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There is much talk about the data as an epicenter of the modern company.We have coined, over the last years, metaphors that equated data such as "new oil" or "gold of the digital era".But information is not an end in itself, but the basic ingredient for the business processes of these times.
We enter with this at the next level, the one in which we have to do something with that data.Words such as advanced analytics or artificial intelligence make their particular emergence on the scene.They are the weapons with which to be able to extract value from the information and obtain the long -awaited efficiency benefits, better experiences for users or the search for new business opportunities.
Above all, challenges and potentialities, we talk with Marcos Carrascosa, Customer Advisory Director at SAS Iberia:
We are immersed in which it is perhaps the greatest revolution for companies in history, with digital technologies as enabling and data as a new epicenter.What is the starting point in these lides?
Business fabric has been drawing its path to digital transformation years.But not equitably, there are much more advanced sectors than others and in general we still have a long way to go.
Pandemia, as we all know, has forced companies to a digital acceleration for an almost survival issue, but the truth is that there is no turning back in this process.And the domain of the data will be the way to face any challenge both in the business field and in the public from now on.
What exactly does that "domain of the data" mean?
I mean that the data has become the new epicenter of all decisions, but for digital transformation to be a reality in organizations, three fundamental pillars must be taken into account: culture of data, quality and government, and operationalization ofThe analytics.
In order for the Cale data culture in an organization, it is necessary for it to put the appropriate focus on taking advantage of the decision -making data and that this is transversal to all departmental areas.It is not just about capturing and storing data, it is about putting them at the service of the optimization of internal processes, efficiency in the market positioning strategy and business results.To do this, it is necessary to establish a fluid communication between the multiple information systems and promote collaboration between all teams.
The quality and government of the data is another key point to be able to implement a solid data culture.In order for the decision makers to trust the data, it is necessary to ensure their quality.Are we complying with the regulation?Protecting our clients' data?Are we using data sources that reflect the reality of our business?An affirmative answer to all these issues contributes the trust that teams need, knowing that they are based on their results report or their strategic changes in reliable information and recognized by their organization.
As for the data of the data, organizations must ensure that there is a common data language and that all teams can have access to them, understand them, know how to use them, know their origin or that of business indicators.Ensuring regulatory compliance is another priorities of the data of the data as a responsible area of safeguarding its quality and that of analytical models.
The operationalization of data analytics is the last mile for any data-driven organization.It is about industrializing analytical models and having a centralized government that allows them to deploy, monitor, document, version and optimize them to automate decision making by meansachieved.
This is the Achilles heel of large companies that have data scientific teams developing models in different programming languages.Some statistics suggest that 90% of the models take to develop between three and four months and that 50% of them never put themselves in production.And this is where from sas we can help and much.Our platform allows the models government, facilitates the production and integrates the main programming languages.
As we say, data analytics is a fundamental tool for companies to anticipate changes in their markets, optimize their processes or detect inefficiencies in their value chain.What are the main fields of application of this technology?
To put some examples, in the retail sector the analytics is key to doing good demand planning, especially in critical moments such as the one we are living where people consumption habits suddenly change.The optimization of the store assortment or the reduction of logistics costs are other current examples in this sector.
In the manufacturing sector, or energy, the use of advanced analytics improves quality in manufacturing processes, minimizing defects and reducing operating costs.Likewise, the detection of anomalies or predictive maintenance favor the continuity of operations.
In the field of fraud prevention or guaranteeing regulatory compliance, which impact both the results account and the reputation of the companies, the advanced data analysis allows to detect new behavior patterns that would otherwise be obvious.
Thus, in banking entities, applications of new high -risk products can be identified in advance and adjust the conditions and controls associated with these applications properly.In the insurance entities we can identify potentially fraudulent accidents that may be obvious by existing controls, and stop payments before they occur.Also in public administrations we can identify irregular requests of aid or improve the efficiency of tax systems.
These are some examples, but we could talk about health, tourism, agriculture, education ... the possibilities are endless.In all sectors there are areas where we can optimize, predict, preserve and improve.
Sas is a pioneer in this field, since 1966 to be more exact.What is the evolution that the technology of data analytics has lived in these decades?
The rise of computing, which has progressively reduced hardware costs at the same time that its power has increased, and connectivity, have been the great promoters of the data as a value for organizations.The development and evolution of Big Data systems and Cloud Computing have allowed companiescould impact your business.And these technological advances have significantly promoted the advanced data analytics.
The automated data analytics was born from the curiosity of two students in North Carolina who, looking for a better way to analyze agricultural crop data, created the SAS platform: a powerful Machine Learning engine, capable of analyzing the data and learning from them.Since then analytical technology has evolved to provide descriptive, diagnostic, predictive and prescriptive analytics.With the application of artificial intelligence we enter the cognitive analytics that allows systems to understand human language and reasoning, capable of learning from them and climbing that knowledge.
SAS, in addition to leading this evolution of advanced analytics, has transformed its platform by expanding its scope to the development of specific analytical solutions to different sectors and business areas, to improve its usability and democratize the use of analytics within organizations.
Another of the great trends that Cloud Computing has encouraged is the concept of Anything as Service (Xaas, in its acronym in English) or all provided as a service, that is, products and processes offered as services, resulting in many benefits for companiessuch as cost reduction, greater agility, flexibility or focus on innovation.
One of the latest trends is the migration of many of the analytical loads to the cloud.Why?What benefits does it contribute to companies?What is your current state of democratization among your clients?
Take advantage of the entire data spectrum of an organization and be able to extract its full potential requires powerful storage infrastructure that are usually expensive and need a lot of resources for its implementation and maintenance.In this context, the main advantage offered by Cloud Computing is the possibility of climbing agilely and effectively, moving the structure to a virtual environment and reducing investment in hardware.
Another of the main benefits is the reduction of the so-called time-to-market, the cloud gives us the elasticity we need to deliver services more quickly, generate IT efficiencies and have payment models based on the subscription.This allows to use on demand, according to needs, to innovate in processes or test new products, quickly implement new technologies, and be able to turn operations to support the business, with greater agility.
As for the current state of our clients, what we are observing is a clear turn towards the cloud.In IDC reports, important growth figures are given in the Cloud market, in the order of 23.2%, and the expenditure in this type of solutions will be expected in 2024.The new SAS clients already buy in Cloud and seek this flexibility for their businesses.In the installed base, what we are doing fundamentally is to help our clients in migration to the cloud.
Could you tell readers some examples of how data analytics in the cloud is assuming a disruption for the future of companies?
Of course.An area where we believe there will be a strong impulse in the coming years and where data analytics can contribute great value is customer intelligence.
The brands are realizing the need for a marketing system that can interact with all contact points regardless of the underlying technology, whether the client data is in their local or cloud systems.
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Therefore, marketing solutions enabled for the cloud are gaining prominence.They can be accessed from anywhere, they are integrated with all the points of contact and have scalability and reliability to support the operational environment of a company 24 hours a day, 7 days a week.I give you an example, if your systems are sized for a certain volume and you decide to increase your capacities for an increase in demand, this is done almost immediately and you can climb your service level quickly without affecting the restof your systems.
Marcos Carrascosa, Customer Advisory Director at SAS Iberia
On the other hand, real -time decision making, the ability to provide context -sensitive communications, at the exact moment in which needs arise, is key when meeting customer expectations and offering personalized experiences.This, in the case of Pandemia, has been critical.
In order for marketing professionals to succeed with real -time decision making, they must have technologies that nourish many types of data and a real -time decisions engine, potentially assisted by artificial intelligence or Machine Learning, capable of proposingThe best offer for each client at the right time.The ability to coordinate all channels to create richer contexts for customization and orchestration of activities that make up the client experience is one of the great advantages of cloud analytics.
We cannot ignore the confluence between artificial and data analytical intelligence.How are you taking advantage of the Potential of the AI to get more value from the information?
We believe that advanced analytics supported by artificial intelligence technologies is key to improving our society in many aspects.
For example, health is an area where artificial intelligence can provide a great value.One of the great challenges we face in the near future is population aging and the high pressure that this will be in the health system.Artificial intelligence will help us detect patterns in the large amount of patient data to be able to define, with greater precision, risk groups of certain diseases and establish more efficient prevention protocols.The more we focus on prevention, the more sustainable our health system will be and the better population health will be.
S As Computer Vision are revolutionizing the medical image.The AI sees injuries that the human eye does not see.It is able to see how a tumor is responding to chemotherapy with precision that our eyes do not reach.Detecting cancer in very little advanced states greatly increases the survival and success of healing therapies.Likewise, as soon as possible we are able to see that applied therapy has not had the desired effect, before we reorient the treatment and look for alternatives that can be more effective.
Another application that I would like to mention is a project that we are carrying out with the Gender Violence Unit of the Secretary of State of the Ministry of Interior, where we apply data analytics and artificial intelligence to improve the classification of the risk of recidivism of aggressions.The unit processes around 50.000 complaints of gender violence per year.
We talk about text information that is then combined with up to 50 additional indicators (degree of vulnerability of the victim; socio -economic conditions of the family nucleus; additions of aggressor; etc.) To establish the victim's degree of risk.This process, which was previously done by traditional media, consumed a significant amount of security forces resources.Today, the management of this information has been automated, the map of the degree of risk of all active cases is done in less than an hour and police forces can devote more resources to the work of protection of victims.
We have talked about the transformation of technology and your customers but what about you?SAS is also immersed in his own revolution to face all these challenges of the 21st century ...
SAS is continuously in transformation.Our curiosity and that of our clients drive us towards continuous improvement because, together, we discover new answers for urgent questions every day.We also invest 26% of our R&D benefit and, therefore, our analytical platform is alive and constantly evolving.
SAS in its origins was born as a statistical analysis tool.The need to extend the application of analytics to other business areas and sectors has led us to develop SAS VIYA, an open, powerful, flexible and user-friendly analytical platform.
Here we started our path to the democratization of data analytics.We have specialized in specific industry solutions and in key initiatives such as client intelligence, fraud and risk, artificial intelligence, IoT, among others.We have improved the integration of our software with different programming languages and achieved interoperability with all information systems.
Subsequently we evolve our SAS VIYA platform to make the most of the benefits of cloud architectures.Thus was born the cloud-native version of Sas VIYA, which will lead us to more flexible the use of our advanced solutions and will allow us to make analytics a reality for all and anywhere.
The strategy that you have launched to embrace open innovation, support for Open Source solutions, integration with third parties and weaving an important network of alliances with other manufacturers in the sector.Tell me more about it ...
One of the great difficulties facing organizations is precisely the difficulty of consolidating information from their departmental processes, whether manual or automatic.Likewise, the integration of different business software solutions to be able to offer an “area” vision of all the organization's data.Here the open nature of our SAS VIYA platform is very helpful since it allows us to integrate all these sources and democratize data and analytics so that everyone can have access and make decisions more agile and reliable.He also does it with very visual and easy interpretation tools for any user, regardless of their technical knowledge level.
Linked this need for systems and data integration, there is the ability to incorporate analytical models developed in different programming languages.
Open source programming languages came to stay as part of companies analytical ecosystems.Today many data scientists learn to program in open source and this, to some extent, is advantageous for companies.However, it also implies additional complexity when administering, implementing and governing all the different open source components.
From SAS we provided the layer that was missing between the models developed in multiple languages and the systems in which they unfold, eliminating the need for recoding, which consumes a lot of time when implementing models.The times of production and obtaining the results for which we have scheduled them are shortened.If, for example, we have a recommendation model to make cross -sale of products, as soon as possible we have implemented, we see the impact on the business and make our investment profit.
The hybrid approach to combine open source with proprietary software can offer the best of both worlds, because proprietary software can address the challenges of bringing a project to production and climbing it for the entire company.
In addition, our recent alliances with Boemska to boost the portability of SAS VIYA and open source models for mobile and business applications;And the agreement with Microsoft that displays the entire potential of SAS in Azure, bring new opportunities for companies to accelerate their path to data-drive decisions.Digital transformation and innovation is something that must be part of the roadmap of companies and institutions and our technology greatly facilitates this evolution process.
I take the opportunity to ask you about the recent commitment announced with Microsoft Azure for close integration with SAS VIYA, your latest version.What will this alliance be in practice?
Companies around the world are moving to the cloud to innovate and move faster towards their business goals.In this sense and facing organizations to accelerate their digital transformation into the cloud, we are working with Microsoft to ensure that SAS products and solutions are successfully implemented and executed in Azure.
On the one hand, analytical products and SAS solutions will be available in Microsoft Azure as a preferential cloud supplier for SAS;And on the other, Microsoft can provide added value to its customers in sectors such as health, financial services, retail and many others, benefiting from the SAS expertise in development solutions with sectoral approach.For example, Microsoft and SAS are already offering a solution that helps to capitalize on a large scale the large amount of data generated by the IoT combining the Microsoft Azure IoT platformSAS.Currently, the city of Cary, in North Carolina, in the United States, is already using this joint IoT solution to boost a critical solution of flood prediction.
This integration facilitates a more fluid transition to the cloud, provides faster and faster access to SAS solutions, and, ultimately, will make the most advanced analytics accessible to more organizations, regardless of its dimension.
Finally, we are talking about everything that has been achieved on the shoulders of data analytics and what is being done right now.I would like to know what the next thing will be, where the lines of work or your roadmap point to the coming years ...
The cloud will undoubtedly be the infrastructure where we see the greatest opportunities to democratize advanced data analytics, for us and for all companies that want to undertake digital transformation.
We will continue to develop our Cloud-Native Sas VIYA platform, betting on flexibility, both at the level of use and payment models.
As for use, our efforts will be in the sense of continuously improving the usability so that the analytics is accessible to any person, regardless of their level of technical knowledge.The objective is that more and more business users can access the analytical tools, know how to handle them and be able to apply analytics in their expertise areas.
Payment for use is another strategic lines where we are focusing.We are evolving towards XAAS models, giving our customers the possibility to access our analytical solutions as a service.We are developing applications in Cloud that will allow companies to hire solutions for specific areas such as marketing or finance.
The operationalization of analytics remains one of the areas where companies find more difficulties, due to the integration of different programming languages, the time to obtain results, government and maintenance of analytical models.We will continue to enhance the automation of processes of creating Machine Learning models and the automation of data preparation processes.At the same time, we will continue to bet on the application of artificial intelligence in Business Intelligence, Data Discovery, generation of reports, improvement of the performance of the models, and very important the improvement of their interpretability, making use of natural language.