Digital has become a centerpiece of innovation. It is no surprise that nearly every business in every sector has transitioned its legacy software ecosystem into one that encompasses the philosophy of product engineering. Today, at the heart of every business, is a tech company that creates a centralized platform-oriented ecosystem over which every business process is aligned, and every customer-facing initiative is planned.
On one side this approach creates a more manageable and flexible innovation ecosystem, but on the other side, it creates the need for a new approach to software product development- one that is driven and validated by data-centric outcomes.
In the digital economy, data is the new oil (some also call it new gold). Businesses today are exposed to truckloads of data streams flowing in and out of their information systems. They cover areas from internal operations to customer experience and are present in multiple formats. Becoming data-driven is no longer a choice, but an imperative.
In fact, studies by McKinsey pointed out that data-driven organizations have 23 times more chances of acquiring customers with 19 times more profitability prospects than organizations where data is less valued.
In this context, product development requires a paradigm shift from its traditional focus on engineering metrics and KPIs. While those traits need to remain in their best form, the changing reality of every product becoming a data product creates a new data-centric policy or framework that any product development activity must follow.
Let us explore the key changes in product development philosophies that are necessary to bring a data-driven objective to their business use-cases.
Prepare for data
The primary change that enterprise leaders must seek from their product development roadmap is creating an environment that fosters data generation and acquisition. A software product may have several interfaces and integration points adhering to multiple transactional processes and operational policies. At each of these venues, there should be a focus on generating data that validates every activity and directing the data to flow into data lakes or stores that capture and store them for further steps. Every workflow that the product is expected to exhibit over the course of its operations, should be dissected to see how well its progress can be plotted with the right data points and dataflows. Capturing every possible data element within the product is the best way to ensure that all facets of the product are covered when data-driven decision-making is made by leaders.
Set governance for data
We have seen in the previous stage the rush to make data generation and acquisition a priority. The next step is to set governance models and policies for utilizing the data thus collected. A product may be serving multiple business departments and helping them run different transactional processes simultaneously. Hence, it is critical to have controls in place that govern policies surrounding data ownership, access permissions, storage, cost parameters, etc. In short, there should be a clear direction on who owns which data and how they can use it or what data flows into which transactional process, without disrupting other stakeholders. Governance is also necessary to prevent data from losing integrity while at storage or in transit but leveraged by multiple stakeholders.
Protect the data
Cybercrime costs and damages are predicted to reach USD 10.5 trillion annually by 2025.
This figure is bigger than the GDP figures of some of the world’s biggest economies. When digital expands its horizons into every walk of life, it is obvious that threat elements will seek new ways to harm and extract their share of ransom from the explosive growth. When building digital products, the security of data handled within the product when deployed needs to be a key focus area. Steps like encrypting data in storage and in transit, selecting only trusted partners for cloud storage or other SaaS capabilities, and implementing powerful access and identity management tools, need to be ensured to help build a digital ecosystem that is always protected from the rising threat landscape.
Learn from the data
One of the biggest areas where data-driven product development can make a change is avoiding repetitive mistakes and flaws. Feedback on previous iterations should be considered when making changes to product versions or when updates are rolled out. It is important to learn from past mistakes and results to see how the product can be improved and its abilities enhanced to support future business needs. It is not just gut feeling and leadership instincts that matter for future product enhancements, but also data-validated feedback from users of the product. It carries more weight when deciding on changes.
Train with the data
Artificial Intelligence (AI) and Machine Learning (ML) are fast becoming mainstream in digital services. For any AI and ML model to work efficiently, it needs a comprehensive training exercise which involves supplying it with a constant stream of operational and transactional data. We have already covered efforts needed to make information systems generate the most accurate data sets. There should also be provisions to utilize such data sets for training AI and ML models and empower them to gradually become self-reliant and ultimately enable better decision making.
The fundamental philosophies of product development will always surround quality, performance, and other known traits. But in a data-driven digital economy, it is equally important to have a roadmap to bring a culture of data-centricity to product development.
The four pillars we covered in this blog form the critical parts of a wider roadmap to building a truly data-driven digital product for your business. Get in touch with us to know more about how Pratiti can help build a data-driven product for you.