There are three primary audiences we write and design for:
There is a single, secondary audience: “Data leaders.” This persona is largely a check writer for our purposes (VP, CDO, CIO), and does not actually use SEP any differently than the primary personas. They are included here for completeness, and should be kept in mind when creating marketing content such as case studies, white papers and ROI-focused materials.
This document describes these audiences as personas - fictional amalgamations - that you can empathize with and solve problems on behalf of. In practice, product personas are given names, faces and backgrounds to aid in discussing their needs as if they were real people, representative of our customers.
Data analysts and scientists will approach Starburst in very similar ways. However, their backgrounds and skill sets are different, so we will separate those out. Their pain points will be treated together.
Chris is a Business Analyst. He's responsible for delivering visualizations and reports to ensure that his leadership is making well-informed, data-driven decisions. Chris cares very deeply that not only are the right questions being asked (and answered), but that the right data is being used to answer the questions. With the wealth of data available, it can be easy to overlook and misuse data. The quality of Chris's work ultimately rests on the quality and reliability of the data he uses, so Chris keeps good working relationships with his data engineering team and often communicates discrepancies and SLAs issues to them. Chris has some solid SQL chops and is often able to prototype a new data source to be productionalized by data engineers. Chris feels that he has just the right combination of technical skills and business acumen.
When you write, design and build for Chris, here are some of the skill sets you can expect him to have:
Cameron is a data scientist. She's responsible for creating data models and that forecast and describe the business. Cameron worries about the impact of seasonality on sales, and feels compelled to deliver models that reflect that impact with a high degree of accuracy. Cameron feels like she brings the answers to "Why?" and "How?" to the table. Her machine learning models help her find the levers that the business can pull - the "how," and her models account for why the business behaved as it did, or will. She feels more like an academic than an engineer, and is very proud of her scientific approach to business. Her digital sales data knowledge is formidable, and her reputation as an SME ensures that she has a robust stream of opportunities in her field.
When you write, design and build for Cameron, here are some of the skill sets you can expect her to have:
Cameron’s and Chris’s pain points include, in no particular order:
Donna Data Engineer is responsible for designing performant data sources that can answer a broad range of business questions at XYZ, Inc. Donna found her way to data engineering through internships in college; it felt like a good blend between the technical chops required for programming jobs, and the big picture, organizational nature of data that she is naturally drawn to. As part of her job, she must understand what data is currently available from what sources, and what new data is needed to fill in any gaps. Donna has to work with stakeholders to source that new data, be it from third parties or through new log entries, message streams or product endpoints. Donna works pretty closely with data analysts and scientists, and tries to anticipate their needs in order to keep up with burgeoning data demands.
Daniel Data Engineer is responsible for delivering data to data analysts and data scientists at Acme Corp. Up until a few years ago, this mostly entailed writing complex ETL in frameworks such as Informatica and Alteryx. Over the last few years, he's worked mostly in python-based frameworks such as Airflow and Bonobo as well as diving into Apache Spark. Daniel really cares about data landing times, because him and his coworkers hear from PagerDuty way more than they would like to.
When you write, design and build for Donna and Daniel, here are some of the skill sets you can expect them to have:
Donna’s and Daniel’s pain points, in no particular order:
Art Administrator is responsible for XYZ, Inc's Starburst cluster. He was an SRE for the data team for years, and switched roles to platform engineering after leading the SREs for a bit. Art really cares about scalability and reliability, especially since XYZ has super aggressive SLAs both on data landing times and of course availability. Art works closely with his colleagues in IT to ensure that his systems adhere to XYZ's strict access policies and support audit requirements.
Ada Administrator is responsible for both Acme Corp's Starburst and Postgres clusters. Ada was a DBA from early to mid-career, and it fell to her at Acme to figure out the HDFS ecosystem when it came along. Now she builds and maintains big data clusters for a living. Ada cares a lot about the using right data platform for the data.
When you write, design and build for Art and Ada, here are some of the skill sets you can expect them to have:
Art’s & Ada’s pain points, in no particular order:
Lauren is CIO at the newly IPO'ed Clouds 'R Us. She's responsible for data infrastructure, data governance and delivery, as well as enabling SOX, GDPR and CCPA compliance. Prior to stepping into her current role, Lauren was a VP of IT at Acme Corp., where she owned the budget for all data infrastructure. She calls this her "real-life MBA," because she learned the hard way from being caught off-guard by explosive growth in under-specified legacy systems in multiple budget cycles. Lauren is also sensitive to scaling, platform lock-in, and staffing around particular technologies.
When you write for Lauren, here are some of her pain points to keep in mind, in no particular order:
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