Saif Abid: The truth is that data is growing at an exponential rate. With all the tools available today, a company's data footprint is constantly expanding. Traditionally, people thought that only big companies like Google or Amazon needed to manage vast amounts of data. However, more data is being collected all the time nowadays, with people interacting on their phones and websites. This goes back to the classic saying: "Garbage in, garbage out." I've seen many companies collect data without thinking about its purpose. They collect data just for the sake of it, often driven by the "Big Data" trend.
What often happens is that years pass, and when they finally want to use that data for meaningful purposes, such as machine learning (ML) or AI, they realize that crucial data elements are missing. This can render years of accumulated data useless. It's not an exaggeration; this scenario happens frequently. Without a data strategy from the beginning, organizations may have to start from scratch. This also applies to machine learning strategy. You need to know what data to collect and ensure it's meaningful to avoid the "garbage in, garbage out" problem. The key is to avoid collecting useless data that you'll later pay to store and then discard.
Saif Abid: Extremely important. Data quality and integration are paramount. Even if you know what data you want to collect, if the quality is poor, it can significantly hinder your efforts. Additionally, data often becomes more valuable when integrated with other sources. Let me provide an example: imagine you're collecting data on self-driving cars. You need to consider factors like the quality of the data from various sensors and how they differ from the original test setup. Data quality and integration can make or break your strategy.
Saif Abid: There are several crucial factors to consider when developing a data strategy. First, think about data collection, quality, governance, and integration. How will you collect data, where will you store it, and who will have access? Ensure you track changes to the data over time. Second, define what you want to achieve with your data. Understand your use cases and align them with your business goals. Third, don't forget about integration. Data can come from various sources, not just internal ones. Finally, remember that data has more uses than just analytics. In today's world, it's also crucial for machine learning and AI. Don't limit your data strategy to analytics alone.
Saif Abid: At Bitstrapped, we conduct Data Readiness Evaluations, which involve various activities. We start with interviews, talking to key stakeholders across the organization, from engineers to business stakeholders. This helps us understand the company's goals and existing data resources. Next, we assess where the data comes from, whether it's primarily first-party or a mix of sources. We also examine how and where data is currently stored. Finally, we provide a roadmap to bridge the gap between their current state and where they want to be, quarter by quarter. We also identify areas where we can accelerate their progress.
Saif Abid: One of the most common challenges is competing priorities. Organizations often have different teams with varying visions of how data can be used. These teams might focus on their specific business units and have distinct priorities. The challenge is aligning these priorities with the overarching business goals. It's essential to balance stakeholder expectations while ensuring a positive return on investment. We often encounter this challenge, especially in organizations with multiple product lines or business units.
Saif Abid: We worked with a healthcare company that initially wanted to implement an AI feature. However, as we dug deeper, we realized they lacked essential data for the feature's success. We proposed developing a comprehensive data strategy first. We conducted interviews, improved data collection methods, ensured data governance, and established data storage for analysis. This laid a solid foundation.
As a result, the company not only improved the AI feature but also had the data foundation to explore additional use cases and AI integration. We demonstrated a tangible increase in performance. The initial solution was just the beginning; with a well-defined data strategy, they could leverage their data for multiple purposes and continually enhance their offerings.
Overall, a well-thought-out data strategy is crucial for businesses to unlock the full potential of their data assets.