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It is a truism at this point to say that the data governance landscape is rapidly evolving. What needs more attention is how enterprises can keep pace or even get ahead of the waves of change.Read More
In this article, we’ll explore the common challenges faced by businesses when purchasing multiple tools and how Alex offers a comprehensive, all-in-one solution that maximizes efficiency and reduces costs.Read More
In today’s digital landscape, data has evolved into the lifeblood of enterprises, steering decision-making processes, powering innovations, and driving competitive advantage.Read More
In the evolving landscape of modern business, harnessing the full knowledge of an organization is the North Star guiding strategic decisions around data and innovation. Deloitte aptly terms Enterprise Knowledge Graphs as "the path to collective intelligence within your company".
In the realm of cutting-edge technologies, few hold as much promise and potential as Generative AI. These AI models are designed to create content, imitate human-like creativity, and generate outputs ranging from images and text to music and more
The past five years have brought about significant changes in enterprise IT, with cloud computing, machine learning, and mobile devices transforming enterprise applications. However, one crucial aspect of enterprise architecture that has remained unchanged is data storage, with data still being stored locally in databases and other storage solutions. Although this approach keeps data safe and secure, it has its drawbacks.
In today's data-driven world, we are constantly collecting and analyzing information. However, with the vast amount of data available, it has become increasingly difficult to keep track of who is doing what with what data. If not careful, sensitive information such as personal health records or financial transactions may fall through the cracks.
In the era of data-driven businesses, managing and leveraging data effectively is crucial for success. However, as data grows exponentially, organizations are struggling to manage and maintain data while ensuring its accessibility and security. This is where an enterprise scale data catalog comes in handy. A data catalog can help organizations keep track of their data assets, understand data lineage, and ensure data governance.
The world of cloud computing is constantly evolving, and multi-cloud architectures are becoming more common. It's crucial for enterprises to find effective ways to manage their cloud resources. Automated data lineage provides a simple way for enterprises to track and audit their cloud data, allowing them to easily manage changes in their infrastructure or monitor the state of their data and applications.
Augmented data catalogs can play a significant role in detecting and resolving data errors within an organization. By serving as a centralized repository of all data assets, they provide a comprehensive view of the data landscape, enabling proactive identification and correction of errors before they affect stakeholders.
An enterprise data catalog is an essential tool for managing your company’s growing volume of data. It provides a single point of access to all your company’s data assets, including files, databases, and more. A unified data catalog makes it easier to find the right asset at the right time so that you can focus on what matters most: delivering results.
The first step in the data management process is metadata management, which is an important component of data governance. It allows you to track information about your data and its source. Metadata management involves creating, organizing, and maintaining metadata, which is data about data. It provides information about a resource, such as its author or creation date. Metadata can be used to organize and search for digital assets, but it also has many other uses in business processes such as workflow automation and knowledge management systems.
Organizational agility is more important than ever, and organizations that can make better decisions faster will have a competitive advantage. This is where self-service data comes in. By enabling business users to work with historical data, including legacy systems and structured data sources, self-service data empowers them to take ownership of their analytics and make better decisions faster. Here's how it works...
Businesses are consuming increasingly large volumes of data, and business analysts are struggling to keep up. Self-service data allows business analysts to access the right data and make better decisions faster. Sometimes, the required data is not easily available in a format that business analysts can access. Self-service data empowers business analysts to find the right data and make better decisions faster. They can also use it to help others make better decisions by discovering new insights or enabling them with tools they didn't have before.
The cloud is only as good as the metadata that powers it; without data lineage, your organization will suffer from visualizations and misunderstandings. Data lineage allows cloud architects to build a better cloud by providing visibility into where your data is, who owns it and where it's going in real time.
Impact analysis is a crucial component of software engineering as it helps identify which parts of a system are impacted by change requests, providing a foundation for implementing changes. Change Impact Analysis (CIA) can be performed manually or through automated methods that identify impacted objects within a software system, tracing dependencies between architectural elements and other artifacts like requirements and designs.
As the amount of data and technology grows exponentially, achieving effective data governance becomes increasingly challenging. Data governance refers to the overall framework that an organization creates for organizing, securing, and managing data. It includes policies for data classification, management, and access control. A business glossary is a collection of key terms and data elements used within an organization, providing definitions, synonyms, and examples to improve clarity and consistency.
In large and complex data ecosystems such as those found in governments, augmented data catalogs are crucial, as manual metadata management can be impractical. By automating the metadata management process, augmented data catalogs can help organizations improve their understanding of their data assets, enhance data governance, and facilitate data-driven decision-making.
As a business analyst, your job is to ensure that your company makes informed decisions. However, determining whether the data sources you're using are reliable can be challenging. The solution is simple: use an automated catalog that provides an overview of all your data.
In the past, only IT professionals handled data cataloging. This meant that business users could only access data when it was requested by IT. However, with today's advanced tools and technology, business users can now access trusted data in a single view. This not only makes them more efficient but also helps protect against fraud and risk.
2022 was a banner year for massive data breaches and tightening privacy regulations worldwide. In this webinar, we will hear from Alex Solutions Privacy and Data Governance Specialist, Evelyn Pinto, on how to jump start enterprise data privacy to ensure a successful 2023.
Data and analytics are transforming the way businesses operate, compete, and grow. In 2023, we can expect to see continued innovation in data and analytics technologies, a greater emphasis on data privacy and security, and a growing focus on data and analytics to drive business value.
Data security is one of the most important aspects of any business today. But as Chief Information Security Officers (CISOs) are quick to point out, protecting data involves more than just your own systems. In fact, most organizations have a significant amount of data stored in the cloud. This poses a huge challenge for CISOs because they’re not able to control how this data is secured – they can only trust that third parties are doing it properly.
Data literacy is the ability to read, work with, analyze, argue with and use data. We now live in a data-driven world. Data is everywhere and is constantly collected about everything. That means that data literacy is a critical capability for success at the enterprise level today.
The role of a data steward is critical for ensuring that an organization's data assets are managed appropriately. Data stewards oversee the overall health and quality of their organization's data assets and make sure they're in compliance with strict regulations that apply to data protection, both generally and industry specific. The Alex enterprise data catalog helps data stewards at some of the world’s largest companies to protect the privacy of their organization's sensitive data assets by automating risk assessments and governance policies to meet compliance standards.
The Australian Prudential Regulation Authority (APRA) has introduced a new data governance standard called CPG 235 Managing Data Risks. Some firms have attempted to comply with this regulation by instituting a bunch of manual processes that are time-consuming, error-prone and unintuitive. Alex helps enterprises to comply with CPG 235 throughout the data lifecycle. We offer an enterprise solution for IT security, data security & compliance across the Financial Services Industry.
Many enterprises have complex data landscapes and data flows that include both structured and unstructured data. While enterprises are increasingly more aware of the importance of managing data privacy, many struggle with the complexities of their data landscape.
As the world has become more connected and reliant on data, there has been a greater need to protect sensitive information. With so much data being collected from an increasing number of sources, CPOs need their people to have the tools they need to provide visibility into the entire compliance management process.
The financial industry is subjected to more data regulations than any industry with the exception of healthcare, and that means companies need to be able to demonstrate that they are using their data ethically. One way that financial institutions can achieve this through using automated data lineage.
“What it takes to get compliant and stay that way is a multifaceted challenge that still requires an enormous amount of manual effort. Hence, one of the overarching challenges for enterprises regarding data privacy and compliance in 2023 is boosting the level of automation applied to these now critical processes.”
2022 has seen some of Australia’s largest companies - including the largest private health insurer and a top two telecom company - be hit with devastating cyberattacks. It is becoming clear that Australia is viewed as an international soft target for cyber criminals.
Data Architects are most often the stewards of data within the enterprise. They must understand all aspects of the data and how it flows through its lifecycle in order to successfully design and govern their architecture. To perform this role effectively, an enterprise needs a complete picture of its data assets and how they interact with each other over time. That's where Data Lineage comes in.
Data mesh is a new approach to managing enterprise data that combines the best features of a data lake with elements of decentralized architecture. In this article we will discuss how Data Mesh works and why it’s becoming more popular than Data Lakes when it comes to managing large volumes of structured and unstructured data.
Metadata is a system of data tags and attributes that can be used to describe, organize, and search for information in an enterprise. It allows you to understand the value of your data, which can help you make better business decisions. Let's look at some examples of metadata in action.
Privacy is a hot topic right now. It's no longer just about how we protect our personal information, but how businesses protect their own sensitive data—and the impact that has on consumers and regulators alike. This blog will explain what makes Data Privacy such a hot topic and why Alex Solutions is the end-to-end Data Privacy partner for your business.
Data is quickly becoming a major tool to deliver excellent services and improve organizational operations. Simultaneously, the forms and amount of data being collected and managed by all manner of organizations are exponentially growing, creating major privacy risks. 30% of Australians have been impacted by data breaches in the past year as in 2022, a number of Australia’s largest companies – including the largest private health insurer – were hit with cyberattacks.
Effective data governance that delivers productivity requires a collaborative effort between people, policy, process and technology. Use the following best practises to avoid some of the pitfalls and overcome frequently experienced challenges to implement a successful data governance initiative.
The word democracy has its origins from the Greek words, ‘demos’ and ‘kratos’ which together mean ‘power to the people.’ Data democratization in a business context still holds true to this meaning. If an organization is able to achieve data democratization their users are empowered to use data.
The traditional technology that investment funds often use merely stores data instead of consuming it. Given the slew of problems and pressures that asset managers face, it is nearly impossible for buy-side firms to be operationally efficient without a sound data management framework in place.
Asset managers have long faced tremendous pressure to achieve high returns and investment outcomes, especially in a time of global pandemic and economic uncertainty. On another front, the world is changing due to the rapid rise of new technologies.
In the aftermath of the 2008 financial crisis and the introduction of strict regulations, financial institutions came to rely on metadata management solutions the hard way. They found that data governance brings more to the table than simply remaining compliant. One of the most important data governance solutions for financial institutions is data lineage.
Having automated data management systems is integral to efficient and trustworthy governance in any firm from retail banks all the way to insurers. This article will outline how the financial industry developed metadata use cases the hard way.
The primary function of data lineage technology is to provide users with an easy way to access and visualize an organization’s datasets, allowing them to make better business decisions. Yet businesses often leave potential value at the table by not making full use of data lineage. This article provides detailed insight into how data lineage best functions within an enterprise.
Enterprises compete in distinct markets, with different types of customers and business problems. Yet, they all needed to adapt to the new climate of big data and some have actually benefited from adopting metadata management and data lineage tools. This article will discuss how data lineage solves business problems and gives companies an edge.
Founded in 2015, Alex Solutions entered the market as a fresh metadata management solution compared to legacy vendors. Alex is led by a team of highly skilled business and technical minds, combining the efforts of industry veterans and the most promising up and coming talent. In just three years, a small but innovative start-up from Melbourne was named a Leader in Metadata Management by Gartner and has retained this status since.
Data protection is becoming increasingly important as businesses manage an increasingly growing influx of data. Data protection is the process by which organizations ensure that their data is safeguarded from corruption, compromise and loss. Many organizations rely on data for their livelihood. It is integral to organizational health to implement a solid data protection framework.
In today’s digital climate, organizations face a plethora of challenges around security and privacy of their data. With the value and volume of data for businesses increasing exponentially while stringent privacy regulations come into effect worldwide, it is integral for business leaders to understand the difference between data security and privacy.
Data governance is a system of processes where data assets are formally managed across an enterprise. Put simply, it is the way that businesses govern their data. An enterprises’ data landscape can be seen as a diverse ecosystem with different kinds of data assets. Today, organizations experience huge volumes of data and the data types are variable. Without data governance in place, the enterprise will be in data anarchy.
The amount of data in our world is growing exponentially while the importance of said data couldn’t be clearer. Rapid data growth presents opportunities to utilize its proliferation to optimize business operations, driving enterprises to implement data catalogs. Yet, while the potential value of data grows radically, so too does the risk it carries to the organization.
All over the world, increasingly strict regulations which govern the data privacy standards for large financial organisations are coming into effect. Enforcement is becoming harsher and more regular, with costly fines and reputational losses following non-compliance. A Deloitte survey found that only 40% of banking customers trust major banks to keep their information secure.
What do you think is the most important foundation for any data project in your organization? Some people answer with ‘technology’. It certainly helps to have the right technology, but there’s something even more fundamental than what technology you use to the success of any data project.
Data catalogs are now an integral part of enterprises; organizing, contextualizing and providing discovery of data. The larger an organization becomes, the greater the potential value of its data catalog. Thus, the importance of ensuring a data catalog is implemented correctly increases as your organization grows.
Business these days is never simple. Take for example educational institutions, who occupy a unique position in a rapidly changing world. The pace of change and growth of the information age means that huge amounts of data are constantly flowing through universities. Complexity needs to be addressed before this information can be leveraged productively. How can this be done?
Catalogs have long been an established enterprise go-to for data management. The value of an organized inventory of useful data is plain to recognise. But manually operating such a catalog is becoming impractible. Gartner has posited the augmented data catalog as the solution.
If you’re reading this, you’re probably well aware of the importance of data privacy today. Governments are aware, too. All over the world, increasingly strict regulations which govern the data privacy standards that organizations must comply with are coming into effect. Enforcement is becoming both harsher and more common.
Rigid technologies and processes were adopted to respond to challenges in the initial phases of data growth. But the new period of data growth is seeing enterprises have to contend with an unprecedented volume, velocity and variety of data. The technologies and processes of the past are no longer fit for enterprise data management.
The COVID-19 pandemic was not when the phenomenon of ‘working from home’ was born. The rise of remote working - particularly that of in-home work - has taken place gradually over the last decade. Technological advancements have brought about the potential for structural changes in how we work, and successive generations have grown more comfortable with the digital era. But the pandemic certainly has accelerated working from home arrangements around the world and many associated trends.
Big Data and accompanying data science has the potential to revolutionize healthcare. Already, modern data technologies and processing systems are driving change where they’re adopted, personalizing treatments and enabling fine-grained analysis. But as this new technology has been adopted to deal with today’s unprecedented scale of data, administrative and security concerns have predictably arisen.
A business glossary done right can be a directory of the importance of data to the business, with specialized definitions and relationships. It can democratize the understanding of specific data, terms and processes in the enterprise. It’s obvious why this is useful, but most implementations of business glossary leave value on the table and fail to enable data users.
The Alex Cloud Platform is designed to be a foundational pillar of any enterprise’s cloud data strategy. The new enhancements make it so that no platform can integrate the number and complexity of cloud and on-premises technologies that Alex can.
The advent of the global COVID-19 pandemic and the associated political, health, logististical and economic fallout has heightened the strain on government departments both big and small. If you work in this the public sector, you know the focus is delivering vital services to your citizens. But to do this under pressure, you need to be operating on the most trustworthy information available. Here's how Alex can help you achieve data-driven excellence.
The past year has thrown up countless unexpected challenges for all businesses, impacting their growth and in many cases, their survival. The rapid shift to remote work and geographically divided workplaces has forced companies to accelerate their digital transformations.
Communications firms connect us all, 24/7. They process, create and facilitate the exchange of the world’s data. It’s not easy to offer increasingly personalised customer experiences without leveraging data to identify opportunities and create value. It’s even more difficult to keep everyone’s personal information safe.
Alex can deliver a solution to both these problems. In one platform.
COVID-19 disruptions had an immense impact on organizations’ digital business initiatives. The overwhelming bulk of organizations accelerated their digital business initiatives and changed their IT operating model in the year of the pandemic. You need to get data-driven, fast. Here's how Alex Solutions can help.
The development and mass-adoption of countless new technologies has disrupted industry, government and people’s lives in previously unimaginable ways. While this wave of digital transformation has created unprecedented opportunities, it has also brought about new challenges. One of the biggest such challenges is the need for corporations, governments and other organizations to safeguard large amounts of people’s personal information.
The nature of enterprise data management as a fragmented patchwork of different technologies, departments, systems and processes requires incisive technical solutions. Without a strong technical basis from data management, it is difficult to extract even the slightest value from your data.
It is more clear than ever that you can’t be a data-driven organisation without a connection between your technical data assets and business knowledge. But for any data to be useful to the business, it must first be accurate. In systems that are only growing ever more disparate and complex, there is a need for centralised understanding and collaboration around data.