Data Management & Analytics Tool
Maplemonk, an early stage startup
I helped MapleMonk envision a low-code/no-code platform with a goal to reduce the time from data extraction to insights by 40. The prototype played a key role in startup was able to secure seed funding.
Founding Designer | 5 months
Disclaimer: Due to NDA the designs are stripped of branding leaving only wireframes.
INTRODUCTION
The Problem Space
The value of combining data from across applications, such as CRM, POS, marketing, and product analytics, is well understood — pooling sources might generate new insights that would otherwise be lost if the data is looked at in silos.
Despite the abundance of available tools, creating a comprehensive data stack can be a challenging and time-consuming task, with up to 40% of analysts' time being dedicated to obtaining the appropriate data. As a result, many companies hire entire teams to manage this process. Maple Monk wanted to develop a low-code/no-code platform to simplify this process.
CONTEXT
As this was the first version, speed and clear UX flows took priority over UI refinement to quickly validate our direction.
SMEs Industry segment
MapleMonk was to be funded by Merilytics, an analytics consulting company catering to SMEs in EU. Hence, it made sense to build the product especially for this target segment and then scale it up for other segments.
3 Target Users
The platform was designed to cater to three common roles found in any Business Intelligence (BI) unit of a company.
The Business users who generate requests and consume the analytics.
Data Analysts who conduct analysis to cater to those requests.
Data Engineers (superuser) who manage the pipelines and ensure that the required data is available for analysis.
Focus on Descriptive Analytics
Business Intelligence (BI) units provide valuable insights to aid decision-making. They achieve this through a 3 tiered approach, starting with descriptive analytics that focuses on informing what has happened in the past and what is currently happening. The 2nd tier, predictive analytics, warns of potential dangers or opportunities to leverage in the future. Finally, prescriptive analytics provides recommendations on the best course of action to take based on the available data. For MVP, the focus was to ensure that data is obtained from various sources and presented in a format that facilitates descriptive analytics.
Process Milestones
Getting to know the Analytics Industry and Product Vision
During the first week, I spent a lot of time getting KT from the founders, trying to understand the analytics industry as a whole, the primary activities, key players, and processes to understand the industry that we were trying to disrupt.
Secondary research, Un-structured Interviews
Simultaneously, I was conducting secondary research understanding the competitors, the offerings available in the market and talking to data analysts and engineers in the office. What I understood was that even though there were many players, they focused on parts of the journey from raw data to insights. I was beginning to see what the founders were trying to build.
Features Prioritization, IA, Wireframes
I then moved toward creating the first wireframes and UI for the product. Speed was given priority over the UI quality as we needed something visual quickly to validate whether we were thinking in the right direction. Given that there were just three of us, most of the discussions happened on whiteboards. We moved to paper prototypes during the COVID lockdown.
UI Prototype + Validation with users + Feedback from Founders
The prototype was built module by module and once a complete flow was ready it was provided to the analysts for validating/feedback. Since we were just three of us, I used to work on the UI and share it with the founders by the EOD, they would each be ready with feedback(comments in Figma) the next day and we would go through them together.
KEY SCREENS
In total 300+ screens were designed as part of the first prototype covering 9 out of 10 planned modules. Below are some of the key screens across them.
Home - Business users
A jumping-off point to all the Business users' answers​
The Home page for Business users brings together the three most commonly performed actions viz. Monitoring key metrics, consuming the requested reports, and generating new requests. The insights tab leverages AI to generate key insights relevant to the exact user. Self analyze tab allows the user to check something on his own, generally, a small analysis including not more than 10 variables. Earlier these reports lived in bookmark tabs or in the email of the users.
Jobs Module - Transformations
Extract, Transform, and Load (ETL/ELT) data in a completely code-free way
Simply put, a job in data management terminology is moving data from point A to point B along with some transformations to get the data in the desired format. Extraction, Transformation, and Load or ETL in short. The above screen shows the 'transformations' step of the process. Users can filter certain records, change the data type, and find and replace certain items among other transformation features. The user can do it using UI (no code) or enter custom SQL queries for advanced transformations.
Raw Data - Creating new tables for reports
Streamlining Creation of Final Tables​
Creating the final tables for reports typically involves pulling data from multiple source tables. During the process of merging these tables, it is essential to assign keys, determine the type of join, and keep track of these keys to gain a comprehensive understanding of the table's source and creation methodology. By doing so, it becomes easier to identify and resolve any issues that may arise during the reporting process. The adjoining screen displays the creation of a final table using source tables.
PROTOTYPE
User Journeys for exploring the details of a table or creation of a new one
Since the user journeys are complex please use the prototype
IMPACT
The prototype played a key role in the startup securing seed funding.
The prototype I designed played a key role in the startup's success. It demonstrated the platform's potential and value to investors. This tangible proof of concept helped the company secure seed funding, providing the resources needed for development and growth.