There is no valid argument against the value of good data preparation as part of an analytics and data management strategy. Organizations require self-service access to valid and reliable data that can be leveraged to make better decisions. Historically, organizations have separated out business and technology decisions and development of business analytics applications. But effective data management requires collaboration between IT and technical requirements and overall business needs. This means taking data acquisition and preparation to the next level by making sure they are managed effectively by IT departments and have business rules that reflect the requirements of business needs.
With this in mind, many more solution providers offer more robust data preparation capabilities. This is in addition to the best of breed providers (like Alteryx and Lavastorm) that base their offerings on self-service data preparation, enabling more complex data flows for analytical processing. Although much of this interactivity requires IT intervention and does not provide a user only approach, what this type of data preparation does provide is the ability to get applications up and running more quickly and ensure that there is support for an agile development environment.
Self-service, agility, and effective data prep all go hand-in-hand. With more solution capabilities supporting data prep, organizations can create more robust analytics applications and manage them centrally creating solutions that support better governance of data sources.
Making the right choice
The expansion of this market means that organizations have more choices when selecting software to meet data prep requirements. The two main considerations to look at are whether it makes more sense to implement a best of breed data prep solution versus leveraging one internal to a broader analytics offering. Organizations should evaluate both types of offerings when looking at analytics expansions or general adoption.
Making the right choice involves looking at the complexities required in order to go from raw data source to valuable information insights. Depending on whose role it is to create initial data sets and the level of self-service to create interactive applications, and both or one type of data prep offering should be considered. Generally, a best of breed solution will provide the best support for technical resources developing end user applications and managing data sources on a broader level. For simple transformations and management within an application, leveraging data prep capabilities within a broader business intelligence solution provides effective data source creation and management. Otherwise, organizations should consider the fact that there isn’t a one-stop solution for full data prep management because organizations need to consider both developer and self-service user needs.