Solutions Review highlights the most common graph database use cases you need to know about so you can select the best software.

A graph database is designed around the concept of a mathematical graph. Unlike relational databases, they allow you to connect data together. This enables users to take advantage of specific functions from within the graph database that help during data analysis. While a graph database stores the same kind of data as any other, it allows you to see how data may be related without having to run a JOIN to understand the relationship. There are a growing number of graph database use cases to be aware of.

The structure of a graph database enables it to map different types of relational and unstructured data. This means that it can provide a view of both simple and complex relationships between seemingly unrelated data. All of these factors mean that graph database users can see the links between data without having to first create a hypothesis about a particular data set and test it.

Graph databases are becoming increasingly popular due to the proliferation of unstructured enterprise data. The structure of a graph database makes it a perfect place to store, manage and link these new data types. Not only do graph databases enable succinct data connectivity, they also provide users with a faster path to  accurate data analytics. With this in mind, our editors have compiled this list of the most common graph database use cases you need to know.

Master Data Management

Master data is made up of essential company-wide data points. This data typically provides insight related to the core of the business, including customers, suppliers, accounts, employees, goals, and operations. Decisions about what constitutes as master data are made by management teams and business stakeholders. Once these data standards have been met, users can analyze the data as they need to identify key metrics that reveal areas of concern so appropriate actions can be taken to improve operations.

Since master data consists of a series of connections, managing it using a relational database structure can be both complex and slow. In addition, real-time querying is a daunting task due to the fact that users often need to integrate master data with cross-enterprise applications. Graph databases support the relationships between data, so they offer a more efficient and effective way to organize it. This means more relevant recommendations for those working with it, and as such, even greater flexibility.

Compliance (Think HIPPA, GDPR)

The growing presence of regulations is putting a strain on the enterprise, especially those organizations that store sensitive customer data. On the whole, these laws require companies in possession of personal information to manage it in a specific way. Companies also must be able to produce the data, as well as its location as it pertains to an audit. It was recently revealed that the majority of organizations are not complying with GDPR. Could this be because of outdated database technologies?

Regulatory compliance is perhaps the most obvious graph database use case. In fact, a database that tracks and retains the relationships among non-alike data is basically a dream. Graph systems enable single queries that can offer a visual representation of the results. In this way they help organizations maintain compliance by tracing data throughout enterprise systems in a more organized manner than a relational database.

Identity and Access Management

Identity and Access Management refers to the protection and secure provisioning of a user’s digital identities. The majority of cyber threats now exploit or otherwise circumvent traditional login systems such as passwords. Through fraudulent logins or stolen identities, hackers can disrupt business practices, steal proprietary data and finances, and cause significant damage to victims’ reputations. Thus Identity and Access Management offers capabilities and tools to ensure that user logins remain secure and their permissions cannot be exploited to devastating effect.

Graph databases provide organizations the ability to manage multiple roles, groups and authorizations in a way that traditional database technologies struggle with. The graph structure enables users to track IAM relationships with speed, as well connect data along different relationship lines.

Machine Learning

Analyst house Gartner, Inc. recently proclaimed that the future of BI and analytics is AI and machine learning. The divide between the open source community and those that use commercial software products is narrowing. Gartner predicts that 75 percent of end-user products will soon be developed with commercial tools in mind. Gartner notes that many software providers are equipping their analytics products with built-in connectors to the open source environment while offering the needed enterprise functionalities.

Since graph databases provide a connective layer between data not present in other database structures, they are ripe for use in conjunction with machine learning software. Machine learning tools can make use of these connections to further enhance and speed up the process of analysis. This is especially beneficial when it comes to machine recommendations; a scenario where the machine scans the database and provides the user with a starting point or initial points to sift through.

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Data governance is being challenged by the emergence of big data. As a result of increasingly expanding stores of unstructured data, Hadoop has emerged as a major player in the data landscape, allowing for increased functionality. New technologies lack the kinds of data governance components that organizations have grown accustomed to deploying inside their legacy data architectures, making enterprise-wide adoption an issue.

If Hadoop is to become the new data warehouse, governance must be optimized for more environments. Data governance should cover both structured data and big data, but that’s not always the case. As the big data market matures, vendors are competing mainly in niche features and software pricing when they should be battling one another on who offers the best governance framework. Legal and compliance requirements for the use of data are going to force the market to evolve, but enterprises first need to re-purpose their environments for these changes to occur.

In our attempt to provide the best resources for whatever stage of the enterprise technology journey you’re currently at, we’ve assorted the six most informative YouTube videos on the topic of data governance. It’s our hope that these videos will help you gain a better understanding of where things have been, where they currently are, and where they’re going in the future.

Access Sciences creates videos and video blogs referencing their experience in information management and governance, compliance, technology, change management, and managed services. In this video, they provide in-depth information about data governance and why it is critical to organizations. They also introduce the five characteristics of a data-driven culture and the Access Sciences 7-Point Data Governance Model that can be utilized to design and develop a scalable, fit-for-purpose data governance program for any organization.

Or, for a more direct “what is” video, we like this explanation here from Intricity101 on YouTube. Their other videos are great too.

Back to basics:

IT Consultant firm EdenTechnologies provides a 30-minute complete explainer on data governance that provides a foundational understanding on the topic. Learn about the 3 Pillars of data governance, tips for gaining control over unstructured corporate data, and reasons why corporate data governance is critical to enterprise security.

DATAVERSITY believes there are several basic components that go into delivering a successful and sustainable data governance program, and that many of these framework items can be developed using tools you already own and without going to great expense. This webinar features Bob Seiner for this video on finding value from your data governance program through the delivery of items that will formalize accountability for the management of data and information assets. Learn more at their website.

Are things different with a data lake?

Many hold the opinion that Data Governance and Management are activities abstracted from the technologies and data patterns they serve. This is somewhat true, but when we look at how technological advances will bring the promise of speed, time-to-market, machine learning, and AI, the face of data management will change. This Knowledgent video describes how things may be different with a data lake.

Now for a data governance roadmap:

Setting up a data governance program is a journey not a project. It is critical that aspects of the three core components of people, process and technology need to be considered. All are important. While many of the issues are often termed “soft,” the approach to make the initiative successful needs to be very structured with clear links to the corporation’s strategies and goals. This video from global data consulting and services firm UtopiaData shares how to get started on the journey, how to get the support needed to make this sustainable, and what role technology should play in the process.

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Collibra has announced the release of a new compliance and data privacy solution called Collibra Privacy & Risk. The product allows customers to proactively manage personal data assets by enabling compliance with privacy regulations. Collibra Privacy & Risk includes modules for the California Consumer Privacy Act (CCPA) and the EU General Data Protection Regulation (GDPR). The tool will be available in September 2019.

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Collibra’s privacy features are aimed at supporting a sustainable compliance approach. Organizations can manage data privacy policies, create and maintain a data process activity register, perform data protection impact assessments, and evidence compliance to regulators and other stakeholders. Users will also be able to complete data inventory and mapping, support the incident response management process, and monitor privacy by design.

The company’s Chief Product Officer Jim Cushman spoke to the new product in a press statement. He said: “With privacy regulations accelerating across the globe, Collibra is at the forefront of embracing data privacy not just as a compliance initiative, but serving as a strategic service that helps organizations liberate and democratize data for the purposes of exploring new analytics, developing new products, engaging and training AI and machine learning, and driving forward Data Intelligence.”

Collibra has seen considerable growth this year as enterprise data governance comes to the forefront. The company has more than 300 customers across the globe in a variety of industries, including the financial services, healthcare, retail and technology. The provider nabbed $100 million in venture capital funding earlier this year from Alphabet’s growth equity and investment arm CapitalG.

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According to new research by Syncsort, more than 70 percent of data analytics leaders report sub-optimal data quality negatively impacts their business decisions. The State of Enterprise Data Quality survey explores the challenges and opportunities for organizations looking to bring data quality across the enterprise. Results were generated from 175 respondents, of which many come from organizations with over 1,000 employees. A range of job titles were polled as well, ranging from CDO to data analyst – with most in data-centric roles.

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Though a surprising number of respondents (69 percent) reported that their leadership and c-suite teams trust data insights enough to inform business decisions on them, just 14 percent of stakeholders have a very good understanding of the data. To add insult to injury, nearly half of those polled claim untrustworthy results from analytics are due to bad data quality in data fed systems such as AI and machine learning. As a result, many sources of data and volumes of data are two of the top-3 data quality challenges organizations are currently up against.

Somewhat unsurprisingly, less than 50 percent of the survey respondents are currently taking advantage of dedicated data profiling software or a data cataloging product. Data analytics leaders rely on other methods — mainly in the form of SQL queries and BI reporting tools — when trying to gain and understanding of data. Syncsort also revealed that only 17 percent do manual data profiling.

Wasted time was the biggest consequence of poor data quality, according to those polled. In fact, 92 percent of the survey responders have experienced this repercussion. Other major consequences include ineffective business decisions (72 percent), customer dissatisfaction (67 percent), and the creation of biased data (39 percent).

We highly encourage you to read the complete report, available here.

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Technology advisory firm Forrester Research has released its latest Forrester Wave for Data Management Platforms, Q2 2019. The researcher notes that data management platforms are the “anchor for marketing orchestration” as advertisers and publishers push for better ways to manage customer data. Marketing data management platform providers are heeding this sentiment and trying to find a space alongside other marketing software in the enterprise technology stack. Key differentiators among vendors in marketing data management include omnichannel marketing integrations and advanced analytics.

In their 34-criteria evaluation of the master data management space, Forrester researchers Tina Moffett and Arleen Chien identified the 7 providers whom they consider most significant in the category: Adobe, Lotame, Neustar, Nielsen, Oracle, Salesforce and The ADEX – then researched, analyzed, and scored them.

The Wave report details their findings and examines how each vendor meets (or falls short of) Forrester’s evaluation criteria and where vendors stand in relation to each other.

Quantity over quality

Forrester recommends that marketers invest in a data management platform to extend first-party data and reach the audiences they seek. Marketing leaders will also want to partner up with vendors that source accurate data using a formal data quality and audit process, which will ensure the quality over quantity measure. Another thing to note is that few vendors offer third-party data source supplementation. Other things buyers should keep in mind include prioritizing consumer consent management and focusing on integrations beyond the adtech ecosystem.

Salesforce and Adobe are the runaway leaders

Salesforce scored a perfect 5.0 in six of Forrester’s data management platforms scorecard that ranks for current offerings, strategy, and market presence. The Salesforce Audience Studio operates on top of automatic data rights and consumer rights controls and protections. These consent controls enable the solution to bridge both known and unknown customer data while respecting compliance. Most of Salesforce’s Audience Studio users are in North America and Europe, and the tool is supported by its parent product called Marketing Cloud.

Neustar is on the cusp

Neustar received perfect 5.0-star scores for its data onboarding and supporting products and services. The vendor offers identity-driven data insights so marketers can create more contextual experiences. Neustar touts more than 400 clients, many of which are concentrated in North America. However, the provider is actively expanding its customer service and technical support around the globe.

Neustar’s placement on the Forrester Wave has them only a stone’s throw away from joining Salesforce and Adobe among the market leaders, a feat which could occur as soon as 2020. Forrester recommends Neustar to organizations looking for strong data connectivity features.

Read the Forrester Wave for Data Management Platforms, Q2 2019.

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According to new data management usage projections by analyst house Gartner, Inc., 75 percent of databases will be cloud-based in three years. The prediction, which comes from Gartner’s The Future of the DBMS Market Is Cloud ($) claims that while three-in-four databases will be deployed or migrated to a cloud platform, only 5 percent will ever be considered for repatriation to on-prem. The researcher believes this trend will be due, in large part, to databases used for analytics, as well as the continued proliferation of the SaaS model.

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Global database management system (DBMS) revenue grew roughly 18 percent in 2018. Cloud DBMS revenue accounted for nearly 70 percent of that growth however, with Microsoft and Amazon Web Services making up more than three-quarters of it. Gartner believes that these trends portend that cloud service provider infrastructures and the services that run on them are quickly becoming the industry standard for data management.

Cloud service providers have done an excellent job embracing the ecosystems that are beginning to form around them, both in terms of integrating services within a single platform and providing early steps toward what Gartner calls “intercloud data management.” The projection does note a small layer of growth for on-prem database products. However, Gartner believes the bulk of that revenue growth is due to price increases and forced upgrades as opposed to new deployments.

Cloud data warehousing enabled users to achieve improved performance and position them to better take advantage of new cloud technologies. However, implementation troubles remain prevalent among those that don’t utilize all the best practices at their disposal. Complexity too remains a major issue. There’s also a growing need for better integration with third-party analytics and BI software.

Gartner’s June Cool Vendors in Data Management, 2019 features several up-and-coming providers that line up with this cloud-based data management worldview that is developing in the enterprise, and we encourage you to read it.

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Waterline Data has announced the release of its new Enterprise Data Catalog aimed at helping organizations navigate the waters of regulatory compliance. The product features data governance capabilities that relate to new and emerging data privacy laws. Enterprise Data Catalog works by automating the process of discovering sensitive data located in databases, data lakes and cross-enterprise applications.

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Waterline Data’s Aristotle machine learning and AI-driven data tagging engine uses a patented fingerprinting technology to automate the discovery, classification, management and governance of sensitive data. The tool also features a new DataOps Dashboard that serves as a regulatory hub where customers can understand the macro risk of their data. The dashboard allows users to locate and view specific files that contain regulated sensitive data and help expedite the identification, remediation and documentation processes to meet GDPR and CCPA requirements.

Enterprise Data Catalog supports native agents running on on-prem clusters and in different clouds, as well as container-based deployments. This architecture allows organizations to scale their deployments to catalog millions of data sets and comply with data residency laws that require all data to be processed locally within a specified jurisdiction. The agents do all their processing within the country and expose allowed metadata in the global catalog.

In a statement to Solutions Review, the company’s founder and CTO Alex Gorelik said: “Two of the most notable new features in the latest release of Waterline‘s flagship Enterprise Data Catalog were driven by direct needs of customers related to GDPR and CCPA compliance. CIOs and CDOs need an automated way to find [data] and a way to get a complete overview of where this data is and how much of it is out there. That’s why Enterprise Data Catalog’s new dashboard is so important.”

Waterline Data released its AI-driven data catalog back in February.

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Source: Zaloni

Zaloni has announced the release of its new Active Data Hub, a data catalog solution built into the Zaloni’s main Data Platform. The tool offers more than traditional, passive data catalog products in that it enables users to interact with, enrich and provision data from a curated catalog that hosts data from any source. Active Data Hub also allows organizations to launch the tool with a single line of business.

Zaloni’s Active Data Hub helps users avoid the struggles typically involved in integrating a data catalog into an organization’s ecosystem of self-service tools. The solution acts as both the data catalog and the starting point of self-service activities that include data transformations and data provisioning. The company notes that its customers have used Active Data Hub to leverage data for new analytics use cases and regulatory compliance as well.

At its core, Active Data Hub is a unified data catalog. The tool helps users discover, catalog and ingest data from cross-enterprise sources, including relational database management systems and data lakes. Users can utilize the catalog to search, filter and group datasets based on all relevant business and technical data. There’s also an improved catalog view that allows users to customize the types of metadata that are highlighted on the quick view of the catalog.

Zaloni customers can prepare or enrich data directly from the catalog and write it to an analytic reporting environment. This eliminates the need to go back and forth between applications. As new data sets are enriched or actioned, they are automatically cataloged. Data consumers can also “shop” for data sets, add them to a shopping cart and provision the data for use in an existing enterprise data warehouse or data mart. Users can provision to the cloud or enable data to be accessed from data visualization software as well.

In a press statement, the company’s Vice President of Product Management Scott Gidley said of the release: “Zaloni’s Active Data Hub solution enables business users to not only understand the data but use it to deliver immediate business results without sacrificing data quality, governance, and security.”

Solutions Review named Zaloni one of The Best 28 Data Management Platforms for 2019 last October.

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Analyst house Gartner, Inc. has released its newest research highlighting three emerging solution providers that data and analytics leaders should consider as compliments to their existing architectures. Cool Vendors in Data Management, 2019 features information on startups that offer some disruptive capability or opportunity not common to the marketplace. Gartner analysts James Richardson, Rita Sallam, Julian Sun, and Gareth Herschel compiled the materials for this report.

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Solutions Review’s Editors have read the complete report, which is available here, and want to take the opportunity to provide a brief, independent introduction to each of the cool vendors listed. Though Gartner’s process for selecting Cool Vendors is somewhat mysterious, we believe our unique view of the space can help you to better understand how these analytics solutions will fit into the marketplace as time goes on.

Dataddo

Dataddo offers a cloud-based data integration, automation and transformation platform. It can transform and wire data to a wide range of database tools, data warehouses, cloud storage, or dashboard and business intelligence software. Users can automate extraction, transformation and delivery internally or to clients. The tool works with your existing IT infrastructure or data visualization tools via a REST API, Direct DB Push or Automatic Data Delivery.

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DataKitchen

DataKitchen offers a product that supports data analytics, incorporating Agile Software Development, DevOps, and manufacturing-based statistical processes to help users manage data. The solution was designed for IT, data warehousing and data science teams. Key features include orchestrating complex data pipelines and development sandboxes, as well as automatic test and monitoring.

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Panoply

Panoply automates data management tasks associated with running big data in the cloud. Smart Data Warehouse require no schema, modeling, or configuration. Panoply features an ETL-less integration pipeline that can connect to structured and semi-structured data sources. It also offers columnar storage and automatic data backup to a redundant S3 storage framework.

Read Gartner’s Cool Vendors in Data Management for 2019.

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Source: Qubole

Qubole has released a new serverless engine called Quantum. Qubole Quantum enables data analysts to query large data volumes using standards-compliant SQL without the need to configure and manage the underlying infrastructure. Since Quantum is serverless, users pay only for the queries they run on object stores or data lakes on AWS.

Quantum is headlined by Intelligent Query Management, featuring reliable query execution through a patent-pending algorithm to estimate and allocate resources. There’s also support for custom metastores that use Qubole’s built-in metastore or an organization’s preferred cloud metastore (there is no need to recreate schemas). Users can switch between managed and serverless mode as needed, and automatically scale Quantum up, down or out to match workload and SLA requirements as well.

Qubole’s flagship data management product is its Autonomous Data Platform, a solution that self-manages and self-optimizes by sending alerts and Insights and Recommendations (AIR) based on cloud agents connected to the customer’s data policies and preferences. Qubole uses a combination of heuristics and machine learning for workload continuity as well.

In a media statement, the company’s co-founder and CEO Ashish Thusoo said of the news: “While serverless is still in the nascent stages of adoption, we know its value, so we have been providing simplification and optimal choice to our customers and partners. Quantum is yet another step in this direction, delivering a self-service way to query their big data on the cloud of their choice, instantly.”

For more technical detail, as well as an introduction of Qubole Quantum, click here.

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