Data Intelligence

What is Cloud Security?

Actian Corporation

September 12, 2022

cloud security

Cloud Security refers to the technologies, policies, controls, and services that protect data, applications, and infrastructure in the cloud from both internal and external threats.

Between essential protection and uncompromising lockdown, it is critical to find the best compromise between security practices and the flexibility that is essential for business productivity.

Business applications, data storage, complete virtual machines… Almost everything can be governed by Cloud Computing. According to forecasts and observations made by IDC, it appears that spending on cloud infrastructure will increase by 22% in 2022 compared to 2021. This means that it will exceed the $90 billion mark by the end of the calendar year. A record number as this is the highest annual growth rate since 2018! But the more our businesses become cloud-based, the more the issue of Cloud Security becomes a top priority.

However, behind a concept as vast and complex as Cloud Security, it must be understood that it is based on a set of strategies, technical means, and control solutions that ensure both the protection of data stored, the availability and reliability of applications and infrastructure services essential to the operation of the cloud.

Protection against external and internal threats and vulnerabilities, resilience, and resistance to cybersecurity issues, Cloud Security is a concept intrinsically linked to Cloud Computing.

Private, Public, Hybrid…Each Cloud Has its Own Security Challenges

To fully understand Cloud Security, we must first differentiate the types of Cloud Computing. Public clouds are hosted by third-party cloud service providers.

When you use a public cloud, you get a turnkey cloud and have no latitude to configure and administer it, and the services are fully managed by the cloud provider.

If, on the other hand, you move to a private cloud service, you have a more secure and potentially more customizable space.

Finally, hybrid cloud services combine the scalability of public clouds with the greater resource control of private clouds while offering lower pricing than private clouds.

In all cases, whether you choose a public, private or hybrid cloud service, it is critical to ensure Cloud Security.

Why is Cloud Security Important?

The more your company leverages the cloud, the more agile it becomes. Even better: the cloud enables small and medium-sized businesses to have the same tools and functionalities as very big companies. The downside is that the power at your disposal increases the cloud usage of your teams and, consequently, increases your exposure to threats that in the past only concerned larger companies.

That’s why Cloud Security is more important than ever. Unleashing usage and productivity through the cloud mechanically increases your dependency on the cloud.

Without the cloud, nothing is possible. Therefore, Cloud Security becomes a priority issue, especially when it comes to data protection. Preventing data leakage and theft is essential to maintaining your customers’ trust. Cloud Security, by guaranteeing data protection, makes it an element of trust between you and your customers.

The Challenges of Cloud Security

The availability of the cloud is a major issue for companies. In this context, the first challenge of Cloud Security is to guarantee maximum availability, particularly by protecting infrastructures from Denial of Service (DDoS) attacks. Analysis of traffic on cloud servers, and detection of suspicious packets, are all practices that are essential to Cloud Security. The fight against data breaches and by extension, against data loss, are two other prominent challenges of Cloud Security.

Finally, the last key challenge of Cloud Security is user identity verification. As employees become increasingly mobile, they connect to cloud services from anywhere in the world, making identity verification more and more complex. This is why multi-factor authentication is highly recommended by cloud players.

Anticipation, visibility, transparency, reactivity, and proactivity are the levers to be used on a daily basis to guarantee Cloud Security.

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About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale, streamlining complex data environments and accelerating the delivery of AI-ready data. The Actian data intelligence approach combines data discovery, metadata management, and federated governance to enable smarter data usage and enhance compliance. With intuitive self-service capabilities, business and technical users can find, understand, and trust data assets across cloud, hybrid, and on-premises environments. Actian delivers flexible data management solutions to 42 million users at Fortune 100 companies and other enterprises worldwide, while maintaining a 95% customer satisfaction score.
Data Security

The State of Data Security Management in 2022

Actian Corporation

September 8, 2022

Hand pointing to digital padlock signaling the importance of state of data security management in 2022

With the current threat landscape, securing information is a high priority for any business with a digital footprint. Cybersecurity threats like ransomware, spyware, phishing, and other malware attacks have become daily occurrences and are increasingly becoming more sophisticated, targeting the lifeblood of any business – its data.

The security of the enterprise is incumbent on the protection of all data associated with the business and user. As work-from-home setups have introduced a multitude of personal devices – such as cell phones, laptops, tablets, and wearables – into the working environment, users are exposed to more points of vulnerability. These devices are often unsecured and exposed to unwanted risk, making it easier for hackers to access valuable data that’s protected within the walls of the enterprise.

Navigating the choppy waters of cybersecurity is tricky. Data security management strategies are a must-have for businesses looking to keep their data safe, secure, and out of the hands of bad threat actors. To understand how to effectively deploy a data security management strategy, businesses must first have a firm grasp on what it is and what it entails.

Data Security Management, Explained

Simply put, data security management is the practice of ensuring that data, no matter its form, is protected. Each business must clearly define its data security program goals and communicate them broadly across the enterprise to ensure all teams know how to handle a cybersecurity event.

Data security acts as the safeguard of data while an organization is storing and using it. Data privacy is the practice of ensuring the data that is stored and used is compliant with standards set by regulatory bodies and internal policies. Security keeps data safe, while privacy ensures confidentiality.

Data security management practices protect users and organizations from unintentional mistakes or hackers that would corrupt or steal your precious resources. Before developing a new strategy, businesses must also understand the top challenges associated with data security, as well as the types of threats that currently exist.

Challenges, Risks, and Threats

Businesses must remember that risks and threats exist internally and externally. A recent report found that poor passwords or credential management, as well as misconfigured cloud data storage, are among the top causes of security breaches.

Having a complete view of where internal data flows in and out of an organization is challenging. Without that clear insight, there may be unintended ways that individuals and teams handle and protect their data. Without proper guidance as to how these systems work, there’s a risk of data mismanagement, creating security gaps where threat actors can attack.

This issue can be compounded by the risks of working from home on devices that aren’t sanctioned by IT teams and business leaders. When personal devices are introduced into the network, any vulnerability that already exists on the device is brought into the fold. This may include improperly using email or social media to share data, as well as the use of other unsanctioned applications, resulting in SaaS sprawl. Additionally, employees using a personal hotspot or public Wi-Fi can invite threats, as these are much less secure than corporate networks.

Another challenge with remote work is a workforce distributed across locations and devices. Monitoring how employees are using and interacting with data and ensuring that their data is safeguarded is critical. Organizations need to know where data is coming from, how it’s created, and how it’s being managed. Privacy issues are a concern if data is not being stored in a way that’s compliant with regulatory laws and internal policies.

Properly understanding the challenges and threats that exist can help a business chart a course towards building an adaptable and effective data security management system strategy.

Adapting Security for Your Enterprise

When building data security management, it’s crucial to know these are not one-size-fits-all solutions. There are many different types of data security management strategies that an organization can choose from based on the needs of the business. There are three strategies that your enterprise can examine:

  • Encryption Keys: Encryption keys transform data into unreadable formats via an algorithm that aids in designing services and can proactively prevent security attacks. Introducing various types of data encryption requires skilled data security measures from trained staff or trusted supplier partners. Taking this route is like holding onto a house key. If an encryption key is lost, its crucial to have a seconder holder of the key should the primary holder be unreachable.
  • Organizational Data Security Management: In this strategy, security roles are assigned to data stewards, administrators, product owners, developers, or other stakeholders. This practice creates a culture of security within the company and can help spread security knowledge-sharing across the organization.
  • Data Deletion, Erasure, and Destruction: The use of software to eradicate data deliberately and completely from a storage device (digital or physical) under the direction of the data owner, data steward, or governance team.

When deployed properly, these strategies can help any organization address the current threat where it lies and prevent the damage of cyberattacks before they can begin.

These challenges may seem daunting, but the risk of being exposed to a cyberattack is worth putting in the time, budget, and effort to secure and protect an organization’s data. Businesses should consider taking a full audit of the data that exists in the enterprise and learn how that data is being accessed by a workforce that works both on-site and remotely. An audit will also help create a comprehensive understanding of where potential security gaps live and where there are opportunities to mitigate those security risks. Once identified, businesses can communicate information security best practices and polices across the organization.

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About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale, streamlining complex data environments and accelerating the delivery of AI-ready data. The Actian data intelligence approach combines data discovery, metadata management, and federated governance to enable smarter data usage and enhance compliance. With intuitive self-service capabilities, business and technical users can find, understand, and trust data assets across cloud, hybrid, and on-premises environments. Actian delivers flexible data management solutions to 42 million users at Fortune 100 companies and other enterprises worldwide, while maintaining a 95% customer satisfaction score.

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Data Intelligence

7 Obstacles to Democratizing Data Access in an Organization

Anthony Corbeaux

September 7, 2022

7 Obstacles To Democratizing Data Access

Many organizations have the objective of becoming data-driven, i.e., basing their strategic decisions not on hunches or trends, but on accurate, reliable data and analysis. This implies a process of storing, documenting, and making available this data to make the most of it. If these companies equip themselves with modern tools to democratize access to data, they are faced with a multitude of difficulties that can slow down the process. This article is based on our experience with the Actian Data Intelligence Platform users from organizations of various sizes and sectors to describe 7 obstacles frequently encountered on the road to data democratization.

Tools are not Sufficient

Among the users of the Actian Data Intelligence Platform solutions, the democratization of data and the desire to switch to a data-driven decision-making model are, of course, major priorities. Moreover, access to the data of these organizations is partially democratized since they are all equipped with dedicated tools like data lakes and data labs. Naturally, the deployment of a Data Catalog in these companies is also an illustration of this, with the use of a unique platform capable of centralizing an entire data ecosystem that is shared with all employees.

These tools are essential building blocks for any data-driven approach, but they do not, on their own, make access to data more democratic. If we take a data catalog, for example, the tool becomes especially effective when it is used by the largest number of people in the organization. It is the multiplication of use cases and the documentation of data assets by as many employees as possible that allows the value of the company’s information to be unlocked. Everyone at their own level can then benefit from the work of their colleagues, a virtuous circle in short. To encourage this, a cultural change is necessary.

Shifting Corporate Culture

There is sometimes a lack of awareness of the value of the data available in the organization and a lack of commitment to the process of documenting and sharing data. The challenge lies in the use of the tools mentioned above, with data often remaining in silos between the different departments and teams. This mindset is even more difficult to change at the business level, whereas IT teams are culturally more aware and inclined to document and share data.

Governance units were created to promote this awareness, but the lack of legitimacy within the organization complicates their work of raising awareness of the central role of data for the company. In data mesh literature, it is recommended to federate/decentralize data governance. Business teams must be integrated into this process, at the risk of creating a language gap: governance teams must work with data owners, data engineers, data analysts, etc. The democratization of data access must involve both data producers and consumers.

The notion of changing the company’s mindset is a necessity to complement the tools in place to democratize data. Research published by Gartner shows that historically, organizations have evolved into a defensive culture of “never share, except” for good reasons to share it. The research institute insists on the need to switch to an “must share, except” philosophy. Tools (data lakes, data labs, data catalogs, etc.) are not enough to democratize data if they are not supported by this cultural shift.

Documenting After the Fact

Many projects are primarily driven by costs and time, and in these cases, data governance and data quality are typically not priority topics from the start. There is a tendency to document after the fact, making sharing and documenting data more difficult. Data quality, and even more so its documentation, is all too often the last task to be executed.

The Lack of Time

The lack of documentation is a bias that is heightened in organizations whose products and value are created through the exploitation of data – where the obstacle to democratization is more related to the lack of time for documenting than a lack of data culture as mentioned above. If we go back to the example of a data catalog and focus on the data scientist profession, we can see that this type of population has more or less the desire to document its activity but does not take the time to do so, since the completeness of the data catalog is not a priority.

Furthermore, documenting and making data available is not always part of the employees’ mission. There is therefore also an HR dimension to data democratization. The documentation mission can be added to the scope of the employees’ responsibilities to promote democratization and accountability.

The Volume of Data

A form of fear sometimes arises when contributors are asked to share their own business data within a large common container (a data lake or data catalog). This is the fear of finding oneself drowning in an ocean of data added by other entities of the organization, and of not being able to find one’s way around.

The data catalog is a valuable tool to alleviate this fear among data producers. Indeed, the tool offers them the possibility to not only easily explore their own data, but also to use data produced by others for their own use cases.

Data Security

The security aspect regularly comes up as a pretext for not sharing data within the company. However, there are effective systems for managing user permissions, such as the one integrated into the the Actian Data Intelligence Platform data catalog, for example, which, coupled with a culture of sharing and accountability, can make it possible to overcome this barrier.

Data Ownership

As far as the notion of ownership is concerned, we too often observe ownership of datasets at a local level. Yet data is a corporate asset, a common heritage, and only regulatory aspects should justify local ownership. In other cases, this ownership quickly becomes an obstacle to documentation: the corporate culture must favor making data available to the greatest number, under the responsibility of an entity or individuals.

If you would like to discuss the obstacles to the democratization of data described in this article, or if you would like a presentation of the Actian Data Intelligence Platform solutions for data-driven companies contact us.

About Anthony Corbeaux

Databases

The Data Challenges of Telemetry

Actian Corporation

September 7, 2022

data challenges telemetry

Telemetry is the automated communications process by which measurements are made and data collected at remote points. The data is then transmitted to receiving equipment for monitoring. The word ‘telemetry’ is derived from Greek roots: tele = remote, and metron = measure.

Telemetry is not a new concept, that’s for sure. We’ve been watching telemetry at work for decades. For example, we’ve strapped transmitters onto migrating animals, weather buoys, seismic monitoring, etc. However, the use of telemetry continues to accelerate, and this technology will bring up huge challenges to those of us responsible for data collection, data integration, and data analysis.

The most recent rise of telemetry is around the use of new and inexpensive devices that we now employ to gather all kinds of data. These can range from Fit Bits that seem to be attached to everyone these days to count the steps we take, to smart thermostats that monitor temperature and humidity, to information kicked off by our automobiles as to the health of the engine.

The rise of the “Internet of Things” is part of this as well. This is a buzzword invention of an industry looking to put a name to the rapid appearance of many devices that can produce data, as well as the ability of these devices to self-analyze and thus self-correct. MRI machines in hospitals, robots on factory floors, as well as motion sensors that record employee activity, are just a few of the things that are now spinning off megabytes of data each day.

Typically, this type of information flows out of devices as streams of unstructured data. In some cases, the data is persisted at the device, and in some cases not. In any event, the information needs to be collected, put into an appropriate structure for storage, perhaps combined with other data, and stored in a transactional database.  From there, the data can be further transferred to an analytics-oriented database, or analyzed in place.

Problems arise when it comes time to deal with that information. Obviously, data integration is critical to most telemetry operations. The information must be managed from point to point and then persisted within transitional or analytics databases. While this is certainly something we’ve done for some time, the volume of information these remote devices spin off is new, and thus we have a rising need to manage a rising volume of data effectively.

Take the case of the new health telemetry devices that are coming onto the market. They can monitor most of our vitals, including blood pressure, respiration, oxygen saturation, and heart rate, at sub-second intervals. These sensors typically transmit the data to a smart phone, where the information is formatted for transfer to a remote database, typically in the cloud.

The value of this data is very high. By gathering this data over time, and running analytics against known data patterns, we can determine the true path of our health. Perhaps we will be able to spot a heart attack or other major health issues before they actually happen. Or, this information could lead to better treatment and outcome data, considering that the symptoms, treatment, and outcomes will now be closely monitored over a span of years.

While the amount of data was relatively reasonable in the past, the number of data points and the frequency of collection are exploding. It’s imperative that we figure out the best path to data integration for the expanding use of telemetry. A few needs are certain:

  • The need to gather information for hundreds, perhaps thousands of data points/devices at the same time. Thus, we have to identify the source of the data, as well as how the data should be managed in-flight, and when stored at a target.
  • The need to deal with megabytes, perhaps gigabytes of data per hour coming off a single device, where once it was only a few kilobytes. Given the expanding number of devices (our previous point), the math is easy. The amount of data that needs to be transmitted and processed is exploding.
  • The massive amounts of data will drive some data governance and data quality issues that must be addressed at the data integration layer. Data is typically not validated when it’s generated by a device, but it must be checked at some point. Moreover, the complexity of these systems means that the use of data governance approaches and technology is an imperative.

This is exciting stuff, if you ask me. We’re learning to gather the right data, at greater volumes, and leverage that data for more valuable outcomes. This data state has been the objective for years, but it was never really obtainable. Today’s telemetry advances mean we have a great opportunity in front of us.

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About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale, streamlining complex data environments and accelerating the delivery of AI-ready data. The Actian data intelligence approach combines data discovery, metadata management, and federated governance to enable smarter data usage and enhance compliance. With intuitive self-service capabilities, business and technical users can find, understand, and trust data assets across cloud, hybrid, and on-premises environments. Actian delivers flexible data management solutions to 42 million users at Fortune 100 companies and other enterprises worldwide, while maintaining a 95% customer satisfaction score.

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Data Intelligence

The Benefits and Challenges of Data for the Banking Industry

Actian Corporation

September 6, 2022

Banking Industry Data

Banks and insurance companies have one thing in common: they collect massive amounts of customer data. Due to rising customer expectations and increased competition from Fintech players, the financial services industry simply cannot afford to let the data collected go unused. Explanations.

The financial services industry has invested heavily in data collection and processing technologies for over a decade. This reality is expected to grow even more with the growth of digital consumer habits and the emergence of new forms of competition. Banks and insurers must leverage existing and future datasets to maximize their understanding of customers and gain a competitive advantage.

The Impact of Digital Transformation in the Banking Industry

Disciplines such as Finance Data or Finance Data Analytics are widely employed in the banking industry to calculate risks, detect fraud, limit losses, and maximize gains. According to a study conducted by IDC, by 2025, the volume of data to be analyzed in the banking sector could reach 163 billion terabytes. Payment card use, multiplication of banking services, online transactions, dematerialization of salaries, online consultation of personal accounts, identification of customers’ consumption habits…banks have a considerable amount of information about their customers. The banking world is undergoing massive digital transformation efforts.

According to a study conducted by the Autorité de contrôle prudentiel et de résolution (ACPR), there are several reasons for these efforts. First, there is a need for easy-to-access, multi-channel digital tools that allow for seamless and perfectly secure customer paths. Secondly, there is a need for immediacy and flexibility in customer relations. And finally, there is a need to personalize the service delivered to the customer, allowing them to become autonomous. All of these needs can be met through the use of data.

The Benefits of Data for Banks

The urgency to carry out this digital transformation, however, is amplified by various structural elements that affect the banking market. Between the evolution of crypto-currencies, the emergence of NFTs, and the appearance of new models favoring new forms of competition with open-banking and Fintech, traditional banks must not only rethink their offers, methods, and organization but also their territorial network, to maximize proximity with customers, minimize operating costs and offer a differentiating customer experience.

However, the use of data in the banking sector is not limited to customer relations.

Through data science, the financial services industry is undergoing a real disruption. By analyzing data, companies can extract valuable information through mathematical and statistical techniques. Finance Data not only allows companies to better understand their customers in order to deliver better service offers but also optimizes the profits of the banking sector – at a time when their business model is being challenged by the emergence of new players such as digital banks and fintech. Data is also used in activities such as high-frequency trading.

The Stakes of Data for Banks

According to Verizon’s 2022 Data Breach Investigation Report, 95% of data compromises identified in the report are motivated by greed. In this context, the financial services industry is, by its very nature, a target for criminal organizations. As a designated target for cyber threats, the banking sector is also the object of particular vigilance on the part of the authorities, particularly with regard to compliance with the provisions of the RGPD. The first challenge of the banking sector in relation to data is indeed that of security and compliance. But it is not the only one.

Indeed, the very nature of banking activity allows the collection and aggregation of large volumes of data that can be of a heterogeneous nature. The banking world is therefore faced with major challenges in terms of data governance, data quality, and the continuous optimization of data assets.

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About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale, streamlining complex data environments and accelerating the delivery of AI-ready data. The Actian data intelligence approach combines data discovery, metadata management, and federated governance to enable smarter data usage and enhance compliance. With intuitive self-service capabilities, business and technical users can find, understand, and trust data assets across cloud, hybrid, and on-premises environments. Actian delivers flexible data management solutions to 42 million users at Fortune 100 companies and other enterprises worldwide, while maintaining a 95% customer satisfaction score.
Data Architecture

Scalability is All About Getting a Smart Start

Actian Corporation

September 6, 2022

Man showing a wide range of solutions illustrating an example of scalability

Scalability is for the most part, a peek into the future. That’s why organizations often bring it up when discussing growth and expansion opportunities – whether it’s scaling up operations to keep pace with increased customer demand or adapting to a new industry-disrupting technology.

Scalability is particularly important to business leaders right now, as the technology landscape is evolving at breakneck speed. A recent McKinsey study found that over 50% of companies think that scaling their business is a top priority, but just 22% have scaled successfully within the past ten years.

What’s often not discussed, however, is the difficulty of growing new projects from the ground up and the pain points encountered when integrating new datasets. This requires careful consideration and proactively planning for growth early on, as opposed to re-tooling systems and applications reactively to meet unexpected shifts.

Business leaders are faced with many challenges, from internal business complexities to external market forces. It’s possible to offset these hurdles by understanding data’s role in enabling scalability and integrating technology into enterprise stacks.

Scalability and What it Means Today

What does scalability mean for an enterprise? Scalability is defined as a measure of how performance changes as available resources or volume of input data changes. A truly scalable solution exhibits a proportional change in performance in response to a change in either of those two variables.

When adding resources to a system, there are two directions to consider:

  • Scaling up refers to increasing the available resources on a single node of the system. This is the traditional model of adding more CPUs, more disk, or more memory to a machine to increase performance.
  • Scaling out refers to increasing available system resources by adding additional nodes. This is the typical distributed cluster model, enlarging the cluster size to increase performance via cluster analysis.

Be it scaling up or scaling out, successful business leaders have an intrinsic understanding of what they can gain through strategic planning and preparation for future growth. Once a clear understanding of the direction of their scalability goals has been established, organizations can move forward and build an enterprise with a solid foundation that can grow as the business does.

Typically, when scaling a new initiative, enterprises can struggle with securing IT and cloud operations, and getting broad buy-in from leadership. To overcome these challenges, ambitious organizations and IT leaders should move their operational workloads to the cloud and offer support for cloud, multi-cloud, or hybrid deployments. This takes a technical backbone that’s built on a foundation of streamlined and democratized data for analysis across business stakeholders.

Building for Success From the Ground Up

Building this foundation requires having systems in place that make data easily accessible across departments. Gartner recently identified data-sharing and data analysis for business use as one of the top trends in data management, signaling that most enterprises have accepted that data is the lifeblood of their business.

Oftentimes, this data is stored in silos that are not only hard to break down but are also disparate from each other, which makes them difficult to access across the enterprise. The Actian Data Platform gives data managers the ability to knock down silos and free the data that’s generated from them to help drive their business forward.

With this platform, business technologists can leverage the power of the cloud to scale to larger and a greater number of concurrent projects, enabling them to iterate and innovate faster. Cloud connectivity helps businesses avoid incomplete and inaccurate insights from spreadsheet sprawl, data being stored in legacy warehouses or lake dumpsters.

This platform empowers business stakeholders to harness real-time data and analytics and get the maximum value out of their data pipelines. Faster innovation and agility ultimately make scaling easier – all with minimal risk or upfront investment into IT resources, CAPEX, and additional training.

The successful scaling of a project or initiative relies on the foundation it’s built on. Actian helps enterprises better understand their business, reduce costs, demonstrate value, and make decisions faster.

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About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale, streamlining complex data environments and accelerating the delivery of AI-ready data. The Actian data intelligence approach combines data discovery, metadata management, and federated governance to enable smarter data usage and enhance compliance. With intuitive self-service capabilities, business and technical users can find, understand, and trust data assets across cloud, hybrid, and on-premises environments. Actian delivers flexible data management solutions to 42 million users at Fortune 100 companies and other enterprises worldwide, while maintaining a 95% customer satisfaction score.

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