Definition
Data Extract is the practice of selecting data from one or more sources for the purpose of storing it, transforming it, integrating it and analyzing it for business intelligence or advanced analytics. Data extracts take all or a portion of data from a source, and is the first step in the process referred to as ETL: Extract, Transform and Load for turning source data into relevant, accurate analysis-ready data products that can be used to create actionable insights and analytics.
Purpose
The purpose of data extracts is to select the portion of data from a source that is desired to support delivery of relevant analysis-ready datasets (e.g., data products) for AI and BI. Data extraction is also known as data collection: gathering data from different sources and types (e.g., web pages, emails, flat files, spreadsheets, databases, documents, video, voice, text). Source data may be structured or unstructured. There are two major types of extracts:
- Full Extract – A full extract is taking the entire source dataset as it is available – this sometimes is referred to as a “full data dump”. It is possible that only a portion of the entire data source may be desired, however, to ensure that the portion obtain is complete, it is often advisable to obtain the entire dataset in raw form, and then extract portions of the data as needed to ensure that as needs change, the desired data will still be available from the source. This approach is particularly popular using cloud technology as the cost to store data is relatively low compared with the risk of not having the desired data available – and it is why the process of ETL is often referred to as ELT – where extraction and loading the data is done before transformations are made.
- Partial Extract – A partial extract is taking a snippet of the data source, and often this approach is used when the entire dataset is not relevant. Often, API’s (Application Protocols) are used to extract and transmit specific fields and views of the data, as well as using standard database query languages such as SQL.
Primary Uses of Data Extract Software
Data Extract is used to select specific data elements from a given data source, in order to make the data ready for analysis and analytics through subsequent steps involving transformation and loading. Data Extract software enables extracts to be made from many different data source types, both structured and unstructured data, as well as data captured entirely or partially as well in batch or continuous mode. Data extract software needs to give the user the flexibility to handle the variety, velocity and volume of data sources at scale, ideally with minimal manual coding and maintenance.
Benefits of Well-Executed Data Extract
The benefits of data transformation are to deliver data that is available for analysis and analytics use with speed, scale and cost effectiveness. Key benefits are listed below:
- Speed – Insights created from data enable actions to be taken faster, because the insights are structured to address business questions more timely and effectively.
- Scale – Data extract processes support an ever increasing number of data sources, users and uses that are also increasingly diverse across functions, geographies and organizations.
- Cost Effectiveness – With more data, comes more cost for data storage, compute and resources to manage. Data Extract processes configured to ensure costs are minimized by focusing on providing consistent, effective processes that are automated, low code / no code self-service oriented, reusable with minimal resources and hand-offs.
- Flexible – Data Extract capabilities should be capable of addressing the myriad options impacting data source types, including volume, variety and velocity.
Common Roles and Responsibilities for Data Extract
Business Intelligence and the resulting creation of actionable insights from data delivered to business users involves the following key roles:
- Data Engineers – Data engineers create and manage data pipelines that transport data from source to target, including creating and managing data transformations to ensure data arrives ready for analysis.
- Analytics Engineers – Analytics engineers support data scientists and other predictive and prescriptive analytics use cases, focusing on managing the entire data to model ops process, including data access, transformation, integration, DBMS management, BI and AI data ops and model ops.
- Data Modelers – Data Modelers are responsible for each type of data model: conceptual, logical and physical. Data Modelers may also be involved with defining specifications for data transformation and loading.
- Technical Architect – The technical architect is responsible for logical and physical technical infrastructure and tools. The technical architect works to ensure the data model and databases, including source and target data is physically able to be accessed, queried and analyzed by the various OLAP tools.
- Data Analyst / Business Analyst – Often a business analyst or more recently, data analyst are responsible for defining the uses and use cases of the data, as well as providing design input to data structure, particularly metrics, topical and semantic definitions, business questions / queries and outputs (reports and analyses) intended to be performed and improved. Responsibilities also include owning the roadmap for how data is going to be enhanced to address additional business questions and existing insights gaps.
Key Business Processes Associated Data Extract
The processes for delivering data extract include the following:
- Access – Data, often in structured ready-to-analyze form and is made available securely and available to approved users, including insights creators and enablers.
- Profiling – Data are reviewed for relevance, completeness and accuracy by data creators and enablers. Profiling can and should occur for individual datasets and integrated data sets, both in raw form as was a ready-to-analyze structured form.
- Extraction / Aggregation – The integrated dataset is made available for querying, including, including aggregated to optimize query performance.
Common Technologies Categories Associated with Data Extract
Technologies involved with data extract are as follows:
- Data Engineering – Data engineering is the process and technology required to move data securely from source to target in a way that it is easily available and accessible.
- Database – Databases store data for easy access, profiling, structuring and querying. Databases come in many forms to store many types of data.
- Data Warehouse – Data warehouses store data that are used frequently and extensively by the business for reporting and analysis. Data warehouses are constructed to store the data in a way that is integrated, secure and easily accessible for standard and ad-hoc queries for many users.
- Data Lake – Data lakes are centralized data storage facilities that automate and standardize the process for acquiring data, storing it and making it available for profiling, preparation, data modeling, analysis and reporting / publishing. Data lakes are often created using cloud technology, which makes data storage very inexpensive, flexible and elastic.
Trends / Outlook for Data Extract
Key trends to watch in the Data Extract arena are as follows:
- Semantic Layer – The semantic layer is a common, consistent representation of the data used for business intelligence used for reporting and analysis, as well as for analytics. The semantic layer is important, because it creates a common consistent way to define data in multidimensional form to ensure that queries made from and across multiple applications, including multiple business intelligence tools, can be done through one common definition, rather than having to create the data models and definitions within each tool, thus ensuring consistency and efficiency, including cost savings as well as the opportunity to improve query speed / performance.
- Automation – Increase emphasis is being placed by vendors on ease of use and automation to increase speed-to-insights. This includes offering “drag and drop” interfaces to execute data-related preparation activities and insights creation / queries without having to write code, including reusing activities and processes, both for repeating use as well as sharing.
- Self-service – As data grows, availability of qualified data technologists and analytics are very limited. To address this gap and increase productivity without having to lean 100% on IT resources to make data and analysis available, Self-service is increasingly available for data profiling, mining, preparation, reporting and analysis. In addition tools like the Semantic Layer offered by AtScale, Inc are also focused on enabling business users / data analysts to model data for business intelligence and analytics uses.
- Transferable – Increased effort is also underway to make data easier to consume, and this includes making data available for publishing easier, including using api’s and via objects that store elements of the insights.
- Observable – Recently, a host of new vendors are offering services referred to as “data observability”. Data observability is the practice of monitoring the data to understand how it is changing and being consumed. This trend, often called “dataops” closely mirrors the trend in software development called “devops” to track how applications are performing and being used to understand, anticipate and address performance gaps and improve areas proactively vs reactively.
AtScale and Data Extraction
AtScale is the leading provider of the Semantic Layer – to enable actionable insights and analytics to be delivered with increased speed, scale and cost effectiveness. Research confirms that companies that use a semantic layer improve their speed to insights by 4x – meaning that a typical project to launch a new data source with analysis and reporting capabilities taking 4 months can now be done in just one month using a semantic layer.
AtScale’s semantic layer is uniquely positioned to support rapid, effective data extraction and analysis: AtScale provides the ability to ensure that data used for AI and BI are consistently defined and structured using common attributes, metrics and features in dimensional form, including automating the process of data inspection, cleansing, editing and refining as well as rapidly adding additional attributes, hierarchies, metrics / features, and extracting / delivering ready-to-analyze data automatically for multiple BI tools, including Tableau, Power BI and Excel. Moreover, this work only requires one resource who understands the data and how it is to be analyzed, eliminating the need for complexity and resource intensity. This approach to data operations automation eliminates multiple data hand-offs, manual coding, the risk of duplicate extracts and suboptimal query performance.
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