business objectives and analytical efforts, moving in opposite directions without achieving the synchronization that would help the business take advantage of the opportunities presented to it.
• The lack of discipline in the measurement of results, which causes imbalances or prevents trends that would force a change in the course of action of the company’s strategy.
• The lack of agility in the planning of Big Data analysis projects, which are usually highly exploratory and with uncertain results since, by focusing on a few high-risk efforts, the probability of failure increases.

As with most initiatives that end up being viewed as a success, in terms of performance and cost-effectiveness, making an IT science program effective can take considerable effort and will most likely require several iterations before it can be structured properly. effective.

But do not give up. In the midst of the analytics era and competing in a data-driven world, leading firms that have already developed strong IT science projects are not only succeeding in excelling in their own industries but are also looking for avenues to enable them. cause disruption in other sectors.

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How Is A Project Aligned With This Strategy Carried Out?

The 360 ° vision provides a comprehensive perspective of the customer link with the organization and thus takes advantage of the data from different contact points and shows a complete picture of who they are. In fact, it is the basis for making the relationship with customers more experiential than transactional, as required by current times.

In turn, a 360 ° vision allows the relationship of customers with the brand to be transparent both in the past (history as a consumer and interactions in the various channels throughout the customer journey), and in the present (current behaviors, how it is linked and interacts with the organization, through which channels). And in doing so, it makes it easier to forecast what is likely to happen in the future.

With this information and the appropriate analytical capabilities, you can have a clearer perception of the customer – of their needs, their level of satisfaction and their value – and boost their loyalty by personalizing and optimizing their experiences. Customers can also be segmented to define differentiated actions and reduce the costs of marketing campaigns using all resources, not only financial but also people, to work only those prospects who are qualified targets for the company. 

How To Do It With Data Analytics?

Today, organizations are supported by modern tools, some powered by AI (artificial intelligence), that enables enormous amounts of data to be converted into useful information for business decision-making.

What is pursued when using these platforms is no longer having Big Data, but rather smart data –that is, data that is properly contextualized, categorized and analyzed–, in order to be able to personalize the strategies, provide content of authentic value and interest and offer unforgettable experiences to customers. Relevant content at the right time.

Now, how is a customer service strategy of these characteristics organized? The first thing is to have the organizational commitment to put the customer at the center of the general business strategy and focus on obtaining the desired customer intelligence or customer intelligence. To achieve the latter, it is necessary to have a data management strategy and design protocols that encourage all departments to enter them in a consistent format. For what reason? Because customer intelligence requires high-quality data: they must be “clean,” organized, and categorized. Otherwise, the knowledge will not be precise or relevant. 

Appropriate Tools

PowerData 360 ° strategy based on data analytics big data technologies allow this comprehensive view of the customer to be obtained more quickly and easily. When creating a 360º model, we must collect a wide range of information and combine it into one system – a tool to manage data.

As the sea of data that companies have can be overwhelming, a good recommendation is, first of all, to identify the commercial need and prioritize the use cases that offer the highest value, since this in turn will allow the types of data to be identified. acquire and then select the analytics to apply. In other words, to properly focus data collection, sales leaders must know what they want to measure and clearly establish a vision of what they want to achieve.

In the second instance, a base for the customer profile must be built: it is best to start with the minimum data elements that identify it. In this instance, it will be necessary to focus on data hygiene and install processes to label and rationalize it.

In initial stages, it will be convenient to take advantage of your own data obtained from a variety of sources (websites, social networks, advertisements, CRM, etc.). Then there will be time to incorporate data from third parties (external databases, public data sources, etc.), which will enrich the comprehensive vision.

Analysis Stage

Once the information is obtained, the data analysis stage will have to be promoted, which is what will allow commercial value to be obtained from it. Better understanding customers and how they interact with the organization is one of the keys to providing you with what you need when you are looking for it. As Mckinsey says,  “If data is the oil of the digital age, analytics is the engine that turns it into energy.” 

As the organization becomes more mature in its data analytics, it will be able to advance with predictive analytics processes that will enable it to detect behavior patterns and anticipate possible future behaviors. From this information, you can adjust your strategies, both to sell more and to optimize customer satisfaction levels.

In short, by having a 360º view of their business and their clients, companies lay the foundations for their data analysis strategy, which may become more complex in the future. In this way, they access the information necessary to offer increasingly satisfying experiences to their customers, enhance 360º engagement, and increase sales, which will naturally have a very positive impact on their bottom line.

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In other words, Big Data and Business Intelligence are designed to act together and elevate the intelligence of the data, making them invaluable assets for the company.

Business Intelligence (BI) is one of the oldest concepts when it comes to data processing, but combining it with Big Data produces a radical reinvention of the initial concept. In fact, Business Intelligence was mainly limited to internal data due to the lack of easy access to data from other sources. 

But now the roles have changed, and it is now possible for companies to access platforms that combine unstructured and structured data for unprecedented flexibility with the combined use of Big Data and Business Intelligence.

Big Data And Business Intelligence: Key Elements Of A Smart Business

Although both are terms that are used together when talking about Data Business Analytics, it is important to keep in mind that they are not the same, and on many occasions they are confused. While Big Data is designed to capture and store high-volume data, Business Intelligence tools are responsible for analyzing all this amount of information, following predictive patterns of behavior, based on the data.

What is true is that both are complementary and help organizations make sound decisions based on data. In addition, when these are transformed into knowledge, they allow them to offer innovative services and experiences to the client, creating a successful business model.

The good results obtained by Big Business have led executives from all sectors to awaken to the potential power of information. So investing in Big Data and Business Intelligence is just the beginning of a cultural shift toward data-driven decision making.

With the right approach, they can use big data to form ideas, transform business operations, and improve customer experiences, but to be a real and effective Big Business, managers will have to avoid three common mistakes:

1. Lack Of Capacities 

In order to exploit the potential of information, it is necessary to have experience in mastering new tools and in applying the most innovative data strategies.

In addition to the technical knowledge of Big Data and Business Intelligence analysts, statisticians, data scientists, and other IT professionals, you need to have the business vision to launch the right questions and decide how to act on the answers.

2. Focus On Technology And Tools

Along the same lines, it is important to look for the business context in each initiative. Focusing on technology investment without a clear purpose aligned with business goals does not help elicit answers or define a business strategy, nor does it make it possible to achieve better business results.

3. Latencies

 The world of Big Data and Business Intelligence is a dynamic environment where every second count. The slowness of manual processes, the lack of resolution in systems architecture renovation projects, or the extension of the terms associated with the development of solutions such as dashboards lead to obsolescence and, therefore, the distance business from its objectives.

The Next Business Intelligence Challenge: Real-Time Analysis

Businesses increasingly demand fast action and more accurate responses based on the right data. For this reason, new business models require a new generation of Business Intelligence tools that include real-time analysis and assets from different operating sources. The goal is to achieve relevant patterns and achieve greater agility.

This is where the Business Intelligence platforms are headed: new methodologies and technologies that give immediate answers when offering quality data at almost any speed, both in cloud environments and on-premises.

In other words, real-time analytics is the ability to establish new business processes and integrate an IT infrastructure that automatically and specifically adapts to all business activities and processes, which are constantly changing. It is a new paradigm: “detect and respond” immediately.

This need requires the integration of a system that can analyze and virtualize all kinds of information, including also unstructured data. It is also necessary to carry out a strategic evaluation to establish which business processes will most benefit from this new technology.  

This is because, although it is true that most organizations already enjoy a significant level of synchronization between the essential data of their business, they still do not have reliable and homogeneous access to all their Business Intelligence tools.

So they need solutions that quickly integrate diverse types of data with advanced management tools within their IT infrastructure. These alternatives should also be adapted to existing software and hardware, so as not to have to resort to a replacement strategy.

The promise of big data and the new Business Intelligence is within the reach of all businesses, but for it to become real results, people, processes, and technology must be backed by a strategy that enables effective transformation.

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The data big is important when it comes to the development of models and statistics. In order to make comparisons, make predictions and provide accurate reports, it is often necessary to work with a lot of information: otherwise, the results are not valid or accurate.

There is no precise amount of data that allows us to talk about big data. The limits that are handled usually revolve around terabytes, petabytes or zettabytes. Working with such an amount of information requires the use of advanced technological resources.

This means that a user with a home computer can hardly work with big data since these teams are not prepared to manage and store so much information.

The data big, anyway, is dumped into databases of different types. It is possible to manage structured data (whose format is already defined), semi-structured data (they are not limited to a specific field but have markers for the distinction of elements) and unstructured data (without specific format). The data, on the other hand, can be captured and visualized using various computer tools according to need.

The importance of this concept is incalculable for many companies since it allows them to obtain answers to thousands of essential questions for its correct operation at a speed that would be impossible through human work. Thanks to the versatility offered by the different data manipulation and consultation systems, it is possible to obtain “customized” results that are easy to understand.

One of the keywords of modern life is ” tendency ” this inclination of the human being to carry out certain activities or consume certain products and services leads some companies to shape their proposals to suit their tastes and needs, and for this The accumulation of large volumes of statistical data is essential.

Big data queries and analysis serve to better target business, accentuating sound decisions, avoiding potentially damaging ones, and targeting those that have not been made so far but could lead to significant growth.

In short, the use of big data is a happy ending for everyone: companies conduct more prolific business, increase the efficiency of their operations and the volume of their profits, while customers receive the products and services they want, And all this has a positive impact on the market.

It is worth mentioning that data storage is not always carried out on the premises of a company; There are big data services in the cloud with very competitive prices, which can represent a significant saving in equipment operation and maintenance, in addition to avoiding the typical problems that memory expansion entails.

To understand all this, let’s think of a very simple example: an individual wants to create a business but does not have a well-defined idea, so he perches on his window to observe his neighbours in order to find inspiration; throughout the day, he hears many people complain about the lack of a grocery store in the area and, based on these data, he decides to open one on his own to satisfy that need. 

Big data analysis allows millions of “stories” like this to be dealt with simultaneously, to open doors to innovative ideas that users themselves were often unaware of.

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