To drive the success of developing or internalizing data products, an enterprise needs to have
an appropriate framework to start with. They have to be agile enough to learn, adapt, and
customize the framework to enable enterprise-wide transformational change. Over a period, this
is internalized and starts to reflect in their business DNA. Let’s look at the steppingstones to
create an ideal framework that teams can adopt:
- Collaborate: Identify and work with key
stakeholders. Build an in-depth understanding of business requirements and complement
with contextual suggestions, thereby creating a forum to collaborate.
Continuous thinking. Instead of taking the traditional approach with
projects that involve fixed value and end date, embrace a modern approach at organizing
around continuous product-like workstreams that iterate and evolve according to the
Try-Fail-Learn-Repeat-Repeat-Repeat. Testing is about trying out different options
and finding what works and what doesn’t. Continuous phases of tests hone experience, a
pillar of success for enterprises in the market.
The true value of data scientists is in not only having the ability to build
great ML models that answer real business needs but also create scalable and deployable
analytical solutions. They need to either collaborate with engineers or be full-stack
data scientists to enable production-grade data apps.
Experts in firms ought to scale their products and keep vying for continuous
improvement. Iterate by focusing on what will increase success along with feedback on
what are the learnings. One needs to go from changing processes to changing mindsets,
growing beyond the scope of data science, to unfold a truly transformational effect.
Building a data product is like cooking a fine dish, you need the right ingredients at
the right time. The various functions that need to collaborate to develop an end-to-end
analytics data product are:
The development phases of a data product are akin to that of general products. The
thought process that goes behind any product development is identifying opportunities to
cater to customers’ needs, build a prototype based on customers’ needs, and then
evaluate and re-evaluate its functionality. However, moving away from the generic
product development arena, the data component adds to the development complexity. In
this context, it is a recommended practice for firms to preach and practice
cross-functional collaboration, contemplate, and prioritize long-term data product
opportunities but start with a less complicated approach.
— the glue that sticks cross-functional teams to achieve product success
Full Stack Development
— designs the product with a view of user interaction & experience
— automated testing to ensure quality control and assurance
— plan for operationalization before jumping into development
TERMINUS — The Result
Toeing the line of quality standards that
help enterprises rise above the noisy supply market arena, MathCo has leveraged its data
product portfolio that helped spinning success stories for many Fortune 500 companies.
Here is one such example:
How TheMathCompany deployed a data product to help a premium shoe manufacturer
boost its brand outreach
A US-based premium shoe manufacturer wanted to leverage a tool that could measure their
marketing campaign efficacy. The existing decentralized decision-making process of the
client added a layer of complexity in the process of identifying campaign events that
were generating high ROI.
TheMathCompany successfully developed and deployed an impact measurement tool that could
seamlessly accept input data, analyze the impact of experimental changes done in the
campaign real-time, and offer actionable insights to the client to optimize their
marketing campaign investments.
Tool Architecture Framework
A presentation layer that featured user access control and enabled visualization of
input and output data.
The application layer in the form of an interface connecting the user interface to
A model runner to process statistical models which were designed to determine the impact
of campaign events. The models had the flexibility to be altered whenever deemed
A data layer that is composed of input data and can store results.
Measurement of KPIs and an optimum level of data granularity had to be identified prior
to using this tool. It was followed by defining parameters such as geography, time,
among others for events whose impact had to be measured. Filters were created for these
parameters to determine their individual impact that triggered the events.
Developed impact measurement framework to determine real time impact of over 100
experiments done across marketing, pricing, and CRM functions
Usage of a serverless platform in the tool reduced the overall expenditure of impact
Accuracy if forecasts furnished by the tool at multiple levels enabled efficient sales
planning and management by the revenue management team.
To know more about the solution in detail and the sustainable impact it had on the
confectionery brand’s operation, read this case study.
EVOLUTION — The Growth
From a business perspective, decision-makers in product firms play vital roles in
creating and ensuring smooth execution of the process funnel of data products.
Considering business and technology teams as the major decision-makers; the presence of
both the teams in discussions around feasibility of executing newer product initiatives
is crucial. While the business team can project market opportunities for such
initiatives, the tech team can confirm the feasibility of executing initiatives. This
makes it obvious that close collaboration between these domain experts can translate
into fruitful identification and materialization of data product opportunities.
Data scientists must have a clear understanding of clients’ business needs which is
generally perceived to be pertinent for business executives. An in-depth understanding
of client needs will offer a definitive direction to data scientists while essaying
their role in the project.
Meanwhile, data scientists must play their part in enterprise-wide democratization of
data. Their parts include opening the access to raw data and everything related to data
models across all the stages of a project
However, democratizing data to individuals who are analytically naïve may not always be
useful and even pose a threat to customer confidentiality. For instance, any incorrect
execution of processes on the open database can leak sensitive client information. To
avert such risks, enterprises must promote data literacy across all the teams.
Including data science teams in discussions related to product and business strategies
will give a significant boost to the necessary cross-team collaboration required in an
enterprise and offer an impetus to data product development.
Data products are the results of a scientific combination of data and algorithms. While
this sounds quite straight-forward, getting to this stage is an uphill task that
involves reaching a common ground between business viability of data product concepts
and R&D efforts required. Over-investing in R&D initiatives before concluding on
business prospects of a conceptual data product will ramify into severe financial losses
and vice versa. Risks can include release of a minimum viable product supported by an
inadequate data model which is bound to receive poor acceptance among users. To reach a
ground that supports the release of a technologically superior data product with maximum
business prospects, some of the MVP approaches are:
Lightweight Models — Start small, ship faster, and build upon over time
New data Sources — Find innovative methods to acquire data
Scope box to MVP — Reduce the scope of challenges, build and launch an
No Big Bang — Start with manual to automated overtime to scale up
The Upside Down
— Engineering and data science run together
Opportunities for data products are ample. But sans an effective business and
technological approach, a majority of enterprises fail in their costly attempts of
mobilizing a data product dream. Seamless collaboration between stakeholders ranging
from business leaders to data scientists, investments without losing focus of future
business prospects, and a simple approach to start, are some of the basic parameters
that must be met to accelerate data product development that can add value to
Involve business in sprints
Full stack data Scientist
Say no to Gantt charts and embrace agile methodology
Collaborate amongst different & relevant folks
Ownership lies with the team
Pawan wears multiple hats from a coder to architect to entrepreneur – he does it all. He has set
up the engineering foundation at various startups that have grown into million-dollar
businesses. His forte lies in envisioning, designing, implementing, marketing, and selling Big
Data & AI products/solutions. At TheMathCompany, he heads the Engineering-India division with a
focus on cloud, big data & operationalizing ML. He has been a corner stone of our engineering
efforts, enabling end-to-end analytical products and solutions for TheMathCompany. Pawan is
inspired by the teachings of Swami Vivekananda. Off work, he is an avid over lander & a