Data is the driving force in today's business landscape, drastically changing the way organizations improve and operate their core operations. However, many organizations are challenged with utilizing this unprecedented access to data to its full potential.
Data comes from a multitude of sources, each with varying formats and structures. As an organization grows, it becomes increasingly difficult to consolidate these data silos into a unified file of pure, clean, complete and accurate data. A variety of users utilize the same data within an organization, including business analysts, data analysts, data scientists, and other self-service data consumers. This is where data preparation comes into play. Ensuring a proper data preparation process prior to analysis and decision-making accelerates business operations and builds a strong foundation of reliable data. This article will take a deep dive into the fundamentals of data preparation and how organizations can utilize this practice to combat today's complex business landscape.
Data preparation, also known as data wrangling, is the process of collecting, cleaning, processing, transforming and consolidating raw data into an accurate and usable file for analysis. Data preparation is the first step in a data analytics project and often involves discrete tasks such as reformatting, making corrections, and combining data sets to enrich data.
There are six key steps involved in the Data Preparation Process:
Data collection is the procedure of gathering data sources from various business departments and measuring the accuracy of each file in order to create a large database of information. This preliminary step sets the foundation for the data preparation process, compiling all the available company data.
Data cleaning is the process of detecting and removing any form of incomplete, inaccurate, or irrelevant records from a data source. This step ensures that any inconsistencies that may have been originally caused by user entry errors are mitigated and corrected.
Data transformation involves changing data into proper formats and structures for reporting and analysis. Its three key components are composed of improving, enriching, and reformatting data. These tasks can be completed through techniques such as normalization, aggregation, and generalization of attributes.
Data reduction is the process of condensing multitudinous amounts of redundant data down to the meaningful parts. There are common techniques used in data reduction, including table diagonalization, layouts, and ordering and rounding measures.
Data consolidation, sometimes used interchangeably with data integration, involves combining and storing varied data into a single place. It allows users to manipulate different types of data from one point of access and turn raw data into insights that drive better decision-making.
The last step involves data storage, which stores the data in a storage medium for future use, such as a computer, database or data warehouse. This step is necessary for compliance, keeping vital information secure within the company walls.
Data preparation is a formal practice within many enterprise systems and business applications maintained by IT teams. It is also an informal practice conducted by business users within an organization, performing tasks such as ad hoc reporting and analytics.
Data preparation is an integral step in generating insights for any business analytics and data-driven processes. Not all of the data a given organization collects is useful, which is why data preparation is needed to unlock data to its greatest value. The data preparation process enriches, transforms, improves and ensures that datasets used for the modelling stage are acceptable and of the utmost quality.
76% of data scientists report that the data preparation phase is the most time-consuming and tedious step. However, proper data preparation is essential in developing reliable models, analyzing high-quality insights and enabling informed decision-making. Data preparation conducted cautiously and effectively can bring a tremendous amount of cost- and time-saving efficiencies.
Here are a few reasons why data preparation is beneficial for companies:
Data preparation helps ensure that error-prone raw data is cleaned and accurate before processing. This is important because data tends to become more difficult to understand and correct once it has been removed from its original source. After processing the data, it becomes a solid and reliable source for decision making, analysis and more.
Higher-quality data is produced from data preparation tasks, such as sanitizing, enriching and consolidating. As organizations begin to make better-informed business decisions from insightful data, end-consumers become happy and satisfied as well.
A good data preparation process streamlines high-quality data in a timely manner to support business decisions. By optimizing the processing speed, important business insights, reports, and data readings can be accessed quickly to promote organizational agility.
As data preparation processes move to the cloud, it comes with even greater benefits including scalability. Cloud data preparation tools grow at the pace of the organization, catering to their growing or fluctuating bandwidth demands.
Data preparation creates a data-driven culture through the early establishment of tuning data into a valuable asset. By constantly converting disparate, raw datasets into a clean view from day one, organizations are able to derive value from their data and create a competitive edge.
According to SAS, data scientists spend an estimated 50 to 80 percent of the typical project time conducting data preparation tasks. For this reason, many organizations consider the data preparation phase as a core challenge in deriving value from data. The substantial time spent during this phase is due to various challenges, including the following:
The data preparation process accounts for a large portion of the time and resources involved in a data project. The more data added, the more time-intensive the process becomes. This is due to the complex nature of data silos, presenting various datasets in different structures, sources and formats. As an organization grows and scales, it is important that their processes are efficient and capable of managing these huge influxes of data.
Collecting, cleansing and consolidating data into a trustworthy file requires having a complete view of an organization's data. The data preparation process often comes across outdated and incomplete data, resulting in data inconsistencies. IT professionals must go through the tedious process of tracking down business users to verify and update the information prior to testing.
An in-depth understanding of a business use case at the preparation stage is essential for any data project. This is often a challenge faced by organizations when outsourcing a data analytics team; having the technical abilities but lacking relevant context and project details. Without use case context, organizations risk spending additional time and effort figuring out how to gain the most value out of the data.
It is estimated that 37% of data analysts need to wait for other departments to get data, with 52% of data analysts using too many tools and processes to retrieve data. Regardless of whether an organization has too little or too many processes and systems in place, data preparation inefficiencies result in missed sales opportunities, reduced ROI, increased costs, and opportunity loss to competitors.
Manual data preparation tools like Microsoft Excel are popular among data analysts and IT professionals within the business landscape. However, this reliance on manual data preparation tools hinders collaboration and efficiency. Rather than creating more time available for analysis, it focuses on mundane data preparation tasks. To avoid this pain point, organizations should consider redirecting their data preparation processes with automated, self-service data solutions.
In order to promote organizational productivity and efficiency, data preparation must become a formalized enterprise best practice. Investing in data preparation tools fast tracks your organization to improve operational efficiency, reduce risk and costs, promote collaboration, and most importantly, create a foundation of trusted data.
Here are some key criteria to consider when selecting a data preparation tool:
Depending on the type of user utilizing the data preparation tool, organizations should look into how data preparation tasks are conveyed. Some solutions offer visual drag-and-drop features, whereas others utilize scripting and coding. If non-technical users plan to use the data preparation tool, a spreadsheet-like user interface is better suited due to the low learning curve with Excel and other business applications. For technical users, having a coding platform is more advantageous, allowing those users to perform their tasks more effectively and efficiently.
As with every new solution used by an organization, integration capabilities with existing platforms is a must. The data preparation tool should be capable of providing the user with different data sources that are accessible and relevant. Having a data preparation tool that provides quick and flexible integration of separate data sources helps create greater efficiencies during the conversion phase.
The data preparation process is intended to empower business users, data scientists, and other self-service data consumers in their decision-making, analysis and operational reporting. A self-preparation tool should encourage collaboration and allow its various users to share and reuse data in different formats, structures and files.
Data preparation tools provide varying data governance features and functions. For each tool you consider, ask how well it works with your existing security processes and systems - eg. data encryption, user permissions, data workflow and data lineage tracking. The goal is to determine the best alignment of tool capabilities with your organization's governance measures.
A data preparation solution can be deployed on-premise, in the cloud or a hybrid solution. Each option offers its own advantages and disadvantages, so when considering your data preparation implementation, select the deployment method that fits best with your current infrastructure, architecture and organizational culture.
Consider the current features and functions that are most critical to your organization's success and place them at the top priority of your requirements list. Data preparation capabilities can include predictive analytics, ETL job scheduling and data testing. As the organization's data volume grows and multiplies, it is also important to anticipate certain features that are essential to future success. The data preparation tool must be able to scale to deliver consistent optimal performance.
A wide range of self-service data preparation tools is available on the market to help organizations increase the value and relevancy of their gathered data. The latest data preparation tools are user-friendly and provide quick and efficient results, contrary to programs used in the past.
The following are industry-leading tools to help you with your data preparation tasks:
And there you have it! A deep dive into the data preparation process, and its encompassing activities through the journey. With improved data preparation practices and technologies, organizations can utilize their most valuable asset with greater confidence during strategic, operational and financial decision-making processes.
Make sure to check out the following parts to our complete 3-layer handbook:
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