Jessica: Yes. The next question I have, and this is an old chestnut that we're constantly asked when talking to organizations throughout all industries. When do you know you have enough data to start your predictive analytics and what type of data should you be looking at? Again, Natalie, would you like to comment on that?
Natalie: Yes, so I think it's difficult to answer that in terms of when do you know you have enough data. There's never a time when you should limit the analysis that you do because of data. You can always do something with whatever data you have. There's always a starting point, and there's always something that you can do.
I think that like for me personally data is obviously what drives us here and the use of it, and one of the first things I would always do with organizations is assess their level of data. What do you have? What could we do? How could we align with your strategic goals. Yeah, so there's no point with which you should limit yourself. In fact we always start with them with small amounts of data.
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All Data Is Valuable
Jessica: Tony any comments there?
Christopher: Well yes, I agree with Natalie that all data is valuable, but we need to recognize, of course, that not all data is of the same quality. The danger really is that if we focus on the data rather than the insights we are looking for, then effectively we'll spend all our lives working on the data rather than actually getting any useful output.
It's interesting that at least one bank I know of provided scoring functions around the quality of the data. In fact, what they do is they recognize the data has value but actually put a weighting against the quality. That might be something worth thinking about.
Just on the question of data, I think many insurers and many other organizations fail to appreciate that they have multiple contact points with the customer and the wider environment through their supply chains. We have what's being called the virtual enterprise, and the ability to pull information and data from that virtual enterprise comes to another dimension to the amount of insight we have.
Of course, that external data may not again be of perfect quality, so at the end of the day again echoing what we said earlier, data has different levels of quality, but all data is valuable.
Natalie: I would just like to come in here and reiterate about what Christopher said in relation to data and the scoring for quality. As part of an end-to-end analytic framework that we would implement with organizations, we have a data readiness check where we would conduct what we refer to as data checks, so it's CHEQ. That looks at coverage history, ease of access, and quality to access the data that you have. That would be part and parcel of any analytics engagement. So we would assess that to see where the data is at essentially.
Data readiness checks are the cornerstone of any successful data-driven initiative, serving as the gatekeepers between raw data and actionable insights. These checks are essential pre-processing steps that ensure data quality, completeness, and reliability before analysis or modeling begins. By verifying data integrity, consistency, and relevance, organizations can mitigate risks associated with flawed or incomplete datasets, thus safeguarding the accuracy and credibility of subsequent analyses.
Jessica: Yeah, it's an interesting one, and these comments about the technology and the data, an awful lot of in our experience is just doing a small focused project aligned with your specific questions, business questions, and saying well can we prove that the data can add value to your decision making in the short-term. Data quality, data governance and the technology is an iterative process that we can build with.
The emergence of new tools in the realm of data governance has revolutionized the way organizations manage, protect, and leverage their data assets. Firstly, advanced data governance platforms offer comprehensive solutions for data cataloging, metadata management, and lineage tracking, enabling organizations to gain unprecedented visibility into their data ecosystem. These tools automate the discovery and classification of data assets, streamlining the process of understanding data lineage and relationships across disparate sources. By providing a centralized repository for metadata and governance policies, these platforms empower data stewards to enforce compliance with regulatory requirements and internal standards, thereby reducing the risk of data breaches or compliance violations.
Secondly, innovative data governance tools leverage artificial intelligence and machine learning algorithms to enhance data quality and integrity. Through proactive monitoring and anomaly detection capabilities, these tools can identify data inconsistencies, errors, or anomalies in real-time, allowing organizations to address issues before they escalate. Moreover, AI-driven data governance solutions can analyze historical data patterns to predict potential data quality issues and recommend corrective actions, enabling organizations to maintain high-quality data and ensure its suitability for analytics and decision-making purposes. By harnessing the power of AI, organizations can optimize data governance processes, minimize manual intervention, and maximize the value derived from their data assets. Lastly, new tools in data governance facilitate collaboration and alignment across diverse stakeholders within an organization.
Modern data governance platforms often feature intuitive user interfaces, collaboration tools, and workflow automation capabilities that streamline communication and decision-making among data governance teams. These tools enable stakeholders from different departments or business units to collaborate on data governance initiatives, share insights, and track progress towards common goals. By fostering a culture of collaboration and accountability, these tools break down silos, promote cross-functional alignment, and ultimately drive more effective data governance practices across the organization.