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Mar 5 2024

How to Modernize Your Data Management Strategy in the Auto Industry

In the data-driven automotive industry, a modern data management strategy is needed to oversee and drive data usage to improve operations, spark innovation, meet customer demand for features and services, create designs and safety features, and inform decisions. Keeping the strategy up to date ensures it meets your current data needs and aligns with business priorities.

With so many data sources now available—and new ones constantly emerging—in addition to data volumes growing rapidly, companies in the automotive industry need a data management strategy that supports this modern reality. A vast rangxe of data is available, including sensor, telematics, and customer data, and it all needs to be integrated and made easily accessible to analysts, engineers, marketers, and others.

Go Beyond Traditional Data Management Approaches

In today’s fast-changing data management environment, the ability to understand and solve data challenges is essential to becoming a true data-driven automotive company. As AWS explains, a robust strategy can help solve data management challenges, improve customer experience and loyalty, build future-proof apps, and deliver other benefits.

By contrast, not having a strategy or taking an outdated approach to data can have negative consequences. “When companies have ineffective strategies, they handle daily tasks less effectively,” according to Dataversity. “Data and data processes get duplicated between different departments, and data management gaps continue to exist.”

A modern data management strategy must go beyond traditional approaches to address present-day needs such as scalability, real-time data processing, building data pipelines to new sources, and integrating diverse data. The strategy should be supported by technology that delivers the capabilities your business needs, such as managing complex and large volumes of data.

Plan to Make Data Readily Available

Your data strategy should cover the variety and complexity of your data, how the data will be brought together, and how the integrated data will be shared in real-time, if necessary, with everyone who needs it. The strategy must ultimately ensure a unified, comprehensive view of the data in order to provide accurate and trusted insights.

Making data readily available with proper governance is essential to fostering a data-driven culture, enabling informed decision-making, and designing vehicles that meet customer wants and needs. The data can also help you predict market changes, gain insights into your supply chains, and better understand business operations.

As best practices and technologies for data management continue to evolve, your strategy and data management tools should also advance to ensure you’re able to optimize all of your data. A modern data management strategy designed to meet your business and IT needs can help you be better prepared for the future of the automotive industry.

Align Your Data Strategy With Business Goals

Your data strategy should support current business priorities, such as meeting environmental, sustainability, and governance (ESG) mandates. As the automotive industry uses data for innovations such as autonomous driving vehicles and intelligent manufacturing processes, there is also a growing pressure to meet ESG goals.

As a result of ESG and other business objectives, your data strategy must address multiple business needs:

Deliver speed and performance to process and analyze data quickly for timely insights.
Offer scalability to ingest and manage growing data volumes without compromising performance.
Integrate technology to ensure data flows seamlessly to apps, platforms, and other sources and locations.
Ensure governance so data follows established processes for security, compliance, and usage.
Build trust in the data so all stakeholders have confidence in the insights for informed decision-making.
Improve sustainability by using data to lower your environmental impact and decrease energy consumption.
Future-proof your strategy with an approach that gives you the agility to meet shifting or new priorities.

The road ahead for the automotive industry requires businesses to continually explore new use cases for data to stay ahead of changing market dynamics, customer expectations, and compliance requirements. Your ability to innovate, accelerate growth, and maintain competitiveness demands a data strategy that reflects your current and future needs.

How Actian Can Support Your Strategy

Modernizing your data management strategy is essential to meet business and IT needs, achieve ESG mandates, and leverage the full value of your data. Actian can help. We have the expertise to help you build a customized strategy for your data, and we have the platform to make data easy to connect, manage, and analyze.

The Actian Data Platform is more than a tool. It’s a solution that enables you to navigate the complex data landscape in the automotive industry. The scalable platform can handle large-scale data processing to quickly deliver answers—which is key in an industry where decisions can have far-reaching implications—without sacrificing performance.

With Actian, you can meet your objectives faster, ensuring your future is data-driven, sustainable, clear, and more attainable. It’s why more than 10,000 businesses trust Actian with their data.

The post How to Modernize Your Data Management Strategy in the Auto Industry appeared first on Actian.

How to Develop a Multi-Cloud Approach to Data Management

A recent 451 Research survey found that an astonishing 98% of companies are using more than one cloud provider. Two-thirds of organizations use services from 2 or 3 public cloud providers and nearly one third of organizations use four or more providers. Using a multi-cloud strategy involves using the services of multiple cloud providers simultaneously. It’s the dominant data management strategy for most organizations.

Top Multi-Cloud Advantages

There’s a long list of reasons why organizations choose to adopt a multi-cloud approach versus just being tied to a single provider. Here’s a look at some of the top reasons.

You Can Match the Right Cloud to the Right Job

The features and capabilities of cloud vendors vary greatly, so using a multi-cloud approach can let you select the best providers for your specific workload requirements. Differences in services for analytics, machine learning, big data, transactions, enterprise applications, and more are factors to consider when deciding where to run in the cloud. Product integrations, security, compliance, development tools, management tools, and geographic locations unique to a cloud provider may also influence your choice.

You Can Save Money

Pricing between providers can differ significantly. These are just a few examples of what you need to take into account when comparing costs:
Providers price the same services differently
Resources such as compute, memory, storage, and networks have different configurations and pricing tiers
The geographic location of a data center can lead to differences in the cost of cloud provider services
Discounts for reserved instances, spot instances, and committed use can save you dollars depending on your usage patterns
Data transfer costs between regions, data centers, and the internet can add up quickly and you should factor these into your costs
The cost of support services can also impact overall expenses

You Can Enhance Business Continuity

Multi-cloud strategies can enhance business continuity so your cloud processing can resume quickly in the face of disruptions. Below are some aspects of multi-cloud business continuity:
There’s no single point of failure
Geographic redundancy enhances resilience against adverse regional events
Cloud provider diversification mitigates the impact of vendor-specific issues such as a service outage or a security breach. Traffic can be redirected to another provider to avoid service disruption.
Data storage redundancy and backup across clouds can help prevent data loss and data corruption
Redundant network connectivity across multiple clouds can prevent network-related disruptions

You Can Avoid Vendor Lock-In

Using multiple cloud providers prevents organizations from being tied to a single provider. This avoids vendor lock-in, giving organizations more freedom to switch providers or negotiate better terms as needed.

You Can Strengthen Your Compliance

Different cloud providers may offer different compliance certifications and different geographic locations for where data is stored. A choice of options helps improve compliance with industry standards and regulations as well as compliance with data residency and data sovereignty-specific regulations.

Some organizations choose to operate a hybrid cloud environment with capabilities stratified across multiple clouds, private and public. Sensitive data applications may be on a private cloud where an organization has more control over the deployment infrastructure.

Actian in a Multi-Cloud World

Despite these advantages, it’s essential for organizations to carefully plan and manage their multi-cloud data management strategy to ensure seamless integration, efficient resource utilization, and strong security.

The Actian Data Platform is a platform that meets multi-cloud data management requirements with features such as a universal data fabric and built-in data integration tools to process and transform data across clouds. You will also benefit from cloud economics, paying only for what you use, having the ability for the service to shut down or go to sleep after a pre-defined period of inactivity, and scheduling starting, stopping, and scaling the environment to optimize uptime and cost. Security such as data plane network isolation, industry-grade encryption, including at-rest and in-flight, IP allow lists, and modern access controls handle the complexities of multi-cloud security.

The post How to Develop a Multi-Cloud Approach to Data Management appeared first on Actian.

Top Capabilities to Look for in Database Management Tools

As businesses continue to tap into ever-expanding data sources and integrate growing volumes of data, they need a solid data management strategy that keeps pace with their needs. Similarly, they need database management tools that meet their current and emerging data requirements.

The various tools can serve different user groups, including database administrators (DBAs), business users, data analysts, and data scientists. They can serve a range of uses too, such as allowing organizations to integrate, store, and use their data, while following governance policies and best practices. The tools can be grouped into categories based on their role, capabilities, or proprietary status.

For example, one category is open-source tools, such as PostgreSQL or pgAdmin. Another category is tools that manage an SQL infrastructure, such as Microsoft’s SQL Server Management Studio, while another is tools that manage extract, transform, and load (ETL) and extract, load, and transform (ELT) processes, such as those natively available from Actian.

Using a broad description, database management tools can ultimately include any tool that touches the data. This covers any tool that moves, ingests, or transforms data, or performs business intelligence or data analytics.

Data Management Tools for Modern Use Cases

Today’s data users require tools that meet a variety of needs. Some of the more common needs that are foundational to optimizing data and necessitate modern capabilities include:

Data management: This administrative and governance process allows you to acquire, validate, store, protect, and process data.
Data integration: Integration is the strategic practice of bringing together internal and external data from disparate sources into a unified platform.
Data migration: This entails moving data from its current or storage location to a new location, such as moving data between apps or from on-premises to the cloud.
Data transformation: Transformative processes change data from one format or structure into another for usage and ensure it’s cleansed, validated, and properly formatted.
Data modeling: Modeling encompasses creating conceptual, logical, and physical representations of data to ensure coherence, integrity, and efficiency in data management and utilization.
Data governance: Effective governance covers the policies, processes, and roles used to ensure data security, integrity, quality, and availability in a controlled, responsible way.
Data replication: Replicating data is the process of creating and storing multiple copies of data to ensure availability and protect the database against failures.
Data visualization: Visualizing data turns it into patterns and visual stories to show insights quickly and make them easily understandable.
Data analytics and business intelligence: These are the comprehensive and sophisticated processes that turn data into actionable insights.

It’s important to realize that needs can change over time as business priorities, data usage, and technologies evolve. That means a cutting-edge tool from 2020, for example, that offered new capabilities and reduced time to value may already be outdated by 2024. When using an existing tool, it’s important to implement new versions and upgrades as they become available.

You also want to ensure you continue to see a strong return on investment in your tools. If you’re not, it may make more sense from a productivity and cost perspective to switch to a new tool that better meets your needs.

Ease-of-Use and Integration Are Key

The mark of a good database management tool—and a good data platform—is the ability to ensure data is easy-to-use and readily accessible to everyone in the organization who needs it. Tools that make data processes, including analytics and business intelligence, more ubiquitous offer a much-needed benefit to data-driven organizations that want to encourage data usage for everyone, regardless of their skill level.

All database management tools should enable a broad set of users—allowing them to utilize data without relying on IT help. Another consideration is how well a tool integrates with your existing database, data platform, or data analytics ecosystem.

Many database management tool vendors and independent software vendors (ISVs) may have 20 to 30 developers and engineers on staff. These companies may provide only a single tool. Granted, that tool is probably very good at what it does, with the vendor offering professional services and various features for it. The downside is that the tool is not natively part of a data platform or larger data ecosystem, so integration is a must.

By contrast, tools that are provided by the database or platform vendor ensure seamless integration and streamline the number of vendors that are being used. You also want to use tools from vendors that regularly offer updates and new releases to deliver new or enhanced capabilities.

If you have a single data platform that offers the tools and interfaces you need, you can mitigate the potential friction that oftentimes exists when several different vendor technologies are brought together, but don’t easily integrate or share data. There’s also the danger of a small company going out of business and being unable to provide ongoing support, which is why using tools from large, established vendors can be a plus.

Expanding Data Management Use Cases

The goal of database management tools is to solve data problems and simplify data management, ideally with high performance and at a favorable cost. Some database management tools can perform several tasks by offering multiple capabilities, such as enabling data integration and data quality. Other tools have a single function.

Tools that can serve multiple use cases have an advantage over those that don’t, but that’s not the entire story. A tool that can perform a job faster than others, automate processes, and eliminate steps in a job that previously required manual intervention or IT help offers a clear advantage, even if it only handles a single use case. Stakeholders have to decide if the cost, performance, and usability of a single-purpose tool delivers a value that makes it a better choice than a multi-purpose tool.

Business users and data analysts often prefer the tools they’re familiar with and are sometimes reluctant to change, especially if there’s a long learning curve. Switching tools is a big decision that involves both cost and learning how to optimize the tool.

If you put yourself in the shoes of a chief data officer, you want to make sure the tool delivers strong value, integrates into and expands the current environment, meets the needs of internal users, and offers a compelling reason to make a change. You also should put yourself in the shoes of DBAs—does the tool help them do their job better and faster?

Delivering Data and Analytics Capabilities for Today’s Users

Tool choices can be influenced by no-code, low-code, and pro-code environments. For example, some data leaders may choose no- or low-code tools because they have small teams that don’t have the time or skill set needed to work with pro-code tools. Others may prefer the customization and flexibility options offered by pro-code tools.

A benefit of using the Actian Data Platform is that we offer database management tools to meet the needs of all types of users at all skill levels. We make it easy to integrate tools and access data. The Actian Platform offers no-code, low-code, and pro-code integration and transformation options. Plus, the unified platform’s native integration capabilities and data quality services feature a robust set of tools essential for data management and data preparation.

Plus, Actian has a robust partner ecosystem to deliver extended value with additional products, tools, and technologies. This gives customers flexibility in choosing tools and capabilities because Actian is not a single product company. Instead, we offer products and services to meet a growing range of data and analytics use cases for modern organizations.

Experience the Actian Data Platform for yourself. Take a free 30-day trial.

Related resources you may find useful:

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Building an Inclusive Data Governance Framework

Gartner defines data governance as the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption and control of data and analytics. Data governance is vital to ensure that your data is consistent and trustworthy and doesn’t get misused.

One of the first steps you’ll need to get started is to create a data governance framework. This will define policies, procedures, and practices that your organization should put in place to manage and protect its data. Establishing a well-defined framework is essential for the success of your data governance initiatives.

Data Governance Framework

There are various data governance frameworks; each has its own pillars and associated responsibilities. I’m using a five-pillar framework, as shown in Figure 1 that includes data ownership, data stewardship and management, data quality, data privacy, compliance, and data protection, as outlined below. I prefer this framework since it’s simple and includes a pillar dedicated solely to privacy and compliance, an area that is increasing in importance to help businesses avoid expensive non-compliance fines, improve their brand image, and gain customer trust.

#1. Data Ownership

Assigning data assets and accountability to specific individuals or roles
Defining data quality, data security, compliance, data access, and data usage policies and processes
Identifying data lifecycle requirements (including creation, storage, usage, archiving, and disposal of data)

#2. Data Stewardship and Management

Overseeing day-to-day data management and oversight of data, including data quality, metadata management (data definitions, data dictionaries, data catalogs, data lineage), and compliance
Collaborating with data owners and data users to ensure that data is used effectively and responsibly

#3. Data Quality

Creating data management practices to ensure data accuracy, completeness, consistency, and reliability
Defining data quality standards, data profiling, data monitoring, data cleansing, and data validation processes

#4. Data Privacy and Compliance

Defining and enforcing standards and guidelines for data classification, data access, and data retention in accordance with legal and regulatory requirements.
Ensuring implementation of compliance measures for relevant data privacy regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA)

#5. Data Protection

Protecting data from unauthorized access, breaches, and cyber threats
Maintaining security to support data confidentiality, integrity, and availability

Your Next Steps

You should consider these five pillars as a guide to developing your own data governance framework. They point out various aspects of data management that your organization should address. However, you will likely need to tailor these to suit your organization’s specific needs based on size of business, industry, regulatory environments, available expertise, priorities, and many other factors.

In addition, a data governance framework is just one checkbox for effective data governance. Building a data governance culture within your organization is crucial for a successful implementation of your framework. Your business may also need invest in data governance software and tools to help automate framework execution as well as training in technical, analytical, communication, and organizational skills and competencies to meet multifaceted data governance demands.

You’ll also need a data platform that offers the right underpinning for your data governance framework. The Actian Data Platform’s modern architecture supports policies, procedures, and best practices for data ownership, data stewardship, data quality, data privacy, compliance, and security, making it easier to implement and scale data governance. Our data platform integrates seamlessly, performs reliably, and delivers at industry-leading speeds.

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9 Aspects of Data Management Your IT Team Must Have

We live in a data-driven world. The amount of data/information generated, gathered, copied, and consumed is forecast to reach 180 zettabytes by 2025. With this rapid expansion comes tremendous opportunities for organizations to gain actionable insights to improve business outcomes. However, to realize the full potential of data, comes the need for effective data management.

Data management is the management of all architectures, policies and procedures that serve the full data lifecycle needs of an organization. It is an IT practice that aims to make sure that data is accessible, reliable, and useful for individuals and the organization. The term can also refer to broader IT and business practices that enable the use of data in the most strategic way possible.

Data Management Skills Every IT Team Should Have

Data analytics involves a broad range of data management skills to effectively handle data collection, storage, deployment, processing, security, governance, analysis, and communication. Here are some of the key data management requirements your IT team should have.

Data Integration

The ability to combine data from diverse sources and systems involves data modeling, extraction, transformation, loading (ETL), data mapping, and data integration tools. Depending on the data integration tool and integration requirements, SQL proficiency may be needed to query and manipulate data.

Data Quality Management

Understanding how to ensure that data is accurate, complete, current, trusted, and easily accessible to everyone who needs it. Techniques include data auditing, data profiling, data cleansing, and data validation.

Data Storage and Processing

Choosing appropriate storage and database technologies for analytics while taking into account your current and future requirements for data volume, velocity, and variety and other uses.

Cloud Deployment

As fast-growing data volumes and advanced analytics are driving deployment in a cloud or hybrid environment, data management professionals need to build their cloud skills.

Database Design and Development

Knowledge of designing the database to handle large volumes of data and to support complex analytical queries. This includes indexing strategies, partitioning techniques, and query optimization to enhance performance.

Data Analysis

Proficiency in how to empower users to extract meaningful insights, identify trends, and make informed decisions based on available information. This involves not only traditional reporting, business intelligence, and data visualization tools, but also includes advanced analytics such as machine learning, to uncover hidden trends and patterns and to forecast future outcomes. In addition, users are seeking real-time data analytics to empower “next best actions” at the moment.

Data Security

Protecting data is a data management must. This includes strong security safeguards and countermeasures to prevent, detect, counteract, or minimize risks, including user authentication, access control, role separation, and encryption.

Data governance

This involves determining the appropriate storage, use, handling, and availability of data. You’ll need to know how to protect privacy, comply with regulations, and ensure ethical use, while still allowing visibility into your stored data.

Communication and Collaboration

Collaboration skills are crucial for working in cross-functional teams and aligning data management efforts with organizational goals. To be successful, you’ll need to understand your users’ needs and how they measure the success and challenges users face in getting the insights they need.

Data management skills for analytics involves a mix of technical, business, and managerial competencies and vary greatly by role and objectives. Also, keep in mind that technology is always advancing, so you’ll have to stay on top of the latest trends and tools and develop new skills as the need arises.

Need Help with Your Data Management?

Actian is a trusted data management advisor, with over 50 years of experience helping customers manage the world’s most critical data. Contact us to learn how we make managing a data platform for analytics easy.

Related Resources

Security, Compliance and Governance

How to Maximize Business Value with Real-Time Data Analytics

How Your Peers Are Experiencing Their Journeys to the Cloud

Are You Accurately Assessing Data? Here are 7 Ways to Improve

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Controlling SAP HANA Data Sprawl

Enterprises running large SAP HANA instances in the cloud are seeing a new challenge appear as their databases continue to grow. Since SAP HANA has a simplified data layout and structure compared to a more complex legacy database, it was assumed this would result in less data sprawl and duplication. But does the data stay […]

The post Controlling SAP HANA Data Sprawl appeared first on DATAVERSITY.

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