Data Intelligence

From Databases to Knowledge Management Systems for Data Power

Actian Corporation

January 29, 2021

Databases

Databases are everywhere. They store data and information organized by fields and tables. There are different types of databases that organize data into relational tables, hierarchical structures, networks, and objects. Databases can be personal, corporate, and shared resources. They can be used for recording business transactions to support decision-making. The value of databases today is tremendous for every industry and every consumer.

With the great value that databases contribute, there are many challenges related to overall management, data integrity, data security, performance, availability, and many others. Yet, the challenges currently do not outweigh the value.

Data collections in databases are growing exponentially, making the business of extracting value from it all increasingly challenging. Much of the data will migrate to the cloud because many companies and personal users do not want to host and manage the data themselves. They want to focus on outcomes, not the technical complexity of hosting and managing data.

Tools and Technology to Get Value From Your Data

Having a great database is nothing without the capability to access, collect, and store the data with tools that simplify the process. Databases are everywhere, and each is unique across the industry. Today, there is no single data source that organizations can use to understand their customers effectively.

Digital tools and technologies leverage big data collections to unlock the stored data’s insights. Organizations learn from how consumers interact with their data, such as what search keywords were used to locate a particular product. This, in turn, enables them to make shopping easier for others.

Public, private, hybrid, and multi-cloud data sources will increase. Artificial Intelligence (AI) and Machine Learning (ML) tools and technologies continue to evolve to help manage and leverage cloud data collections and improve the quality of the decisions made in a business.

Collaboration

All data has to be collected and collaborated across multiple platforms, database types, and sources today. Tools and technologies are needed to understand various architectures, including the databases and the platform and the various infrastructures.

Data in the cloud increases the organization’s capability to collaborate and share data for the common good of both the organization and its customers. Data in the cloud can readily be used for collaborative technologies, including smart robots, virtual assistants, smart devices, IoT interfaces, and many others. This data has enabled innovation across many industries.

Knowledge Management Systems

A Knowledge Management System (KMS) is a collaborative interrelated, sometimes normalized system for storing data and information with the ability to retrieve any related data across the system for decisions. The data can be accessed using specific tools that enable the centralization of the data and information. Data can be extracted from various data stores and applications, including multiple cloud platforms. Each platform and tool can be viable independently. Still, the system brings together the various data sources to transform data to information, information to knowledge enabling centralized access to knowledge.

The more data, the more the capability for a smart decision can be made with the caveat the data is being used efficiently and effectively. Having lots of data does not mean success, but what specific data and how it is utilized can significantly differ.

Knowledge management systems are not just for consumption by people but also for smart technology. Natural Language Processing (NLP) technology, for example, relies on a very efficient knowledge management system.

Moving the “Normal” – From Data-Driven to Data-Powered

Using data to support consumer and business outcomes is normal for many businesses today but this evolving and changing. Data collection and reactive responses will evolve to be more proactive and service-oriented. The tools and technologies that you purchase today will need to evolve with you to the new normal.

Actionable data will evolve driven by data normalization and unique data models that enable fast data consumption. The goal is to move from data-driven to data-powered, transforming the data collected for specific decisions by understanding and normalizing what it takes to make a decision.

Real-time decision-making is dependent upon the ability to empower a data-driven enterprise. Analytics, data integration, superior architecture, and an intelligent platform can change the current normal to the new normal enabled by fast timely data consumption to drive intelligent high performing results.

Actian empowers the data-driven enterprise with a single data management platform to provide for the needs of the most demanding analytics use cases and mission-critical applications. Actian Data Platform is a fully managed, hybrid cloud data warehouse service designed from the ground up to deliver high performance and scale across all dimensions – data volume, concurrent users, and query complexity, providing high-speed analytics at a much lower cost than alternatives.

About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale. Actian data intelligence solutions help streamline complex data environments and accelerate the delivery of AI-ready data. Designed to be flexible, Actian solutions integrate seamlessly and perform reliably across on-premises, cloud, and hybrid environments. Learn more about Actian, the data division of HCLSoftware, at actian.com.

Data Platform

The Best Cloud Data Strategy for Value

Actian Corporation

January 29, 2021

Which Cloud Makes Sense?

Nothing is perfect, but we want to approach perfection with continuous improvement and the management of risk in our business environments. IT capabilities and resources underpin our business environments. On-premise capabilities and cloud capabilities have to deliver a return on investment (ROI) for our efforts and, at the same time, help the organization reduce IT cost. Every cloud is architected differently with a range of functionality, integrations, models, and specific characteristics. Pricing differs based on your cloud data capability choices. To deliver effective and efficient business value in a complex, growing cloud environment requires advanced analytics. Guessing is not an option.

A multi-cloud environment using multiple public cloud services, usually from different service providers, allows the distribution of risk across multiple cloud environments to improve and assure business continuity. Individual cloud environments can have more risks due to the unique service that the provider delivers. Hybrid, which includes public and private, can have value for some organizations but may seem more costly to manage.

In either case, organizations have to be consumer-driven and drive superior experiences for their customers. If a customer cannot access a supplier’s services because of a cloud failure, that supplier is in danger of becoming optional to that customer. Architecting a multi-cloud environment may be the best choice for business continuity, overall cost, and lowering the risk of failure due to your ability to deliver services to the customer.

Cloud Growth for the Customer

Cloud environments have to perform well. The performance of your cloud environment is partially based on how easily you can obtain data and make decisions. Decision insight capabilities have to be real-time and predictive. The data for the entire enterprise has to be integrated to support the mission of the organization. This includes current cloud data and the ability to easily integrate future data platforms.

As your organization grows, so does the data. Dynamic analytics is a must across all environments, especially if you chose a multi-cloud environment. Since each cloud is different, you have to have a common data warehouse solution such as Actian that can address your current cloud architectures and your future cloud initiatives.

Speed, real-time data, performance, and cost are key factors for the empowerment of a data-driven enterprise. Data that each decision-maker can use quickly, in a collaborative real-time fashion that improves the performance of the organization for its customers is mandatory for managing costs, customer experiences, and dynamic decisions.

Services should continuously improve over time, as competition and innovation are never-ending. The use of the cloud will continue to grow, and organizations have to take advantage of the technology growth in the industry. As this happens, customers want more from every company from whom they consume services or products. Cloud growth then becomes synonymous with customer growth. The data will continue to grow, along with the maturity of data management capabilities across all the platforms. Build a strong enterprise data management foundation and select technology to support your current build and future projects.

Improving Performance, Costs, and Value

On-premise capabilities can also play a significant role in managing multi-platform environments. Remember the old saying, “He who owns the gold makes the rules.” This is also true for your data. An on-premises solution leveraged with a cloud solution can help you manage risk to your organization’s data. Keeping sensitive data on-premises and exploit the elasticity of the cloud as a strategic data decision.

Improving performance and reducing cost always has value to an organization. Including risk avoidance brings with it business opportunity values that improve return on investment (ROI) and the overall value of the investment (VOI) for the organization. Empowering a data-driven enterprise with cost-effective solutions for data analytics anytime and anywhere is a good investment for managing risks.

Building Knowledge for Decisions

The decision support foundation strategy that is set for business intelligence and analytics is important. The key is building on a great platform for supporting cloud intelligence and analytics that services your current needs and is positioned for the future. The technology that you choose has to be built with the following capabilities.

Real-time decision-making.
The ability to manage all clouds.
The ability to support existing on-premises data.
The ability to easily integrate enterprise data.
High performance and low cost.

Data empowers decisions and has to be enabled and empowered with leading technology.

Actian is a fully managed hybrid cloud data warehouse service designed from the ground up to deliver high performance and scale across all dimensions – data volume, concurrent user, and query complexity – at a fraction of the cost of alternative solutions.

Actian is a true hybrid platform that can be deployed on-premises as well as on multiple clouds, including AWS, Azure, and Google Cloud, enabling you to migrate or offload applications and data to the cloud at your own pace.

About Actian Corporation

Data Management

From Data Management to Decision-Making for Empowerment

Actian Corporation

January 28, 2021

person pointing to represent decision-making

Everyone in an organization makes decisions every day. They go through the process of identifying that a decision needs to be made, then gather data and relevant information from various sources to identify the choices that they can make. By Analyzing the data and other considerations based on their experience, they can make a choice and finally take action. The decisions are reviewed for continuous improvement in their decision-making process.

The Four Stages of Decision-Making

Decision-making is one of the most important responsibilities people have in any organization they work in. For every decision, people go through the four stages of learning.

The four stages are:

Unconscious incompetence.
Conscious incompetence.
Conscious competence.
Unconscious competence.

The stages of conscious incompetence and conscious competence are stressful and require help with data that has to be gathered or presented to them for the decision.

Delivering Services and Products

Every company delivers a service or product to its consumers. The company’s assets enable these services or products. An important asset is the people in the organization. Every person is unique and at a different stage of maturity and expertise in their functional role. Each person does their job a little differently and at a different pace or performance level.

Delivery of the organization’s services and products has to be predictable and consistent. To enable this, an efficient and effective value chain of activities needs to occur between functional units in the organization enabled with technologies, data, and tools that need to be collaborative.

Identifying and managing constraints helps the organization to improve performance, but decision-making can be a constraint for productivity. The process can be slowed down when technology does not support the collaboration and the data exchanges need to make informed decisions. Enterprises are people and data-driven. The data needs to be accurate and timely, and the people need to be empowered to use it for decision-making.

Role of Data and Data Relationships

A decision can be automated and should enable improved performance of the organization and its people. One of Enterprise Resource Planning (ERP) systems’ biggest values was the enablement of consistent, collaborative data exchanges across the organization for decision support.

Today as with ERP systems, organizations need to improve the empowerment of the data-driven enterprise. The data managed by an ERP is important, but all the organization’s data is also important, including external data sources that support the organization’s goals. The organization is one team with different roles and should perform in an efficient collaborative way to enable high performance.

Data Capture to Understanding Experience

Enterprise data systems should capture data from anywhere and everywhere, both internally and from cloud sources. Capturing data from all organizational functions, including other enterprise data integrations, will improve its ability to make decisions based on data and improve people’s overall performance and abilities.

Understanding experiences when making a decision can help enrich data capture and presentation usage for faster decisions. Understanding your employees’ experiences and understanding your customer and other stakeholders is driven by cloud data collection, enterprise data collection, and the integration between them. Using a solution like Actian DataConnect enables quick and easy deployment and management across on-premises, cloud, and hybrid environments. It allows you to connect to virtually any data source, format, location, using any protocol, and use this data to understand the experience of customers or suppliers.

Consistency for Success

Enterprise data integration improves decision support and consistency for success. Leverage a solution like DataConnect that supports current needs to drive a data-enabled enterprise. Remove the stress as quickly as possible for decision-making by leveraging the analytical performance, data collection, knowledge intelligence, no matter where the data resides, for continuous improvement of service and product delivery.

Improving consistency using an enterprise-class data-driven solution is just a good service to yourself. Taking care of your needs for less stressful decisions empowered by enterprise data is also good for your business objectives and your customers. Learn more here.

About Actian Corporation

Data Analytics

Data Analytics is a Core Element Creating Business Value

Actian Corporation

January 22, 2021

Valued Assets

Organizational assets are defined as capabilities and resources. To be valued strategic assets, they have to contribute to the success of the organization. There is no business value contributed to an asset that does not have an understood return on investment (ROI) for the organization.

Capabilities can be defined as the ability, power, potential, or competence to do something or carry out an activity for producing a service or product. Examples of categories of capabilities are people, organization, process, knowledge, and management. Capabilities can be generalized or specialized. When specialized, they can create a unique advantage for an organization. For example, an organization can follow a standard industry process and achieve the same results as another organization or the organization can improve on the standard industry process and achieve much better results by making the process specialized and more efficient.

Resources can be defined as a supply, materials that enable functionality, or the building blocks within an organization to produce services or products. Examples of resources are financial capital, infrastructure, applications, information, and people. Resources are drawn on to extract value for transformation into the needed product or service. Extraction of value has a direct relationship to the capability of the resource.

Valued strategic assets contribute to the organization’s success by supporting the effective, efficient, and economical delivery of products and services. Assets that do not contribute to the organization’s top and bottom line are liabilities and can contribute to organizational failure.

People-Driven

The most valuable assets in an organization are its people. People need to be enabled to perform at their highest level. This can mean investing in people’s capabilities, especially their abilities to make decisions rapidly and effectively. People deliver specialized capabilities for the organization in various functions. Each of the functions that people serve contributes to a business value chain for producing a product or service for consumption. People are enabled by a process, technology, and other organizational capabilities, such as management systems.

Although, people are directed with the process, procedure, and work instructions. Then further enabled with tools, technology, and automation. There remains lots of manual work. Manual repetitious activities performed by people need to be automated to improve organizational performance. Data is continuously processed. Information is extracted from data. With information, knowledge is obtained for decision support. Contributing to the manual activities exists in functional silos, not entirely integrated across the organization.

A Team Sport

Behind every organizational initiative is a person or team of people. We are still a long way from the age where machines and artificial intelligence can completely drive an organization. Also, if this was the case, there are still people entablements that have to occur for automated organizations.

Enablement of teams and people is improved with the proper management of data lakes. Data needs to be collected efficiently and economically for the end goal of enablement of decision support capabilities for the organization. Data automation increases people’s capabilities by replacing manual daily activities with time to think, learn, study, improve, and utilize the unique capability of a human that a machine cannot accomplish.

Experience and Data

Experience is what builds expertise in people. Experience is not just the participation in an activity but includes the combination of empathy and analysis of the activity for improvement and evolution. The same applies to decisions for business innovation, evolution, and transformation. These things don’t just happen but happen when people have time to do higher-level activities. High-level activities are enabled to successfully manage data, information, and knowledge for organizational decision-making.

Simplifying data collection and analysis helps an organization understand customer habits and helps with predictions. Collected experiences interpreted by experienced people help us understand the customer in a way that machines cannot comprehend yet. Combining both as a capability and, over time, creates a unique capability for an organization with data and human decisions can improve overall service, product, and business value.

Accelerate the use of data analytics by assuring and addressing people collaboration and decision-making across the organization. Improve real-time decision-making by understanding how to enable people with actionable data and information.

Using the Actian Data Platform Hybrid Data Warehouse solution includes data integration and unique capabilities such as blazing fast analytics, real-time data ingestion, and an ability to deploy on-premises and across multiple cloud platforms, so you can combine your business experience and data to make decisions in the business moment.

About Actian Corporation

Data Intelligence

Data Governance, a Reinforced Priority for Companies in 2021?

Actian Corporation

January 19, 2021

With both the increasing need for digital transformation and the power of IT solutions, the place of data in corporate strategies is exploding. A reality that makes the notion of data governance an unavoidable priority. Here’s a look back at a challenge that will remain crucial in 2021.

With the growing importance of new technologies, companies are at a crossroads. On the one hand, they are collecting and producing huge volumes of data. On the other hand, they must be able to harness the full richness of this data to adapt to their markets in real time.

The challenge? Implement robust data governance strategies to ensure not only the accuracy and relevance of data but also its reliability and security.

But the challenge does not stop there. They must also provide their teams, internally, with the information they need to fulfill their missions. According to estimates published in Statista’s Digital Economy Compass 2019, the annual volume of data created worldwide has increased more than twentyfold between 2010 and 2020 and reached 50 zettabytes this year. 50 zettabytes, that’s 500 million 100TB hard disks. A dizzying figure, which only goes to illustrate the importance of defining a real data governance policy.

The question is not limited to a simple concern for storage or security, but also, and above all, for the exploitation of the data. An exploitation that allows the company to develop a precious asset to facilitate the daily life of its teams and the satisfaction of its customers.

Gartner stated, “The uncertainty ushered in by 2020 will stay with us for multiple years to come. But with disruption comes an enormous opportunity to not just restart what we used to do but forge new paths. Data and analytics leaders who thrive will design and execute on a strategy that accelerates change, builds resilience, and optimizes business impact.”

Starting Data Governance

No one doubts the importance of a data governance policy anymore. The COVID-19 crisis is a clear illustration of this. Health data are critical to controlling the epidemic and when governance is not properly in place, the consequences can be disastrous.

Strictly speaking, data governance is the overall management of the availability, usability, integrity and security of the data used in an organization. But behind this principle, there are the facts… and organizational or technical difficulties. Within a company, the definition of an appropriate data governance policy must rely on the right people. The team in charge of the data governance policy guarantees the determination of standards, the use and integration of data between projects, domains and business sectors…a demanding mission that requires taking up complex challenges.

Meeting Today’s Data Governance Challenges

Since the place of data is central to the life of a company, it is, more than ever, essential to abolish the silos that too often hinder the optimal use of data. This is the very heart of a data governance project: ensuring that data becomes valuable information. A challenge that involves democratizing data access to non-IT profiles.

All business departments must be able to manipulate, exploit and interrogate data.

To achieve this, the solutions deployed in organizations must offer an intuitive and ergonomic experience. But behind the sharing of information, which brings with it the notion of data quality, there is the constant challenge of securing data… especially when your employees are not physically present in the company and access this strategic asset from home, for example. Identity management and compliance with “best practices” in terms of IT security must be the subject of constant support. This support must be the immediate counterpart to the development of an internal culture of data governance.

Developing policies, procedures and practices that enable effective control and protection of data, while at the same time strengthening the way it is handled and used, is the DNA of a Data Governance policy.

About Actian Corporation

Data Intelligence

IoT in Manufacturing: Why Your Enterprise Needs a Data Catalog

Actian Corporation

January 12, 2021

Digital transformation has become a priority in organizations’ business strategies, and manufacturing industries are no exception to the rule! With stronger customer expectations, increased customization demands, and the complexity of the global supply chain, manufacturers are in need of finding new, more innovative products and services. In response to these challenges, manufacturing companies are increasingly investing in IoT (Internet of Things).

In fact, the IoT market has grown exponentially over the past few years. IDC reports the IoT footprint is expected to grow up to $1.2 trillion in 2022, and Statista, by way of contrast, is confident its economic impact may be between $3.9 and $11.1 trillion by 2025.

In this article, we define what IoT is and some manufacturing-specific use cases, as well as explain why the Actian Data Intelligence Platform Data Catalog is an essential tool for manufacturers to advance in their IoT implementations.

What is IoT?

According to Tech Target, the Internet of Things (IoT), “a system of interrelated computing devices, mechanical and digital machines, objects, or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.”

A “thing” in the IoT can therefore be a person with a heart monitor implant, an automobile that has built-in sensors to alert the driver when tire pressure is low or any other object that can be assigned an ID and is able to transfer data over a network.

From a manufacturing point of view, IoT is a way to digitize industry processes. Industrial IoT employs a network of sensors to collect critical production data and uses various software to turn this data into valuable insights about the efficiency of manufacturing operations.

IoT Use Cases in Manufacturing Industries

Currently, many IoT projects deal with facility and asset management, security and operations, logistics, customer servicing, etc. Here is a list of examples of IoT use cases in manufacturing:

Predictive Maintenance

For industries, unexpected downtime and breakdowns are the biggest issues. Hence manufacturing companies realize the importance of identifying potential failures, their occurrences and consequences. To overcome these potential issues, organizations now use machine learning for faster and smarter data-driven decisions.

With machine learning, it becomes easy to identify patterns in available data and predict machine outcomes. This works by identifying the correct data set, combining it with a machine to feed real-time data.This kind of information allows manufacturers to estimate the current condition of machinery, determine warning signs, transmit alerts and activate corresponding repair processes.

With predictive maintenance through the use of IoT, manufacturers can lower the maintenance costs, lessen the downtime and extend equipment life, thereby enhancing quality of production by attending to problems before equipment fails.

For instance, Medivators, one of the leading medical equipment manufacturers, successfully integrated IoT solutions across their service and experienced an impressive 78% boost of the service events that could be easily diagnosed and resolved without any additional human resources.

Asset Tracking

IoT asset tracking is one of the fastest growing phenomena across manufacturing industries. It is expected that by 2027, there will be 267 million active asset trackers in use worldwide for agriculture, supply chain, construction, mining, and other markets.

While in the past manufacturers would spend a lot of time manually tracking and checking their products, IoT uses sensors and asset management software to track things automatically. These sensors continuously or periodically broadcast their location information over the internet and the software then displays that information for you to see. This therefore allows manufacturing companies to reduce the amount of time they spend locating materials, tools, and equipment.

A striking example of this can be found in the automotive industry, where IoT has helped significantly in the tracking of data for individual vehicles. For example, Volvo Trucks introduced connected-fleet services that include smart navigation with real-time road conditions based on information from other local Volvo trucks. In the future, more real-time data from vehicles will help weather analytics work faster and more accurately; for example, windshield wiper and headlight use during the day indicate weather conditions. These updates can help maximize asset usage by rerouting vehicles in response to weather conditions.

Another tracking example is seen at Amazon. They are using WiFi robots to scan QR codes on its products to track and triage its orders. Imagine being able to track your inventory—including the supplies you have in stock for future manufacturing—at the click of a button. You’d never miss a deadline again! And again, all that data can be used to find trends to make manufacturing schedules even more efficient.

Driving Innovation

By collecting and audit-trailing manufacturing data, companies can better track production processes and collect exponential amounts of data. That knowledge helps develop innovative products, services, and new business models. For example, JCDecaux Asia has developed their displaying strategy thanks to data and IoT. Their objective was to have a precise idea of the interest of the people for the campaigns they carried out, and to attract their attention more and more via animations. “On some screens, we have installed small cameras, which allow us to measure whether people slow down in front of the advertisement or not.”, explains Emmanuel Bastide, Managing Director for Asia at JCDecaux.

In the future, will displaying advertising be tailored to individual profiles? JCDecaux says that in airports, for example, it is possible to better target advertising according to the time of day or the landing of a plane coming from a particular country! By being connected to the airport’s arrival systems, the generated data can send the information to the displaying terminals, which can then display a specific advertisement for the arriving passengers.

Data Catalog: One Way to Rule Data for any Manufacturer

To enable advanced analytics, collect data from sensors, guarantee digital security and use machine learning and artificial intelligence, manufacturing industries need to “unlock data,” which means centralizing in a smart and easy-to-use corporate “Yellow Pages” of the data landscape. For industrial companies, extracting meaningful insights from data is made simpler and more accessible with a data catalog.

A data catalog is a central repository of metadata enabling anyone in the company to have access, understand and trust any necessary data to achieve a particular goal.

Actian Data Intelligence Platform Data Catalog x IoT: The Perfect Match

Actian Data Intelligence Platform helps industries build an end-to-end information value chain. Our data catalog allows you to manage a 360° knowledge base using the full potential of the metadata of your business assets.

Success Story in the Manufacturing Industry

In 2017, Renault Digital was born with the aim of transforming the Renault Group into a data-driven company. Today, this entity is made up of a community of experts in terms of digital practices, capable of innovating while delivering agile delivery and maximum value to the company’s business IT projects. Jean-Pierre Huchet, Head of Renault’s Data Lake, states that their main data challenges were:

Data was too siloed.
Complicated data access.
No clear and shared definitions of data terms.
Lack of visibility on personal/sensitive data.
Weak data literacy.

By choosing the Actian Data Intelligence Platform Data Catalog as their data catalog software, they were able to overcome these challenges and more. Actian Data Intelligence Platform today has become an essential brick in Renault Digital’s data projects. Its success can be translated into:

Its integration into Renault Digital’s onboarding: mastering the data catalog is part of their training program.
Resilient documentation processes & rules implemented via the Actian Data Intelligence Platform.
Hundreds of active users.

Now, the Actian Data Intelligence Platform is their main data catalog, with Renault Digital’s objectives of having a clear vision of the data upstream and downstream of the hybrid data lake, a 360 degree view on the use of their data, as well as the creation of several thousands of Data Explorers.

Actian Data Intelligence Platform’s Unique Features for Manufacturing Companies

Our data catalog has the following features to solve your problematics:

Universal connectivity to all technologies used by leading manufacturers.
Flexible metamodel templates adapted to manufacturers’ use-cases.
Compliance to specific manufacturing standards through automatic data lineage.
A smooth transition in becoming data literate through compelling user experiences.
An affordable platform with a fast return on investment (ROI).

Are You Interested in Unlocking Data Access for Your Company?

Are you in the manufacturing industry? Get the keys to unlocking data access for your company by downloading our new Whitepaper “Unlock Data for the Manufacturing Industry”. Download our Whitepaper.

About Actian Corporation

Data Management

Leveraging IoT and Industry Data for Managing Experiences and Decisions

Actian Corporation

January 12, 2021

Leveraging Data

Data is everywhere. Data comes from social interactions, personal devices, cloud service interactions, the environment, and many other places and things. There is a wide variety of data and a significant volume of data being collected every second. The velocity and quality of data are increasing every minute.

Big data can be considered a moment in society that will continue to grow by double figures each year. Any research on big data will give you insights into the magnitude that this moment is growing across the globe. People, organizations, and machines crave data to support their existence.

Every person is a participant in data collection. Every industry wants to leverage this data for truthful decisions that can affect their organizations. Many personal devices and organizational devices are connected to the Internet, which is usually referenced as the Internet of Things (IoT).

Leveraging all this data takes careful planning to support organizational strategic intent and overall decision-making capabilities. People and organizations use data to help remove slow manual interactions to improve the ability to react and be proactive for decisions and the overall well-being of the person or organization.

Data Visualization, Interoperability, and Connectivity

Quick decisions require the ability to visualize the data message from the noise easily. No one system functions without connectivity to another system for data exchanges by turning inputs into outputs for processing and analytics. Interfaces and data relationships have to be completely understood and interoperable across many distinct platforms. Enterprise data solutions are needed to leverage interoperability and connectivity across multiple data sources.

Data Decisions for Continuity

People and organizations want continuity of services. Continuity consists of:

Ensuring the service is available when needed.
Having enough capacity to support requests and analytic computations.
Secure enough to protect the confidentiality, integrity, and information privacy.

Organizations have to continuously monitor and collect data, perform analysis and generate analytics, quickly implement solutions, perform constant tuning, and repeat these activities to listen to their systems and customers, including monitoring for threats, such as those related to cybersecurity.

If a threat to business continuity becomes apparent through data analysis, the organization needs to respond to the threat as soon as possible. If the threat creates an incident, the incident must be first detected, then addressed as soon as possible. If the incident creates damage, the damage must be repaired as soon as possible. The recovery actions need to be evaluated to stop the loss of business continuity as early in the cycle as possible. The ability to do proactive data detection and trend analysis across the organization’s products and services and across connected devices of their customers becomes important, especially for critical services impacting customers.

To accomplish this, business continuity plans need to be created, and IT continuity plans have to support business plans. Services have to be classified as critical or non-critical especially if they impact customers or revenue. Organizations may formally perform impact analysis and risk assessments. People may do the same informally to determine the criticality of products and services. There needs to be an exchange of data from the customer’s perspective to the organization for the creation of a proper response for the benefit of both the customer and the organization.

Organizations need to formalize the creation of proactive plans, testing, training, audits, and invocation of plans to collect data and help assure that the organization continues to function following a disruption. Without these formal activities, people will behave in a reactive manner to try to restore the capability of a service, which can compound an incident.

Managing Experience and Decision

Organizational needs and people needs have to be aligned. Understanding how people experience the services and products, including their dependency, continuity, and security needs, becomes very important for organizational survival. People who use critical products or services from an organization and then face a challenge with that deliverable will usually try to make a rapid decision to support their outcomes with the new provider. Employees who engage a new provider may never return to the provider that they had the issues with. An organization that loses a customer usually spends over three times more time and effort trying to regain loyalty.

An enterprise data strategy built on high-performing technology can help ensure high customer retention, low cost, and mitigate reputational damage to the organization. It is important to obtain and manage the right data about your customers across multiple data sources. Actian DataConnect makes it easy to connect operational data from any data source and transform it to facilitate effective data analysis. DataConnect makes it easy for data-driven enterprises to design, deploy, and manage data flows across on-premises, cloud, and hybrid environments.

About Actian Corporation

Data Analytics

Approaching Holistic Data Models for Organizational Efficiency

Actian Corporation

January 12, 2021

Futuristic banner with abstract geometric shape

Why Model Anything?

There are many types of models with many types of usages, such as physical, scientific, mathematical, computer, concrete, communication, etc. The overall purpose is to define, support analysis and decisions, and to communicate or validate a system or perspective.

Models can also be used to help speed things up or slow things down if needed or enable integration and collaboration. Holistic data models are used to understand how systems influence each other. Models can help with realizing economic and operational benefits for an organization. These perspectives are sometimes separated.

Models have scope and detail. The scope defines one or different types of elements in the model. For example, a model can be created that only shows one type of a thing, such as a door, the scope of the model is doors, but if I add floors, windows, and other things related to a house, I have increased the scope of the model. Each item in the scope should be unique, required for the intent, manageable, and subject to change control. Model detail is data related to the individual scope of each item for clarity of the capability of the item.

Service Models

Service models can be used to understand logically how a service is delivered and supported. These models usually consist of the service’s definition as it is delivered to an internal or external customer, sometimes called the business service. This may include services that are packaged together for delivery that cannot be separated by the customer for the purchase. For example, most people who have cable service in their households have to buy the basic service package and cannot pick only a few of the channels that are included in this package. Services can be sold individually if the company decides to do so, but in most cases, individual channels are considered as add on to the primary service or as an enhancing service or premium service.

The service model has underpinning relationships built based on how the service should be managed. The next level in a service model is the IT service/application that supports the higher-level business service delivered to the customer. After that, the levels may follow a typical cloud model detailing additional interconnected services or products, the infrastructures, and the platforms used for the service.

Service models can be created or viewed from the business service, IT service, or any perspective needed for the consumer of the model’s data to make a decision.

Financial Models

Financial models normally show a summary of a company’s expenses and earnings, but there are various types of financial models for gaining insight into the company’s finances from different perspectives for making specific decisions. Accounting, budgeting, and charging are key outcomes from some financial models.

One-way financial models are used is to understand the cost of service versus prices of service. Organizations want to make sure that they are not losing money on any service or product produced by the company. The return on investment (ROI) and the total cost of ownership (TCO) must be considered. Not understanding this can result in the company producing a product or service that has no economic value, which can ultimately lead to bankruptcy.

An important factor that is left out of many financial models is the service perspective. Within financial models, the data and insights from the service model are usually not apparent. Accounting for an IT service from end-to-end, understanding the cost and the price can be a challenge. Financial insight into business and IT services or products for decisions can be very challenging without understanding the service model.

Enterprise Data Models

Leveraging financial models holistically with service models can have a great benefit to the organization. Finance can understand better how to charge for services and products based on the total cost of ownership. Finance can simplify charging by focusing on one higher aspect of the service instead of all the components once they understand the cost deviations. For example, in a service such as onboarding a new employee, the employee receives the equipment, applications, etc., depending on their function in the organization. Finance can set one internal cost for onboarding, saving time, making them more effective, efficient, and economical overall for the organization.

Today with data everywhere, in the cloud, on-premises, on personal devices, connecting and leveraging data related to services and products becomes a daunting task without the proper enterprise technology solutions to help. Actian DataConnect makes it easy to connect operational data from any data source and transform it to facilitate effective data modeling and analysis.

Service data related to the service model then consumed by finance can provide intelligent insights into the real cost of delivering and supporting a service or product. Organizations can better understand which service or product delivers the highest return on investment (ROI), the real total cost of ownership, and insights into continuous improvement for value to the customer and the organization. Learn more about how DataConnect makes it easy for data-driven enterprises to design, deploy, and model data flows across on-premises, cloud, and hybrid environments.

About Actian Corporation

Data Intelligence

How Has Data Impacted the Manufacturing Industry?

Actian Corporation

January 11, 2021

The place of data is – or should be – central to a manufacturing industry’s strategy. From production flows optimizations through predictive maintenance to customization, data exploitation is a major lever for transforming the industry. However, with great data comes great responsibility. Here are some explanations.

The manufacturing industry is already on the way to becoming data-driven. In the 2020 edition of the Industry 4.0 Barometer, Wavestone reveals that 86% of respondents say they launched Industry 4.0 projects. From the deployment of IoT platforms, complete redesigns of historical IT architecture, movements towards the Cloud, and data lake implementations… data is at the heart of manufacturing industry transformation challenges.

“In 2020, we are starting to see more and more projects around data, algorithmics, artificial intelligence, machine learning, chatbots, etc.” Wavestone explains.

All sectors are impacted by this transformation. According to Netscribes Market Research forecasts, the global automotive IoT market, for example, is expected to reach $106.32 billion by 2023. The driving force behind the adoption of data-driven strategies in the industry is the need for increased productivity at a lower cost.

What are the Data Challenges in the Manufacturing Industry?

The use of data in the manufacturing industry is also a question of responding to a key issue: that of the mass customization of production. A growing topic that particularly affects the automotive sector. Each consumer is unique and intends to have products that resemble them. However, in the past, manufacturing industries based their production methods on the volume of production and industry-specific standards.

Mass customization of production is, therefore, the lever of the data-driven revolution currently underway in the manufacturing industry. Nevertheless, other considerations come into play as well. A “smart” industrial tool makes it possible for these enterprises to reduce the costs and delays of production as well as respond to the general acceleration of the time-to-market. Data also contributes to meeting ecological challenges by reducing the production machines’ environmental footprint.

Whether it is integrating IoT, Big Data, Business Intelligence, or Machine Learning, these technologies are all opportunities to reinvent a new data-based industry (embedded sensors, connected machines and products, Internet of Things, virtualization).

But behind these perspectives, there are many challenges. The first of these is the extremely rigorous General Data Protection Regulation (GDPR) in application since May 2018 in Europe. The omnipresence of data in the industrial world has not escaped mafia organizations and cybercriminals who have been multiplying attacks on industry players’ IT infrastructures since 2017 with the infamous Wannacry ransomware.

This attention is fueled by another difficulty in the industrial sector: older and legacy IT environments that are often described as technological hassles, multiplying potential vulnerabilities. The heterogeneity of data sources is another sensitive difficulty for the manufacturing industry. Marketing data, product data, logistics data, are often highly siloed and difficult to reconcile in real time.

The Benefits of Data for the Manufacturing Industry

According to the Wavestone Barometer statistics, 74% of the companies surveyed recorded tangible results within 2 years. Nearly 7 out of 10 companies (69%) report a reduction in costs, and 68% report an improvement in the quality of services, products or processes.

On average, transformation programs regarding the creation or processing of data have led to the optimization of energy performance by 20 to 30% and a reduction in downtime from better monitoring of equipment that can reach up to 40% in some sectors.

Increased traceability of operations and tools, real-time supervision of the operating conditions of production tools, all of which contribute to preventing errors, optimizing product tracking, but also to detecting new innovation levers related to the analysis of weak signals thanks to AI solutions for example.

At the heart of the manufacturing industry’s transformation is the need to rely on data integration and management solutions that are powerful, stable and ergonomic, to accelerate the adoption of a strong data culture.

Are You Interested in Unlocking Data Access for Your Company?

Are you in the manufacturing industry? Get the keys to unlocking data access for your company by downloading our new Whitepaper “Unlock Data for the Manufacturing Industry”.

About Actian Corporation

Data Analytics

Delivering Real-Time Customer 360 Insights Faster and Easier

Actian Corporation

January 11, 2021

A long time ago (read: before the COVID-19 pandemic started), companies around the world were interested in developing a full 360-degree view of their customers. The importance of creating a tailored, data-enhanced customer experience was understood, but formidable challenges—the fact that the data comprising an individual customer profile resides in so many disparate systems, that individual customers use several different devices and interact through different channels at different times and for different reasons, and more—often frustrating operations and executives alike.

Since the emergence of COVID-19, though, that interest has become imperative. COVID-19 has affected the buyers’ journey much the same way it has affected every other activity in which humans engage. A study by McKinsey found that 75% of US customers tried a new brand or a new way of shopping since the start of the pandemic, and 84% have used digital channels more than they did in 2019.

This is the perfect storm that creates real urgency for a 360-degree customer view and the ability to analyze the dynamics of customer behavior and act appropriately—in real-time. The challenges associated with data collection, analysis, and response are still present. In many ways, they have only grown more formidable, for we have come to realize that a snapshot view of the customer is insufficient. Organizations need a way to model and remodel the customer journey in real-time, as creating an engaging customer experience is a dynamic effort that evolves with each interaction. Resolving a customer’s identity across multiple channels and under various personas, mapping those identities to historical transactions, and profile data stored in CRM, ERP, and operational systems are both important and monumental tasks. But it’s also only a beginning, a first step in creating an actionable 360-degree view of the customer.

Building a Broader View

A true 360-degree view of a customer can’t be sourced exclusively from internal data—let alone from a single system or department—nor will the picture created remain unchanged. The necessary insights that can drive improved customer acquisition and retention will arise only from an ongoing effort to aggregate data sources and to analyze data and signals in real-time across operational ERP and CRM systems, social channels, selling tools, transaction systems, and more. A 2020 study from Aquia surveying the top three challenges for marketing organizations cited the number one challenge as adopting new marketing solutions, followed by the need to integrate new solutions with existing ones.

This is where the Actian Data Platform can make all the difference. With the platform, organizations can leverage drag-n-drop integration to aggregate data from all the disparate sources and apply real-time analytics to create dynamic customer profiles, measure sentiment, and personalize each customer’s experience. The Actian Data Plaform analytics can enable micro-segmentation and the ability to identify the next best actions for individual personas. You can conduct market basket analyses to increase upsell. Additional insights can also improve customer acquisition and retention through campaign optimization and churn analyses. Strategically implemented, all these customer experience management (CXM) outcomes can increase overall customer loyalty and value.

A Peek Under the Hood

Let’s look at four key aspects of the 360-degree customer journey that Actian Data Platform can enable:

Unlock the Value of Customer Data by Bringing Together All the Pieces of the Puzzle

The Actian Data Platform has built-in data integration capabilities through DataConnect. It acts as self-service data preparation and ingestion engine for on-prem and SaaS applications, disparate databases, data lakes, and document stores. It supports static and streaming sources, including clickstreams, IoT, and event-driven data.

The drag-and-drop and menu-driven features of DataConnect empower your data engineers, data scientists, business analysts, and other non-IT users to develop and maintain a true Customer 360 view on their own. There’s little need to engage the IT team to code solutions for data integration, extraction, transformation, or loading. Actian comes with pre-built connectors to popular applications from Salesforce, Marketo, Microsoft Dynamics, NetSuite and others, as well as a full range of database environments, web service APIs, JSON objects, flat files, and more. It also provides native integration with AWS S3, Azure Data Lake Services, and Google Cloud data stores, so you can pull data into the Actian Cloud Data Warehouse from on-premises as well as multi-cloud environments.

Empower Large and Diverse Teams With Real-Time Decision-Making Capabilities

The delivery of a truly engaging customer experience requires real-time analytics that can run with sub-second response times. Yet the data pools can be vast—with queries running against multiple terabytes of data—and all this data is being accessed simultaneously by cross-functional teams. Nor is the dataset static; it evolves with every customer interaction and update. But this is the dynamic environment for which the patented vector technology of Actian was designed. Actian delivers the industry’s fastest performance at scale with no tuning required nor caching of stale data to achieve superior performance results.

Improve Business Agility While Avoiding Customer Data and Cloud Lock-In

The Actian cloud data warehouse enables you to unlock the data in all your key systems and enables you to conduct analytics that transcends the limits of an individual CRM or ERP system. DataConnect empowers your business analysts, data engineers, and data scientists to unlock the true value of the data in your CRM, ERP, and other systems by enabling the exploration of these data sets in combination with other customer-related data—both from sources within your organization as well as from external sources such as Facebook, LinkedIn, and others.

Actian runs on AWS, Azure, and Google Cloud and offers one-click deployment in your cloud environment of choice. Should your choice change for any reason, you can easily migrate your Actian data to a new cloud while remaining connected to the tools you had been using to analyze and visualize your customer 360 data.

Analyze, Visualize, and Report Without Additional Training or IT Support and Internal Resources

Actian runs as a fully-managed cloud service, so your business analysts, data engineers, and data scientist can access it directly, without the need to allocate internal IT resources. Once, your users have determined what data they want to use and how they want to structure it in Actian, they can use either SQL or user-defined functions to query and manipulate the data for analysis, visualization, and real-time decision-making. Those responsible for your CXM efforts can make real-time changes to business process execution through the use of existing BI and advanced analytics tools to, in turn, change the policies and business process driving offers, programs, and so forth as needed.

The Perfect Umbrella for the Perfect Storm

In this perfect storm, Actian provides you with an easy and fast way to build the complete Customer 360 views that you have been seeking. You can easily aggregate data from all the relevant yet disparate sources and move quickly from access and enrich to analyze and act—all of which puts you in a far better position to engage, meet the needs of, and retain customers even as the storms of change continue to rage around you.

About Actian Corporation

Data Analytics

Four Data Analytics Predictions for 2021

Actian Corporation

December 28, 2020

In a year dominated by the dark cloud of the COVID-19 pandemic and the tragedies that ensued, we seem to be ending on a hopeful note. We saw the incredible power of human ingenuity, as not one but several vaccines have been developed at breakneck speed, surpassing even the most optimistic of expectations. We also saw how technology could be used to effect amazing transformation on a global scale.

With this positive and promising reminder, here are my four predictions for analytics in 2021:

Data Analytics Will Fundamentally Transform the Supply Chain, Bringing Greater Visibility to Lead Times, Inventory Levels, and Logistics. We saw a classic case of a broken supply-and-demand chain at the onset of the COVID-19 outbreak in March. Demand for specific products surged, while supply plummeted due to unexpected manufacturing factory shutdowns, causing consumer panic, disruption, and delays. Leveraging analytics to look at real-time data for existing supply chain processes, distribution networks, and transportation solutions can help find pain points and opportunities, which in turn can proactively address supply chain points of vulnerability before issues arise. Harnessing data to understand delivery lead times, logistics scenarios, and inventory asset levels will drive greater levels of responsiveness, efficiency, and effectiveness across a broad spectrum of industries.
Demand for Interoperable Multi-Cloud Platform Solutions Will Dramatically Increase. As SaaS tools and applications create further data fragmentation not just between existing on-premises data but across cloud-based operational workload data, the need for loosely coupled, cloud-based data ecosystems will emerge. Paradoxically, many organizations that have a “cloud-first” policy are seeing their costs rise over time due to increased consumption, inflexible deployment models, and lack of financial governance capabilities in cloud-based solutions. These “experienced organizations” will demand the ability to consume cloud services from many sources and the ability to combine data, leading to an unprecedented level of cost savings and new generations of solutions. For a modern data stack to work, it needs to be open to all origination sources, analysis, and visualization destinations.
Technology Solutions That Can Deliver Real-Time Insights Will Be One of the Heroes of the Pandemic. The ability to gain real-time insights from federated but connected systems will enable organizations globally to respond to and gain control over the pandemic’s impact, whether that be for contact tracing, understanding infection rates, or vaccine distribution. But almost as important as saving lives and mitigating the spread of COVID-19 will be the need to rebuild the economy. The ability to rapidly assess changing market conditions will have to be fundamentally data-driven, following the same recipe of combining the right data from the right sources in real time.
Container Technology That Has Played Such a Vital Role in Transforming the Data Center Will Also Move to the Edge, Bringing New Levels of Intelligence, Data Privacy, and the Next Generation of Services. Virtualization technologies and their ability to unlock the value of software on an increasingly intelligent converged infrastructure will move from the physical data center to the cloud, which in turn will lay the foundation for the new connected Edge. In 2021 expect to see hyper-converged infrastructure with container technology bring a new richness to software developed and deployed for mobile and IoT environments. We won’t see full monetization of 5G just yet, but these supporting technologies will give innovators and investors alike the confidence that the 5G wave is real and will be big.

At Actian, we have done our best to adapt to the unprecedented challenges of 2020. As we look ahead to the new year, we are excited to help our customers achieve new levels of innovation with our data management, integration, and analytics solutions.

Have a safe and successful 2021!

About Actian Corporation

Data Platform

Google GKE Containers: Technology for Cloud Data Warehouses

Actian Corporation

December 21, 2020

Actian on Google Kubernetes Engine

Google Kubernetes Engine (GKE) is now running containerized versions of Actian Data Platform (formerly known as Avalanche), designed to power an enterprise’s most demanding operational analytics workloads. Why is that important? Because now an enterprise can deploy one of the world’s most advanced data warehouse systems in about five minutes—a fraction of the time it takes to deploy in other cloud environments.

Let’s break this down a bit. The Google Kubernetes Engine—GKE—works with containers, which are, effectively, self-contained, pre-built component images. That’s important because in other, non-containerized, cloud environments data warehouses are typically deployed by running a series of scripts and/or REST API calls that build out each component from a base OS VM. In those scenarios, every component needs to be installed and configured—in sequence—so building out a complete cluster can easily take 25 minutes or more. That’s not a huge amount of time if you are expecting to set it and forget it, but in the age of DevOps there’s less and less setting and forgetting. The needs of a DevOps team change constantly, and in such a dynamic environment the need to reconfigure and redeploy—at 25 minutes per pop—can quickly become a source of real frustration. It’s also worth noting that a 25-minute projected deployment time assumes that everything runs without incident, and that may not always occur. The sheer number of operations that need to be executed to build these highly complex systems increases the possibility that something will not go as planned at some point in the process. There are lots of dots to connect, and each connection presents a point of vulnerability where something could go awry. The more you need to iterate, replicate, and expand deployments over time, the greater the likelihood that something is not going to go the way it should go and you’ll spend far more than 25 minutes trying to work out why.

Containers, in contrast, obviate the need to run these complicated setup procedures—because they have already been run and the dots connected when the containers were built. That’s right: it’s as though someone else ran through all the scripts and captured images of what a fully deployed Actian instance should look like—and then froze these images in a form that could be used and reused anywhere. Those pre-built images are the containers, and once built can be deployed quickly on Google Cloud via GKE.

In fact, it’s not even as complicated as deploying the containers via GKE. All an organization needs to do is select Google Cloud as the target when deploying an Actian cloud data warehouse. Actian invokes GKE to do the work of deploying the containers for you and within minutes you’re up and running with a world-class data warehouse.

Making the Most of the Google Cloud Infrastructure

That brings us to the second part of why it’s great to run Actian using GKE. Actian is designed to make optimal use of the compute resources at hand. The more CPU power and RAM one can configure in an Actian cluster, the more performance you’re going to experience. While that may be true for many systems, when it comes to the cloud, distinctly different infrastructures can be implemented. And while the question of which cloud vendor has the most performant infrastructure will vary from one investment cycle to the next, users of Google Cloud can take advantage of more readily available offerings with advanced, high-performance CPU/memory configurations than found on alternative platforms, and that can be crucial in certain business scenarios where speed-to-insight is critical. The whole physical infrastructure—not just the CPUs, but also the storage and network infrastructure on which GKE itself runs—enables Actian to take advantage of CPUs with larger on-chip cache and faster RAM, which it has been designed to leverage. This more innovative cloud infrastructure makes it easier to access more of the processing power than in other cloud offerings.

The containerized architecture that GKE is managing is important here: containers are largely agnostic when it comes to the underlying machine hardware, which means that a containerized deployment of Actian can easily take advantage of new hardware as it becomes available in Google Cloud. Conversely, an environment where Actian—or Snowflake or any other cloud data warehouse—is constructed without the benefits of containerization, will be more tightly tied to the architecture of the VM upon which the cluster components are running. Because an organization can easily subscribe to Google Cloud services that are configured to extract the highest performance achievable from the most current CPU and memory technologies, Google Cloud and GKE make it significantly easier to build a solution that will enable Actian to operate at peak performance.

Given the more optimal infrastructure provided by GKE in Google Cloud, it’s not surprising that provisional benchmarks conducted by Actian show Actian on Google Cloud delivering a 20% throughput improvement on average when compared to alternative cloud platforms. For those organizations looking for the data warehouse that delivers highest performance and throughput from the cloud, Actian on GKE presents a clear winning choice.

More Advantages Arising from Running Actian on Google Cloud

Does Actian gain other advantages from running on GKE? Yes, but we’ll flesh those out in part 2 of this blog. For teasers, though, let me say this: Anthos and security. We’ll say more about each in future discussions about Google Cloud and Actian. For now though, suffice it to say that there is an early adaptor program for Actian on Google that will enable you to kick Actian’s tires yourself and see how it can meet your pressing operational analytical workload needs more effectively than ever.

Give it a shot and see if you are moved by the power of Actian on GKE.

From Databases to Knowledge Management Systems for Data Power

Databases

Tools and Technology to Get Value From Your Data

Collaboration

Knowledge Management Systems

Moving the “Normal” – From Data-Driven to Data-Powered

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The Best Cloud Data Strategy for Value

Which Cloud Makes Sense?

Cloud Growth for the Customer

Improving Performance, Costs, and Value

Building Knowledge for Decisions

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From Data Management to Decision-Making for Empowerment

The Four Stages of Decision-Making

Delivering Services and Products

Role of Data and Data Relationships

Data Capture to Understanding Experience

Consistency for Success

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Data Analytics is a Core Element Creating Business Value

Valued Assets

People-Driven

A Team Sport

Experience and Data

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Data Governance, a Reinforced Priority for Companies in 2021?

Starting Data Governance

Meeting Today’s Data Governance Challenges

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IoT in Manufacturing: Why Your Enterprise Needs a Data Catalog

What is IoT?

IoT Use Cases in Manufacturing Industries

Predictive Maintenance

Asset Tracking

Driving Innovation

Data Catalog: One Way to Rule Data for any Manufacturer

Actian Data Intelligence Platform Data Catalog x IoT: The Perfect Match

Success Story in the Manufacturing Industry

Actian Data Intelligence Platform’s Unique Features for Manufacturing Companies

Are You Interested in Unlocking Data Access for Your Company?

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Leveraging IoT and Industry Data for Managing Experiences and Decisions

Leveraging Data

Data Visualization, Interoperability, and Connectivity

Data Decisions for Continuity

Managing Experience and Decision

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Approaching Holistic Data Models for Organizational Efficiency