Posted by Jon Pilkington on October 9, 2017

Note: This article originally appeared in SmartDataCollective.

Self-service data preparation solution should enable users to prep less and analyze more, while providing true enterprise collaboration and agility.

You’ve completed the research, and you now know that the return on investment (ROI) of self-service data preparation technology is not only proven, but invaluable.It automates and simplifies data retrieval and preparation processes, enabling even novice business users to easily and rapidly extract, manipulate, enrich and combine disparate data from virtually any source, and then prepare it for analysis and reporting – without coding, manual data entry or involvement from IT. And eliminating labor-intensive and error-prone manual processes improves data accuracy, saves countless hours, and enables data users to focus on analysis that results in faster, better business decisions.

But which self-service data preparation solution is right for your business? Below are three steps to make the seemingly daunting selection process an easy one.

Step One: Identify and Research Existing Providers

There are many effective ways to identify data preparation providers. You can ask colleagues who work in the same profession for their advice. You can turn to Google or other search engines and conduct in-depth research of your own. Hiring consultants to do the upfront work for you is another option. And referencing analyst reports that explain and rank self-service data preparation vendors and solutions can also be helpful.

Whichever method you choose to identify relevant providers, extensive research must be done on each to verify capabilities and ensure you’re choosing the solution that best meets the needs of your business.

Step Two: Prepare a Comprehensive List of Requirements

To ensure the data preparation vendor you choose offers the right capabilities and features for your organization, it’s a good idea to create a list of requirements that the technology solution must have. It’s a common practice for companies to list their detailed requirements as a series of capability questions, and then indicate if each vendor: 1) Currently has the feature in its standard product, 2) will have the feature by a specified date, 3) can make the feature available on a custom basis for an additional cost, or 4) doesn’t have the feature and can’t accommodate the request.

What capabilities should you look for? Here are seven important ones to consider:

  1. Data access – Enables users to easily and quickly access and import structured, semi-structured and unstructured data from virtually any source.
  2. Data marketplace – Creates a centralized database enabling users to interactively prepare, search, sample, profile, catalog and inventory data assets, as well as tag and annotate data for future exploration.
  3. Data collaboration – Produces repeatable models and workflows that can be scheduled, automated, shared and distributed, eliminating the need for users to start from scratch for every analytics project.
  4. Data socialization – Facilitates the sharing, commenting, curation and promotion of datasets, recipes and exports.
  5. Data transformation, blending and modeling – Supports data enrichment, data mashup and blending, data cleansing, filtering, and user-defined calculations, groups and hierarchies.
  6. Data curation and governance – Supports workflows for data stewardship; includes capabilities for data encryption, user permissions and data lineage; and incorporates governance features, such as data masking, platform authentication and security filtering at the user/group/role level.
  7. Machine learning – Automates and improves the self-service data preparation process.

Step Three: Prepare a Request for Proposal (RFP)

An RFP can be a very effective way to evaluate vendors and their offerings, as it allows you to solicit feedback on your outlined requirements in a common format. When preparing an RFP, consider including an overview of your organization, your software needs, and system functionality and IT specifications, so vendors have a clear picture of what you’re looking for. It’s also a good idea to specify architecture and technical requirements, including security, accessibility and compatibility with specific databases, Web technology, reporting and server/desktop requirements. And, per step two, make sure to clearly outline required or desired solution features.

On the flip side, asking each vendor to provide the following information can prove extremely helpful in the evaluation process:

  • The product roadmap for the next several years
  • Company financial viability, market share, strategic partnership, industry leadership, number of employees, years in the business, and support model
  • Software costs (ask that the provider include all fees associated with SaaS or on-premises software, support, training and implementation)
  • Integration requirements from both a personnel and price perspective
  • The number of customers the provider serves and the retention rate of those customers; also ask for information on whether the vendor supports other organizations in your industry
  • Integration capabilities with existing enterprise applications, BI platforms, visualization tools or other systems
  • Whether the solution is part of a broader analytics platform
  • Customer references

Making the Right Choice

Together, the product requirement and RFP responses can help you thoroughly evaluate each vendor offering by considering factors such as deployment model, end-to-end capabilities, cost, support for data sources, end-user roles and integration with existing platforms. Armed with this information, you can choose the most appropriate solution for your business and remain confident that your investment will yield maximum ROI – both from a technology and business perspective. Remember, your self-service data preparation solution should enable users to prep less and analyze more, while providing true enterprise collaboration and agility, and ensuring data governance and compliance. The result is better and faster decision-making, and an analytics community that works together to move the business forward.

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