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Without good-quality data in usable datasets, executives base their decisions on flawed, incomplete, or inaccurate analysis. As companies adopt data-driven cultures, people throughout the organization increasingly depend on data quality and — whether they know it or not — the quality of their company’s data preparation processes.
Published on: July 14, 2023
Data preparation is the process that turns raw data from disparate internal and external sources into usable datasets.
These processes address quality issues in the source data, make the data’s format and structure more consistent, and enrich the final datasets.
Surveys of data scientists and engineers often report that data preparation consumes most of the schedule in any big data project. Yet, that investment pays dividends when it generates better insights for more effective decision-making.
At its core, data prep eliminates garbage in, garbage out as an operating mode in your company’s data pipelines. Optimizing every dataset’s quality, consistency, and accuracy ensures data users have the best possible information at their fingertips.
Addressing any quality or structural issues early in the pipeline saves time and effort at the other end, where fixing datasets is much more expensive. Furthermore, data preparation can reduce storage costs in data warehouses and data lakes by culling duplicate or obsolete data.
Data pipelines that deliver clean, well-documented datasets enhance a data-driven business culture. Analysts can access data faster through Tableau or other analytics and data visualization tools. Data scientists can feed high-quality data into their machine learning algorithms and artificial intelligence projects. Dashboards deliver the most complete and accurate picture of the business to executives.
These benefits shorten time-to-insight, promoting a more robust decision-making culture and driving the company’s long-term success.
Traditionally, data engineers manage the preparation process by developing extract, transform, and load (ETL) or extract, load, and transform (ELT) pipelines. These data preparation tools improve the quality of ingested data and make analysis easier.
Input sessions with the project’s stakeholders help set the data team’s objectives and priorities as they begin this phase of the preparation pipeline. Engineers explore internal and external data sources to discover data that might be relevant to the project’s analytical goals.
They must then profile each source to understand the lineage, format, structure, and other properties of any data they may extract. These profiles help the team determine whether the data in each source is truly relevant and identify issues for resolution in the next stage.
Inevitably, the data collected in the Extract phase will not be fit for consumption. Data transformation involves cleaning, structuring, formatting, and enriching data from each source to prepare it for transfer.
First, the data team must address outliers, duplicates, redundancies, missing values and inaccuracies in the source data.
The pipeline must also bring consistency to data formats. For example, each data source could have a different format for recording dates. The final dataset should only use the company’s standard format for date records.
Likewise, incoming structured data must be aligned to make row and column names consistent. Data enrichment uses tags and other metadata to enhance later analysis or structured and unstructured data.
At this point the pipeline is ready to load the clean data into an analytics platform’s database, data warehouse, or other destination. The data team must validate this final aggregated dataset.
Validation goes beyond checking data quality and fit for purpose. It must also verify compliance with all data governance policies. For example, access controls must ensure only authorized users can see sensitive or protected data.
Data preparation lets business intelligence and other data analysts spend more time extracting data insights and less data wrangling.
Unless data quality issues are particularly egregious, their effects may not be immediately apparent. Business analysts could get most of the way through a project before spotting an issue. Then they have to figure out that the source of their problems is in the data rather than their analysis.
At that point, analysts must engage with the data team to trace the data quality issue through the pipeline and back to the source.
Taking the time to address quality issues upfront avoids the analytical errors and troubleshooting delays caused by poor data quality.
Similar frustrations and delays occur when data from different sources do not share formats or structure. Business users may have to create their own fixes, increasing the opportunity for errors in their analyses.
Structuring and formatting the final dataset to comply with governance standards helps eliminate these inconsistencies.
The point of data prep is to ensure the variation between datasets gets out of the way, letting analysts identify the patterns and insights that will inform business decisions.
With ever-increasing data volumes and velocities, data preparation has only gotten more challenging.
Data-driven businesses must strike a careful balance between data accessibility and data security. On the one hand, people need access to get the information they need to make sound business decisions. On the other hand, the company must protect sensitive data from unauthorized or inappropriate access.
Data privacy regulations add to this challenge by requiring strict controls over access to any personally identifiable information the company handles.
Data governance policies should dictate how the company’s data management processes comply with data privacy and security frameworks. Since security risks increase as data moves through pipelines, data teams must factor compliance into their data preparation processes.
As we’ve discussed, data preparation is an essential investment in success. Unfortunately, much of the work involves repetitive, manual tasks such as data profiling every data source for the ETL pipeline. Treating every project on an ad hoc basis magnifies these effects as data teams reinvent the wheel for every project.
Combining consistent documentation with automation tools can streamline preparation processes. Rather than doing routine work, engineers can devote more time to higher-level activities and support more analytics projects.
Centralized data teams in large enterprises face a particular challenge when combining data from across the organization. Both website clickstreams and manufacturing line sensors are real-time data sources, but very different assumptions go into their designs.
Without domain-specific knowledge and expertise, engineers cannot fully understand the data’s context at the source.
Preparing data for machine learning and other big data project poses additional challenges. These projects rely on extremely large datasets. Transforming these datasets burdens the company’s storage and compute resources.
Proprietary databases and data warehouses are particularly vulnerable to scalability issues. Companies must build enough capacity to handle peak demands even though daily requirements are much lower.
Modern, cloud-based data analytics architectures can help meet this challenge by providing more affordable ways to optimize storage and compute resources at scale.
Starburst provides a single point of access to every data source in a streamlined and user-friendly platform. Data users can explore these sources using their preferred analytics tools and SQL query techniques they already know. Thanks to this democratized access, data users can answer questions themselves without calling on data teams for help.
Freeing data engineers from these simple requests lets them focus on more complex projects that require data preparation pipelines. As with general users, engineers get results faster thanks to Starburst’s simple drag-and-drop user interface.
Starburst layers performance features, from cached views to dynamic filtering, on top of Trino’s massively parallel SQL engine to deliver highly performant queries across multiple data sources. Pushing down queries into the data sources can improve performance further while reducing network traffic.
Our data lakes analytics platform creates a virtual data layer that decouples storage from compute. You no longer need just-in-case capacity that lies idle most of the time. Instead, Starburst features like cluster autoscaling elastically handle the most demanding data preparation workflows, while cost-based optimization ensures your queries generate results at the lowest cost.
Starburst connects to over fifty enterprise data sources, including Amazon Redshift, Snowflake, and Splunk. These connectors let Starburst deliver a holistic view of your business by breaking down the silos that once isolated your data.
End-to-end data encryption, role-based access control (RBAC), and other security features let you enforce compliance policies in the virtual data layer. These fine-grained controls protect data at the source and ensure only authorized users can see sensitive, protected data.
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