Council Post: Power of “Data as a product”
The industry loves to toss around new terms or buzz phrases, like ‘data is the new oil’ or ‘miracle stepchild’, but an adaptation of data in everyday business remains elusive on a large scale. So, what if we accept and treat data as simply data, in form and shape, which is self-sufficient, reliable, and, most importantly, trustworthy? The biggest impediment to creating a data-driven organisation is the lack of executives trust in the organisational data. A survey from KPMG says one in three executives do not trust their organisational data while 50% of the knowledge workers spend their time sourcing, correcting and co-relating that data. This is the reason chief of analytics, or CDOs, struggle to justify value returns from analytical platforms, including most ambitious AI initiatives. The impact of a trust deficit in organisational data is directly impacting business decisions and outcomes accordingly. The huge investment into AI/ML initiatives does not yield promised value because AI/ML models are only as accurate and as intelligent as the data quality in terms of business relevance, completeness, and precision. How equipped are Data and Analytics teams (D&A) to deliver business worthy outcomes? If one were to query about what the purpose of a D&A team would be, the most plausible answer is to enable the business to make the best possible decision through data/information/insights/knowledge at the right point in time. How do we measure the effectiveness of the team? The effectiveness of the team can be measured with the number of breakthroughs or insights delivered to increase the customer experience. What does an average day in a D&A team look like? The work of a D&A team implies that they execute a variety of tasks on an everyday basis, including answering questions on data, dashboards, and sources, justifying the source and accuracy of the data, much like desktop support. If something breaks, you can raise a ticket and get it resolved. But that is not creating insights to enable the best possible decisions. What is the impact of this view on the operating model? In essence, the D&A team is looked at like a service desk. Though nothing is wrong with that perspective, it does have an impact on the operating model. There is a lack of ownership with the output delivered by the D&A team. The team is engaged in answering the technical questions but they are measured with the business insights/wisdom that they generate. What’s the proposal? Approach the data with a product mindset. Since adoption is the primary outcome metric for product creation, nearly all product development methodologies place a strong emphasis on discovering and meeting the unmet needs of end users. Agility and iterability are other key essential features of product development and they should be treated as such by the analytics teams. Apart from product development methodology, organising a D&A team for product development with clearly defined roles and responsibilities would help teams transit from project orbit to that of product. The critical aspect of adapting a product mindset is to start generating value before seeking the budget, which is challenging in the project or fixed mindset. Bridging the gap between data and business When businesses view data delivered by a data team, they wonder if they can bet their decision on what they see. One way to build confidence in data is having greater involvement by decision-makers in identifying and sourcing the right data from the right source system. This makes them confident about what data they are getting and what transformation it is going through. In order to build a bridge between a data engineering team and businesses, it would be useful to create a role as a decision partner who understands business application, consumer needs and data engineering. This enables both teams to communicate in common language and builds confidence in the insights they are discovered or presented with. Lastly, the need to enable data engineers to transition from the data/technology orbit to the business world and help them wear multiple hats of business analysts and data experts. This will help in erasing the walls between data engineering teams and business stakeholders. Business Analytics: A business unit Most of the business analytics are combined and made part of the IT department. The industry is yet to recognise its independent working of it. Making business analytics a business function  brings required attention to outcome-focused analytics and ensures that the data insights generated align with business objectives and goals. This also helps with better collaboration between different teams, such as sales, marketing, and operations, to drive data-centric decisions. However, it’s critical that a strong partnership between the data analytics team and IT is maintained to ensure seamless data management across enterprise and infrastructure support. Treating data as product brings a shift in the mindset which brings focus on the quality and value of data, thus leading to better utilisation, reliable insights, and improved decision-making. Valuable products win deeper emotional connections and lifetime relationships with the customers. Similarly, by providing valuable data products to customers, organisations can establish deeper relationships with them and improve customer satisfaction and gain an advantage over the competition and differentiate in the market. To unlock the full value of enterprise data assets and drive business growth, let’s start treating data as a product. This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. 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Treating data as product brings a shift in the mindset which brings focus on the quality and value of data, thus leading to better utilisation, reliable insights, and improved decision-making. Valuable products win deeper emotional connections and lifetime relationships with the customers.
Treating data as product brings a shift in the mindset which brings focus on the quality and value of data, thus leading to better utilisation, reliable insights, and improved decision-making. Valuable products win deeper emotional connections and lifetime relationships with the customers.
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["data science", "Go", "TPU", "AI", "ML", "RAG", "Aim", "analytics", "Rust", "R"]
["AI", "ML", "data science", "analytics", "Aim", "RAG", "TPU", "R", "Go", "Rust"]
With over two decades of experience, Shan is a context-driven leader with a demonstrated successful history of building Data Engineering and Agile Analytics structures from the ground up. He is highly skilled in Data Analytics, AI, Data modernisation, Data Architecture &amp; Digital Transformation. Currently, he is the Data &amp; AI leader - Senior Director at Ascendion.