Organizational data strategy needs a redesign for companies to extract the most value from their data infrastructure. Through this series of blog posts I plan to describe in detail, conceptual positions designed to meet contemporary organizational needs. For this first post, I describe the role of a data experience designer.
Position goal: Increase the value of an organization’s data and evidence by improving quality, accessibility, and usefulness.
- While companies are putting larger investments into storage and analytics, they are missing opportunities to increase value at an organization-wide level.
- A data experience designer is a hybrid role that is somewhere between a data scientist and a UX/UI designer.
- Experienced social scientists with practical data experience, who can understand context along with technology and design, can help improve the quality, accessibility, usefulness, and overall value of data.
How could a data experience designer help your organization?
Improving Data Quality
Garbage in/garbage out applies to big data too. Low quality data is the result of poorly structured datasets, irregularly formatted spreadsheets, cloud-based data sprawl, weak data collection methods, and a lack of appropriate data cleaning processes.
Organizations often understand the amount of work required to locate high quality data sets, and how to properly clean data for analytics (because the highly paid data analysts and data scientists spend most of their time gathering and cleaning data). But there is rarely anyone who is assigned the responsibility of improving cross-organization data quality. By focusing on fixing what’s broken, quality improvements can open up new data sources for analysts and increase the overall speed to insight and reliability of results.
Improving Data Accessibility
If a data warehouse is completely inaccessible, is the data still valuable? In other words, what good is having a lot of data if your employees do not know what exists or how they can gain access?
Old school reporting services are often clunky, hard to access, or hyper focused only on certain data sources. Simple data frequencies and charts can be useful, but the form limits potential value. Additionally, the use of a wide range of specialized cloud-based systems has created a level of reporting redundancy and disconnect. Through the use of platform agnostic data analytics software, sources can be connected and brought together in new exploratory interfaces designed to increase access for the people who really need that access.
Boosting Data Usefulness
Intention is inadequate, even the best conceptualized and most expensive dashboard platforms can sit unused. Usefulness can only be boosted with the help of current or potential data users.
Reports that are created based on an assumed audience, or merely from a dataset perspective, are doomed to underdeliver when it comes to usefulness. By incorporating human centered, user experience focused, and appreciative methods data can be made more useful. The rise of UX/UI design and research has also provided professionals with the right blend of expertise the tools needed to support this work and boost the usefulness of data. Improved user-derived performance metrics can increase adoption and positively impact the bottom line, not just create a prettier report.
What kind of person can fill this role?
The right person for the role will depend on the context of your organization and which areas are most in need of support. But overall you should seek someone with a breadth of experience across different facets of the data lifecycle.
To increase data quality, look for someone who has experience with survey design, data collection management, and has a handle on modern research methods. A general awareness of the use of SQL databases, data warehousing architecture, cloud services, and API connections is useful, but not the most important thing. It’s more important that this person can speak to and work with IT staff within your organization. This person could easily be a master’s level social scientist or other major that puts an emphasis on research methods.
To increase data accessibility, look for someone who is generally data savvy. They have to be able to inform the collection and aggregation of data from across a variety of platforms. Depending on your organization, you may have lots of spreadsheet data sources scattered throughout your company. You may have something more robust, like a data warehouse, that is no more less accessible.
This person has to understand how data connects, and what can be brought together to meet specific business objectives. They may have some background in SAS, SPSS, R, Salesforce, Python, Excel, or other stats tools, but understanding the logic behind data structure and the potential of data is more important than any individual tool. Ultimately someone who can organize and aggregate data with a tool like Tableau prep or build unique interfaces using Tableau desktop could be just what you need.
To increase data usefulness, look for someone who can learn what is needed, or desired, by going directly to the people who need the data. This would be someone with a practical understanding of things like qualitative research methods, facilitation, user experience design, user experience research, or human centered design. Ultimately, this person will need to iteratively ideate and develop data products to meet the needs of key users. These people also tend to give fantastic presentations and reports, as they deeply think about and research the needs of an audience before developing the product.
How can a data experience designer work within your organization?
A data experience designer is a data person in an agile world. Anyone who has ever worked with big company coders or designers knows at least a little bit about agile methodology. Data experience design is a specialized form of digital product design, albeit one that is lead by the kind of person usually associated with academic work. The tools and methodologies surrounding agile (including sprints, backlogs, and stand ups) can also be incredibly useful process methods for a data experience designer.
Within an organization, the data experience designer is most likely cross-function and semi-independent. Corporate silos limit data’s usefulness and working with a single project team can limit the potential benefit a data experience designer can bring to an organization.
Embedding a data experience designer within an IT team or unit will likely give them the broadest access to the people they need to support. Given that IT teams can often support the needs of multiple project teams, it seems to be a good placement for a data experience designer. Other department options include marketing, UX design, business intelligence, communications, strategic initiatives, or business management consulting.
What should you expect to pay a data experience designer?
Given the breadth of experience and alternative functions a potential data experience designer could fill, they are not likely an entry level employee.
A mid-level data experience designer with a graduate degree (i.e. Masters, MBA, PhD) and at least 5 – 10 years of experience in related fields, would probably expect an annual salary in the range of $70,000 to $110,000.
A senior-level data experience designer with a graduate degree (i.e. Masters, MBA, PhD) and at least 10 – 15 years of experience in related fields, would probably expect an annual salary in the range of $90,000 to $130,000.
Considering the large scale investments organizations make towards data storage, analytics, and strategy, an investment in data experience designers can pay huge dividends.