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

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 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.

About Actian Corporation

Data Analytics

The Need for Speed in Data Analysis

Emma McGrattan

December 16, 2020

Why Performance Matters

My very first sales call to an analytics prospect was with a customer using an OLTP database for reporting. Some of their reports took hours to run and one particularly challenging report typically ran for more than thirteen hours. Our performance benchmarking had shown that for analytics workloads, Actian would be up to 100 times faster than an OLTP database and I was anxious to see if that would hold true in the real world. Indeed, we were able to take the 13-hour report and complete it in less than twenty seconds.

It felt like a punch to the gut when the customer informed us that speed did not matter to him because that particular report was run on Saturday nights and so long as it was completed by Monday at 9 am, it didn’t matter how long it took to run. That is when I first realized that for performance to really matter, it has to deliver business value.

For this prospect, that weekly report was parceled out to a team of representatives who spent their week working off aging information and wasting time visiting accounts where the status had changed. By taking advantage of the performance that Actian could deliver and switching to real-time access to their customer information, they were able to eliminate wasted customer visits and grow revenue by 12% in the first year after adopting Actian.

Fast forward to today, data sets are exponentially more extensive, and many industries are reliant on fast analytics performance to achieve improvements in business outcomes, remain competitive, and add value to their customers. Here are a few examples of how Actian’s continued performance gains help its clients improve their data analytics capabilities.

Performance Matters in Healthcare

Equian uses Actian for healthcare claims processing because it enables them to process claims much faster than any other technology they have evaluated. Equian is paid a fee for each claim they process, and the more claims they process, the more money they make. The fees they receive for processing claims are dependent on the age of the claim, so this is a prime example of an instance where Actian’s performance delivers directly to the bottom line. But it isn’t always so obvious.

Performance Matters for Insurance Providers

The UK’s largest motoring organization, The AA, uses Actian technology to quickly generate accurate car insurance quotes. They provide quotes for comparative car insurance websites, and the faster they can return a quote, the more prominent position they get on the page of competitive quotes. Generating the quote quickly is obviously important, but generating an accurate quote, enriched by myriad other data sources, is just as important to the insurance underwriters – they need to be sure that the quote accurately represents the insurable risk. For example, if the car that you’re requesting the quote for has been in a number of accidents, that needs to be factored into the quote, as does the driver’s driving record and the demographics of the neighborhood where the car will be parked; it’s only when all of this information has been factored in that the quote can be provided to the customer and typically the AA wants to complete the entire quoting process in a matter of a second or two.

Performance Matters in Financial Services

In the financial services world, time is money; shaving a millisecond off the time it takes to analyze data can mean millions made or lost. Refinitiv, now part of the London Stock Exchange, built their Eikon analytics platform on Actian technology, and they set an SLA of 20ms for completing analytics queries. Refinitiv’s analytics platform provides its customers with the data and analysis needed to make trading decisions faster than customers of their competitors. In the rapidly changing financial markets, time is money. As a result, people put tremendous value on a performance advantage.

Performance You Can Try for Yourself

The recently released GigaOm Cloud Data Warehouse benchmark report clearly demonstrates the Actian Data Platform, formerly Avalanche, performance superiority over the competition with a typical decision support workload comprised of a mix of complex queries, representative of the types of queries we encounter in every customer, no matter what industry they are in.

Scalable to Meet Your Performance Demands

While many of our competitors charge a premium for the type of performance that Actian Data Platform delivers, we pride ourselves on the value our platform delivers. Not only do we deliver the best value per query, but we also keep your costs low by enabling you to scale the compute environment to meet your ever-changing business needs. Additionally, we identify when the system has been inactive for a specified period and will shut it down to stop the meter from running. Gone are the days of purchasing systems to match your peak workload and hoping that your gut feeling regarding anticipated growth was accurate. You can grow and shrink your Actian Data Platform environment to meet the needs of the business.

actian more cost-effective than snowflake

I’m very proud of the technology that my team has built and of the results that the researchers at GigaOm were able to achieve when benchmarking Actian Data Platform relative to our competitors. I’d love to hear your stories of how performance translated into business value for you.

About Emma McGrattan

Emma McGrattan is CTO at Actian, leading global R&D in high-performance analytics, data management, and integration. With over two decades at Actian, Emma holds multiple patents in data technologies and has been instrumental in driving innovation for mission-critical applications. She is a recognized authority, frequently speaking at industry conferences like Strata Data, and she's published technical papers on modern analytics. In her Actian blog posts, Emma tackles performance optimization, hybrid cloud architectures, and advanced analytics strategies. Explore her top articles to unlock data-driven success.

Data Analytics

Innovation and the Power of Data

Actian Corporation

December 15, 2020

What is Innovation?

Innovation is doing something new that does not currently exist or something new that improves efficiency, effectiveness, cost, and/or creates a competitive advantage. A new method, process, product, or service can be considered an innovation. This also includes the integration of different markets with the utilization of a new product that services both markets. An example of this is the cellular phone market, which has leveraged and integrated other markets such as photography and electronic payments on one mobile device. These innovations have created a new mobile device market, making the cellular phone market almost obsolete.

There are many other examples of innovation in many start-up companies. Some companies have leveraged cloud computing to deliver products or services without having traditional on-premises IT infrastructure. Others like Uber, Getaround, and Turo have innovative business models in use such that they do not own any of the major assets required to run their business, such as vehicles.

The opportunities for innovation are everywhere and in everything. The caution is that a consumer may not need every innovation. Someone can have the world’s most creative idea for innovation of something, yet it may never be adopted. There are also barriers to entry into various markets that can be obstructed by many constraints and challenges.

Consumers and businesses need specific product and service benefits to accomplish their outcomes and may want products and services that are not necessary. Needs and wants are fulfilled through market offerings in which constraints are managed.

Maturity and Innovation

It is hard for an organization to mature and innovate if the organization uses most of its resources for firefighting, including data analytics. The organization has to have some stability in the markets that it serves. The products and services also have to provide the basic needs and wants of its customers; otherwise, the company may end up just trying to keep up with its competitors instead of getting ahead of its competitors.

To innovate, organizations must have strategic intent on innovation and invest budget for innovation-related activities, including the data acquisition and analytics needed. The challenge is, where should the organization invest? The question is, should it be in new products, services, processes, or in other areas? The answer cannot be found just by simply guessing.

Organizations should always do strength, weakness, opportunity, and threat (SWOT) analysis at strategic, tactical, and operational levels. It is not good enough to only do this analysis at a strategic level in the organization. It is also a mistake to do this analysis at any level without external market data to back up findings.

Organizations may find that they need to evolve existing products, solutions, or practices; or completely throw out and transform how they currently do things or deliver products to their customers. Survival of the fittest can be defined by the ability to innovate rapidly.

Data Trending to the Next Innovation

An enterprise data-driven strategy can help with next-generation innovation in your organization. Collected data can help with accurate decision making for innovation initiatives. Trends can be easily identified, and actionable decisions can be made.

Here are some of the steps:

Collect and pay attention to organizational metrics in all areas for continuous improvement.
Define market spaces and adjacent market spaces – The adjacent spaces give insight into possible market integrations and innovation possibilities.
Categorize the data based on market and services, then further based on each capability in the service or product.
Measure, collect, and integrate the data with the intent to innovate, not just for organizational health.

Here are some questions to ask that need data support:

What is the difference between the benefit that a consumer gains for using a product or service and the price?
Can the markets that the organization serves be integrated and simplified for the consumer or the organization?

And here are data decisions:

Anticipate demand for a new product or service.
Set measure success criteria.
Look at data trends across the organization.
Note the constraints to performance, efficiency, effectiveness in each area.
Create service-oriented and customer-oriented integrated data maps to other supporting data.
Tie the trending data into organizational value knowledge for strategic, tactical, and operational innovations, including budgeting decisions.

Empowering Data for Innovation

The most reliable way to make innovation decisions is to base the decision on enterprise-wide integrated data. 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. Bringing together internal operational data, customer requirements, and external market data innovation investments can be funded and assessed more effectively.

Meta description: Actian DataConnect makes it easy to connect operational data from any data source and transform it to facilitate practical data analysis to drive innovation.

About Actian Corporation

Data Integration

System Thinking to Run, Grow, and Transform the Business

Actian Corporation

December 11, 2020

Systems Thinking and the Organization

There are many ways to describe system thinking. We can say system thinking is a way of viewing or thinking about a system in terms of structure, patterns, cycles, and data exchanges. System thinking can also be thought of as a way of integrating a person into the organizational flows of the system or the way the organization works to make decisions. Then there is system-1 and system-2 thinking. System 1 is based on one’s experiences. System 2 is based on analytics or data. There have been studies to categorize system-1 as fast thinking and system-2 as slow thinking.

Organizations usually have an intended strategy that is budgeted to run, grow, or transform the business. The Run the Business strategy usually is aimed at fixing issues, customer wishes, and keeping the light on. The Grow strategy is usually aimed at creating competitive capabilities. The transformation of the business strategy is aimed at innovation.

The strategy is transformed into tactics and tactics into operations. Another model to consider is that strategy transforms into missions, missions to goals, and goals to objectives. Each organizational structure and person in the organization supports the objectives with projects and daily activities.

Organizational structure influences the behavior of the organization. There are different types of structures, such as hierarchical, which can include functional, divisional, and horizontal structures. There are matrix organizational structures and other types. Each structure supports behavior, data exchanges, and communication of the people in the particular roles in the structure.

Within these structures are many complexities and challenges. There is a value chain of interactions, linking the top strategy to the bottom objectives. These objectives to meet the goals have critical success factors. Each critical success factor is measured based on a system 1 approach, or a system 2 approach, or a combination of system 1 and 2.

System 1, expert opinion can be good enough for some objectives. System 2, a metric can be good enough for some objectives. The combination of both is always best.

Enablement of Decision and Data Exchanges

Data is exchanged between people and technology for accomplishing work efforts. The person or automated system transforms data for consumption. Data effectiveness strives to be done so that there are no data silos that could affect the health of the system. Work efforts across the organization need to be focused on the organization’s strategy. Data management tools should support the elimination of data silos, such as supporting the integration of data in the cloud and on-premises solutions.

Decisions are enabled with the transformation of data to information, information to knowledge, and knowledge to decisions. A decision can be made without data, such as the discussion on system-1 thinking. A decision can be made with data alone, such as with system-2 thinking. Enterprise data systems help connect the data value chains between the organization structure types with people for decision support.

Many organizations have been concerned with what is called the “graying” of the IT community—in other words, losing key decision capabilities that are in the minds of the employee who has years of experience in a particular area or from working for the company. Many of these people are sometimes hired back as consultants because of the knowledge they have. The empowerment of a data-driven enterprise system helps collect people’s knowledge and discovered knowledge for the benefit and system thinking of the organization. Important decision-making data does not get lost.

Run, Grow and Transform the Business

Day-to-day analytics captured for running the business can help experts in key customer-facing functions or areas make faster and more accurate decisions. Enterprise data analytics can be used to discover business constraints and change the business trajectory for continuous growth. Business transformation requires knowledge from a system 1 and system 2 perspective.

Enterprise data to support an overall service knowledge management system in your organization for agile, quick, empowered, trusted, and high performing decisions can only be enabled with technology. Each function and many people in the same functional structure in the organization uses different tools and processes to do their job within their service value chains. The organization’s data is the organization’s data and should be leveraged appropriately across the enterprise. To do this effectively requires the collection of analytical data from as many sources as possible, then transforming this data to appropriate information and knowledge for decision support across the organization. Using a solution like Actian DataConnect enables quick and easy design, deployment, and management across on-premises, cloud, and/or hybrid environments.

An organization that functions as a one-team with many unique, specialized capabilities and responsibilities should be enabled with analytical system 2 data to support their system 1 experience and expertise for high-performing organizational decision support. Customer insights, organizational performance, and many other valuable decisions can be made for effective and efficient running, growing, or transforming the business with a system thinking approach. Actian DataConnect enables rapid onboarding and delivers rapid time to value, and allows you to connect to virtually any data source, format, location, using any protocol. Learn more here.

About Actian Corporation

Data Intelligence

Marquez: The Metadata Discovery Solution at WeWork

Actian Corporation

December 10, 2020

Created in 2010, WeWork is a global office and workspace leasing company. Their objective is to provide space for teams of any size including startups, SMEs, and major corporations, to collaborate. To achieve this, what WeWork provides can be broken down into three different categories:

Space: To ensure companies with optimal space, WeWork must provide the appropriate infrastructure, which consists of booking rooms for interviews / one on ones or even entire buildings for huge corporations. They also must make sure they are equipped with the appropriate facilities such as kitchens for lunch and coffee breaks, bathrooms, etc.
Community: Via WeWork’s internal application, the firm enables WeWork members to connect with one another, whether it’s local within their own WeWork space, or globally. For example, if a company is in need of feedback for a project from specific job titles (such as a developer or UX designer), they can directly ask for feedback and suggestions via the application to any member, regardless of their location.
Services: WeWork also provides their members with full IT services if there are any problems as well as other services such as payroll services, utility services, etc

In 2020, WeWork represents:

More than 600,000 memberships.
Locations in 127 cities in 33 different countries.
850 offices worldwide.
Generated $1.82 billion in revenue.

It is clear that WeWork works with all sorts of data from their staff and customers, whether that be individuals or companies. The huge firm was therefore in need of a platform where their data experts could view, collect, aggregate, and visualize their data ecosystem’s metadata. This was resolved by the creation of Marquez.

This article will focus on WeWork’s implementation of Marquez mainly through free & accessible documentation provided on various websites, to illustrate the importance of having an enterprise-wide metadata platform in order to truly become data-driven.

Why Manage and Utilize Metadata?

In his talk “A Metadata Service for Data Abstraction, Data Lineage & Event-Based Triggers” at the Data Council back in 2018, Willy Lulciuc, Software Engineer for the Marquez project at WeWork explained that metadata is crucial for three reasons:

Ensuring Data Quality: When data has no context, it is hard for data citizens to trust their data assets: are there fields missing? Is the documentation up to date? Who is the data owner and are they still the owner? These questions are answered through the use of metadata.
Understanding Data Lineage: Knowing your data’s origins and transformations are key to being able to truly know what stages your data went through over time.
Democratization of Datasets: According to Willy Lulciuc, democratizing data in the enterprise is critical! Having a central portal or UI available for users to be able to search for and explore their datasets is one of the most important ways companies can truly create a self-service data culture.

To sum up: creating a healthy data ecosystem. Willy explains that being able to manage and utilize metadata creates a sustainable data culture where individuals no longer need to ask for help to find and work with the data they need. In his slide, he goes through three different categories that make up a healthy data ecosystem:

Being a self service ecosystem, where data and business users have the possibility to discover the data and metadata they need, and explore the enterprise’s data assets when they don’t know exactly what they are searching for. Providing data with context, gives the ability to all users and data citizens to effectively work on their data use cases.
Being self-sufficient by enabling data users the freedom to experiment with their datasets as well as having the flexibility to work on every aspect of their datasets whether they input or output datasets for example.
And finally, instead of relying on certain individuals or groups, a healthy data ecosystem allows for all employees to be accountable for their own data. Each user has the responsibility to know their data, their costs (is this data producing enough value?) as well as keeping track of their data’s documentation in order to build trust around their datasets.

Room Booking Pipeline Before

As mentioned above, utilizing metadata is crucial for data users to be able to find the data they need. In his presentation, Willy shared a real situation to prove metadata is essential: WeWork’s data pipeline for booking a room.

For a “WeWorker”, the steps are as follows:

Find a location (the example was a building complex in San Francisco).
Choose the appropriate room size (usually split into the number of attendees – in this case they chose a room that could greet 1 – 4 people).
Choose the date for when the booking will take place.
Decide on the time slot the room is booked for as well as the duration of the meeting.
Confirm the booking.

Now that we have an example of how their booking pipeline works, Willy proceeds to demonstrate how a typical data team would operate when wanting to pull out data on WeWork’s bookings. In this case, the example exercise was to find the building that held the most room bookings, and extract that data to send over to management. The steps he stated were the following:

Read the room bookings from a data source (usually unknown).
Sum up all of the room bookings and return the top locations.
Once the top location is calculated, the next step is to write it into some output data source.
Run the job once a hour.
Process the data through .csv files and store it somewhere.

However, Willy stated that even though these steps seem like it’s going to be good enough, usually, there are problems that occur. He goes over three types of issues during the job process:

Where can I find the job input’s dataset?
Does the dataset have an owner? Who is it?
How often is the dataset updated?

Most of these questions are difficult to answer and jobs end up failing. Without being sure and trusting this information, it can be hard to present numbers to management. These sorts of problems and issues are what made WeWork develop Marquez.

What is Marquez?

Willy defines the platform as an “open-sourced solution for the aggregation, collection, and visualization of metadata of [WeWork’s] data ecosystem”. Indeed, Marquez is a modular system and was designed as a highly scalable, highly extensible platform-agnostic solution for metadata management. It consists of the following components:

Metadata Repository: Stores all job and dataset metadata, including a complete history of job runs and job-level statistics (i.e. total runs, average runtimes, success/failures, etc).
Metadata API: RESTful API enabling a diverse set of clients to begin collecting metadata around dataset production and consumption.
Metadata UI: Used for dataset discovery, connecting multiple datasets and exploring their dependency graph.

Marquez’s Design

Marquez provides language-specific clients that implement the Metadata API. This enables a diverse set of data processing applications to build a metadata collection. In their initial release, they provided support for both Java and Python.

The Metadata API extracts information around the production and consumption of datasets. It’s a stateless layer responsible for specifying both metadata persistence and aggregation. The API allows clients to collect and/or obtain dataset information to/from the Metadata Repository.

Metadata needs to be collected, organized, and stored in a way to allow for rich exploratory queries via the Metadata UI. The Metadata Repository serves as a catalog of dataset information encapsulated and cleanly abstracted away by the Metadata API.

According to Willy, what makes a very strong data ecosystem is the ability to search for information and datasets. Datasets in Marquez are indexed and ranked through the use of a search engine based keyword or phrase as well as the documentation of a dataset: the more a dataset has context, the more it is likely to appear first in the search results. Examples of a dataset’s documentation is its description, owner, schema, tag, etc.

You can see more detail of Marquez’s data model in the presentation itself here: https://www.youtube.com/watch?v=dRaRKob-lRQ&ab_channel=DataCouncil

The Future of Data Management at WeWork

Two years after the project, Marquez has proven to be a big help for the giant leasing firm. They’re long term roadmap is to solely focus on their solution’s UI, by including more visualizations and graphical representations in order to provide simpler and more fun ways for users to interact with their data.

They also provide various online communities via their Github page, as well as groups on LinkedIn for those who are interested in Marquez to ask questions, get advice or even report issues on the current Marquez version.

Sources

A Metadata Service for Data Abstraction, Data Lineage & Event-Based Triggers, WeWork. Youtube: https://www.youtube.com/watch?v=dRaRKob-lRQ&ab_channel=DataCouncil

29 Stunning WeWork Statistics – The New Era Of Coworking, TechJury.com: https://techjury.net/blog/wework-statistics/

Marquez: Collect, aggregate, and visualize a data ecosystem’s metadata, https://marquezproject.github.io/marquez/

Marquez: An Open Source Metadata Service for ML Platforms Willy Lulciuc

About Actian Corporation

Data Analytics

Turning Data Measurements, Metrics, and Performance Indicators into Results

Actian Corporation

December 1, 2020

Data is everywhere. There is data inside our organizations and outside. Some data is in the form of measurements. Measurements can be descriptive, predictive, or for diagnostic reasons. In either case, we measure to make decisions based on the quantity and quality of something. Measurements can give us actionable operational, tactical, and strategic metrics for influencing human and artificial decisions. Organizational experts at every level, besides just using their opinionated industry expertise, also rely on data to enhance their decisions.

This leads to challenges with what data to collect and how to use it effectively for specific outcomes. Collecting data is the easy part; integrating and orchestrating data collaboration across a value chain is the hard part. Every functional unit uses different tools, automation, and manual interfaces for performing their job across a chain of interconnected activities for producing a service or product for the organization. In many cases, data analytics are siloed within a function or require manual people-oriented exchanges between functions in the organization. Data needs to be integrated, without being constrained by organizational boundaries for people, processes, and technologies to effectively harmonize.

If data, information, and knowledge interchanges are not done with strategic intent, we risk ineffective organizational collaboration and poor use of our assets. This can cause challenges with decision-making in an organization at every level. Data and information do not move in one direction across an organization’s value chain of activities, but there should also be a feedback mechanism through the use of data exchanges to help organizations be more agile and precise in how they use and interpret information for strategic, tactical, and operational intents.

Data Transformation

Data transforms into information, information into knowledge, and knowledge into decisions. This is the DIKW model. Information systems need integrations at various levels, across various tools and technologies to enable informed, precise decisions across the organization.

When transforming data, consideration needs to be given to how to transform measurements into metrics and metrics into key performance indicators. Key performance indicators (KPIs) take data metrics and help an organization focus on what matters the most. The KPIs should be related to critical success factors (CSFs) for each organizational objective or project. Each organizational objective should relate to the strategic intent and investment strategy of the organization. As these data elements are connected across the organization, visibility from strategy, and tactics to operations can be achieved.

Fully integrated and automated heterogeneous systems expedite data exchanges, workflows, and decisions for people, including artificial intelligence-enabled technologies. This helps all business processes perform better and improves forward and rearward visibility for agility.

Data and Business Strategy, Tactics and Operations

Some business strategy concerns are usually related to decisions that affect the return on investment (ROI), value of investment (VOI), and total cost of ownership (TCO) of the organization’s capabilities and resources for delivering and supporting the portfolio of products and services to the market. These financial concerns affect the performance of the entire organization, including influencing the budget for innovation, providing customer-requested enhancements, customer fixes, and competitive features. Each of these areas has strategic intent and receives a portion of the budget for execution.

Most organizations decide business strategy investments for a year and then review their decisions at year-end for modification of the next year’s decisions. Although the organization may have a multi-year vision and mission established, this is usually the case, especially for managing the budget investments.

Using agile tactical and operational feedback across their service value chains can modify or shift the budget spend quickly based on data feedback integrated into their business systems, quickly affecting the top line and the bottom line in their organizations. Daily monitoring, watching trends, environmental issues, the success of tactics, and production of operations across the organization is enabled with organizational-wide integrated data, information, and knowledge.

Improving Business Outcomes

In today’s highly competitive environment, decisions must be made quickly to improve the long-term viability of the business. Information superiority for competitiveness is a necessity. Actian DataConnect simplifies data integration across the organization. Offering them the strategic ability to answer the following questions and more using data-rich metrics instead of just people expertise.

Where are we now?
Where do we want to be?
How do we get there?
Was the change effective?
How do we measure our progress?
Are resources and capabilities being used effectively and efficiently?
Are there any constraints?
Is the current strategy and tactics effective?
Are operations working effectively, efficiently, and economically?

Technology, data integration, people collaboration, and communication go hand in hand. To improve results and overall business outcomes, organizations must work as one, sharing data and information seamlessly to support strategic intent, programs, projects, and overall successful operational decisions. Enterprise data integration improves the business overall, including customer expectations and experiences.

Actian DataConnect provides the technology platform you need to achieve your Enterprise Data Integration objectives. Through a highly scalable hybrid deployment model, robust integration design capabilities, and automated deployment capabilities – DataConnect can help you deliver more effectively and faster than other solutions. To learn more, visit www.actian.com/data-integration/dataconnect/.

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?

About Actian Corporation

Subscribe to the Actian Blog

Subscribe

Thank you for subscribing to the Actian Blog!

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

About Actian Corporation

Related Tags

Subscribe to the Actian Blog

Subscribe

Thank you for subscribing to the Actian Blog!

Ready to Get Started?

Approaching Holistic Data Models for Organizational Efficiency

Why Model Anything?

Service Models

Financial Models

Enterprise Data Models

About Actian Corporation

Related Tags

Subscribe to the Actian Blog

Subscribe

Thank you for subscribing to the Actian Blog!

Ready to Get Started?

How Has Data Impacted the Manufacturing Industry?

What are the Data Challenges in the Manufacturing Industry?

The Benefits of Data for the Manufacturing Industry

Are You Interested in Unlocking Data Access for Your Company?

About Actian Corporation

Subscribe to the Actian Blog

Subscribe

Thank you for subscribing to the Actian Blog!

Delivering Real-Time Customer 360 Insights Faster and Easier

Building a Broader View

A Peek Under the Hood

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

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

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

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

The Perfect Umbrella for the Perfect Storm

About Actian Corporation

Related Tags

Subscribe to the Actian Blog

Subscribe

Thank you for subscribing to the Actian Blog!

Ready to Get Started?

Four Data Analytics Predictions for 2021

About Actian Corporation

Related Tags

Subscribe to the Actian Blog

Subscribe

Thank you for subscribing to the Actian Blog!

Ready to Get Started?

Google GKE Containers: Technology for Cloud Data Warehouses

Actian on Google Kubernetes Engine

Making the Most of the Google Cloud Infrastructure

More Advantages Arising from Running Actian on Google Cloud

About Actian Corporation

Related Tags

Subscribe to the Actian Blog

Subscribe

Thank you for subscribing to the Actian Blog!

Ready to Get Started?

The Need for Speed in Data Analysis

Why Performance Matters

Performance Matters in Healthcare

Performance Matters for Insurance Providers