Top Tools for your Data Management Stack

[Free Takeaway Inside – Must-Use Analytical Techniques across the Customer Lifecycle]

“You can’t act on what you can’t see!”

Data is the most important asset that helps businesses ‘see’ their customers and spot trends in the market.

In today’s ever-changing data landscape, organizations need to be able to not only capture data, but act on it to drive business success. This is why it’s paramount that your data and analytics strategy is built on a robust data management foundation.

A solid data management foundation relies on several architectural components that organizations need to consider in order to operationalize strategic decision-making. These platforms are a combination of interoperable and scalable technologies working together to deliver an enterprise’s overall data needs.

It essentially lays the groundwork to implement effective data management practices.

Here’s an outline of the different types of platforms used in each stage of data management that rounds up to form a modern data architecture stack:

1. Data Ingestion

Manually extracting data from disparate first, second and third-party sources is cumbersome. That’s where data ingestion tools come into play. It automatically collects data from a wide range of sources and facilitates the transfer of data streams into a central repository. In addition to data extraction and transfer, some data ingestion tools also help with normalizing and formatting data into a standard format or schema, before passing it on to a storage layer. Some types of data ingestion tools include:

  • Hand Coding – Ingest data through hard coding and control the movement of data through a pipeline.
  • Open Source Data Ingestion Tools – Automate ingestion models using a drag and drop interface to connect data streams and transform data. Eg - Apache Flume, AirByte, Coefficient, Wavefront, Talend.
  • ETL Data Pipelines – Extract data from different sources, transform and process it to a standard format, and finally load data to its destination. Eg – Airflow, AWS Glue, Stitch, Fivetran.

2. Data Storage

Data storage has evolved a long way since the days of disk-based and manual systems. Now all data is attached to a network or software-defined and is a key component of data management. From cloud, block to file and hybrid storage, different types of data storage technologies exist today. Here are some types of data storage tools:

  • Data Lake – A vast pool that houses significant amounts of raw data in its native format until it’s needed for analytics applications. Eg – Hadoop Distributed File Systems, NoSQL databases, Amazon S3, Google Dataproc, Azure HDInsight
  • Data Warehouse – A storage repository for structured and filtered data that has already been processed for a specific purpose. Eg – Relational database systems, Snowflake, Google BigQuery, Amazon Redshift, Azure Synapse, IBM D2 Warehouse

3. Data Integration

If data has been collected and stored in standalone silos or separate data stores, it’s vital to unify and integrate it to create a single version of truth. Data integration can consolidate all kinds of data, including — structured, unstructured, batch, and streaming — to do everything from basic querying of inventory databases to complex predictive analytics. Data integration platforms generally include many of the following tools:

  • Master Data Management Software – Create uniform sets of data on customers, suppliers, products and other business entities to ensure semantic consistency and accountability of the enterprise’s official shared data assets. Eg – Attacama One, Dell Boomi, IBM Infosphere, Oracle EDM, SAP Master Data Governance.
  • Data Management Platform – Integrate first, second and third-party data sources to generate granular and dynamic customer segments for targeting and online media buying. Eg – Salesforce DMP, Lotame, SAS Data Management, Nielsen.
  • Customer Data Platform – Collect, organize and unify cross-channel customer data from first and second-party sources to create a 360-degree view of the customer. Eg – Segment, Tealium, Salesforce CDP, Adobe Real-time CDP.
  • Data Connectors – Mix and match data sources together into one integrated space to build effective analytical models that will crack the answers you need. Eg – Hevo Data,, Informatica Cloud Connector.

4. Data Governance

A well-designed data governance program is a critical component of effective data management strategies, particularly in organizations with distributed data environments. It is the process that defines internal standards and policies that control access to data, and how it’s stored and used further in business operations and analytics. It often incorporates data security initiatives to ensure that data is consistent, trustworthy and complies with privacy regulations. Data governance tools help organizations automate various aspects of managing a governance program including:

  • Data Catalog – Provide a glossary of data assets that enable users to add a title, description and other metadata to promote searchability, accessibility, linkability, and compliance. Eg – Google Data Catalog, Alation, Cloudera Navigator, Tableau Catalog, SAP Data Intelligence.
  • Data Modeling and Documentation – Create a data dictionary that will provide full context to why it exists including the structure of the data and its contents, and any alterations done. Eg – ApexSQL, Datalogz, Microsoft Word or Excel.
  • Data Stewardship – Empower the experts that are well-versed with data management to clean, certify and reconcile data and delegate tasks to others who know the data best. Eg – Talend, Informatica, Ataccama.
  • Data Lifecycle Management – Control the flow of data through its lifecycle – from creation to storage to when it becomes obsolete and is deleted. Eg – AWS Data Lifecycle Manager, Spirion, Qlik

5. Data Activation and Usage

The final step of data management is to act on the data captured, stored and integrated into the core data stack. This phase entails deep integrations throughout the ad and marketing ecosystem. A few applications of data activation include customer segmentation, content personalization, executing dynamic ad campaigns, monetizing audience data, creating real-time reporting dashboards and more. Here are some data activation tools to put your data into action:

  • Personalization Engines – Orchestrate a central decisioning engine based on a set of business rules and regression models to match specific messages, offers and experiences to customer propensity scores and buying signals. Eg - Adobe Target, Optimizely Intelligence Cloud, Dynamic Yield, Emarsys Customer Engagement Platform, Acquia.
  • Campaign Management System – Push customer data such as enriched customer personas, and segments to campaign management tools and execute dynamic ad campaigns across channels based on pre-defined triggers and content delivery mechanisms. Eg – MailChimp, HubSpot Marketing Hub, Adobe Campaign, SharpSpring, ActiveCampaign.
  • Marketing Analytics – Create customized reporting dashboards and insights into ad performance to maximize its effectiveness and optimize ROI. Eg – Google Analytics, Adobe Analytics, Matomo, Hotjar.

Data Management is for all

Data management helps brands deeply understand their customers through a holistic, unified, centralized and real-time view of data. Regardless of the size or scale of the business, brands must take the first step to invest in a solid data management infrastructure to democratize data across their organization. Armed with the right tools and technologies, organizations can gain an upper hand to make sense of data and deliver superior customer experiences.