What are the best data management solutions?

As data volumes have grown, businesses have invested heavily in data management technology, with the aim of administering their entire data estate. Initially this focus was a reactive, compliance, security and governance-driven need to protect the company and its data while meeting regulations (such as GDPR, and HIPAA). Data management was seen as a cost-center, rather than as something that added to revenues.

Particularly thanks to the rise of AI, data management is moving beyond compliance. Companies want to ensure that their data delivers value through wider, secure sharing and consumption across the entire business. This requires an updated data management technology stack, focused on management, consumption and collaboration, alongside governance and security. 

However, there are hundreds of separate tools now available in this space, across a wide range of categories. Which ones should data teams look to deploy, and how will they deliver ROI and support business objectives? Discover the main types of tools needed to build an effective data stack in your organization.

When it comes to managing data, businesses have to operate in an increasingly complex environment. Data is being generated on an ongoing basis by business systems and Internet of Things sensors, collected or bought from third parties. It is stored in cloud-based solutions such as Amazon Web Services or Google Cloud, locally in data warehouses/lakes or lakeshouses, or in business tools such as Google Drive or Microsoft SharePoint.

None of this raw data has value in its basic form. It needs to be processed, certified, checked and enhanced to make it understandable and usable in areas such as analytics, AI initiatives, operational reporting, regulatory compliance, and cross-functional decision-making. It is vital that data is available across the business to all types of user, meaning it has to be accessible to those without technical skills.

Successfully managing this data complexity requires high-level architecture choices that balance centralization and federation, such as between data fabric, data mesh or hybrid approaches. 

Given the choice of tools and solutions on the market, where should businesses look to invest to maximize value and ROI? As shown in the graphic, there are five key layers to focus on to build a successful modern data stack.

Data ingestion is the foundation of the data stack. It covers the process of collecting, importing, and moving raw data from various sources into a centralized destination, so that it can be processed, cataloged, enhanced and ultimately used. Data can be moved in real-time or through batch processing, with a range of different tools available depending on the source and ultimate destination. 

Examples of data ingestion & integration include: 

• Fivetran, 
• Apache Kafka, 
• AWS Glue
• Google Cloud Dataflow.

Data integration tools such as Talend, move and transform data between sources and destinations, using techniques such as Extract Transform, Load (ETL) to create consistency and common formats between different systems. They enable the creation of automated data pipelines that ensure that data is integrated, verified and trustworthy across the business. 

Moving or duplicating data can be difficult or inefficient. Compliance rules might make it hard to centralize certain datasets in another solution, while duplication adds to storage and management costs, and can impact creating a single version of the truth. Data virtualization aims to overcome these challenges, making data available securely where it is needed, ensuring flexibility and control. Denodo is a leading data virtualization player, integrating with data consumption solutions such as Huwise to drive effective data sharing.

By centralizing data and breaking down silos, businesses are able to better manage, query and analyze their information. Four main types of tool provide this centralization and single version of the truth:

• Data warehouse: a single, centralized, large repository for storage, analysis and reporting of structured or semistructured data. 
• Data mart: a smaller subset of a data warehouse, containing less data which means that analysis and processing is faster.
• Data lake: a large-scale, centralized repository which stores and processes structured, semistructured, and unstructured data in its raw format. 
• Data lakehouse: a hybrid approach that combines the ability to use structured analytics (as in a data warehouse), with the opportunity to store data in its raw form (as in a data lake).

The main difference between a data warehouse and a data lake is how data is stored, and what that means for its usage. A data warehouse contains structured data that has been cleansed and standardized to fit with specific models or use cases. By contrast, a data lake contains raw data. This means it can be accessed for a variety of immediate or future uses, rather than specific, pre-set uses. 

Platforms such as Databricks and Snowflake deliver a centralized repository that bring together data and make it available to technical analysts.

Ensuring that data is reliable, accurate and high-quality is vital to being able to analyze and consume it successfully. Without clear quality processes in place, data can be incomplete, unclear or inconsistent, preventing its use.

Master data is non-transactional data that provides context by describing transactional data and making it easier to categorize, understand and manage. Master Data Management (MDM) solutions cover how this master data is created, shared, updated, and used. By setting and enforcing consistent and uniform definitions across an organization MDM solutions and processes ensure accuracy and trust in data. Key vendors include Informatica.

Data quality measures the condition of data, based on areas including accuracy, completeness, timeliness, consistency and reliability. Data quality tools, such as Talend, therefore profile, validate, and cleanse data so that it meets set quality standards, making it usable and trustworthy. Data observability tools take this a step further, continuously monitoring the data estate to detect when those standards are at risk, before they cause failures. Sifflet and Informatica both offer powerful observability tools.

Ensuring that data is protected and compliant is a key part of the CDO role. Data governance is central to this, covering how you identify, organize, handle, manage, and use data collected in your organization. It comprises processes and frameworks, and the technology needed to manage and ensure governance rules are followed at all times. Effective data governance reduces risk and enables agility, innovation and greater consumption by ensuring that data is trusted by both humans and AI. Atlan is a leading vendor in this space, with its modern data catalog technology evolving from handling governance to additionally providing the context layer for AI. Huwise is an Atlan Context Layer Partner, helping organizations to make their context layer complete.

One of the foundations of effective data governance is knowing exactly what data the organization owns, where it is located, and how it is used. Data catalogs provide a technical inventory of all data based on structured metadata, ensuring compliance and control. However, data catalogs essentially act as an index to data – they do not enable users to directly access it. Equally, they are technical tools with interfaces designed for data and IT teams, rather than business users, limiting their adoption. Both these factors mean that data catalogs such as Collibra do not drive data consumption across the business, limiting business impact and data value. 

Analytics and business intelligence tools enable trained data analysts to query data, normally in the data warehouse or data lake, in order to produce reports and dashboards for business use. Tools such as Tableau and Microsoft Power BI deliver insights, but can only be used by those with specialist, technical skills. They are too complex for business users to benefit from directly, meaning that they must rely on technical teams to run queries on their behalf. This slows down the availability of insights, adds to data team workloads and prevents business users from directly interacting with data.

Data product marketplaces provide a seamless, centralized and self-service consumption layer for all business users and AI. An e-commerce style experience and features such as AI-powered search, recommendations and ratings all help non-technical employees discover and consume data products, and other data assets, that they need in their daily working lives. Unlike a data catalog, data is immediately accessible to users from the data marketplace, with granular access management providing security and compliance. A business glossary ensures consistency of terms across data, building trust, while data lineage provides end-to-end tracking of where data has been used by humans and AI. 

Essentially, the data product marketplace completes the data management stack, making curated, high-quality and well-governed data available to all, without requiring support from data or IT teams. Rated by Gartner as “essential”, data marketplaces transform data into a core strategic asset, drive consumption at scale, underpin effective and compliant AI use cases and maximize ROI from investments in data. Huwise has been cited multiple times by Gartner as a leading solution in the data marketplace market.

The complexity of data management stacks has added to the workload of data teams, as they need to integrate and manage multiple, overlapping solutions. This adds to cost and can reduce flexibility and agility, particularly in terms of making trusted, understandable data immediately available to business users and AI.

To overcome this issue, converged data management platforms (DMPs) are now appearing, evolving from point solutions to cover multiple layers of the data stack. They offer multiple capabilities, consistency and scalability across the organization. This reduces cost, while also providing easier management and monitoring, greater agility, improved governance, and the faster supply of AI-ready data. Gartner believes that DMPs can reduce expenditure by 50%, freeing up resources for more strategic and impactful investments. However, while DMPs rationalize much of the stack, they do not provide best-of-breed capabilities in every area. Gartner therefore recommends that they are complemented with data marketplaces to deliver effective data sharing and consumption capabilities. 

Precisely is a strong player offering a DMP, and through its partnership with Huwise is able to provide a data product marketplace as part of its solution, maximizing value and minimizing complexity.

CDOs understand that they must build a data stack that is focused on consumption and value for the business, rather than simply managing data. This requires a range of interoperable tools that can integrate with each other seamlessly, while supporting transversal capabilities such as quality, lineage, governance and now context and semantics.

However, traditionally organizations have struggled to drive greater data discovery and use by business teams. That makes it vital for CDOs to complete their data management architecture, adding a consumption layer that provides secure, self-service access to data for business teams and AI – the data marketplace. This turns data into value and delivers ROI from data management investments, future-proofing operations and maximizing impact.