By Mayank Baid, Regional VP- India and South Asia, Cloudera
Most modern companies have access to rich and varied data sources embedded into their daily operations, from customer preferences and social media sentiment to financial transactions and purchasing trends. A recent EY Future Consumer Index (FCI) survey, revealed that 62% of Indian consumers make purchases based on AI recommendations, compared to 30% globally, and 61% find AI-driven suggestions highly beneficial.
Yet, not many C-suites and key decision makers truly grasp the importance of data or how to fully leverage insights from data to inform business decisions. This results in a missed opportunity; according to Forrester, companies that use data-driven insights when making decisions are nearly three times more likely to achieve double-digit growth.
Though dealing with data may seem intimidating and overly complicated, it really isn’t. C-suites just need to remember these three things: what different types of data can be used for, why reliable data is essential, and the importance of building a strong data culture.
Using different kinds of data
There are three main categories of data businesses can use: customer data, operational data, and market data.
Simply put, customer data is information about who is buying the product and includes information such as demographics, purchase history, and website behaviour. This kind of data is crucial for sales and marketing, as well as the product development team. By analysing this data, these functions can understand the customer journey, personalise experiences to increase loyalty and retention, and create new products based on predicting future needs.
When it comes to operational data, businesses can optimise and streamline their processes in order to increase cost efficiency and productivity. This includes analysing resource allocation and workflow bottlenecks to identify areas for improvement, as well as ensuring maintenance is done on time or inventory levels meet current demand.
Lastly, market data keeps businesses informed about the broader market landscape, including tracking competitors and identifying new possible opportunities. Using data to analyse market trends could mean companies spot emerging whitespace to capitalise on and grow revenue, identifying areas for product innovation to adapt to market shifts.
C-suites need to combine all three different kinds of data to extract the most value from their insights. For instance, analysing purchasing data trends may reveal that a product has become less popular, which means C-suites might want to lower inventory of that product and invest in creating a new product in an area where there is not much competition.
Why reliable and trusted data is essential
Not all data is good data. Much like sailing a ship and navigating at sea, companies running with unreliable data are similar to a captain with a faulty compass, potentially leading the vessel into uncharted, hazardous waters. As organisations increasingly turn to data-driven approaches to innovate at scale and maintain competitiveness, having clean and accurate data that is easy to use allows business leaders to steer confidently by insights, not hunches.
With massive volumes of data to make sense of, having reliable and scalable modern data architectures that can organise and store data in a structured, secure, and governed manner while ensuring data reliability and integrity is critical. This is especially true in the hybrid, multi-cloud environment in which companies operate today.
Furthermore, as we face a new “AI summer”, executives are experiencing increased pressure to respond to the tsunami of hype around AI and its promise to enhance efficiency and competitive differentiation. This means companies will need to rely on high-quality, verifiable data to implement AI-powered technologies Generative AI (Gen AI) and Large Language Models (LLMs) at an enterprise scale.
However, just throwing money at emerging technologies to solve their problems is not the solution. C-suites need to know how to operationalise and trust AI models and technologies. The best way to do this is by leveraging trustworthy hybrid multi-cloud platforms to ensure data quality, governance, and security. This creates a reliable foundation for AI initiatives, allowing organisations to move past the hype and actually capitalise on trusted data.
Building a strong data culture
Beyond infrastructure, companies in India need to look at ways to create a culture of data. In today’s digital-first organisations, many businesses require real-time analytics to operate efficiently. To enable this, organisations need to create data platforms that are easy to use and equipped with the latest tools and controls so that employees at every level can get their hands on the right data to unlock productivity, saving them valuable time for other strategic priorities.
Building a data culture also needs to come from the top; it is imperative to ensure that data is valued and used strategically and consistently to drive decision-making. C-suites should lead by example, using data-derived insights to make decisions on growth, investments, and mitigating risks.
Executives can take three practical steps to foster a culture of data:
- Find internal champions to build a robust internal ‘advocacy’ network where everyone can gather to share best practices and resources that help drive data literacy.
- Encourage a curiosity for how data flows through the business by providing access and guidance to tools that enable self-directed discovery of new insights, patterns, and connections.
- Deploy the right tools that meet evolving business needs to ensure that business-critical data is readily available, manageable, and governable.
By continuously investing in employees to upskill and embrace new perspectives driven by data, leaders can help create an environment where data analytics becomes an integral and natural part of everyday decision-making across the organisation.
Technology and culture need to work hand in hand
Data is a technological phenomenon with a cultural dimension that remains elusive for many companies. Focusing solely on the technical aspect of data could force trade-offs that stifle data-driven innovation. Navigating the complexity of establishing a data-driven culture might seem daunting, but it is no longer an option if companies want to remain relevant, competitive, and resilient in this digital era.
