In today’s competitive business landscape, leveraging data effectively is critical to success. The vast amounts of data generated can offer key insights and drive informed decision-making, but only if businesses are able to cut through the noise and reveal the meaningful patterns hidden within.

As the digital landscape evolves, businesses must stay agile and adapt to new technologies that can help them stay ahead of the curve. The ability to identify and understand the connections between data points is essential for businesses seeking to harness the full power of their data. This is especially true when dealing with geospatial information, as the relationships between locations, time, and events can reveal critical trends and patterns.

One such technology is the combination of graph databases and geospatial intelligence, which allows organizations to gain deeper insights into customer behavior, supply chain efficiencies, and market trends. This potent fusion empowers businesses to make data-driven decisions with greater speed and accuracy, ultimately translating into improved customer experiences, increased revenue, and sustainable growth. By embracing this innovative approach to data analysis, companies can unlock the full potential of their data assets and create a strong foundation for continued success in an increasingly data-driven world.

By integrating graph databases and harnessing the power of geospatial intelligence, businesses can uncover new opportunities, optimize operations, and gain a competitive edge in their respective industries. Graph databases change the game because they view data points as relationships, rather than individual tables and documents. By intuitively calling attention to relationships in seemingly unrelated datasets, graph technologies help data scientists spend less time cleaning data, and more time discovering invaluable insights.

Transform Your Business Strategy with Geospatial Intelligence

Businesses across industries are using unique insights derived from geospatial data to make smarter, more efficient business decisions and to improve customer experiences.

One example is the analysis of social media posts in combination with GPS data. With the right analysis and interpretation, a business can gain insight into customer behavior and preferences when joining the sentiment from public social media posts with the location they are geotagged in. With a graph database, these data points are intuitively linked together to increase accuracy and enhance attributes. Consumer insights extracted from the data can then be used to make decisions, such as where to invest in a new storefront location or what inventory to stock up on for the season.

Leaning on graph technologies can help businesses unlock the power of geospatial data in three important ways:

Streamlining Geospatial Data Analysis

Often, 80 percent of a data scientist’s time is spent searching and cleaning data, while only 20 percent is actually performing analysis and discovering insights. In addition to this time and personnel commitment, analyzing complex geospatial data traditionally requires specialized resources and tools, such as GIS software.

With a graph database, teams can leverage the power of graph analytics to perform complex queries and analysis, without the need for specialized resources. This significantly reduces the time it takes to gain insights from geospatial data, and minimizes the laborious hours spent aggregating and cleaning datasets, thus freeing up strategic work on initiatives that impact the business bottom line.

Take telecommunications and network planning as an example. Graph databases can examine the relationship between communication nodes, network infrastructures, and geographic locations to optimize network performance and coverage. This enables more efficient planning and deployment of network resources, saving time compared to traditional relational databases that require extensive data preparation and manual queries.

In solving for the stringent preparations that go into data analysis to free up a data scientist’s time for value-focused work, the graph database accelerates time to insights.

Unlocking Geospatial Data Insights

The potential value of what geospatial data could deliver to every department within an organization is massive. However, historically, business users have had to rely on highly-skilled data science and engineering teams to derive and understand insights relevant to the decisions they’re making.

By simplifying complex data processes and making interpretation easier, a graph database allows non-technical users to gain insights from data without needing to rely on specialized data analysts or engineers. With a graph database, employees at every skill level and in every department are empowered to make more informed, data-driven decisions in their daily work.

Lowering the barrier to entry for geospatial data analytics also reduces strain on engineering and IT teams, lending to improved agility and responsiveness in a competitive business environment.

Breaking Down Data Silos

One of the biggest challenges in working with geospatial data is the fact it is often stored in disparate systems or databases, making it difficult to access and join effectively. Data silos slow down the analysis process, and lead to insights lacking accuracy or that may not paint a comprehensive enough picture by the time they’ve reached fruition.

Real estate is one industry where having geospatial data locked away in silos can be detrimental to business success. Real estate companies often deal with data from various sources, such as property listings, market trends, demographics, and geographic information. By congregating this data in a graph database, real estate firms can access a holistic view of the market. This enables more informed decision making and provides better services, leading to happier clients and overall business success.

Any business, from real estate to retail and supply chain management, can use a graph database to consolidate different geospatial datasets into a single, interconnected data model that can be accessed by multiple teams. This facilitates collaboration, enables deeper insights and ultimately supports common business goals. While traditional databases dilute data into separate documents and files, graph databases break down data silos to foster connections between data points.

The future is Graph

By streamlining the data analysis process, democratizing access to valuable insights and breaking down data silos, graph databases enable organizations to harness the full potential of their data assets. As the digital landscape continues to evolve and the importance of data-driven decisions intensifies, the adoption of graph databases and geospatial intelligence will become increasingly essential for staying ahead in an ever-competitive business world. Embracing this cutting-edge technology is a strategic move that will propel businesses forward, driving growth, innovation, and long-term success in the era of big data and beyond.

In fact, Gartner predicts that graph technology will be used in 80 percent of data and analytics innovations by 2025. It’s clear to see that the future of data analytics is being shaped by technologies like graph databases.

The post Three Ways Graph Databases Can Revolutionize Geospatial Data appeared first on Best Data Management Software, Vendors and Data Science Platforms.

Solutions Review editors created this short resource highlighting the most common data lake myths to stand clear of.

In the realm of big data, data lakes have emerged as a popular and powerful solution for storing, processing, and analyzing vast amounts of structured and unstructured data. However, misconceptions and myths surrounding data lakes can impede organizations from fully harnessing their potential. In this article, we aim to debunk some of the greatest data lake myths, providing insights and best practices to help organizations navigate the complexities and unleash the true value of their data lake implementations.

Data Lake Myths

Myth 1: Data Lakes Are Just Data Warehouses in Disguise

One prevalent myth surrounding data lakes is that they are simply data warehouses with a different name. While both data lakes and data warehouses store data, they differ significantly in their architecture, purpose, and flexibility. Unlike traditional data warehouses, data lakes embrace a schema-on-read approach, allowing for the ingestion of raw, unstructured data without predefined schemas. Data lakes are designed to handle diverse data types, enable data exploration and discovery, and support advanced analytics. Understanding the distinctions between data lakes and data warehouses is crucial for leveraging the unique capabilities of each.

Myth 2: Data Lakes Are a Solution for All Data Challenges

Some organizations believe that implementing a data lake will automatically solve all their data-related challenges. However, a data lake is not a silver bullet solution. It is a powerful tool that requires proper planning, governance, and data management practices to be effective. Without appropriate data governance, metadata management, and data quality controls, data lakes can quickly become data swamps, with unorganized and unreliable data. To maximize the benefits of a data lake, organizations must invest in comprehensive data management strategies, including data cataloging, data lineage, and data stewardship.

Myth 3: Data Lakes Lead to Data Chaos and Lack of Control

Another myth is that data lakes promote data chaos and make it difficult to maintain control over data assets. While it is true that data lakes allow for the ingestion of diverse data without rigid structures, proper data governance can ensure control, security, and compliance. Implementing robust metadata management, access controls, and data lineage tracking mechanisms enables organizations to maintain visibility, traceability, and control over data in the data lake. With effective governance practices in place, organizations can strike a balance between data accessibility and data security.

Myth 4: Data Lakes Eliminate the Need for Data Preparation

There is a misconception that data lakes eliminate the need for data preparation or data cleaning processes. In reality, data preparation remains a crucial step in the data pipeline, even within a data lake environment. While data lakes offer flexibility in ingesting raw data, data preparation tasks such as data cleansing, data transformation, and data enrichment are essential for ensuring data quality and usability. Organizations should incorporate data preparation workflows and tools into their data lake strategies to optimize the accuracy and reliability of the data.

Myth 5: Data Lakes Are Only for Data Scientists and Analysts

It is often believed that data lakes are exclusively meant for data scientists and analysts, leaving other business users out of the equation. However, data lakes have the potential to benefit a wide range of stakeholders across the organization. With proper data governance and self-service analytics capabilities, data lakes can empower business users, executives, and decision-makers to explore, query, and derive insights from the data lake. By democratizing data access and fostering a data-driven culture, organizations can unlock the full potential of their data lake investments.

Final Thoughts

Data lakes have revolutionized the way organizations store and analyze data, but misconceptions can hinder their successful adoption and utilization. By dispelling these common myths surrounding data lakes, organizations can embrace the true power of this technology. Understanding the distinctions between data lakes and data warehouses, implementing robust data governance practices, acknowledging the need for data preparation, and expanding the usage of data lakes beyond data scientists and analysts, organizations can optimize their data lake implementations.

It is crucial to approach data lakes with a holistic understanding of their capabilities and limitations. By debunking these myths, organizations can harness the full potential of their data lakes, enabling them to unlock valuable insights, support data-driven decision-making, and drive innovation.

Data lakes have revolutionized the way organizations store, manage, and analyze data. However, misconceptions surrounding data lakes can hinder their successful adoption and utilization. By dispelling these myths and understanding the true capabilities of data lakes, organizations can effectively leverage this powerful tool to maximize the value of their data assets. Through proper planning, governance, data management practices, and democratized data access, organizations can harness the full potential of their data lakes and gain a competitive edge in the data-driven era.

The post The 5 Greatest Data Lake Myths and How to Avoid Them appeared first on Best Data Management Software, Vendors and Data Science Platforms.

Solutions Review editors created this short resource highlighting the most common master data management myths to stand clear of.

In the age of data-driven decision-making, effective master data management (MDM) has become essential for organizations seeking to maintain data integrity, improve operational efficiency, and enhance customer experiences. However, misconceptions and myths surrounding MDM can hinder organizations from fully leveraging its potential. In this article, we aim to debunk some of the greatest MDM myths, providing insights and best practices to help organizations harness the true power of MDM and achieve data harmony.

Master Data Management Myths

Myth 1: Master Data Management is Only for Large Enterprises

One common misconception is that MDM is exclusively relevant to large enterprises with complex data ecosystems. In reality, organizations of all sizes can benefit from implementing MDM practices. Whether a small business or a multinational corporation, MDM helps in establishing consistent and accurate master data across systems, enhancing data quality, and driving better decision-making. By tailoring MDM strategies to their specific needs and scalability requirements, organizations can effectively manage and leverage their master data assets, regardless of their size.

Myth 2: Master Data Management is a One-Time Project

A prevalent myth surrounding MDM is that it is a one-time project with a definite endpoint. However, MDM is an ongoing process that requires continuous attention and maintenance. Master data evolves as business processes change, new data sources emerge, or regulatory requirements evolve. Organizations must establish robust data governance frameworks, data stewardship practices, and data quality management processes to ensure the accuracy, consistency, and relevance of master data over time. Treating MDM as a continuous effort enables organizations to adapt and align their master data with evolving business needs.

Myth 3: Master Data Management is an IT Responsibility

Another misconception is that MDM falls solely within the purview of the IT department. While IT plays a critical role in implementing MDM solutions and technologies, effective MDM requires collaboration between IT and business stakeholders. Business users, data owners, and data stewards possess valuable domain knowledge and play an active role in defining data standards, data governance policies, and data validation rules. By involving all relevant stakeholders, organizations can ensure that master data is accurate, relevant, and aligned with business objectives.

Myth 4: Master Data Management is Expensive and Time-Consuming

There is a misconception that implementing MDM is a costly and time-consuming endeavor. While MDM requires investment in tools, technologies, and resources, the long-term benefits outweigh the initial costs. MDM streamlines data processes, reduces data redundancy, improves data quality, and enhances data consistency across systems. By implementing MDM best practices, organizations can reduce operational costs, optimize data-related workflows, and improve overall business efficiency. Proper planning, resource allocation, and leveraging MDM technologies can make the implementation and maintenance of MDM solutions more cost-effective.

Myth 5: Master Data Management is a Technical Issue, Not a Business Priority

Some organizations view MDM as a technical issue rather than a business priority. However, MDM has a direct impact on business outcomes, customer satisfaction, and regulatory compliance. High-quality master data enables organizations to make accurate forecasts, provide personalized customer experiences, ensure regulatory compliance, and drive strategic decision-making. By recognizing MDM as a business priority, organizations can prioritize data governance, establish data stewardship roles, and integrate MDM initiatives with broader business strategies.

Final Thoughts

Master data management is a critical component of effective data governance and business success. By debunking the myths surrounding MDM, organizations can harness its true potential. MDM is not exclusive to large enterprises or limited to the IT department alone. It is an ongoing process that requires collaboration, continuous effort, and a focus on data governance and data quality. Implementing MDM practices enables organizations to establish a single source of truth, improve data quality, and drive informed decision-making across the organization.

Debunking the myths surrounding master data management is crucial for organizations to fully leverage its power and achieve data harmony. MDM is not limited to large enterprises or confined to the IT department; it is a cross-functional initiative that requires collaboration between IT and business stakeholders. By recognizing MDM as an ongoing effort, prioritizing data governance, investing in the right tools and technologies, and aligning MDM with broader business objectives, organizations can optimize their master data, enhance data quality, and drive business success. Effective master data management paves the way for accurate insights, streamlined processes, and improved decision-making, enabling organizations to stay ahead in the data-driven world.

The post The 5 Greatest Master Data Management Myths and How to Avoid Them appeared first on Best Data Management Software, Vendors and Data Science Platforms.

Solutions Review editors created this short resource highlighting the most common metadata management myths to stand clear of.

In the realm of data management, metadata plays a crucial role in providing context, understanding, and accessibility to data assets. However, misconceptions and myths surrounding metadata management can hinder organizations from fully leveraging its potential. In this article, we aim to debunk some of the greatest metadata management myths, providing insights and best practices to help organizations harness the true power of effective data governance and metadata management.

Metadata Management Myths

Myth 1: Metadata Management is Optional

One common misconception is that metadata management is an optional or secondary aspect of data management. In reality, metadata is the backbone of effective data governance and management. Metadata provides critical information about the structure, meaning, and lineage of data, enabling organizations to understand, organize, and utilize their data assets effectively. By implementing metadata management practices, organizations gain insights into data quality, lineage, and usage, leading to improved decision-making, data discovery, and regulatory compliance.

Myth 2: Metadata Management is Only for IT Professionals

Another prevalent myth is that metadata management is solely the responsibility of IT professionals. While IT plays a vital role in implementing metadata management systems and technologies, effective metadata management requires collaboration between IT and business stakeholders. Business users, data owners, and data stewards possess valuable domain knowledge that contributes to meaningful metadata creation and maintenance. By involving all relevant stakeholders, organizations can ensure that metadata reflects the business context, making it more valuable and actionable for users across the organization.

Myth 3: Metadata Management is a One-Time Project

Some organizations mistakenly view metadata management as a one-time project with a definitive endpoint. However, metadata management is an ongoing process that requires continuous attention and maintenance. As data assets evolve and new data sources are introduced, metadata needs to be updated and expanded. Organizations must establish metadata governance practices, define metadata standards, and regularly review and update metadata definitions to keep pace with the changing data landscape. Treating metadata management as an ongoing effort ensures the accuracy, relevance, and usefulness of metadata over time.

Myth 4: Metadata Management is Costly and Time-Consuming

There is a misconception that metadata management is a costly and time-consuming endeavor. While metadata management does require investments in tools, technologies, and resources, the long-term benefits far outweigh the initial costs. Effective metadata management improves data understanding, facilitates data integration, enhances data search capabilities, and reduces the time spent searching for relevant information. By implementing automation, standardization, and data governance practices, organizations can optimize the efficiency of metadata management and maximize its value without breaking the bank.

Myth 5: Metadata Management is Irrelevant in the Age of Artificial Intelligence

With the rise of artificial intelligence (AI) and machine learning (ML), there is a myth that metadata management is no longer relevant. However, metadata remains critical in AI and ML initiatives. Metadata provides essential information about the context, quality, and characteristics of data, enabling AI algorithms to make informed decisions. Metadata helps in data preprocessing, feature engineering, and model training, ensuring the accuracy and reliability of AI and ML outputs. Effective metadata management ensures the success and integrity of AI and ML initiatives, making it more crucial than ever in the age of advanced analytics.

Final Thoughts

Metadata management is a fundamental aspect of effective data governance and management. By debunking the myths surrounding metadata management, organizations can unlock its true potential. Metadata management is not optional but a critical component of data management strategies. It requires collaboration between IT and business stakeholders, ongoing attention, and the right combination of tools, standards, and practices. By embracing metadata management, organizations can enhance data understanding, improve decision-making, and maximize the value of their data assets. Effective metadata management is a cornerstone of successful data governance, empowering organizations to navigate the data landscape with confidence, ensure data quality and compliance, and derive actionable insights from their data assets.

Debunking the myths surrounding metadata management is crucial for organizations to fully leverage its power in effective data governance and management. Metadata serves as the bridge between data and understanding, providing essential context and meaning to data assets. By recognizing metadata management as a vital component of data management strategies, involving both IT and business stakeholders, treating it as an ongoing effort, and leveraging automation and standards, organizations can unlock the true value of metadata. Effective metadata management enhances data discovery, decision-making, and regulatory compliance, enabling organizations to harness the full potential of their data assets and stay ahead in the data-driven world.

The post The 5 Greatest Metadata Management Myths and How to Avoid Them appeared first on Best Data Management Software, Vendors and Data Science Platforms.

Solutions Review editors created this short resource highlighting the most common data warehouse myths to stand clear of.

In the world of data management, data warehouses have long been a cornerstone for organizations seeking to store, organize, and analyze their data. However, several myths and misconceptions surrounding data warehouses have led to misunderstandings and challenges in their implementation. In this article, we aim to debunk some of the greatest data warehouse myths, providing insights and best practices to help organizations optimize their data warehousing strategies and unlock the full potential of their data assets.

Data Warehouse Myths

Myth 1: Data Warehouses are Only for Large Enterprises

One common misconception is that data warehouses are only suitable for large enterprises with massive amounts of data. In reality, data warehouses are valuable for organizations of all sizes. Whether a small business or a multinational corporation, a well-designed data warehouse enables efficient data storage, easy data retrieval, and meaningful data analysis. By tailoring the data warehouse architecture to their specific needs and scaling it as required, organizations can effectively manage and leverage their data assets, regardless of their size.

Myth 2: Data Warehouses are Just for Storing Data

Another prevalent myth is that data warehouses are merely storage repositories for data. While data warehousing involves storing data, its purpose extends far beyond that. A data warehouse serves as a centralized platform that integrates and organizes data from various sources. It involves data transformation, data cleansing, and data modeling processes to ensure data quality and consistency. Data warehouses provide a foundation for business intelligence and analytics, enabling organizations to derive meaningful insights and make data-driven decisions.

Myth 3: Data Warehouses are Inflexible and Slow to Adapt

Some organizations believe that data warehouses are rigid structures that cannot accommodate changes and evolving business needs. However, modern data warehousing technologies have evolved to address these concerns. Agile data warehouse methodologies, such as Data Vault and dimensional modeling, enable flexibility and adaptability to changing requirements. With proper data modeling techniques and well-designed ETL (Extract, Transform, Load) processes, organizations can efficiently incorporate new data sources, modify existing structures, and respond to evolving business demands.

Myth 4: Data Warehouses are Expensive to Implement and Maintain

There is a misconception that implementing and maintaining a data warehouse is a costly endeavor. While building and maintaining a data warehouse does require investments, the benefits outweigh the costs in the long run. Data warehouses provide a centralized and structured environment that simplifies data analysis, improves data quality, and enhances decision-making. By implementing scalable architectures, leveraging cloud-based solutions, and adopting automation, organizations can optimize costs and ensure a cost-effective data warehousing strategy.

Myth 5: Data Warehouses are Becoming Obsolete in the Era of Big Data

With the rise of big data technologies, there is a myth that data warehouses are becoming obsolete. However, data warehouses continue to play a vital role in managing and analyzing structured data alongside big data. While big data technologies like Hadoop and NoSQL databases excel in handling unstructured and semi-structured data, data warehouses provide a structured and reliable foundation for integrating and analyzing structured data. In fact, the integration of data warehouses with big data platforms can yield powerful insights by combining structured and unstructured data sources.

Final Thoughts

Data warehouses remain a fundamental component of effective data management strategies, despite the misconceptions surrounding them. By debunking these myths and understanding the true capabilities of data warehouses, organizations can optimize their data warehousing initiatives. Data warehouses provide a structured, scalable, and flexible environment for data storage, integration, and analysis, empowering organizations to make informed decisions and gain actionable insights. By embracing data warehousing best practices, organizations can unlock the true potential of their data assets and stay ahead in the data-driven landscape.

The post The 5 Greatest Data Warehouse Myths and How to Avoid Them appeared first on Best Data Management Software, Vendors and Data Science Platforms.

Solutions Review editors created this short resource highlighting the most common data governance myths to stand clear of.

In today’s data-driven world, data governance has become a crucial aspect of managing and protecting valuable information. However, with the growing importance of data governance, numerous myths and misconceptions have emerged, causing confusion and hindering organizations from implementing effective data governance strategies. In this article, we aim to debunk some of the greatest data governance myths, shedding light on the truth behind these misconceptions and providing actionable insights for businesses.

Data Governance Myths

Myth 1: Data Governance is Solely an IT Responsibility

One prevailing myth surrounding data governance is that it falls solely within the purview of the IT department. In reality, effective data governance requires a collaborative effort involving multiple stakeholders across the organization. While IT plays a crucial role in implementing technical controls and ensuring data security, data governance must involve participation from business leaders, data owners, data stewards, and compliance professionals. A comprehensive data governance framework involves defining data policies, establishing accountability, and aligning data management with business objectives.

Myth 2: Data Governance Hampers Data Accessibility

Another common misconception is that data governance stifles data accessibility, making it difficult for employees to access and utilize data when needed. In fact, data governance aims to strike a balance between accessibility and security. By implementing robust data governance practices, organizations can ensure that data is accessible to authorized personnel while safeguarding it against unauthorized access and misuse. Effective data governance frameworks establish data access controls, define data usage policies, and enable data discovery tools to empower employees with the right data at the right time.

Myth 3: Data Governance is a One-Time Project

Data governance is often mistaken as a one-time project with a definite endpoint. However, data governance is an ongoing process that requires continuous monitoring, assessment, and improvement. It involves establishing data quality standards, implementing data cleansing procedures, and regularly auditing data assets for compliance. Data governance frameworks must evolve with changing business needs, technological advancements, and regulatory requirements. By treating data governance as a continuous effort, organizations can ensure the integrity, accuracy, and reliability of their data assets over time.

Myth 4: Data Governance is Only Relevant for Large Organizations

Some small and medium-sized enterprises (SMEs) believe that data governance is a luxury reserved for large corporations with vast amounts of data. However, data governance is equally crucial for SMEs, as they also handle sensitive customer information, financial records, and proprietary data. Implementing appropriate data governance practices can help SMEs mitigate risks, comply with data protection regulations, and gain a competitive advantage. A tailored data governance strategy that aligns with the size and scope of the organization can provide SMEs with the necessary structure to effectively manage their data assets.

Myth 5: Data Governance is an Expense, Not an Investment

One of the most prevalent myths surrounding data governance is that it is merely an expense without any tangible return on investment (ROI). However, data governance should be viewed as a strategic investment rather than an operational cost. By implementing robust data governance practices, organizations can enhance data quality, improve decision-making processes, increase operational efficiency, and reduce the risk of data breaches or regulatory non-compliance. A well-implemented data governance framework can lead to significant cost savings, increased customer trust, and improved business outcomes.

Final Thoughts

Data governance is an integral component of modern organizations’ data management strategies. By dispelling these prevalent myths, organizations can approach data governance with a clearer understanding of its significance and benefits. It is crucial to recognize that data governance is a collaborative effort involving stakeholders across the organization, that it enables data accessibility without compromising security, and that it requires ongoing commitment and adaptation. Regardless of the organization’s size, investing in data governance is a strategic decision that yields long-term benefits, empowering businesses to effectively leverage their data assets, drive informed decision-making, and maintain a competitive edge in the digital landscape.

As organizations continue to grapple with the challenges of managing and protecting data, debunking these data governance myths becomes increasingly important. By adopting a holistic and informed approach to data governance, businesses can unlock the true potential of their data while ensuring compliance, mitigating risks, and fostering a culture of data-driven innovation.

Data governance is not a mere buzzword or a set of rigid rules. It is a strategic practice that empowers organizations to harness the value of their data, optimize operations, and gain a competitive advantage. By dispelling common data governance myths, businesses can make informed decisions and implement effective data governance frameworks that align with their specific needs and objectives. In this data-rich era, embracing data governance is not a choice but a necessity for organizations aiming to thrive in the digital landscape.

The post The 5 Greatest Data Governance Myths and How to Avoid Them appeared first on Best Data Management Software, Vendors and Data Science Platforms.

Solutions Review editors created this short resource highlighting the most common data quality myths to stand clear of.

In today’s data-driven world, the quality of data has a profound impact on business decisions, operational efficiency, and overall success. However, there are several prevalent myths surrounding data quality that can mislead organizations and hinder their ability to leverage data effectively. In this article, we aim to debunk some of the greatest data quality myths, providing insights and best practices to help businesses navigate the complex landscape of data quality management.

Data Quality Myths

Myth 1: Data Quality is Solely an IT Responsibility

One of the most common misconceptions is that data quality is the sole responsibility of the IT department. In reality, data quality is a collective effort that requires collaboration between IT and business stakeholders. While IT plays a crucial role in implementing data quality tools and technologies, it is the responsibility of business users, data owners, and data stewards to ensure the accuracy, completeness, and relevance of data. Establishing clear roles, responsibilities, and processes that involve both IT and business teams is essential for effective data quality management.

Myth 2: Data Quality is Expensive and Time-Consuming

Another prevalent myth is that achieving high-quality data is a costly and time-consuming endeavor. While it’s true that data quality management requires an investment of resources, the long-term benefits far outweigh the initial costs. By proactively addressing data quality issues, organizations can avoid the detrimental effects of poor data, such as incorrect analytics, flawed reporting, and flawed decision-making. Implementing data quality best practices, such as data profiling, data cleansing, and data validation, can significantly improve the accuracy and reliability of data without breaking the bank.

Myth 3: Data Quality is a One-Time Fix

Some organizations believe that data quality is a one-time fix that can be achieved by cleansing data once and for all. However, data quality is an ongoing process that requires continuous monitoring, maintenance, and improvement. Data can degrade over time due to factors such as system changes, data migrations, or human error. Therefore, organizations must establish data quality monitoring mechanisms, implement data governance frameworks, and regularly assess the quality of their data to maintain its integrity and relevance. Viewing data quality as a continuous effort is essential for sustained data excellence.

Myth 4: Data Quantity Trumps Data Quality

In the era of big data, there is a prevalent misconception that the sheer volume of data outweighs its quality. While the abundance of data can provide valuable insights, it is essential to prioritize data quality over quantity. Poor-quality data can lead to misleading analysis, erroneous conclusions, and flawed decision-making, rendering the vast amount of data meaningless. Instead of accumulating massive volumes of data indiscriminately, organizations should focus on capturing high-quality data that is relevant to their business objectives. Quality data ensures that organizations make informed decisions based on accurate and reliable information.

Myth 5: Automated Tools Solve All Data Quality Issues

Automated data quality tools are undoubtedly valuable in identifying and addressing data quality issues. However, relying solely on automation is a common misconception. While these tools can streamline certain aspects of data quality management, they are not a silver bullet solution. Data quality management requires a human touch, involving the expertise of business users and data professionals who understand the context, business rules, and nuances of the data. Combining automated tools with human oversight and intervention ensures a comprehensive approach to data quality management.

Final Thoughts

Data quality is a critical aspect of modern business operations, and debunking these prevalent myths is essential for organizations to unlock the full potential of their data assets. By acknowledging that data quality is a collective responsibility, investing in proactive data quality management, embracing it as an ongoing process, prioritizing quality over quantity, and combining automation with human expertise, organizations can elevate their data quality practices to ensure accurate, reliable, and actionable data.

In today’s data-driven landscape, debunking these data quality myths is crucial for organizations striving to make informed decisions, improve operational efficiency, and gain a competitive edge. By dispelling these misconceptions, businesses can adopt a proactive and holistic approach to data quality management. It involves collaboration between IT and business stakeholders, recognizing data quality as an ongoing process, prioritizing accuracy over quantity, and leveraging a combination of automated tools and human expertise.

Organizations that invest in effective data quality management practices are better equipped to derive meaningful insights, drive innovation, and build a solid foundation for successful business strategies. By debunking these myths and implementing data quality best practices, businesses can harness the true value of their data assets, gain a competitive advantage, and ensure sound decision-making based on accurate and reliable information.

Data quality is not a trivial matter or an isolated IT concern. It is a fundamental aspect of organizational success that requires a collective effort, ongoing commitment, and the right blend of technology and human expertise. By dispelling the myths surrounding data quality, organizations can navigate the complex data landscape with confidence, ensuring that data becomes a strategic asset that empowers informed decision-making and drives sustainable business growth.

The post The 5 Greatest Data Quality Myths and How to Avoid Them appeared first on Best Data Management Software, Vendors and Data Science Platforms.

Solutions Review editors created this short resource highlighting the most common data management myths to stand clear of.

In today’s digital age, effective data management is crucial for businesses to thrive and make informed decisions. However, there are numerous myths and misconceptions surrounding data management that can hinder organizations from harnessing the full potential of their data assets. In this article, we aim to debunk some of the greatest data management myths, providing insights and best practices to help organizations navigate the complexities of data management and unlock its true value.

Data Management Myths

Myth 1: Data Management is Only for Large Enterprises

One prevailing myth is that data management is solely relevant to large enterprises with vast amounts of data. In reality, data management is essential for organizations of all sizes. Whether a startup, small business, or multinational corporation, effective data management ensures data accuracy, consistency, and accessibility. By implementing data management best practices, organizations can optimize their operations, streamline decision-making processes, and gain a competitive advantage, regardless of their size.

Myth 2: Data Management is Strictly an IT Responsibility

Another common misconception is that data management falls solely within the purview of the IT department. While IT plays a vital role in implementing data management systems and technologies, data management is a collaborative effort involving various stakeholders across the organization. Business leaders, data owners, data stewards, and compliance professionals must actively participate in data management initiatives. Effective data management requires defining data governance policies, establishing data ownership, and fostering a data-driven culture throughout the organization.

Myth 3: Data Management is a One-Time Project

A prevalent myth is that data management is a one-time project with a definite endpoint. However, data management is an ongoing process that requires continuous attention and improvement. Data is dynamic and constantly evolving, and organizations must adapt their data management practices accordingly. Regular data quality assessments, data cleansing, and data integration are essential to maintain data integrity and relevance. By treating data management as an ongoing effort, organizations can ensure that their data remains accurate, up-to-date, and valuable.

Myth 4: Data Management Is Solely About Data Storage

Data management is often mistakenly reduced to data storage and infrastructure. While data storage is an important aspect, data management encompasses much more. It involves the entire lifecycle of data, including data collection, data governance, data integration, data quality assurance, and data analysis. Data management strategies should focus not only on storing data but also on organizing, securing, and leveraging data to support business objectives and facilitate data-driven decision-making.

Myth 5: Data Management Hinders Data Accessibility

There is a misconception that robust data management practices hinder data accessibility and impede productivity. In reality, effective data management enhances data accessibility by establishing standardized data formats, organizing data repositories, and implementing data retrieval mechanisms. Through data management, organizations can ensure that authorized personnel can access relevant and accurate data in a timely manner. By striking a balance between data security and accessibility, data management empowers employees to make informed decisions and drive business growth.

Final Thoughts

Data management is a critical discipline that organizations must embrace to leverage the power of their data. By dispelling the myths surrounding data management, organizations can develop a comprehensive understanding of its significance and benefits. It is crucial to recognize that data management is not limited to large enterprises or the IT department alone. It requires collaboration, continuous effort, and the involvement of multiple stakeholders. Effective data management practices enable organizations to harness the true value of their data, enhance decision-making processes, and achieve sustainable business success in the digital era.

The post The 5 Greatest Data Management Myths and How to Avoid Them appeared first on Best Data Management Software, Vendors and Data Science Platforms.

Solutions Review editors curated this list of the most noteworthy data management news items for the week of June 30, 2023, including a number of features from the Snowflake Summit.

Keeping tabs on all the most relevant big data and data management news can be a time-consuming task. As a result, our editorial team aims to provide a summary of the top headlines from the last week, in this space. Solutions Review editors will curate vendor product news, mergers and acquisitions, venture capital funding, talent acquisition, and other noteworthy big data and data management news items.

Top Data Management News for the Week Ending June 30, 2023

Alation Drops Open Data Quality Framework for Snowflake, New Connected Sheets

With the Open Data Quality Framework, Alation customers can strengthen data governance for Snowflake by making data quality information visible. Launch partners include Acceldata, Anomalo, Bigeye, Datactics, Experian, FirstEigen, Lightup, and Soda. This product enables users to access governed, trusted, and up-to-date data in Snowflake directly from Microsoft Excel and Google Sheets.

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ALTR Expands Data Access Control Solution with Snowflake

The solution has an industry-first, format-preserving data protection module running natively on Snowpark, Snowflake’s development framework, providing better security, up to 1000x faster results on queries that need detokenization, cost efficiencies, and additional use cases for data sharing through easy tokenization of sensitive data.

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Anomalo Now Includes Metadata-Based Observability

Enterprises can now do basic monitoring of the entire data warehouse in minutes and at low cost and use that as a pathway into deep data quality monitoring to identify issues with the contents of their data. Anomalo will be showcasing this new capability this week at the Data + AI Summit by Databricks and Snowflake Summit.

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Ascend.io Joins Snowflake Partner Ecosystem

Ascend’s Data Pipeline Automation Platform is a single platform with intelligence to detect and propagate change across a company’s ecosystem—ensuring data accuracy and eliminating wasteful spend on data reprocessing. Pipelines powered by Ascend seamlessly ingest, transform, orchestrate, and share data for the business, across the entire end-to-end journey.

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New BMC AMI Cloud Sets Site on Mainframe Data Management

The BMC AMI Cloud portfolio allows customers to leverage hybrid cloud technologies with all the advantages of on-premises mainframe computing for large-scale, business-critical transactions while gaining the benefits of the cloud. By unlocking their mission-critical data, organizations gain increased business intelligence to make better decisions with greater security, flexibility, scalability, and efficiency.

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Boomi’s Intelligent Automation Toolset Now Available on Amazon Web Services

AWS customers can accelerate their automation initiatives by seamlessly connecting and integrating enterprise-wide data sources, applications, and systems – such as enterprise resource planning (ERP), customer relationship management (CRM), and ecommerce platforms, among others – both on-premises and on the cloud with Boomi’s low-code platform.

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Cloud Software Group Secures Strategic Partnership with Midis Group

The partnership provides Cloud Software Group with the local resources customers need to support their transformative technology journey and the scale required to expand its reach in these regions. A leading technology partner providing managed IT services and consultancy, system integration, cloud and data center capabilities, infrastructure, software, and hardware solutions, the Midis Group serves customers through a network of 170 companies across 70 countries.

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Cloudera Enhances Open Data Lakehouse Offerings

Cloudera Data Platform (CDP) provides a safe, fast path to trusted Enterprise AI based on an advanced open data lakehouse that enables deployment of the latest AI models with data anywhere. Cloudera makes emerging technologies like large language models (LLM) and real-time self-service analytics at scale easily accessible to all its customers.

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Coalesce Unveils New Collaborative Data Transformation Features

The latest platform upgrades include a new feature called “Projects,” which organizes data transformation pipelines into distinct workspaces within Coalesce. Over the next few months, customers will be able to share transformed data and track its evolution across projects.

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data.world Announces Governance & Data Catalog Integrations with Snowflake Snowpark

Through the integration, data.world provides data catalog, data governance, and DataOps applications that help Snowflake customers accelerate Snowpark migrations and govern Snowpark metadata. The integration follows data.world’s recent achievements of Powered by Snowflake, Data Governance Accelerated, and Snowflake Premier Partner status.

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Dataiku Now Runs in New Snowpark Container Services

Snowflake, the Data Cloud company, announced the launch of Snowpark Container Services to expand the scope of Snowpark to help organizations run third-party software and full-stack applications — all within their account. By being able to access and run commercial software and apps like Dataiku directly in their Snowflake account, joint customers can seamlessly enhance the value of their data using cutting-edge tools without moving or compromising its security.

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Datometry Announces Airlife for Oracle to PostgreSQL Migrations

Datometry Airlift is Datometry’s new product line for the direct-to-customer market. Airlift makes applications written for Oracle instantly run on PostgreSQL without the need for code changes. Based on a decade of research and development, Airlift makes the fidelity and accuracy of Datometry’s enterprise products available to marketplace customers.

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DQLabs Drops its Data Quality Platform on Snowflake Cloud

Joint customers will be able to leverage out-of-the-box automation of business quality checks using DQLabs’ Modern Data Quality Platform, Powered by Snowflake. This automation has helped customers drastically reduce inefficiencies across business processes, decreased 85% of the hours spent on manual upkeep of their data quality checks and gain 10x improvement in operational efficiency.

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Dremio Unveils Platform Performance & Versatility Updates

These new capabilities empower organizations to accelerate their data analytics and enable faster, more efficient decision-making. Dremio is ensuring easy self-service analytics—with data warehouse functionality and data lake flexibility—across customer data.

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Edge Delta Announces New Visual Observability Pipeline Flows

Visual Pipeline was designed and architected with large enterprise organizations in mind. It gives these organizations the ability to support and scale pipelines spanning hundreds of teams across thousands of data sources, thereby unlocking open observability.

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Immuta Drops Platform Update for Simplifying Data Security & Monitoring on Snowflake

The partnership between Immuta and Snowflake continues to have strong momentum, fueled by the growing adoption of their solutions at organizations across industries, including JB Hunt, Roche, and Thomson Reuters. In the last year, Snowflake has awarded Immuta with competency recognitions in the Healthcare & Life Sciences and Financial Services sectors.

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Informatica Announces SuperPipe for Snowflake for Faster Integration & Replication

Informatica announced the launch of four new product capabilities to bring greater simplicity, speed, and performance to data integration and replication for customers in the Snowflake ecosystem: Informatica Superpipe for Snowflake, Enterprise Data Integrator Snowflake native application, Cloud Data Integration-Free for Snowflake, and support for Apache Iceberg on Snowflake.

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Kensu Drops the First Data Observability Solution For Matillion

By integrating with Matillion, Kensu retrieves valuable context regarding data runs as well as information about the data sources used in the run. In parallel, Kensu connects to tables to retrieve schemas and metrics, feeding this information back to data practitioners.

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Matillion Announces New Productivity Platform for Teams

It empowers all practitioners, from low-code users to dbt developers, to engineer their data’s movement, transformation, and orchestration at an unprecedented scale. The platform provides a best-in-class experience for data teams to collaborate via low-code and high-code frameworks that unify every workload across a cloud data infrastructure.

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Parabola Secures $24 Million in Series B Funding

Parabola provides a solution for businesses beyond hiring more people for every new problem. Instead, non-technical teams can stop doing things manually, and start creating, documenting and sharing reusable workflows. Combining the power of AI with familiar spreadsheet functionality, Parabola enables users to extract, analyze, categorize and experiment with data to solve real use cases.

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Qlik and Talend Extend Transformation, Quality & Analytics Tools on Snowflake

The combination of Qlik’s data integration and analytics solutions, alongside Talend’s hybrid-cloud approach to data connectivity and data quality, is a unique end-to-end portfolio of solutions that help Snowflake customers find, transform, trust, and analyze their data at scale.

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Redpanda Raises Cool $100 Million Dollars in Series C Funding Round

Redpanda continues to cement its position as the streaming data platform of choice for both transactional and analytical applications. With the ongoing cross-industry transformation of applications from batch to real-time and the rapid adoption of AI and ML, Redpanda has experienced a bumper fiscal year including 5X revenue growth and a workforce that has more than doubled.

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Reltio Adds AI-Driven Real-time Capabilities to MDM Platform

Reltio has leveraged AI/ML so that customers can quickly and accurately achieve value from their Customer, Product, Supplier, and other Core business data domains. Unlike other solutions that require weeks of manual effort before the ML recommendations can be effective, Reltio’s entity resolution capability delivers instant value.

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Safe Software Expands Snowflake Partnership That Began in 2019

Safe Software’s consumption-based pricing model option, similar to Snowflake’s, allows customers to deploy FME in their preferred environment (on-premises, cloud, hybrid) and only pay for what they use. This partnership provides a new level of flexibility and convenience for customers to leverage Snowflake’s single, integrated platform and data for their unique data integration needs.

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SAS Enables Provisioning of Viya AI on Snowflake Data Cloud

The integration supports the complete AI and analytics life cycle, from data discovery and modeling development to decisioning. The SAS and Snowflake partnership empowers organizations with interactive, real-time insights across industries – including banking, government, health care and financial services – in a single, secure environment.

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Snowflake Makes Slew of Product Enhancements at Snowflake Summit 2023

With new innovations like Document AI (private preview), Snowflake is launching a new large language model (LLM) built from Applica’s pioneering generative AI technology to help customers understand documents and put their unstructured data to work. Snowflake is also unveiling updates to Iceberg Tables (private preview soon) to further eliminate data silos and allow organizations to use open table formats with fast performance and enterprise-grade governance for both data in Snowflake’s catalog and data managed by another catalog.

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Soda Merges Generative AI and Data Quality into SodaGPT

SodaGPT combines the domain-specific language capabilities of SodaCL with the Natural Language Processing (NLP) power of gen-AI, to provide a platform for data consumers and data engineers to work together to produce data that can be trusted.

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Tamr Unveils New ‘Smart Curation’ Functionality on Snowflake

Tamr’s integrated turn-key solutions combine data quality capabilities, machine learning-based matching models, referential data, and third-party enrichment to help data teams deliver more impact. With Tamr Smart Curation, a Snowflake Native App, customers can expect a myriad of benefits.

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Expert Insights Section

Watch this space each week as Solutions Review editors will use it to share new Expert Insights Series articles, Contributed Shorts videos, Expert Roundtable and event replays, and other curated content to help you gain a forward-thinking analysis and remain on-trend. All to meet the demand for what its editors do best: bring industry experts together to publish the web’s leading insights for enterprise technology practitioners.

Solutions Review Set to Host Alteryx for Exclusive Spotlight Webinar on July 27

With the next Solutions Spotlight event, the team at Solutions Review has partnered with leading analytics, data science, and automation vendor Alteryx. Through case studies and practical examples, Alteryx’s Field Chief Data & Analytics Officer, Heather Harris, will help you learn the keys to capturing the business impact or your analytic solutions.

Read on for more.

‘Data Governance Coach’ Nicola Askham Set to Host Free Online Masterclass entitled Building the Business Case for Data Governance on July 6

Making the case for Data Governance can be extremely hard. For many reasons, the most important of which we’ll explain in this masterclass. Join Alex and Nicola for this free masterclass as they explore how to build a successful Data Governance business case.

Read on for more.

For consideration in future data management news roundups, send your announcements to the editor: tking@solutionsreview.com.

The post Data Management News for the Week of June 30; Updates from Alation, Qlik, Snowflake & More appeared first on Best Data Management Software, Vendors and Data Science Platforms.

Solutions Review editors curated this list of the most noteworthy data management news items for the week of June 23, 2023.

Keeping tabs on all the most relevant big data and data management news can be a time-consuming task. As a result, our editorial team aims to provide a summary of the top headlines from the last week, in this space. Solutions Review editors will curate vendor product news, mergers and acquisitions, venture capital funding, talent acquisition, and other noteworthy big data and data management news items.

Top Data Management News for the Week Ending June 23, 2023

Acryl Data Raises $21 Million Series A Funding

Acryl’s product – Acryl Cloud – is an enterprise-ready data management platform powered by the open-source DataHub Project. The team at Acryl leads the DataHub Project and Community with over 7,500 data practitioners, and enables practitioners to collaborate on industry-leading data management capabilities.

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Aerospike Unveils New Real-Time Graph Database Solution

Aerospike Graph delivers millisecond multi-hop graph queries at extreme throughput across trillions of vertices and edges. Benchmarks show a throughput of more than 100,000 queries per second with sub-5ms latency — on a fraction of the infrastructure.

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Alation Joins Databricks Partner Connect

With Alation and Databricks’ expanded partnership, customers can now scale data access for lakehouse adoption, discover and migrate high-value data, and with integration with Databricks Unity Catalog, govern and catalog metadata across multiple workspaces. This enables customers to answer bigger, more impactful questions with their data than ever before.

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Astera Releases New Suite of AI-Driven Integration & Extraction Tools

The initial integration will be with Astera’s data extraction solution, ReportMiner. With new AI capabilities, users can generate report models 90 percent faster, providing customers with more accurate, actionable insights in a fraction of the time. The primary feature of this AI-driven tool is its ability to automate template creation, which streamlines the process of creating report models.

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Bigeye Announces Acquisition of Data Advantage Group

The integration of Data Advantage Group’s extensive data lineage capabilities allow Bigeye to automatically map data lineage across transactional databases, ETL platforms, data lakes, data warehouses, and business intelligence tools.This acquisition gives Bigeye customers the most sophisticated view of data pipelines available to date from a data observability provider.

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ClickHouse Launches on Google Cloud Platform

With transparent, pay-as-you-go pricing, ClickHouse Cloud users can get started on GCP with a few clicks. Backed by a robust serverless architecture, ClickHouse Cloud dynamically scales based on project needs and eliminates the operational overhead of having to self-manage, over-provision or pay for unused capacity.

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New Cloudera Research Focuses on Enterprise AI Readiness in EMEA

Conducted by Coleman Parkes Research, Cloudera’s survey evaluated the opinions of 850 IT decision makers with responsibility for data analytics and tooling in their organization across the EMEA region. Respondents came from over ten industries. The research was conducted between March and April 2023. The next report on open data lakehouse will be published in the fall of 2023.

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New Confluent Research Shows Key Benefits of Data Streaming

Conducted by Coleman Parkes Research, Cloudera’s survey evaluated the opinions of 850 IT decision makers with responsibility for data analytics and tooling in their organization across the EMEA region. Respondents came from over ten industries. The research was conducted between March and April 2023. The next report on open data lakehouse will be published in the fall of 2023.

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data.world Launches Data Governance App with Generative AI

data.world’s inclusion of Eureka Bots in both its standard Data Catalog Application and in the new premium Data Governance Application ensure that teams at every stage of data catalog adoption can utilize robust governance capabilities. With diverse governance automations, teams can elevate the focus of data governance teams from purely tactical program execution to strategic initiative leadership.

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DiffusionData is Now Available on Amazon Web Services

The company’s Diffusion Data Platform consumes raw data in any size, format, or velocity; enriches the data in-flight; and distributes the data in real time reliably and at scale with secure, fine-grained, role-based access control. By listing through the AWS Marketplace, organizations that use AWS can streamline the purchase and deployment of Diffusion, giving development teams a simplified way to deploy the platform into their environments.

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Informatica Launches in Japan Region

Informatica’s IDMC is a comprehensive cloud-native data management platform that enables enterprises to visualize, analyze and collaborate with their data regardless of location or platform. The expansion of this cloud footprint further extends the years-long collaboration between Informatica and AWS.

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Manta Announces New Partnership with Teclever Solutions

Manta has partnered with Teclever solutions to bring automated data lineage to businesses around the world, expanding the reach of Manta’s data lineage platform.  Manta is the provider of a unified and automated data lineage solution and has global operations based in the US, Czech Republic, Portugal, Ireland, and the UK. This partnership will provide additional support to the APAC region.

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Matillion Publishes New Data Productivity Survey

The survey polled more than 900 experts across the United States and the United Kingdom to determine the impact of data and business demands on data teams. Survey results illustrate that data teams are overextending themselves to meet business demands.

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Privacera Drops New AI Governance Solution, Databricks Unity Catalog Connector

With native enforcement of security and privacy controls across diverse data estates, and architectures, and built on open standards, Privacera’s latest innovation helps companies reduce sensitive data exposure, increase privacy and ethics, and address regulatory and legal compliance issues with AI.

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Promethium Enhances Data Fabric Tool with Generative AI Features

The company’s Diffusion Data Platform consumes raw data in any size, format, or velocity; enriches the data in-flight; and distributes the data in real time reliably and at scale with secure, fine-grained, role-based access control. By listing through the AWS Marketplace, organizations that use AWS can streamline the purchase and deployment of Diffusion, giving development teams a simplified way to deploy the platform into their environments.

Read on for more.

ThinkData Works Unveils New Data Lineage Solution

The new tool will provide critical visibility into data relationships, enabling upstream and downstream monitoring of data pipelines for rapid impact analysis, quality control, and enhanced governance. The solution, which uses ThinkData’s powerful data catalog to predict lineage relationships between datasets, is supported by ThinkData Works’ characteristically intuitive user interface.

Read on for more.

Expert Insights Section

Watch this space each week as Solutions Review editors will use it to share new Expert Insights Series articles, Contributed Shorts videos, Expert Roundtable and event replays, and other curated content to help you gain a forward-thinking analysis and remain on-trend. All to meet the demand for what its editors do best: bring industry experts together to publish the web’s leading insights for enterprise technology practitioners.

Solutions Review Set to Host Qlik for Exclusive Spotlight Webinar on June 29

With the next Solutions Spotlight event, the team at Solutions Review has partnered with leading analytics and data integration platform vendor Qlik for an exclusive webinar showcasing a leading embedded analytics platform. The demo will be presented by their customer, PERSUIT.

Read on for more.

Solutions Review Set to Host Alteryx for Exclusive Spotlight Webinar on July 27

With the next Solutions Spotlight event, the team at Solutions Review has partnered with leading analytics, data science, and automation vendor Alteryx. Through case studies and practical examples, Alteryx’s Field Chief Data & Analytics Officer, Heather Harris, will help you learn the keys to capturing the business impact or your analytic solutions.

Read on for more.

For consideration in future data management news roundups, send your announcements to the editor: tking@solutionsreview.com.

The post Data Management News for the Week of June 23; Updates from Alation, Cloudera, Informatica & More appeared first on Best Data Management Software, Vendors and Data Science Platforms.