In a world where everything is changing too quickly, the evolution towards agile, interoperable data services is a welcome change. Data is now able to be delivered as a service, without the need for costly investments in data centers and the resources needed to manage them. As more companies embrace the cloud, data integration and data quality need to be a more important consideration.

As a result, organizations are focusing on delivering products and services at a faster pace, and to achieve this, operational analytics is more critical than ever. Today, organizations are reliant on using their data, along with external data, to make better decisions.

And just as the cloud alleviated the expense and expertise needed to manage infrastructure, data is also seeing accelerated value from the cloud. Data lakes and cloud data warehouses make it affordable and easy to store and use all your data. So why are companies still struggling to maximize their data potential?

It’s probably due to one of these 3 culprits:

1. You failed to alight stakeholders and create a data-driven culture. This is, by far, the primary reason why most data projects fail. In fact, according to a 2021 survey of Fortune 1000 companies “executives report that cultural challenges – not technology challenges – represent the biggest impediment to successful adoption of data initiatives and the biggest barrier to realizing business outcomes.” For any data project to succeed, there needs to be strong leadership at the top of the organization and a data culture that permeates throughout the organization.
2. Your data is – literally – everywhere. I’m sure I’m not telling you anything new, but it really can’t be overstated – your data is living in places you don’t know about. It’s in third-party systems, spreadsheets on personal devices, and in public online repositories. It’s also in legacy systems which can pose a significant challenge since these are often proprietary and not always the most cooperative when you need to retrieve data regularly. These older systems are often also considered mission-critical, so if you don’t create a data-driven culture, there may be resistance from application owners. As you put together your Rockstar team of stakeholders, this is a good time to audit the systems in use by every department. This leads me to my last point on what is limiting data insights….
3.  Your data quality sucks. While it stands to reason that your data isn’t going to be perfect, it should be as accurate and consistent as possible to drive better business decisions. At a minimum, data quality requires:
   • Discovery and Profiling. Know where your data lives and what it does. Understand the accuracy and completeness of your data and use that as a baseline. Data quality is like laundry, it never ends.
   • Standard, conformant and clean data. Once you’ve done the work to understand your data, it’s important to define what “good” looks like and create rules that maintain that definition going forward. If you have a team that is focused on this today, understanding what those rules are and why they exist is a critical component of a successful data project.
   • Deduplicated data. While no one wants to forecast revenue twice, with many databases and storage residing in the cloud, duplicate data can cause more than incorrect reports. Cloud costs can easily spiral if you’re storing and analyzing duplicate data.

Today, more organizations than ever are facing the challenge of increasing data and technological complexity, but few are seeing a significant return.  To thrive in the digital era, organizations must embrace new thinking. Infusing data obsession into the corporate DNA will allow data to start driving better decisions and better results. Check out how Actian’s DataConnect integration platform can help with your data quality goals.

The post Developing your Data DNA appeared first on Actian.

In a world where everything is changing too quickly, the evolution towards agile, interoperable data services is a welcome change. Data is now able to be delivered as a service, without the need for costly investments in data centers and the resources needed to manage them. As more companies embrace the cloud, data integration and data quality need to be a more important consideration.

As a result, organizations are focusing on delivering products and services at a faster pace, and to achieve this, operational analytics is more critical than ever. Today, organizations are reliant on using their data, along with external data, to make better decisions.

And just as the cloud alleviated the expense and expertise needed to manage infrastructure, data is also seeing accelerated value from the cloud. Data lakes and cloud data warehouses make it affordable and easy to store and use all your data. So why are companies still struggling to maximize their data potential?

It’s probably due to one of these 3 culprits:

1. You failed to alight stakeholders and create a data-driven culture. This is, by far, the primary reason why most data projects fail. In fact, according to a 2021 survey of Fortune 1000 companies “executives report that cultural challenges – not technology challenges – represent the biggest impediment to successful adoption of data initiatives and the biggest barrier to realizing business outcomes.” For any data project to succeed, there needs to be strong leadership at the top of the organization and a data culture that permeates throughout the organization.
2. Your data is – literally – everywhere. I’m sure I’m not telling you anything new, but it really can’t be overstated – your data is living in places you don’t know about. It’s in third-party systems, spreadsheets on personal devices, and in public online repositories. It’s also in legacy systems which can pose a significant challenge since these are often proprietary and not always the most cooperative when you need to retrieve data regularly. These older systems are often also considered mission-critical, so if you don’t create a data-driven culture, there may be resistance from application owners. As you put together your Rockstar team of stakeholders, this is a good time to audit the systems in use by every department. This leads me to my last point on what is limiting data insights….
3.  Your data quality sucks. While it stands to reason that your data isn’t going to be perfect, it should be as accurate and consistent as possible to drive better business decisions. At a minimum, data quality requires:
   • Discovery and Profiling. Know where your data lives and what it does. Understand the accuracy and completeness of your data and use that as a baseline. Data quality is like laundry, it never ends.
   • Standard, conformant and clean data. Once you’ve done the work to understand your data, it’s important to define what “good” looks like and create rules that maintain that definition going forward. If you have a team that is focused on this today, understanding what those rules are and why they exist is a critical component of a successful data project.
   • Deduplicated data. While no one wants to forecast revenue twice, with many databases and storage residing in the cloud, duplicate data can cause more than incorrect reports. Cloud costs can easily spiral if you’re storing and analyzing duplicate data.

Today, more organizations than ever are facing the challenge of increasing data and technological complexity, but few are seeing a significant return.  To thrive in the digital era, organizations must embrace new thinking. Infusing data obsession into the corporate DNA will allow data to start driving better decisions and better results. Check out how Actian’s DataConnect integration platform can help with your data quality goals.

The post Developing your Data DNA appeared first on Actian.

Hybrid clouds seem to be the way the wind is blowing. According to the Enterprise Strategy Group, the number of organizations committed to or interested in a hybrid cloud strategy has increased from 81% in 2017 to 93% in 2020. But what exactly is a hybrid cloud? Turns out, there are a lot of definitions. I’ll share a definition from Deloitte that I like:

“Hybrid cloud is cloud your way. It’s integrating information systems—from on-premises core systems to private cloud, public cloud, and edge environments—to maximize your IT capabilities and achieve better business outcomes. It’s designing, building, and accelerating your possible.”

Why Hybrid Cloud?

Data warehouse deployments on premises and in the public cloud play equally important roles in a hybrid cloud strategy. The Enterprise Strategy Group found that 89% of organizations still expect to have a meaningful on-premises footprint in three years. At the same time, Gartner predicts that public cloud services will be essential for 90% of data and analytics innovation by 2022. Accordingly, organizations are adopting a hybrid cloud strategy to leverage the right mix of locations to meet their needs.

Consider: The cloud provides the flexibility to build out and modify services in an agile manner, the potential to scale almost infinitely, the assurance of enhanced business continuity, and the ability to avoid capital expenditures (CapEx)—all of which continue to accelerate the adoption of cloud-based data warehouses. But data warehouses running on premises in your own data center deliver their advantages:

• Data gravity: Sometimes data is hard to move to public clouds since there’s so much of it and/or the data have interdependencies with other systems and databases.
• More control over governance and regulatory compliance: You know where and under what geographic or other restrictions your data is operating.
• More control over deployment infrastructure: You may want to use hardware, operating systems, databases, applications, and tools you already have.
• Avoiding high operational expenditure (OpEx): Consumption-based pricing models in public clouds can lead to high OpEx when usage is frequent – particularly if that data is fluid, moving between public clouds and on-premise locations. 

Hybrid Cloud Evaluation Criteria

To get optimal benefits from a hybrid cloud data warehouse, though, you’ll need a solution that can drive better business outcomes while using fewer resources. For starters, you’ll want a single-solution architecture that can operate in both public and on premises environments. Solutions from many data warehouse vendors either don’t do this well or don’t do this at all. Many vendor’s data warehouse solution runs in the public cloud or on-premises, and their “hybrid” versions have been cobbled together to meet the increase in demand. However, without the same data and integration services on-premises and in the cloud, the same data model, the same identity, and the same security and management systems, these solutions effectively saddle you with two siloed data warehouse deployments.

Why are common data and integration services, the same data model, the same identity, and the same security and management systems important? Let me tell you:

Same Data Services

It is essential that your data warehouse supports the same data services for public cloud and on-premises data warehouses. Without this, you will wind up with data redundancy and data consistency issues, duplications of effort and resources (human and technical), increased costs, and an inability to provide seamless access across environments.

Same Data Model

A data model determines how data is organized, stored, processed, and presented. Having a single data model for the on-premises and cloud-based sides of your data warehouse eliminates incongruencies across source systems. It also strengthens data governance by ensuring that data is created and maintained in accordance with company standards, policies, and business rules. As data is transformed within the data warehouse—on-premises or in the cloud—it continues to adhere to data definitions and integrity constraints defined in the data model.

Same Identity Authentication

Your users should be able to sign on to on-premises and cloud data warehouses using the same login ID and password. Data warehouse support for single sign-on (SSO) access helps eliminate password fatigue for users and, more importantly, can help you ensure that your organization’s identity policies are extended to protect both data warehouse locations.

Same Security and Management Services

Shared security services are also critical for hybrid cloud data warehouses. I’ve already written two blog posts that provide details on security, governance, and privacy requirements for the modern data warehouse, one on database and one on cloud service security, so I will refer you to those for more details. But I would like to point out in this discussion that you will need integrated security services across your on-premises and public cloud environments to ensure a strong and consistent security posture for your hybrid data warehouse.

Finally, shared services for management tasks offer clear advantages in terms of cost, control, and simplicity:

• You’ll need fewer staff members to develop, maintain, and monitor the components in your hybrid deployment.
• You’ll improve control through consistent upgrades, patches, and backups.
• You’ll simplify metering and licensing requirements across environments.

Actian Avalanche Hybrid Cloud Data Warehouse

It should come as no surprise that a hybrid cloud data warehouse that meets all these criteria does exist: the Actian Avalanche™ hybrid-cloud data warehouse, integration, and management platform is a true hybrid cloud data warehouse that can be deployed on-premises as well as in multiple public clouds, including AWS, Azure, and Google Cloud. You can read more about the Avalanche solution here.

This originally appeared on iTechnologySeries on October 12, 2021.

The post Hybrid Cloud Data Warehouses: Don’t Get Stuck on One Side or the Other appeared first on Actian.

Hybrid clouds seem to be the way the wind is blowing. According to the Enterprise Strategy Group, the number of organizations committed to or interested in a hybrid cloud strategy has increased from 81% in 2017 to 93% in 2020. But what exactly is a hybrid cloud? Turns out, there are a lot of definitions. I’ll share a definition from Deloitte that I like:

“Hybrid cloud is cloud your way. It’s integrating information systems—from on-premises core systems to private cloud, public cloud, and edge environments—to maximize your IT capabilities and achieve better business outcomes. It’s designing, building, and accelerating your possible.”

Why Hybrid Cloud?

Data warehouse deployments on premises and in the public cloud play equally important roles in a hybrid cloud strategy. The Enterprise Strategy Group found that 89% of organizations still expect to have a meaningful on-premises footprint in three years. At the same time, Gartner predicts that public cloud services will be essential for 90% of data and analytics innovation by 2022. Accordingly, organizations are adopting a hybrid cloud strategy to leverage the right mix of locations to meet their needs.

Consider: The cloud provides the flexibility to build out and modify services in an agile manner, the potential to scale almost infinitely, the assurance of enhanced business continuity, and the ability to avoid capital expenditures (CapEx)—all of which continue to accelerate the adoption of cloud-based data warehouses. But data warehouses running on premises in your own data center deliver their advantages:

• Data gravity: Sometimes data is hard to move to public clouds since there’s so much of it and/or the data have interdependencies with other systems and databases.
• More control over governance and regulatory compliance: You know where and under what geographic or other restrictions your data is operating.
• More control over deployment infrastructure: You may want to use hardware, operating systems, databases, applications, and tools you already have.
• Avoiding high operational expenditure (OpEx): Consumption-based pricing models in public clouds can lead to high OpEx when usage is frequent – particularly if that data is fluid, moving between public clouds and on-premise locations. 

Hybrid Cloud Evaluation Criteria

To get optimal benefits from a hybrid cloud data warehouse, though, you’ll need a solution that can drive better business outcomes while using fewer resources. For starters, you’ll want a single-solution architecture that can operate in both public and on premises environments. Solutions from many data warehouse vendors either don’t do this well or don’t do this at all. Many vendor’s data warehouse solution runs in the public cloud or on-premises, and their “hybrid” versions have been cobbled together to meet the increase in demand. However, without the same data and integration services on-premises and in the cloud, the same data model, the same identity, and the same security and management systems, these solutions effectively saddle you with two siloed data warehouse deployments.

Why are common data and integration services, the same data model, the same identity, and the same security and management systems important? Let me tell you:

Same Data Services

It is essential that your data warehouse supports the same data services for public cloud and on-premises data warehouses. Without this, you will wind up with data redundancy and data consistency issues, duplications of effort and resources (human and technical), increased costs, and an inability to provide seamless access across environments.

Same Data Model

A data model determines how data is organized, stored, processed, and presented. Having a single data model for the on-premises and cloud-based sides of your data warehouse eliminates incongruencies across source systems. It also strengthens data governance by ensuring that data is created and maintained in accordance with company standards, policies, and business rules. As data is transformed within the data warehouse—on-premises or in the cloud—it continues to adhere to data definitions and integrity constraints defined in the data model.

Same Identity Authentication

Your users should be able to sign on to on-premises and cloud data warehouses using the same login ID and password. Data warehouse support for single sign-on (SSO) access helps eliminate password fatigue for users and, more importantly, can help you ensure that your organization’s identity policies are extended to protect both data warehouse locations.

Same Security and Management Services

Shared security services are also critical for hybrid cloud data warehouses. I’ve already written two blog posts that provide details on security, governance, and privacy requirements for the modern data warehouse, one on database and one on cloud service security, so I will refer you to those for more details. But I would like to point out in this discussion that you will need integrated security services across your on-premises and public cloud environments to ensure a strong and consistent security posture for your hybrid data warehouse.

Finally, shared services for management tasks offer clear advantages in terms of cost, control, and simplicity:

• You’ll need fewer staff members to develop, maintain, and monitor the components in your hybrid deployment.
• You’ll improve control through consistent upgrades, patches, and backups.
• You’ll simplify metering and licensing requirements across environments.

Actian Avalanche Hybrid Cloud Data Warehouse

It should come as no surprise that a hybrid cloud data warehouse that meets all these criteria does exist: the Actian Avalanche™ hybrid-cloud data warehouse, integration, and management platform is a true hybrid cloud data warehouse that can be deployed on-premises as well as in multiple public clouds, including AWS, Azure, and Google Cloud. You can read more about the Avalanche solution here.

This originally appeared on iTechnologySeries on October 12, 2021.

The post Hybrid Cloud Data Warehouses: Don’t Get Stuck on One Side or the Other appeared first on Actian.

Digital transformation requires use of the latest technologies. However, as you probably already know, modernizing a mission-critical database and the applications that interact with it can be risky and expensive, often turning into a long disruptive journey. But I have good news! According to a recent Bloor Spotlight report, Actian’s Ingres NeXt strategy for modernizing Ingres and OpenROAD applications either avoids or proactively addresses these potential pain points.

Bloor Senior Analyst Daniel Howard comments:

“Ingres NeXt is worth paying attention to because it acknowledges both the massive need and desire for digital transformation and modernization as well as the difficulties and shortcomings of conventional approaches to them, then takes steps to provide the former while mitigating the latter.”

Let’s look at the top four obstacles that stand in the way of modernization.

It’s Risky

Less than half of modernization projects are successful. Complex dependencies among databases, applications, operating systems, hardware, data sources, and other structures increase the likelihood  that something will go wrong. In addition, organizations are likely to make poor decisions at some point since there are few modernization best practices to guide the way.

It’s Expensive

Modernization typically requires Capital Expenditure (CapEx) justification. Although modernization can potentially save money and increase revenue in the long run, it can be difficult to prove that this will significantly outweigh the costs of maintaining your legacy systems over time. It can also be challenging to get a modernization initiative approved as part of an innovation budget. Innovation budgets are often quite small. According to Deloitte’s analysis, the average IT department invests more than half of its technology budget on maintaining business operations and only 19% on building innovative new capabilities.

It’s a Long Journey

Modernization can involve replacing thousands of hours’ worth of custom-developed business logic. Code may be stable, but it is perceived as brittle if it cannot be changed without great pain. Missing documentation, third-party applications, and libraries that are often no longer available can add time and complexity to a modernization project. Plus, many developers are simply unaware of conversion tools for updating “green screen” ABF applications and creating web and mobile versions.

It’s Disruptive

Mission-critical databases and applications require near 100% availability, so modernization requires careful planning and execution. Plus, technical staff and business users will need to be retrained and upskilled to make the most of new technologies.

How exactly does Ingres NeXt avoid or address these pain points?

Read the Bloor Spotlight on the Ingres NeXt Database and Application Modernization program for the answer to that question. The report discusses how automated migration utilities, asset reuse, and a high degree of flexibility and customization—among other things—result in a solution that can streamline your organization’s path to a modern data infrastructure.

The post Bloor Spotlight Highlights How Actian’s Ingres NeXt Strategy Avoids Common Modernization Pitfalls appeared first on Actian.

Digital transformation requires use of the latest technologies. However, as you probably already know, modernizing a mission-critical database and the applications that interact with it can be risky and expensive, often turning into a long disruptive journey. But I have good news! According to a recent Bloor Spotlight report, Actian’s Ingres NeXt strategy for modernizing Ingres and OpenROAD applications either avoids or proactively addresses these potential pain points.

Bloor Senior Analyst Daniel Howard comments:

“Ingres NeXt is worth paying attention to because it acknowledges both the massive need and desire for digital transformation and modernization as well as the difficulties and shortcomings of conventional approaches to them, then takes steps to provide the former while mitigating the latter.”

Let’s look at the top four obstacles that stand in the way of modernization.

It’s Risky

Less than half of modernization projects are successful. Complex dependencies among databases, applications, operating systems, hardware, data sources, and other structures increase the likelihood  that something will go wrong. In addition, organizations are likely to make poor decisions at some point since there are few modernization best practices to guide the way.

It’s Expensive

Modernization typically requires Capital Expenditure (CapEx) justification. Although modernization can potentially save money and increase revenue in the long run, it can be difficult to prove that this will significantly outweigh the costs of maintaining your legacy systems over time. It can also be challenging to get a modernization initiative approved as part of an innovation budget. Innovation budgets are often quite small. According to Deloitte’s analysis, the average IT department invests more than half of its technology budget on maintaining business operations and only 19% on building innovative new capabilities.

It’s a Long Journey

Modernization can involve replacing thousands of hours’ worth of custom-developed business logic. Code may be stable, but it is perceived as brittle if it cannot be changed without great pain. Missing documentation, third-party applications, and libraries that are often no longer available can add time and complexity to a modernization project. Plus, many developers are simply unaware of conversion tools for updating “green screen” ABF applications and creating web and mobile versions.

It’s Disruptive

Mission-critical databases and applications require near 100% availability, so modernization requires careful planning and execution. Plus, technical staff and business users will need to be retrained and upskilled to make the most of new technologies.

How exactly does Ingres NeXt avoid or address these pain points?

Read the Bloor Spotlight on the Ingres NeXt Database and Application Modernization program for the answer to that question. The report discusses how automated migration utilities, asset reuse, and a high degree of flexibility and customization—among other things—result in a solution that can streamline your organization’s path to a modern data infrastructure.

The post Bloor Spotlight Highlights How Actian’s Ingres NeXt Strategy Avoids Common Modernization Pitfalls appeared first on Actian.